Abagyan Group
}
leftfooter:
{
Ruben Abagyan Research Group
Molecular Biology, TSRI
10550 North Torrey Pines Rd., TPC-28
La Jolla, CA 92037
}
>>>main{Home}
h1-- Abagyan Lab
Welcome to the Abagyan Lab! Please feel free to explore our website and get
acquainted with our research and goals.
We focus on developing efficient methods and algorithms
in several areas, such as `molmod{molecular modeling}, `bioinfo{bioinformatics}
and `drugdesign{drug design & flexible docking}. If you are interested in
details of our most recent developments, please refer to our `research{Research}
section. We also gathered some of our achievements and put them together as
several `servers{servers}, with full open access to the scientific community.
Please feel free to `feedback{submit} us your
questions or experimental suggestions. You might end up with a tool that is
tailor-made for your needs!
Our lab supports the `public{Public Library of Science} initiative
{Abagyan lab 2001}
>>>research{Research,lab research,science}
h1-- Research
{Docking of a known RAR-alpha specific agonist. The RAR-alpha
selective agonist Am580 was docked into the modeled ligand binding pocket of RAR-alpha. A: The complexed
ligand (white sticks) superimposes withe the crystal structure of bound all-trans RA (green). Hydrogens are
not shown for clarity. B: Am580 (CPK display) fits tightly into the receptor's pocket (yellow wire), but for a
ketone oxygen, which shares an hydrogen with Ser234 of the receptor (displayed as stick).The receptor in
vicinity of the ligand is shown as white ribbon. Carbons, hydrogens, oxygens and nitrogens are colored white,
gray, red and blue respectively. (Image generated with MolSoft ICM)}
The development of new tools, techniques, algorithms and its use to decipher
biological mysteries - these are our main goals.
Here we present our major research topics as well as the grants that support our work.
Our group is interested in four major research topics. More detailed
descriptions are available for every project, as well as its corresponding
papers.
Have a look at our `gallery{gallery} section too. Some results and
concepts are nicely portrayed as graphical schemes and representations.
>>>molmod{molecular modeling}
h2-- Molecular modeling
{Comparison of molecular surfaces in the region identified as important for
species selective function (residues 40-66). The surface of regions 1-39 and 66-77 that are not important for species selectivity is colored white.
Conserved residues previously identified as functionally important in human CD59 are colored magenta and are not numbered. Side chains
of all nonidentical residues within the 40-66 sequence (and potentially responsible for species selective activity) are colored red (negatively
charged residues), blue (positively charged residues), yellow (hydrophobic residues), and green (other residues). Backbone atoms of other residues,
as well as side chains of residues that are identical in human and rat CD59 and therefore not important for species
selectivity, are shown in white.In collaboration with `http://www2.musc.edu/MIC/Stomlinson.htm#{Dr. Stephen Tomlinson}}
Molecular modeling is an essential method used to help predict the main structural features of a protein. From these models it is sometimes possible to
derive useful functional information about the proteins concerned.
The basis behind it is to use as much information as possible derived from the
solved structures and apply them to the wealth of newly generated gene
sequences, derived from several genome programs. All the available
parameters are considered. Whenever there are variables
that are too uncertain to derive from experimental data, we use powerful
prediction algorithms of the ICM program to find the most probable solution.
In today's need for high-throughput, molecular modeling is often one of the
best approaches to define priorities for researchers and corporations.
<>
Prediction of three-dimensional structures of proteins and peptides by global optimization of the free energy estimate
has been attempted without much success for over thirty years. The key problems were the insufficient accuracy of
the free energy estimate and the giant size of the conformational space. Global optimization of the free energy
function of a peptide in internal coordinate space is a powerful method of structure prediction that out-performs both
Molecular Dynamics, bound by the continuity requirement, and Monte Carlo, bound by the Boltzmann ensemble
gener-ation requirement. We demonstrate that stochastic global optimization algorithms of the first order, i.e., with
local minimization after each iteration (e.g., Monte Carlo-Minimization), have a greater chance of finding the global
minimum after a fixed number of function evaluations. Recently, the principle of optimal bias was mathematically
justified and the Optimal-Bias Monte Carlo-Minimization algorithm (a.k.a. Biased Probability Monte Carlo-minimization)
was successfully applied to theoretical ab initio folding of several peptides, resulting in more than a 10-fold increase in
efficiency compared to the Monte Carlo-Minimization method. The square-root bias is shown to be comparable in
performance with the previously derived linear bias. A 23-residue peptide of beta-beta-alpha structure can be
predicted from any random starting conformation.
`58{[J.Computational Physics - 1999]} `58.pdf#{[PDF]}
>>>loopmod {loop modeling}
h3-- Loop modeling and protein design
We collaborate with laboratory of
Prof. Rik Wierenga from University of Oulu, Finland.
In protein design our goal is to demonstrate that our ICM structure prediction technology
can predict new backbone conformations ~~ de novo ~~ with high accuracy and that the results can be
used as guidelines for real ~~ in vitro ~~ experiments. For example:
The `icm{ICM} method was applied to redesign a 15-residue loop-3 of trypanosomal
triosephosphate isomerase (TIM) dimer. The purpose of the design
was conversion of the dimer into a monomer. Several polypeptide chain
fragments of different amino acid sequence and length were tried and the ICM
global energy optimization was performed for each of them. An eight-residue
connection with sequence GNADALAS replacing the native sequence IAKSGAFTGEVSLPI
between positions 68 and 82 was predicted to fold into a strainless loop with an
additional helical turn at the N-terminus of helix 3. The modified polypeptide
was synthesized and the first suggested variant was experimentally proven to
form a stable, monomeric structure with TIM activity `30{[Abs.]}. Subsequent
crystallographic studies of the redesigned protein, referred to as mono-TIM,
demonstrated that the protein retains the characteristic TIM-barrel fold and
that the new loop was correctly predicted with a main-chain atom rms difference
of 0.4 Å for the loop residues `23{[Abs.]}.
Interestingly, two other loops
of the original dimer interface, loop-1 and loop-4, were found to change their
conformational state `34{[Abs.]}. Loop-1 became disordered, which in turn influenced
the ability of an active site residue Lys13 to reach the substrate. This
inspired another design project in which loop-1 was rigidified `34{[Abs.]}. This loop
design was the second blind test of the ICM loop prediction algorithm. A design
scheme similar to the scheme used previously for loop-3 was employed. A series
of ICM simulations suggested shortening of the eight-residue loop by one residue
as well as some modifications of the sequence. The predicted structure was
deposited in the PDB and the crystallographic structure was determined. The
experimental structure confirmed that the loop became rigid and was predicted
correctly. The direct superposition of the lowest energy structure of the
proposed loop KSGSPDS to the crystallographic structure results in an rms
difference of 0.5 Å for the 28 main-chain atoms.
{An example of the modelled loop compared to the crystallographically solved structure.
}
In summary, two (out of two)
blind predictions aimed at designing loop-1 (eight residues) and loop-3 (seven
residues) in triosephosphate isomerase were successful examples of the ICM ab
initio loop prediction technique. Let us note, however, that these were not
single loop predictions, but rather a series of iterative sequence modifications
followed by structure predictions. In this setup, even slightly wrong initial
predictions can be stabilized by further sequence modifications. Loop
predictions in modeling by homology are much more challenging because a sequence
cannot be adjusted and, more importantly, because the structural environment
(loop ends and surrounding residues) of the loop on a homologous template may be
strongly distorted with respect to the true environment `36{[Abs.]}.
// ADD RIK VIERENGA'S PAPERS HERE !!!!!
>>>homology {homology modeling}
h3-- Homology modeling
{Correlation of the probability of local structural
difference calculated in a window of 11 residues using the
analytical formulae for structural significance of sequence alignment
with the RMS deviation of the backbone Ca
atoms between the template and the experimentally determined
structure for T0009. The reliability function is shown by color (blue,
reliable; red, unreliable) on a structural superposition of our model
of T0009 and the experimental structure (green ribbon).}
Five models by homology containing insertions and deletions and ranging from 33% to 48% sequence
identity to the known homologue, and one high sequence identity (85%) model were built for the CASP2
meeting. For all five low identity targets: (i) our starting models were improved by the Internal Coordinate
Mechanics (ICM) energy optimization, (ii) the refined models were consistently better than those built
with the automatic SWISS-MODEL program, and (iii) the refined models differed by less than 2% from the
best model submitted, as judged by the residue contact area difference (CAD) measure [Abagyan,
R.A., Totrov, M.J. Mol. Biol. 268:678-685, 1997]. The CAD measure is proposed for ranking models built by
homology instead of global root-mean-square deviation, which is frequently dominated by insignificant
yet large contributions from incorrectly predicted fragments or side chains. We demonstrate that the
precise identification of regions of local backbone deviation is an independent and crucial step in the
homology modeling procedure after alignment, since aligned fragments can strongly deviate from the
template at various distances from the alignment gap or even in the ungapped parts of the alignment.
We show that a local alignment score can be used as an indicator of such local deviation. While four
short loops of the meeting targets were predicted by database search, the best loop 1 target T0028, for
which the correct database fragment was not found, was predicted by Internal Coordinate Mechanics
global energy optimization at 1.2 A accuracy. A classification scheme for errors in homology modeling is
proposed.
`48{[Proteins - 1997]} `48.pdf#{[PDF]}
>>>e_strain {energy strain}
h3-- Energy strain
Steric strain in protein three-dimensional structures is related to unfavorable inter-atomic interactions
and may be due to packing or functional requirements, or indicate errors in a structure's coordinates.
Detailed energy functions are usually considered too noisy for error detection. After a short energy
refinement, a full-atom detailed energy function becomes a sensitive indicator of errors. Statistics of
the energy distribution of amino acid residues in high-resolution crystal structures represented by models
with idealized covalent geometry were calculated. Interaction energy of each residue with the whole
protein structure and with the solvent was considered. Normalized deviations of amino acid residue
energies from their average values were used for detecting energy-strained and, therefore, potentially
incorrect fragments of a polypeptide chain. Protein three-dimensional structures of different origin (X-ray
crystallography, nuclear magnetic resonance spectroscopy, theoretical models and deliberately
misfolded decoys) were compared. Examples of the applications to loop and homology modeling are
given. Elevated level of energy strain may point at a problematic fragment in a protein
three-dimensional structure of either experimental or theoretical origin. The approach may be useful in
model building and refinement, modeling by homology, protein design, folding calculations, and
protein structure analysis.
`55{[Folding & Design - 1998]}
{Two structures of gene V binding protein solved in the same crystal form.
Left: PDB code 2gn5, resolution 2.3Å; Right: PDB code 1bgh, resolution 1.8Å. Residues are colored according to the
normalized residue energy (NRE) values.}
>>>dommotion {domain motion}
h3-- Domain motion
{Two crystal forms of Bence-Jones protein (2bjl): (a)BJ closed form; (b) BJ
open form. Two subdomains are colored in blue and white}
A method for modeling large-scale rearrangements of protein domains connected by a single- or a
double-stranded linker is proposed. Multidomain proteins may undergo substantial domain
displacements while their intra-domain structure remains essentially unchanged. The method allows
automatic identification of an inter-domain linker and builds an all-atom model of a protein structure in
internal coordinates. Torsion angles belonging to the inter-domain linkers and side-chains potentially
able to form domain interfaces are set free while all remaining torsions, bond lengths and bond angles
are fixed. Large-scale sampling of the reduced torsion conformational subspace is effected with the
`29{"biased probability Monte Carlo-minimization" method}.
Solvation and side-chain entropic contributions are added to the energy function. A
special procedure has been developed to generate concerted deformations of a double-stranded
inter-domain linker in such a way that the polypeptide chain continuity is preserved. The method was
tested on Bence-Jones protein with a single-stranded linker and lysine/arginine/ornithine-binding (LAO)
protein with a double-stranded linker. For each protein, structurally diverse low energy conformations
with ideal covalent geometry were generated, and an overlap between two sets of conformations
generated starting from the crystallographically determined "closed" and "open" forms was found. One
of the low energy conformations generated in a run starting from the LAO "closed" form was only 2.2 A
away from the structure of the "open" form. The method can be useful in predicting the scope of
possible domain rearrangements of a multidomain protein.
{Backbone display of BJ protein conformations generated in the calculations
starting from the BJ closed (a) and the BJ open (b) crystal structures.}
`42{[Proteins - 1997]}
>>>bioinfo{bioinformatics}
h2-- Bioinformatics
Bioinformatics is the application of computational power to scrutinize large
amount of data with high degree of reliability. These are important issues when
we consider the abudancy and richness of the data outputted by all the genome
projects.
Its goals include, but are not limited to, the recognition of genes and
regulatory sequences and the extraction of relevant
information from nucleotide sequences.
The search of any subtle functional patterns that may be hidden in the highly
complex and intricate data (as obtained by gene chips, for instance) are also
possible with apropriate treatment of such information, as well as
predictions and correlations of several gene functions.
Moreover, a large amount of data will also result from the efforts to map intricate
protein-protein interactions, suggested as the main explanation for the wide range of
functionality presented by the ~30,000 human genes. Once again, bioinformatics will play the main
role in the comprehensible integration of the raw data.
{Distribution of sequence identities in 1,330,931 pairwise Needleman & Wunsch (1970) alignments with zero
end gap penalties between sequences of structurally unrelated protein domains as a function of length of the
shorter sequence (L). Only alignments with sequence overlap greater than 50% were retained. Coloring represents the
probability density (i.e. the fraction of alignments with the given number of identical residues and minimal sequence
length), with higher density represented by red color. Dark blue corresponds to zero density (no alignments). At low sequence identities, two sequences cannot be
aligned with the global alignment algorithm utilizing a non-positive comparison matrix. This explains the zero density
at very low identities. The upper continuous curve combined with the straight line shows the Sander & Schneider
(1991) dependence of sequence identity threshold with the safety margin m = 3%, as used in the HSSP
database. The broken line is the 4sigma-level threshold for this comparison setup. The four continuous
curves represent the derived thresholds at the following levels: 1, 4, 10 and 20%, from top to bottom.}
`44{[J. Molecular Biology - 1997]}
>>>funcann {functional annotation, structural data}
h3-- Functional annotation of structural data
>>>seqsrch {sequence, database, search}
h3-- Sequence database searches
>>>drugdesign{drug, design, flexible, docking}
h2-- Drug design & docking
{RAR antagonists. Two known antagonists (A and B) and two novel
antagonists (C and D). (Left) Chemical structure. (Right) Conformation docked into the receptor (part of the
receptor is displayed as a ribbon representation, and the binding pocket boundary is displayed in yellow).
Cyan, carbons; red, oxygen: blue, nitrogen; magenta, flourine; yellow, sulfur. Hydrogens are not represented
for clarity.}
One of the main approaches that computational biology has pursued
with high success is the simulation and prediction of flexible protein-ligand
and protein-protein docking.
This technology has been achieved by combining an enourmous amount of
state-of-the-art scientific data of structural biology, chemistry and statistics
into computational algorithms.
Flexible protein-ligand docking are extremely important for the discovery of new drugs.
Through the exhaustive integration and fine tuning of different variables, the
latest programs and algorithms (such as `icm{ICM}, for instance) are capable
of predicting the behavior of chemical compounds and protein molecules in
order to better help researchers find a more efficient drug leads. This method
significantly reduces the necessary cost money and time consummed, as well as minimizing the
non-specific interaction of drug molecules (and thus reflecting in reduced
side-effects)
Protein-protein interactions are also extremely important, since they are
responsible for many necessary biological functions. Prediction of such interactions is extremely important
to the complete understanding of human physiology.
<>
{Predicted docking conformations are shown in red and
conformations determined by x-ray crystallography are shown in
green. Analytical molecular surface of protein receptors was
generated with the contour-buildup method as implemented in
the ICM program.}
Eight protein-ligand complexes were simulated by using global optimization of a complex energy
function, including solvation, surface tension, and side-chain entropy in the internal coordinate space of
the flexible ligand and the receptor side chains `29{[Abs]}.
The procedure uses two types of efficient random moves, a pseudo-brownian positional move
`32{[Abs]} and a Biased-Probability
multitorsion move `29{[Abs]}, each accompanied by
full local energy minimization. The best docking solutions were further ranked according to the
interaction energy, which included intramolecular deformation energies of both receptor and ligand,
the interaction energy, surface tension, side-chain entropic contribution, and an electrostatic term
evaluated as a boundary element solution of the Poisson equation with the molecular surface as a
dielectric boundary. The geometrical accuracy of the docking solutions ranged from 30% to 70%
according to the relative displacement error measure at a 1.5 A scale. Similar results were obtained
when the explicit receptor atoms were replaced with a grid potential.
`49{[Proteins - 1997]} `49.pdf#{[PDF]}
>>>virtscrn {virtual screening, ligand, small molecule, drug, lead compound}
h3-- Virtual screening
Virtual ligand screening (VLS) based on high-throughput flexible docking is an emerging
technology for rational lead discovery based on receptor structure. Rapid
accumulation of high-resolution three-dimensional structures, further accelerated by the
structural proteomics initiative and the improvements of docking and scoring
technology, are making VLS an attractive alternative to the traditional methods of lead
discovery. VLS can sample a virtually infinite chemical diversity of drug-like molecules
without synthesizing and experimentally testing every screened molecule. Typically, a
corporate high-throughput screening (HTS)-ready compound library ranges from 200,000
to 1,000,000 molecules. Even with corporate libraries as large as these, however, the
experimental HTS often does not result in viable leads (Martin Rosenberg, personal
communication). The high cost of such massive experimental testing and its technical
complexity are further motivation for the theoretical alternative. Finally, the virtual
experiment, as opposed to a high-throughput assay, can be easily designed to select for
a particular binding site or receptor specificity.
Docking and screening methods have a long history that is described elsewhere.We
have not tried to cover docking and binding energy-prediction methods that are too
computationally expensive for high-throughput applications and take more than 3 5
minutes per ligand per processor. `73{Here} we will review the most recent advances in
the area of high-throughput flexible docking and computer screening,as well as
applications of these techniques to lead discovery.
Receptor Modeling Correct receptor pocket model(s) Sources: X-ray, NMR, or homology modeling.Apo-form or liganded-form.Alternative conformations predicted by simulations Receptor model does not reflect the induced fit. Alternative conformations are missed
* 
Library Generation Sufficiently large and diverse set of relevant compounds In-house collection, HTS hits, commercially available compounds, virtual libraries computed from accessible scaffolds and side-chains The library is too restricted, molecules are not chemically feasible or not drug-like
* 
Flexible Docking Correct prediction of the binding geometry MC or GA, Stochastic global optimization with gradient minimization, Incremental construction, grid or explicit receptor representations, etc. Inaccurate energy function, poor optimization algorithm. Wrong receptor model, inadequate ligand flexibility.
* 
Ligand Scoring Maximal separation between binders and non-binders Weighted interaction terms,Statistical potentials, combination of binding score with QSAR if binders are known Poorly predicted binding geometries, score over-training to a particular case/family, large number of false positives.
* 
Hit List Post-Processing The best task for the chemist, screener or compound vendor Clustering, diversity, selection of scaffolds and/or side-chains for a small combinatorial library of parallel synthesis Domination of one chemical family, lack of chemical availability, or ADME-tox and patent considerations.
<>
`73{[Curr. Opin. Chemical Biology - 2001]} `73.pdf#{[PDF]}
>>>flppdock {flexible protein-protein docking, protein-, protein-protein, dock}
h3-- Flexible protein-protein docking
{Docking simulations of the interaction between trypsin and BPTI:
3-D model of the unbound ligand BPTI (backbone and binding side-chains, in green)
after rigid-body docking onto the unbound trypsin (receptor surface in blue). The
ligand binding side-chains are optimized after the refinement step (shown in red),
and can be compared to the conformation of the ligand binding side-chains in the
crystallographic structure (in white).}
Association of two biological macromolecules is a fundamental biological phenomenon
and an unsolved theoretical problem. In recent years, several groups have developed
a variety of tools in an attempt to solve the so-called protein-protein docking
problem, that is, the prediction of the geometry of a complex from the atom
coordinates of its uncomplexed constituents. These predictive docking methods
usually fail when they are applied to a large set of complexes, mostly because of
inaccuracies in the scoring function and/or difficulties on simulating the
rearrangement of the interacting side-chains of the individual molecules upon
binding. Our lab have developed and optimized a two-step docking procedure
(pseudo-Brownian rigid body docking `32{[Abs.]} followed by Biased Probability Monte Carlo
minimization `29{[Abs.]} of
the ligand interacting side chains) that uses a fast soft interaction energy
function pre-calculated on a grid `49{[Abs.]}. The use of grid potentials, instead of the explicit energy,
increased drastically the speed of the procedure. The method was tested in a
benchmark of twenty-four protein-protein complexes where the 3D structures of their
subunits (bound and free) are known. The results shown that the rank of the
near-native conformation in a list of candidate docking solutions was below 20 in
85 % of complexes with no major backbone motion upon binding. Among them, as many
as seven protease-inhibitor complexes out of eleven (64 %) can be successfully
predicted as the highest rank conformations.
[Fernandez-Recio, J., Totrov, M. & Abagyan, R., `79{[Protein Sci.]}].
A web server of the protein-protein docking algorithm is available `http://www.scripps.edu/~jfrecio/ICMprotdock#{here}.
>>>methalgo{methods, algorithms}
h2-- Methods and algorithms
{(a) A histogram of the CAD values and (b) the cRMSD versus CAD
dependence for six sets of pairwise structure comparisons: PDB domains related by non-crystallographic
symmetry and solved by X-ray crystallography (red), pairs of the NMR models submitted in one PDB entry (blue),
models built by homology for the HPR protein(green), models by homology of CRABP (yellow), unfolded models
with preserved secondary structure (violet), extended polypeptide chains (brown), deliberately misfold models
(dark green). The N scale for the NMR set have been divided by 20 to show the distribution on the same plot.}
The creation of tools is a critical step towards the development and evolution of any science, and it
is no different in our field. The protein structural and genomic sciences, two big and virtually unexplored
research lines, demand very particular, specifically designed tools. This is part of our work
- provision of new and efficient tools to uncover the wealth of biological information hidden
behind the data we and others gather.
{The Internal Coordinates Mechanics main variables: torsion angles, bond angles, bond lenght and phase angle}
`32{[J. Computational Chemistry - 1994]}
>>>homomod{modeling homology, methods}
h3-- Modeling by homology
Five models have been built by the ICM method for the Comparative Modeling section of the Meeting
on the Critical Assessment of Techniques for Protein Structure Prediction. The targets have homologous
proteins with known three-dimensional structure with sequence identity ranging from 25 to 77%. After
alignment of the target sequence with the related three-dimensional structure, the modeling procedure
consists of two subproblems: side-chain prediction and loop prediction. The ICM method approaches
these problems with the following steps: (1) a starting model is created based on the homologous
structure with the conserved portion fixed and the nonconserved portion having standard covalent
geometry and free torsion angles; (2) the Biased Probability Monte Carlo (BPMC) procedure is applied to
search the subspaces of either all the nonconservative side-chain torsion angles or torsion angles in a
loop backbone and surrounding side chains. A special algorithm was designed to generate low-energy
loop deformations. The BPMC procedure globally optimizes the energy function consisting of ECEPP/3
and solvation energy terms. Comparison of the predictions with the NMR or crystallographic solutions
reveals a high proportion of correctly predicted side chains. The loops were not correctly predicted
because imprinted distortions of the backbone increased the energy of the near-native conformation
and thus made the solution unrecognizable. Interestingly, the energy terms were found to be reliable
and the sampling of conformational space sufficient. The implications of this finding for the strategies of
future comparative modeling are discussed.
{General scheme of the modeling by homology method}
`36{[Proteins - 1995]}
>>>elecsolv {electrostatics & solvation, protein }
h3-- Protein electrostatics and solvation
{The composite boundary elements compared to the basic triangles. Left half of the
molecular surface of the polypeptide (shown in solid) has composite boundary elements colored
randomly to illustrate the distribution of sizes and shapes. Right half of the molecular surface is
displayed as a mesh directly produced by triangulation procedure. Drastic difference in the number
of surface elements can be observed.}
Solvation effects play a profound role in the energetics of protein folding. While a continuum dielectric model of
solvation may provide a sufficiently accurate estimate of the solvation effects, until now this model was too
computationally expensive and unstable for folding simulations. Here we proposed a fast yet accurate and robust
implementation of the boundary element solution of the Poisson equation, the REBEL algorithm. Using our earlier
double-energy scheme, we included for the first time the mathematically rigorous continuous REBEL solvation term in
our Biased Probability Monte Carlo (BPMC) simulations of the peptide folding. The free energy of a 23-residue
betabetaalpha-peptide was then globally optimized with and without the solvation electrostatics contribution. An
ensemble of betabetaalpha conformations was found at and near the global minimum of the energy function with
the REBEL electrostatic solvation term. Much poorer correspondence to the native solution structure was found in the
"control" simulations with a traditional method to account for solvation via a distance-dependent dielectric constant.
Each simulation took less than 40 h (21 h without electrostatic solvation calculation) on a single Alpha 677 MHz CPU
and involved more than 40,000 solvation energy evaluations. This work demonstrates for the first time that such a
simulation can be performed in a realistic time frame. The proposed procedure may eliminate the energy evaluation
accuracy bottleneck in folding simulations.
`77{[Biopolymers - 2001]} `77.pdf#{[PDF]}
>>>montcarl {monte-carlo, simulations}
h3-- Monte-Carlo simulations
Two major components are required for a successful prediction of the three-dimensional structure of
peptides and proteins: an efficient global optimization procedure which is capable of finding an
appropriate local minimum for the strongly anisotropic function of hundreds of variables, and a set of
free energy components for a protein molecule in solution which are computationally inexpensive
enough to be used in the search procedure, yet sufficiently accurate to ensure the uniqueness of the
native conformation. We here found an efficient way to make a random step in a Monte Carlo
procedure given knowledge of the energy or statistical properties of conformational subspaces (e.g.
phi-psi zones or side-chain torsion angles). This biased probability Monte Carlo (BPMC) procedure
randomly selects the subspace first, then makes a step to a new random position independent of the
previous position, but according to the predefined continuous probability distribution. The random step
is followed by a local minimization in torsion angle space. The positions, sizes and preferences for
high-probability zones on phi-psi maps and chi-angle maps were calculated for different residue types
from the representative set of 191 and 161 protein 3D-structures, respectively. A fast and precise
method to evaluate the electrostatic energy of a protein in solution is developed and combined with
the BPMC procedure. The method is based on the modified spherical image charge approximation,
efficiently projected onto a molecule of arbitrary shape. Comparison with the finite-difference solutions
of the Poisson-Boltzmann equation shows high accuracy for our approach. The BPMC procedure is
applied successfully to the structure prediction of 12- and 16-residue synthetic peptides and the
determination of protein structure from NMR data, with the immunoglobulin binding domain of
streptococcal protein G as an example. The BPMC runs display much better convergence properties
than the non-biased simulations. The advantage of a true global optimization procedure for NMR
structure determination is its ability to cope with local minima originating from data errors and
ambiguities in NMR data.
{Types of random moves used by ICM}
`29{[J. Molecular Biology - 1994]}
>>>cad{contact area difference, evaluation, 3d model}
h3-- Protein 3D model accuracy evaluation
{The surface area of contact between the two residues i and j. Absolute differences between the Aij values for two
different models are accumulated in the CAD number.}
A simple unified measure to evaluate the accuracy of three-dimensional atomic protein models is
proposed. This measure is a normalized sum of absolute differences of residue-residue contact surface
areas calculated for a reference structure and a model. It employs more rigorous quantitative
evaluation of a contact than previously proposed contact count based measures. We argue that the
contact area difference (CAD) number is a robust single measure to evaluate protein structure
predictions in a wide range of model accuracies, from ab initio and threading models to models by
homology, since it reflects both local secondary structure and packing geometry, is smooth, continuous
and threshold-free, is not sensitive to typical crystallographic errors and ambiguities, adequately
penalizes domain and/or secondary structure rearrangements and protein plasticity, and has
consistent linear and matrix representations for more detailed analysis. The CAD quality of
crystallographic structures, NMR structures, models by homology, and unfolded and misfolded
structures is evaluated. It is shown that the CAD number discriminates between models better than
Cartesian root-mean-square deviation (cRMSD). The source code of the program calculating the CAD
measures is available from the authors.
`45{[J. Molecular Biology - 1997]} `45.pdf#{[PDF]}
>>>fullatom {full atom, energy, calculations}
h3-- Detailed full-atom energy calculations
>>>visual {molecular visualization, graphics, rendering, solid, 3D}
h3-- High-quality molecular visualization
>>>animation {animation, movies, flics, graphics, rendering, solid, 3D}
h3-- Molecular animations
>>>funding{fundings,support, grants}
h2-- Fundings and Support
** Aknowledgements **
Our work is funded and supported by several institutions (below) that believe in the development and improvement through scientific research.
Here we have compiled a list of our publications, subdivided into papers and book chapters.
Inside the papers section, you will find the publications separated by year, as well as a comprehensive grouping of the same papers into their respective
research fields.
The search engine (on the left part of your page) is capable of returning you links to references that matches the word(s) you type in.
>>>papers{resumed list}
h2-- Papers
Here you can find all the papers we have or have helped to write, arranged in
chronological order. If you just want papers about a specific topic, please
follow the links below to go to the subset of papers that better suits your
interests.
The search engine (located at the left upper corner of this window) can be used to search for papers that contains the word(s) you enter as a query.
Every paper is linked to its abstract and/or PDF file (when available).
**1993
* 24. Borchert, T.V., Abagyan, R.A., Kishan, K.V. R., Zeelen, J.Ph., and Wierenga, R.K. (1993). The crystal structure of an engineered monomeric triosephosphate isomerase, monoTIM: the correct modeling of an eight-residue loop. Structure, 1, 205-213 `24{[Abs.]}
* 26. Gibson, T.J., Thompson, J.D. and Abagyan, R.A (1993). Proposed structure for the DNA-binding Domain of the Helix-Loop-Helix family of eucaryotic gene regulatory proteins. Protein Engineering, 6, 41-50 `26{[Abs.]}
*
*
**1994
* 29. Argos, P., and Abagyan, R.A. (1994). The protein folding problem: finding a few minimums in a near infinite space. Computers & Chemistry, 18, 225-232 `29{[Abs.]}
* 30. Abagyan, R.A., and Totrov, M.M. (1994). Biased Probability Monte Carlo Conformational Searches and Electrostatic Calculations for Peptides and Proteins. J. Mol. Biol., 235, 983-1002 `30{[Abs.]}-`30.pdf#{[PDF]}
* 31. Borchert, T.V., Abagyan, R.A., Jaenicke, R., and Wierenga, R.K. (1994). Design, creation, and characterization of a stable, monomeric triosephosphate isomerase. Proc. Natl. Acad. Sci. USA, 91, 1515-1518 `31{[Abs.]}-`31.pdf#{[PDF]}
*
*
**1995
* 35. Borchert, T.V., Kishan, K.V.R., Zeelen, J.Ph., Schliebs, W., Thanki, N., Abagyan, R.A., Jaenicke, R., and Wierenga, R.K. (1995). Three new crystal structures of point mutation variants of monoTIM: conformational flexibility of loop-1, loop-4 and loop-8. Structure, 3, 669-679 `35{[Abs.]}-`35.pdf#{[PDF]}
* 37. Cardozo, T., Totrov, M., and Abagyan, R. (1995). Homology modeling by the ICM method. Proteins: Structure, Function, Genetics, 23, 403-414 `37{[Abs.]}
* 36. Houbrechts, A., Moreau, B., Abagyan, R., Mainfroid, V., Preaux G., Lamproye, A., Poncin, A., Goormaghtigh, E., Ryusschaert, J.-M., Martial, J.A., Goraj, K. (1995). Second-generation octarellins: two new de novo (??)8 polypeptides designed for investigating the influence of ?-residue packing on the ?/? -barrel structure stability. Protein Engineering, 8, 249-259 `36{[Abs.]}
*
*
**1997
* 49. Abagyan, R., Batalov, S., Cardozo, T., Totrov, M., and Zhou, Y. (1997). Homology modeling with ICM: deformation zone mapping and improvements of models via conformational search. Proteins, Supplement 1, 29-37 `49{[Abs.]}-`49.pdf#{[PDF]}
* 43. Maiorov, V.N., and Abagyan, R.A. (1997). A new method for modeling large-scale rearrangements of protein domains. Proteins, 27, 410-424 `43{[Abs.]}
* 53. Mathieu, M., Modis, Y., Zeelen, J. Ph., Engel, C.K., Abagyan, R.A., Ahlberg, A., Rasmussen, B., Lamzin, V.S., Kunau W.H., and Wierenga, R.K. (1997). The 1.8 Crystal Structure of the Dimeric Peroxisomal 3-Ketoacyl-CoA Thiolase of Saccharomyces cerevisiae: Implications for Substrate Binding and Reaction Mechanism. J. Mol. Biol., 273, 714-728 `53{[Abs.]}-`53.pdf#{[PDF]}
* 42. Thanki, N., Zeelen, J.Ph., Mathieu, M., Jaenicke, R., Abagyan, R.A., Wierenga R.K., and Schliebs, W. (1997). Protein engineering with monomeric triosephosphate isomerase (monoTIM): the modelling and structure verification of a seven residue loop. Protein Eng., 10, 159-167 `42{[Abs.]}-`42.pdf#{[PDF]}
* 44. Yu, J., Abagyan, R., Dong, S., Gilbert, A., Nusenzweig, V., and Tomlinson, S. (1997). Mapping of the Active Site of CD59. J. Expt. Medicine, 185, 745-754 `44{[Abs.]}-`44.pdf#{[PDF]}
* 47. Yu, J., Dong, S., Rushmere, N.K., Morgan, B.P., Abagyan, R., and Tomlinson, S. (1997). Mapping the regions of the complement inhibitor CD59 responsible for its species selective activity. Biochemistry, 36, 9423-9428 `47{[Abs.]}-`47.pdf#{[PDF]}
*
*
**1998
* 54. Cardozo, T.J., and Abagyan, R. (1998). Molecular Modeling of the Domain Shared Between CED-4 and its Mammalian Homologue Apaf-1: A Structural Relationship to the G-proteins. J. of Mol. Model., 4, 83-93 `54{[Abs.]}
* 56. Maiorov, V., and Abagyan, R. (1998). Energy strain in three-dimensional protein structures. Folding & Design, 3, 259-269 `56{[Abs.]}-`56.pdf#{[PDF]}
*
*
**1999
* 60. Abagyan, R., and Totrov, M. (1999). Ab initio folding of peptides by the optimal-bias Monte Carlo minimization procedure. Journal of Computational Physics, 151, 402-421 `59{[Abs.]}-`59.pdf#{[PDF]}
* 67. Zhou, Y., and Abagyan, R. (1999). Efficient stochastic global optimization for protein structure prediction. Rigidity Theory and Application (M.F. Thorpe & P.M. Duxbury eds.), 345-356 `66{[Abs.]}
*
*
**2001
* 76. Norledge, B.V., Lambeir, A.M., Abagyan, R.A., Rottmann, A., Fernandez, A.M., Filimonov, V., Peter, M.G., and Wierenga, R.K. (2001). Modeling, mutagenesis, and structural studies on the fully conserved phosphateloop (loop 8) of triosephosphate isomerase: toward a new substrate specificity. Feb 15;42(3), 383-9 `75{[Abs.]}-`75.pdf#{[PDF]}
*
*
**2001
* 85. Schapira, M., Totrov, M. and Abagyan, R. (2002). Structural Model of Nicotinic Acetylcholine Receptor Isotypes Bound to cetylcholine and Nicotine. BMC Structural Biology 2:1 `85{[Abs.]}-`85.pdf#{[PDF]}
<>
>>>r_bioinfo{references, bioinformatics}
h3-- Bioinformatics
**1994
* 28. Abagyan, R.A., Frishman, D., and Argos, P. (1994). Recognition of distantly related proteins through energy calculations. Proteins, 19, 132-140 `28{[Abs.]}
*
*
**1997
* 45. Abagyan, R.A., and Batalov, S.V. (1997). Do aligned sequences share the same fold? J. Mol. Biol., 273, 355-368 `45{[Abs.]}
* 48. Koonin, E.V., and Abagyan, R.A. (1997). TSG101 may be the prototype of a class of dominant negative ubiquitin regulators. Nature Genetics, 16, 330-331 `48{[Abs.]}
*
*
**1999
* 61. Gates, M., Kim, L., Egan, E., Cardozo, T., Sirotkin, H., Dougan, S., Lashkari, D., Davis, R., Abagyan, R., Schier, A,. and Talbot, W. (1999). An integrated genetic linkage map of the zebrafish genome. Genome Research 9, 334-47 `60{[Abs.]}
*
*
**2000
* 72. Kelly, P.D., Chu, F., Wood, I.G., Ngo-Hazelett, P., Cardozo, T., Huang, H., Kimm, F., Liao, L., Yan, Y.L., Zhou, Y., Johnson, S.L., Abagyan, R., Schier, A.F., Postlethwait, J.H., Talbot, W.S. (2000). Genetic linkage mapping of zebrafish genes and ESTs. Genome Res. Apr. 10 (4), 558-567 `72{[Abs.]}-`72.pdf#{[PDF]}
*
*
**2001
* 83. Volkman, S.K., Hartl, D.L., Wirth, D.F., Nielsen, K.M., Choi, M., Le Roch, K.G., Abagyan, R., Winzeler, E.A. (2002). Excess Polymorphisms in Genes for Membrane Proteins in Plasmodium Falciparum. Science 298, 216-218 `83{[Abs.]}-`83.pdf#{[PDF]}
<>
>>>r_drugdesign{docking,drug design,references,flexible docking}
h3-- Drug design & flexible docking
**1994
* 32. Totrov, M.M., and Abagyan, R.A. (1994). Detailed ab initio prediction of lysozyme-antibody complex with 1.6 accuracy. Nature Structural Biology, 1, 259-263 `32{[Abs.]}
*
*
**1996
* 40. Strynadka, N.C.J., Eisenstein, M., Katchalski-Katzir, E., Shoichet, B.K., Kuntz, I.D., Abagyan, R., Totrov, M., Janin, J., Cherfils, J., Zimmerman, F., Olson, A., Duncan, B., Rao, M., Jackson, R., Sternberg, M., and. James, M.N.G. (1996). Molecular docking programs successfully predict the binding of a beta-lactamase inhibitory protein to TEM-1 beta-lactamase. Nature Struct. Biol., 3, 233-239 `40{[Abs.]}
*
*
**1997
* 50. Totrov, M., and Abagyan, R. (1997). Flexible protein-ligand docking by global energy optimization in internal coordinates. Proteins, Supplement 1, 215-220 `50{[Abs.]}-`50.pdf#{[PDF]}
*
*
**1999
* 62. Li, D., Desai-Yajnik, V., Lo, E., Schapira, M., Abagyan, R., and Samulels, H.H. (1999). NRIF3 is a novel co-activator mediating functional specificity of nuclear hormone receptors. Molecular and Cellular Biology Oct, 19 (10), 7191-7202 `62{[Abs.]}-`62.pdf#{[PDF]}
* 63. Schapira, M., Totrov, M., and Abagyan, R. (1999). Prediction of the binding energy for small molecules, peptides and proteins.} J. of Molecular Recognition, 12, 177-190 `63{[Abs.]}
* 64. Stigler, R.-D., Hoffmann, B, Abagyan, R. and Schneider-Mergener, J. (1999). Soft docking an L and a D peptide to an anticholera toxin antibody using internal coordinate mechanics. Structure, 7, 663-670 `64{[Abs.]}-`64.pdf#{[PDF]}
* 65. Totrov, M., and Abagyan R. (1999). Derivation of sensitive discrimination potential for virtual ligand screening. Proceedings of the Third Annual Intl. Conf. on Comp. Mol. Bio. 312-320 `65{[Abs.]}
*
*
**2000
* 69. Filikov, A.V., Mohan, V., Vickers, T.A., Griffey, R.H., Cook, P.D., Abagyan, R.A., and James, T.L. (2000). Identification of Ligands for HIV-1 TAR RNA via Structure Based Virtual Screening. JCAMD. Aug 14(6), 593-610 `69{[Abs.]}
* 71. Jin, E., Katritch, V., Olson, W.K., Kharatisvili, M., Abagyan, R., Pilch, D.S. (2000). Aminoglycoside binding in the major groove of duplex RNA: the thermodynamic and electrostatic forces that govern recognition. J. Mol. Biol. Apr 21 298 (1), 95-110 `71{[Abs.]}-`71.pdf#{[PDF]}
* 73. Schapira, M., Raaka, B.M., Samuels, H, H. and Abagyan, R. (2000). Rational discovery of novel nuclear hormone receptor antagonists. PNAS, Feb 1;97 (3), 1008-1013 `73{[Abs.]}-`73.pdf#{[PDF]}
*
*
**2001
* 75. Abagyan, R. and Totrov, M. (2001). High-throughput Docking for Lead Generation. Current Opinion in Chemical Biology. Aug;5(4):375-82 `75{[Abs.]}-`75.pdf#{[PDF]}
* 77. Schapira, M., Raaka, B. M., Samuels, H. H., and Abagyan, R. (2001). In Silico Discovery of novel Retinoic Acid Receptor Agonist Structures. BMC Structural Biology Journal 2001;1(1):1 `77{[Abs.]}-`77.pdf#{[PDF]}
* 78. Totrov, M., and Abagyan, R. (2001). Protein-ligand docking as an energy optimization problem. Drug-Receptor Thermodynamics: Introduction and Applications. Editor: R.B. Raffa, John Wiley & Sons, RV, 603-624 `78{[Abs.]}
*
*
**2002
* 85. Schapira, M., Totrov, M. and Abagyan, R. (2002). Structural Model of Nicotinic Acetylcholine Receptor Isotypes Bound to cetylcholine and Nicotine. BMC Structural Biology 2:1 `85{[Abs.]}-`85.pdf#{[PDF]}
<>
>>>r_methalgo{references, methods, algorithms}
h3-- Methods and algorithms
**1992
* 19. Abagyan, R.A., and Argos, P. (1992). Optimal Protocol and Trajectory Visualization for Conformational Searches of Peptides and Proteins. J. Mol. Biol., 225, 519-532 `19{[Abs.]}
* 22. Petukhov, M.G., Dorofeev, V.E., Abagyan, R.A., Mazur, A.K. (1992). Global optimization of the conformational energy of oligopeptides using a tunnel algorithm. Biofizika, 37, 226-230 `22{[Abs.]}
*
*
**1993
* 23. Abagyan, R.A. (1993). Towards protein folding by global energy optimization. FEBS Letters, 325,17-22 `23{[Abs.]}
* 25. Eisenmenger, F., Argos, P., and Abagyan, R.A., (1993). A method to configure protein side-chains from the mainchain trace in homology modeling. J. Mol. Biol., 231, 849-860 `25{[Abs.]}
* 27. Kuznetsov, D.A., and Abagyan, R.A. (1993). A technique for identifying atoms from a screen image. J. Mol. Graph., 11, 245-247 `27{[Abs.]}
*
*
**1994
* 30. Abagyan, R.A., and Totrov, M.M. (1994). Biased Probability Monte Carlo Conformational Searches and Electrostatic Calculations for Peptides and Proteins. J. Mol. Biol., 235, 983-1002 `30{[Abs.]}
* 33. Abagyan, R.A., Totrov, M.M., and Kuznetsov, D.A. (1994). ICM: a new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J. Comp. Chem., 15, 488-506 `33{[Abs.]}
* 34. Totrov, M.M., and Abagyan, R.A. (1994). Efficient parallelization of the energy, surface and derivative calculations for internal coordinate mechanics. J. Comp. Chem., 15, 1105-1112 `34{[Abs.]}
*
*
**1996
* 38. Totrov, M.M., and Abagyan, R.A. (1996). The Contour-Buildup Algorithm to Calculate the Analytical Molecular Surface. J. Struct. Biol., 116, 138-143 `38{[Abs.]}-`38.pdf#{[PDF]}
* 39. Chalikian, T.V., Totrov, M.M., Abagyan, R.A., Breslauer, K.J. (1996). The hydration of globular proteins as derived from volume and compressibility measurements: cross correlating thermodynamic and structural data. J. Mol. Biol., 260, 588-603 `39{[Abs.]}-`39.pdf#{[PDF]}
*
*
**1997
* 46. Abagyan, R., and Totrov, M. (1997). Contact Area Difference (CAD): A robust measure to evaluate accuracy of protein models. J. Mol. Biol., 268, 678-285 `46{[Abs.]}-`46.pdf#{[PDF]}
* 51. Abagyan, R. (1997). Protein structure prediction by global energy optimization. Computer Simulation of Biomolecular Systems: Theoretical and Experimental Applications, (van Gunsteren, W.F., et al., eds.). 3, 363-394 `51{[Abs.]}-`51.pdf#{[PDF]}
* 52. Rashin, A.A., Rashin, B.H., Rashin A., Abagyan, R. (1997). Evaluating the energetics of empty cavities and internal mutations in proteins. Protein Science, 6, 2143-2158 `52{[Abs.]}
*
*
**1999
* 67. Zhou, Y., and Abagyan, R. (1999). Efficient stochastic global optimization for protein structure prediction. Rigidity Theory and Application (M.F. Thorpe & P.M. Duxbury eds.), 345-356 `67{[Abs.]}
*
*
**2000
* 68. Cardozo, T., Batalov, S., and Abagyan, R. (2000). Estimating local backbone structural deviation in homology models. Estimating local backbone structural deviation in homology models. Computers & Chemistry . Jan 24(1), 13-31 `68{[Abs.]}-`68.pdf#{[PDF]}
*
*
**2001
* 75. Abagyan, R. and Totrov, M. (2001). High-throughput Docking for Lead Generation. Current Opinion in Chemical Biology. Aug;5(4):375-82 `75{[Abs.]}-`75.pdf#{[PDF]}
* 79. Totrov, M., and Abagyan, R. (2001). Rapid boundary element solvation electrostatics calculations in folding simulations: Successful folding of a 23-residue peptide. Biopolymers 2001;60(2):124-33 `79{[Abs.]}-`79.pdf#{[PDF]}
*
*
**2002
* 80. Fernandez-Recio, J., Totrov, M., and Abagyan, R. (2002). Soft Protein-Protein Docking in Internal Coordinates. Protein Science 11:280-91 `80{[Abs.]}-`80.pdf#{[PDF]}
* 81. Zhou, Y. and Abagyan, R. (2002). Match-Only Integral Distribution (MOID) Algorithm for High-Density Oligonucleotide Array Analysis. BMC Bioinformatics 3:3 `81{[Abs.]}-`81.pdf#{[PDF]}
* 82. Fernandez-Recio, J., Totrov, M., and Abagyan, R. (2002). Screened charge electrostatic model in protein-protein docking simulations. Pac Symp Biocomput. 2002;:552-63 `82{[Abs.]}-`82.pdf#{[PDF]}
*
*
**2003
* 86. Katrich, S., Totrov, M., and Abagyan, R. (2003). ICFF: A new method to incorporate implicit flexibility into an internal coordinate force field. J. Comput. Chem. 24:254-265 `86{[Abs.]}-`86.pdf#{[PDF]}
<>
>>>r_applic{applications}
h3-- Applications
**1993
* 26. Gibson, T.J., Thompson, J.D. and Abagyan, R.A (1993). Proposed structure for the DNA-binding Domain of the Helix-Loop-Helix family of eucaryotic gene regulatory proteins. Protein Engineering, 6, 41-50 `26{[Abs.]}
*
*
**1996
* 41. Goodman, A.R., Cardozo, T., Abagyan, R.A., Altmeyer, A., Wisniewski, H.G., and Vilcek, J. (1996). Long Pentraxins: an Emerging Group of Proteins with Diverse Functions. Cytokine & Growth Factor Reviews, 7, 191-202 `41{[Abs.]}
*
*
**1997
* 53. Mathieu, M., Modis, Y., Zeelen, J. Ph., Engel, C.K., Abagyan, R.A., Ahlberg, A., Rasmussen, B., Lamzin, V.S., Kunau W.H., and Wierenga, R.K. (1997). The 1.8 Crystal Structure of the Dimeric Peroxisomal 3-Ketoacyl-CoA Thiolase of Saccharomyces cerevisiae: Implications for Substrate Binding and Reaction Mechanism. J. Mol. Biol., 273, 714-728 `53{[Abs.]}-`53.pdf#{[PDF]}
*
*
**1998
* 55. Patel, I.R., Attur, M.G., Patel, R.N., Stuchin, S.A., Abagyan, R.A., Abramson, S.B., and Amin, A.R. (1998). TNF-a convertase enzyme from human arthritis-affected cartilage: Isolation of cDNA by differential display, expression of the active enzyme, and regulation of TNF-a. J. of Immunology, 160, 4570-4579 `55{[Abs.]}-`55.pdf#{[PDF]}
* 57. Isakoff, S.J., Cardozo, T., Andreev, J., Li, Z., Ferguson, K.M., Abagyan, R., Lemmon, M.A., Aronheim, A and Skolnik, E.Y. (1998). Identification and analysis of PH domain-containing targets of phosphatidylinositol 3-kinase using a novel in vivo assay in yeast. Embo J. 17 (18), 5374-87 `57{[Abs.]}-`57.pdf#{[PDF]}
* 58. Zhou, Y. and Abagyan, R. (1998). How and why phosphotyrosine-containing peptides bind to the SH2 and PTB domains. Folding and Design, 3, 513-522 `58{[Abs.]}-`58.pdf#{[PDF]}
*
*
**1999
* 66. Zhang, H-F., Yu, J., Chen, S., Morgan, B.P., Abagyan, R. and Tomlinson, S. (1999). Identification of the Individual Residues that Determine Human CD59 Species Selective Activity. J.Biol.Chem., 274, 10969-10974 `66{[Abs.]}-`66.pdf#{[PDF]}
*
*
**2000
* 70. Gantt, S., Persson, C., Rose, K., Birkett, A.J., Abagyan, A. and Nussenzweig, V. (2000). Antibodies against TRAP do not inhibit Plasmodium sporozoite infectivity in vivo. Infection and Immunity Jun; 68(6), 3667-3673 `70{[Abs.]}-`70.pdf#{[PDF]}
* 74. Tomko, R.P., Totrov, M., Abagyan, R. and Philipson, L. (2000). Expression of the Adenovirus Receptor and Its Interaction with the Fiber Knob. Experimental Cell Research, Feb 25;255(1), 47-55 `74{[Abs.]}-`74.pdf#{[PDF]}
*
*
**2001
* 77. Schapira, M., Raaka, B. M., Samuels, H. H., and Abagyan, R. (2001). In Silico Discovery of novel Retinoic Acid Receptor Agonist Structures. BMC Structural Biology Journal 2001;1(1):1 `77{[Abs.]}-`77.pdf#{[PDF]}
*
*
**2002
* 85. Schapira, M., Totrov, M. and Abagyan, R. (2002). Structural Model of Nicotinic Acetylcholine Receptor Isotypes Bound to cetylcholine and Nicotine. BMC Structural Biology 2:1 `85{[Abs.]}-`85.pdf#{[PDF]}
*
*
**2003
* 87. Eneqvist, T., Lundberg, E., Nilsson, L., Abagyan, R., Sauer-Eriksson, A.E.(2003). The transthyretin-related protein family. Eur. J. Biochem. 270(3):518-32 `87{[Abs.]}-`87.pdf#{[PDF]}
<>
>>>r_list{references}
h3-- Chronological list
>>>1{ref 1, Abagyan, 1983, Diffraction, variable, sign, helix, Biophysics}
h4-- Abagyan, R.A. (1983). Diffraction from a variable sign helix. Biophysics, 28, 388-392
>>>2{ref 2,Abagyan, 1983, diffraction, effects, appearing, packing, helical, molecules, Biophysics}
h4-- Abagyan, R.A., Rogulenkova, V.N., Tumanyan, V.G., Esipova, N.G. (1983). Investigation of diffraction effects appearing on packing of helical molecules. Biophysics, 28, 897-904
>>>3{ref 3,Abagyan, 1983, Molecular, arrangement, collagen, fibrils, Biophysics}
h4-- Abagyan, R.A. (1983). Molecular arrangement in collagen fibrils. Biophysics, 28, 498-500
>>>4{ref 4,Abagyan, 1984, tripeptide, conformations, collagen, Calculation, structures, Bioorganic Chemistry}
h4-- Abagyan, R.A., Tumanyan, V.G., Esipova, N.G. (1984). Two types of tripeptide conformations in collagen. Calculation of the (Gly-Pro-Ser)n and (Gly-Val-Hyp)n structures. Bioorganic Chemistry, 10, 476-482
>>>5{ref 5,Tumanyan, 1984, Conformational, analysis, polytripeptides, problem, collagen structure, Biopolymers}
h4-- Tumanyan, V.G., Abagyan, R.A., Esipova, N.G. (1984). Conformational analysis of polytripeptides (Gly-Pro-Ala)n, (Gly-Ala-Hyp)n, and (Gly-Ala-Ala)n in connection with the problem of collagen structure. Biopolymers, 23, 1499-1512
>>>6{ref 6,Ivanitskii, 1985, improvement, genetic text, factor, acceleration, Biofizika}
h4-- Ivanitskii, G.R., Esipova, N.G., Abagyan, R.A., Shnol, S.E. (1985). Block improvement of the genetic text as a factor of acceleration of biological evolution. Biofizika, 30, 418-421
>>>7{ref 7,Abagyan, 1988, Simple, Quantitative, Polypeptid, Chain, Folds, Comparison, Protein Tertiary Structures, J Biomol Struct Dyn}
h4-- Abagyan, R.A., and Maiorov, V.N. (1988). A Simple Quantitative Representation of Polypeptide Chain Folds: Comparison of Protein Tertiary Structures. J. Biomol. Struct. Dyn., 5, 1267-1279
>>>8{ref 8,Chuprina, 1988, Anomalous, properties, adenine-thymine, Nature}
h4-- Chuprina, V.P., Abagyan, R.A. (1988). Anomalous properties of adenine-thymine tracts. Nature, 332, 117
>>>9{ref 9,Chuprina , 1988, Structural, Stable Bending, DNA, Oligo, J. Biomol Struct Dyn}
h4-- Chuprina, V.P., Abagyan, R.A. (1988). Structural Basis of Stable Bending of DNA Containing Oligo(dA) tracts. Different Types of Bending. J. Biomol. Struct. Dyn., 6, 121-138
>>>10{ref 10,Mazur, 1989, New Methodology, Computer-Aided, Modelling, Biomolecular, Dynamics, J Biomol Struct Dyn}
h4-- Mazur, A.K., and Abagyan, R.A. (1989). New Methodology for Computer-Aided Modelling of Biomolecular Structure and Dynamics. 1. Non-Cyclic Structures. J. Biomol. Struct. Dyn., 6, 815-832
>>>11{ref 11,Abagyan, 1989, Computer-Aided, Modelling, Biomolecular, Dynamics, J Biomol Struct Dyn}
h4-- Abagyan, R.A., and Mazur, A.K. (1989). New Methodology for Computer-Aided Modelling of Biomolecular Structure and Dynamics. 2. Local Deformations and Cycles. J. Biomol. Struct. Dyn., 6, 833-845
>>>12{ref 12,Abagyan, 1989, Automatic Search, Similar Spatial Arrangements, Helices, Strands, Globular Proteins, J Biomol Struct Dyn}
h4-- Abagyan, R.A., and Maiorov, V.N. (1989). An Automatic Search for Similar Spatial Arrangements of ß-Helices and ß-Strands in Globular Proteins. J. Biomol. Struct. Dyn., 6, 1045-1060
>>>13{ref 13,Abagyan, 1989, General Patterns, DNA Sequences, Interaction, Proteins, Doklady Academii Nauk SSSR}
h4-- Abagyan, R.A., and Ul'yanov, A.V. (1989). General Patterns of DNA Sequences in Regions of Interaction with Proteins. Doklady Academii Nauk SSSR, 304, 741-745
>>>14{ref 14,Abagyan, 1990, Conformational, Polypeptides, Flexible Proline Rings, Computers Chem}
h4-- Abagyan, R.A., and Mazur, A.K. (1990). Conformational Energy Derivatives for Polypeptides with Flexible Proline Rings. Computers & Chem., 14, 169-175
>>>15{ref 15,Abagyan, 1990, Electrophoretic behaviour, DNA bending model, Nucl Acids Res}
h4-- Abagyan, R.A., Mironov, V.N., Chernov, B.K., Chuprina, V.P., Ulyanov, A.V. (1990). Electrophoretic behaviour of d(GGAAAAAAGG)n, d(CCAAAAAACC)n, and (CCAAAAAAGG)n and implications for a DNA bending model. Nucl. Acids Res., 18, 989-992
>>>16{ref 16,Eisenhaber, 1990, Structure, Hydration Shells, Oligo, B-Type Conformation, Monte Carlo Calculations, Biopolymers}
h4-- Eisenhaber, F., Tumanyan, V.G., Abagyan, R.A. (1990). Structure of the Hydration Shells of Oligo(dA-dT)*Oligo(dA-dT) and Oligo(dA)*Oligo(dT) Tracts in B-Type Conformation on the Basis of Monte Carlo Calculations. Biopolymers, 30, 563-581
>>>17{ref 17,Gromova, 1990, Sequence dependent modulating effects, camptothecin, DNA cleaving activity, calf thymus type I topoisomerase, Nucl Acids Res}
h4-- Gromova, I.I., Buchman, V.L., Abagyan, R.A., Ulyanov, A.V. and Bronstein, I.B. (1990). Sequence dependent modulating effects of camptothecin on the DNA cleaving activity of the calf thymus type I topoisomerase. Nucl. Acids Res., 18, 637-645
>>>18{ref 18,Mazur, 1991, Derivation, Testing, Explicit Equations, Internal Coordinates, J Comput Phys}
h4-- Mazur, A.K., Dorofeev, V.E., and Abagyan, R.A. (1991). Derivation and Testing of Explicit Equations of Motion for Polymers Described by Internal Coordinates. J. Comput. Phys., 92, 261-272
>>>19{ref 19,Abagyan, 1992, Optimal Protocol, Trajectory Visualization, Conformational Searches, Peptides and Proteins, J Mol Biol}
h4-- Abagyan, R.A., and Argos, P. (1992). Optimal Protocol and Trajectory Visualization for Conformational Searches of Peptides and Proteins. J. Mol. Biol., 225, 519-532
Conformational searches by molecular dynamics and different types of Monte Carlo or build-up methods usually aim to
find the lowest-energy conformation. However, this is often misleading, as the energy functions used in conformational
calculations are imprecise. For instance, though positions of local minima defined by the repulsive part of the
Lennard-Jones potential are usually altered only slightly by functional modification, the relative depths of the minima could
change significantly. Thus, the purpose of conformational searches and, correspondingly, performance criteria should be
reformulated and appropriate methods found to extract different local minima from the search trajectory and allow
visualization in the search space. Attempts at convergence to the lowest-energy structure should be replaced with efforts to
visit a maximum number of different local energy minima with energies within a certain range. We use this quantitative
criterion consistently to evaluate performances of different search procedures. To utilize information generated in the
course of simulation, a "stack" of low energy conformations is created and stored. It keeps track of variables and visit
numbers for the best representatives of different conformational families. To visualize the search, projection of
multidimensional walks onto a principal plane defined by a set of reference structures is used. With Met-enkephalin as a
structural example and a Monte Carlo procedure combined with energy minimization (MCM) as a basic search method, we
analyzed the influence on search efficiency of different characteristics as temperature schedules, the step size for variable
modification, constrained random step and response mechanisms to search difficulties. Simulated annealing MCM had
comparable efficiency with MCM at constant and elevated temperature (about 600 K). Constraining the randomized choice
of side-chain chi angles to optimal values (rotamers) on every MCM step did not improve, but rather worsened, the search
efficiency. Two low-energy Met-enkephalin conformations with parallel Tyr1 and Phe4 rings, a gamma-turn around the
Gly2 residue, and Phe4 and Met5 side-chains forming together a compact hydrophobic cluster were found and are
suggested as possible structural candidates for interaction with a receptor or a membrane.
>>>20{ref 20,Abagyan, 1992, conformational searches, peptides and proteins, Computer Aided Innovation of New Materials}
h4-- Abagyan, R.A., Eisenmenger, F., and Argos, P. (1992). Techniques for conformational searches of peptides and proteins. Computer Aided Innovation of New Materials II (Doyama, M., Kihara, J., Tanaka, M., and Yamamoto, R., eds.), 2, 1241-1246
>>>21{ref 21, Abagyan, 1992, side-chain conformation, prediction, packing optimization}
h4-- Abagyan, R.A. and Argos, P. (1992). Prediction of protein side-chain conformation by packing optimization. Chemtracts - BIochemistry and Molecular Biology, 2, 324-327.
>>>22{ref 22,Petukhov, 1992, Global optimization, conformational energy, oligopeptides, tunnel algorithm, Biofizika}
h4-- Petukhov, M.G., Dorofeev, V.E., Abagyan, R.A., Mazur, A.K. (1992). Global optimization of the conformational energy of oligopeptides using a tunnel algorithm. Biofizika, 37, 226-230
The tunneling algorithm has been suggested as a method for the searching of the low energy conformations of the oligopeptides. The
efficiency of the method has been compared with other global energy minimization methods such as grid search and molecular dynamics.
It has been shown that tunneling algorithm reached global minimum of potential energy of the molecule of 3-4 residues more effectively
than other methods. Experiments with oligopeptides of more than 4 residues showed that although during reasonable time tunneling
algorithm does not reach the global minimum it can very effectively find the low energy minimum.
>>>23{ref 23,Abagyan, 1993, Towards protein folding, global energy optimization}
h4-- Abagyan, R.A. (1993). Towards protein folding by global energy optimization. FEBS Letters, 325,17-22
Different components of the theoretical protein folding problem are evaluated critically. It is argued that : (i) as a rule,
small- and medium-sized proteins are in the free energy minimum; (ii) long-living metastable states may either appear
occasionally with growing protein size, or be selected by evolution for a specific function; (iii) functions discriminating
against incorrect folds would fail if they were used directly in the global optimization, unless they approximate the true free
energy accurately; (iv) surface and electrostatic free energies should be treated separately; (v) conformational entropy (of
side chain in particular) should be taken into account; (vi) Monte Carlo procedure considering all free energy terms and
combining global knowledge-based random moves with local optimization have the largest potential for success.
>>>24{ref 24,Borchert, 1993, crystal structure, engineered monomeric triosephosphate isomerase, monoTIM, Structure}
h4-- Borchert, T.V., Abagyan, R.A., Kishan, K.V. R., Zeelen, J.Ph., and Wierenga, R.K. (1993). The crystal structure of an engineered monomeric triosephosphate isomerase, monoTIM: the correct modeling of an eight-residue loop. Structure, 1, 205-213
The triosephosphate isomerase (TIM) fold is found in several different classes of enzymes, most of which are oligomeric.
TIM itself always functions as a very tight dimer. It has recently been shown that a monomeric form of TIM (monoTIM) can
be constructed by replacing a 15-residue interface loop, loop-3, with an eight-residue fragments; modeling suggests that
this should result in a short strain-free turn, resulting in the subsequence helix, helix-A3, having an additional turn at its
amino terminus. The crystal structure of monoTIM shows that it retains the characteristic TIM-barrel (beta/alpha)8 fold
and that the new loop has a structure very close to that predicted. Two other interface loops, loop-1 ad loop-4, which
contain the active site residue Lys13 and His95, respectively show significant changes in structure in monoTIM compared
with dimeric wild-type TIM. The observed structural differences between monoTIM and wild-type TIM indicate that the
dimeric appearance if TIM determines the location and conformation of two of the four catalytic residues .
>>>25{ref 25,Eisenmenger, 1993, configure protein side-chains, mainchain trace, homology modeling, J Mol Biol}
h4-- Eisenmenger, F., Argos, P., and Abagyan, R.A., (1993). A method to configure protein side-chains from the mainchain trace in homology modeling. J. Mol. Biol., 231, 849-860
Protein homology modelling typically involves the prediction of side-chain conformations in the modelled protein while
assuming a main-chain trace taken from a known tertiary structure of a protein with homologous sequence. It is generally
believed that the need to examine all possible combinations of side-chain conformations poses the major obstacle to
accurate homology modelling. Methods proposed heretofore use only discrete or limited searches of the side-chain torsion
angle space to mitigate the combinatorial problem and also rely on simplified energy functions for calculational speed. The
configurational constraints are typically based upon use of frequently observed torsion angles, fixed steps in torsion angles,
or oligopeptide segments taken from tertiary structural databanks that are similar in sequence and conformation with the
target structure. In the present work, a more fundamental approach is explored for several protein structures and it is
demonstrated that the combinatorial barrier in side-chain placement hardly exists. Each side-group can be configured
individually in the environment of only the backbone atoms using a systematic search procedure combined with extensive
local energy minimization. Tests, using the main-chain or both the main-chain and remaining side-chain atoms to calculate
low energy geometries for each residue, established the dominance of the main-chain contribution. The final structure is
achieved by combining the individually placed side-chains followed by a full energy refinement of the structure. The
prediction accuracy of the present homology modelling technique was assessed relative to other automated procedures and
was found to yield improved predictions relative to the known side-chain conformations determined by X-ray
crystallography.
>>>26{ref 26,Gibson, 1993, Proposed structure, DNA-binding Domain, Helix-Loop-Helix family, eucaryotic gene regulatory proteins, Protein Engineering}
h4-- Gibson, T.J., Thompson, J.D. and Abagyan, R.A (1993). Proposed structure for the DNA-binding Domain of the Helix-Loop-Helix family of eucaryotic gene regulatory proteins. Protein Engineering, 6, 41-50
A modelled tertiary structure for the dimeric HLH domain of the E47 protein is presented. Structural information was obtained from the
aligned sequences of > 40 members of the HLH family. The information was used to model each monomer as an alpha-helical hairpin,
with knobs-into-holes packing of side-chains as found in antiparallel coiled-coil. The dimer forms a four-helix bundle with additional
knobs-into-holes packing at the dimer interface. The size and electrostatic properties of core-forming residues are all accounted for in
the model. The model does not violate any known properties of protein structure. The monomers are related by two-fold rotational
symmetry, in agreement with the observed DNA-binding sites which are imperfect inverted repeats. The N-terminal basic region, in
which DNA binding and base specificity reside, forms the first part of helix 1. A prediction based on the model structure is that the HLH
domains do not bind to DNA in its B form but require a partially unwound conformation in order to enter the major groove.
>>>27{ref 27,Kuznetsov, 1993, technique, identifying atoms, screen image, J Mol Graph}
h4-- Kuznetsov, D.A., and Abagyan, R.A. (1993). A technique for identifying atoms from a screen image. J. Mol. Graph., 11, 245-247
Improving the interfaces in molecular graphics applications, making them more natural and easy to use, is an important task, given the
current complexity of the displayed objects and of modeling operations. Clicking near an atom center is the usual method of atom
selection. However, this method has certain disadvantages when working with images composed of different atomic representations such
as sticks, CPK, or dotted surfaces. We propose another technique allowing the user to obtain the correct answer when he or she clicks on
any element of the atom image.
>>>28{ref 28,Abagyan, 1994, Recognition, distantly related proteins, energy calculations, Proteins}
h4-- Abagyan, R.A., Frishman, D., and Argos, P. (1994). Recognition of distantly related proteins through energy calculations. Proteins, 19, 132-140
A new method to detect remote relationships between protein sequences and known three-dimensional structures based on
direct energy calculations and without reliance on statistics has been developed. The likelihood of a residues to occupy a
given position on the structural template was represented by an estimate of the stabilization free energy mad after explicit
prediction of the substituted side chain conformation. The profile matrix derive from these energy values and modified by
increasing the residue self-exchange values successfully predicted compatibility of heat-shock protein and globin sequences
with the three-dimensional structures of actin and phycocyanin, respectively, from a full protein sequence databank search.
The high sensitivity of the method makes it a unique tool for predicting the three-dimensional fold for the rapidly growing
number of protein sequences.
>>>29{ref 29,Argos, 1994, protein folding problem, finding few minimums, Computers & Chemistry}
h4-- Argos, P., and Abagyan, R.A. (1994). The protein folding problem: finding a few minimums in a near infinite space. Computers & Chemistry, 18, 225-232
Folding a protein from only a knowledge of its amino acid sequence is a formidable many-body problem. Since it is computationally
impossible to test all possible atomic conformations to determine the global minimum representing the compact state, methods need to
be developed to sample only a small part of the configurational space and yet delineate the free energy optimum (or nearly so). This article
largely reviews such techniques as applied by the authors and their colleagues.
>>>30{ref 30,Abagyan, 1994, Biased Probability Monte Carlo Conformational Searches, Electrostatic Calculations, Peptides and Proteins, J Mol Biol}
h4-- Abagyan, R.A., and Totrov, M.M. (1994). Biased Probability Monte Carlo Conformational Searches and Electrostatic Calculations for Peptides and Proteins. J. Mol. Biol., 235, 983-1002
Two major components are required for a successful prediction of the three-dimensional structure of peptides and
proteins: an efficient global optimization procedure which is capable of finding an appropriate local minimum for the
strongly anisotropic function of hundreds of variables, and a set of free energy components for a protein molecule in
solution which are computationally inexpensive enough to be used in the search procedure, yet sufficiently accurate to
ensure the uniqueness of the native conformation. We here found an efficient way to make a random step in a Monte Carlo
procedure given knowledge of the energy or statistical properties of conformational subspaces (e.g. phi-psi zones or
side-chain torsion angles). This biased probability Monte Carlo (BPMC) procedure randomly selects the subspace first, then
makes a step to a new random position independent of the previous position, but according to the predefined continuous
probability distribution. The random step is followed by a local minimization in torsion angle space. The positions, sizes and
preferences for high-probability zones on phi-psi maps and chi-angle maps were calculated for different residue types
from the representative set of 191 and 161 protein 3D-structures, respectively. A fast and precise method to evaluate the
electrostatic energy of a protein in solution is developed and combined with the BPMC procedure. The method is based on
the modified spherical image charge approximation, efficiently projected onto a molecule of arbitrary shape. Comparison
with the finite-difference solutions of the Poisson-Boltzmann equation shows high accuracy for our approach. The BPMC
procedure is applied successfully to the structure prediction of 12- and 16-residue synthetic peptides and the determination
of protein structure from NMR data, with the immunoglobulin binding domain of streptococcal protein G as an example.
The BPMC runs display much better convergence properties than the non-biased simulations. The advantage of a true
global optimization procedure for NMR structure determination is its ability to cope with local minima originating from
data errors and ambiguities in NMR data.
`30.pdf#{[PDF]}
>>>31{ref 31,Borchert, 1994, Design, creation, characterization, monomeric triosephosphate isomerase, Proc Natl Acad Sci USA}
h4-- Borchert, T.V., Abagyan, R.A., Jaenicke, R., and Wierenga, R.K. (1994). Design, creation, and characterization of a stable, monomeric triosephosphate isomerase. Proc. Natl. Acad. Sci. USA, 91, 1515-1518
Protein engineering on trypanosomal triosephosphate isomerase (TIM) converted this oligomeric enzyme into a stable,
monomeric protein that is enzymatically active. Wild-type TIM consists of two identical subunits that form a very tight
dimer involving interactions of 32 residues of each subunit. By replacing 15 residues of the major interface loop by another
8-residue fragment, a variant was constructed that is a stable and monomeric protein with TIM activity. The length,
sequence, and conformation of the designed fragment were suggested by extensive modeling.
`31.pdf#{[PDF]}
>>>32{ref 32,Totrov, 1994, ab initio prediction, lysozyme-antibody complex with 1.6 accuracy, Nature Structural Biology}
h4-- Totrov, M.M., and Abagyan, R.A. (1994). Detailed ab initio prediction of lysozyme-antibody complex with 1.6 accuracy. Nature Structural Biology, 1, 259-263
The fundamental event in biological assembly is association of two biological macromolecules. Here we present a successful,
accurate ab initio prediction of the binding of uncomplexed lysozyme to the HyHel5 antibody. The prediction combines
pseudo Brownian Monte Carlo minimization with a biased-probability global side-chain placement procedure. It was
effected in an all-atom representation, with ECEPP/2 potentials complemented with the surface energy, side-chain entropy
and electrostatic polarization free energy. The near-native solution found was surprisingly close to the crystallographic
structure (root-mean-square deviation of 1.57 A for all backbone atoms of lysozyme) and had a considerably lower energy
(by 20 kcal/mol) than any other solution.
>>>33{ref 33,Abagyan, 1994, ICM, new method, protein modeling and design, docking and structure prediction, distorted native conformation, J Comp Chem}
h4-- Abagyan, R.A., Totrov, M.M., and Kuznetsov, D.A. (1994). ICM: a new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J. Comp. Chem., 15, 488-506
An efficient methodology, further referred to as ICM, for versatile modeling operations and global energy optimization on
arbitrarily fixed multimolecular system is described. It is aimed at protein structure prediction, homology modeling,
molecular docking, nuclear magnetic resonance (NMR) structure determination, and protein design. The method uses and
further develops a previously introduced approach to model biomolecular structures in which bond lengths, bond angles,
and torsion angles are considered as independent variables, any subset of them being fixed. Here we simplify and generalize
the basic description of the system, introduce the variable dihedral phase angle, and allow arbitrary connections of the
molecules and conventional definitions of the torsion angles. Algorithms for calculation of energy derivatives with respect to
internal variables in the topological tree of the system and for rapid evaluation of accessible surface are presented.
Multidimensional variable restraints are proposed to represent the statistical information about the torsion angle
distributions in proteins. To incorporate complex energy terms as solvation energy and electrostatics into a structure
prediction procedure, a "double-energy" Monte Carlo minimization procedure in which these terms are omitted diring the
minimization stage of the random step and included for the comparison with the previous conformation in a Markov chain
is proposed and justified. The ICM method is applied successfully to a molecular docking problem. The procedure finds the
correct parallel arrangement of two rigid helices from a leucine zipper domain as the lowest-energy conformation (0.5 A
root mean square, rms, deviation from the native structure) starting from completely random configuration. Structures with
antiparallel helices or helices staggered by one helix turn had energies higher by about 7 or 9 kcal/mol, respectively. Soft
docking was also attempted. A docking procedure allowing side-chain flexibility also converged to the parallel configuration
starting from the helices optimize individually. To justify an internal coordinate approach to the structure prediction as
opposed to a Cartesian one, energy hypersurfaces around the native structure of the squash seeds trypsin inhibitor were
studied. Torsion angle minimization from the optimal conformation randomly distorted up to the rms deviation of 2.2 A or
angular rms deviation of 10 degrees restored the native conformation in most cases. In contrast, Cartesian coordinate
minimization did not reach the minimum from deviations as small as 0.3 A or 2 degrees. We conclude that the most
promising detailed approach to the protein folding problem would consist of some coarse global sampling strategy
combined with the local energy minimization in the torsion coordinate space.
>>>34{ref 34,Totrov, 1994, Efficient parallelization energy, surface, derivative calculations, internal coordinate mechanics, J Comp Chem}
h4-- Totrov, M.M., and Abagyan, R.A. (1994). Efficient parallelization of the energy, surface and derivative calculations for internal coordinate mechanics. J. Comp. Chem., 15, 1105-1112
An efficient algorithm for parallelization of a molecular mechanics program operation in the space of internal coordinates
such as dihedral angles, bond angles, and bond length, is described. The iterative procedure to calculate analytical energy
derivatives with respect to the internal coordinates was modified to allow parallelization. Computationally intensive
modules that calculate energy and its derivatives, solvent-accessible surface, electrostatic polarization energy and that
update lists of interactions were parallelized with nearly 100% efficiency. The proposed strategy for the shared-memory
computer architecture is easily scalable and requires minimum changes in a program code. The overall speedup for a
realistic calculation minimizing the energy of a myoglobin reaches a factor of 3 for 4 processors.
>>>35{ref 35,Borchert, 1995, Three new crystal structures, point mutation variants, monoTIM, conformational flexibility, Structure}
h4-- Borchert, T.V., Kishan, K.V.R., Zeelen, J.Ph., Schliebs, W., Thanki, N., Abagyan, R.A., Jaenicke, R., and Wierenga, R.K. (1995). Three new crystal structures of point mutation variants of monoTIM: conformational flexibility of loop-1, loop-4 and loop-8. Structure, 3, 669-679
Wild-type triosephosphate isomerase (TIM) is a very stable dimeric enzyme. This dimer can be converted into a stable
monomeric protein (monoTIM) by replacing the 15-residue interface loop (loop-3) by a shorter, 8-residue, loop. The
crystal structure of monoTIM shows that two active-site loops (loop-1 and loop-4), which are at the dimer interface in
wild-type TIM, have acquired different rather different structural properties. Nevertheless, monoTIM gas residual catalytic
activity.
Three new structures of variants of monoTIM are presented, a double-point mutant crystallized in the presence and
absence of bound inhibitor, and a single-point mutant in the presence of a different inhibitor. These new structures show
large structural variability for the active-site loops, loop-1, loop-4 and loop-8. In the structures with inhibitor bound, the
catalytic lysine (Lys13 in loop-1) and the catalytic histidine (His95 in loop-4) adopt conformations similar to those observed
in wild-type TIM, but very different from the monoTIM structure.
The residual catalytic activity of monoTIM can now be rationalized. In the presence of substrate analogues the active-site
loops, loop-1, loop-4 and loop-8, as well as the the catalytic residues, adopt conformations similar to those seen in the
wild-type protein. These loops lack conformational flexibility in wild-type TIM. The data suggest that the rigidity of these
loops in wild-type TIM, resulting from subunit-subunit contacts at the dimer interface, is important for optimal catalysis.
`35.pdf#{[PDF]}
>>>36{ref 36,Houbrechts, 1995, Second-generation octarellins, polypeptides designed, barrel structure stability, Protein Engineering}
h4-- Houbrechts, A., Moreau, B., Abagyan, R., Mainfroid, V., Preaux G., Lamproye, A., Poncin, A., Goormaghtigh, E., Ryusschaert, J.-M., Martial, J.A., Goraj, K. (1995). Second-generation octarellins: two new de novo (??)8 polypeptides designed for investigating the influence of ?-residue packing on the ?/? -barrel structure stability. Protein Engineering, 8, 249-259
The sequence of octarellin I, the first de novo (beta/alpha)8 polypeptide, was revised according to several criteria, among others the
symmetry of the sequence, beta-residue volume and hydrophobicity, and charge distribution. These considerations and the overall
conclusions drawn from the first design led to two new sequences, corresponding to octarellins II and III. Octarellin II retains perfect
8-fold symmetry. Octarellin III has the same sequence as octarellin II, except for the beta-strands which exhibit a 4-fold symmetry. The
two proteins were produced in Escherichia coli. Infrared and CD spectral analyses of octarellins II and III reveal a high secondary
structure content. Non-denaturing gel electrophoresis, molecular sieve chromatography and analytical ultracentrifugation suggest that
both of these second-generation artificial polypeptides exist as a mixture of a monomer and a dimer form. Octarellins II and III are at
least 10 times more soluble than octarellin I. Urea-induced unfolding followed by fluorescence emission suggests that the tryptophan
residues, designed to be buried in the (beta/alpha)8, are indeed packed in the hydrophobic core of both proteins. However, octarellin III
displays a higher stability towards urea denaturation, indicating that introducing 4-fold symmetry into the beta-barrel might be important
for stability of the overall folding.
>>>37{ref 37,Cardozo, 1995, Homology modeling, ICM method, Proteins Structure Function Genetics}
h4-- Cardozo, T., Totrov, M., and Abagyan, R. (1995). Homology modeling by the ICM method. Proteins: Structure, Function, Genetics, 23, 403-414
Five models have been built by the ICM method for the Comparative Modeling section of the Meeting on the Critical
Assessment of Techniques for Protein Structure Prediction. The targets have homologous proteins with known
three-dimensional structure with sequence identity ranging from 25 to 77%. After alignment of the target sequence with the
related three-dimensional structure, the modeling procedure consists of two subproblems: side-chain prediction and loop
prediction. The ICM method approaches these problems with the following steps: (1) a starting model is created based on
the homologous structure with the conserved portion fixed and the nonconserved portion having standard covalent
geometry and free torsion angles; (2) the Biased Probability Monte Carlo (BPMC) procedure is applied to search the
subspaces of either all the nonconservative side-chain torsion angles or torsion angles in a loop backbone and surrounding
side chains. A special algorithm was designed to generate low-energy loop deformations. The BPMC procedure globally
optimizes the energy function consisting of ECEPP/3 and solvation energy terms. Comparison of the predictions with the
NMR or crystallographic solutions reveals a high proportion of correctly predicted side chains. The loops were not correctly
predicted because imprinted distortions of the backbone increased the energy of the near-native conformation and thus
made the solution unrecognizable. Interestingly, the energy terms were found to be reliable and the sampling of
conformational space sufficient. The implications of this finding for the strategies of future comparative modeling are
discussed.
>>>38{ref 38,Totrov, 1996, Contour-Buildup Algorithm, Analytical Molecular Surface, J Struct Biol}
h4-- Totrov, M.M., and Abagyan, R.A. (1996). The Contour-Buildup Algorithm to Calculate the Analytical Molecular Surface. J. Struct. Biol., 116, 138-143
A new algorithm is presented to calculate the analytical molecular surface defined as a smooth envelope traced out by the
surface of a probe sphere rolled over the molecule. The core of the algorithm is the sequential build up of multi-arc
contours on the van der Waals spheres. This algorithm yields substantial reduction in both memory and time requirements
of surface calculations. Further, the contour-buildup principle is intrinsically "local", which makes calculations of the
partial molecular surfaces even more efficient. Additionally, the algorithm is equally applicable not only to convex patches,
but also to concave triangular patches which may have complex multiple intersections. The algorithm permits the rigorous
calculation of the full analytical molecular surface for a 100-residue protein in about 2 seconds on an SGI indigo with
R4400++ processor at 150 Mhz, with the performance scaling almost linearly with the protein size. The contour-buildup
algorithm is faster than the original Connolly algorithm an order of magnitude.
`38.pdf#{[PDF]}
>>>39{ref 39,Chalikian, 1996, hydration of globular proteins, volume, compressibility measurements, cross correlating thermodynamic, J Mol Biol}
h4-- Chalikian, T.V., Totrov, M.M., Abagyan, R.A., Breslauer, K.J. (1996). The hydration of globular proteins as derived from volume and compressibility measurements: cross correlating thermodynamic and structural data. J. Mol. Biol., 260, 588-603
We report the first thermodynamic characterization of protein hydration that does not depend on model compound data
but rather is based exclusively on macroscopic (volumetric) and microscopic (X-ray) measurements on protein molecules
themselves. By combining these macroscopic and microscopic characterizations, we describe a quantitative model that
allows one for the first time to predict the partial specific volumes, v(zero), and the partial specific adiabatic
compressibilities, ks(zero), of globular proteins from the crystallographic coordinates of the constituent atoms, without
using data derived from studies on low-molecular-mass model compounds. Specifically, we have used acoustic and
densimetric techniques to determine v(zero) and ks(zero) for 15 globular proteins over a temperature range from 18 to 55
degrees C. For the subset of the 12 proteins with known three-dimensional structures, we calculated the molecular volumes
as well as the solvent-accessible surface areas of the constituent charged, polar and nonpolar atomic groups. By combining
these measured and calculated properties and applying linear regression analysis, we determined, as a function of
temperature, the average hydration contributions to v(zero) and ks(zero) of 1 A2 of the charged, polar, and nonpolar
solvent-accessible protein surfaces. We compared these results with those derived from studies on low-molecular-mass
compounds to assess the validity of existing models of protein hydration based on small molecule data. This comparison
revealed the following features: the hydration contributions to v(zero) and ks(zero) of charged protein surface groups are
similar to those of charged groups in small organic molecules. By contrast, the hydration contributions to v(zero) and
ks(zero) of polar protein surface groups are qualitatively different from those of polar groups in low-molecular-mass
compounds. We suggest that this disparity may reflect the presence of networks of water molecules adjacent to polar
protein surface areas, with these networks involving waters from second and third coordination spheres. For nonpolar
protein surface groups, we find the ability of low-molecular-mass compounds to model successfully protein properties
depends on the temperature domain being examined. Specifically, at room temperatures and below, the hydration
contribution to ks(zero) of protein nonpolar surface atomic groups is close to that of nonpolar groups in small organic
molecules. By contrast, at higher temperatures, the hydration contribution to ks(zero) of protein nonpolar surface groups
becomes more negative than that of nonpolar groups in small organic molecules. We suggest that this behaviour may reflect
nonpolar groups on protein surfaces being hydrated independently at low temperatures, while at higher temperatures some
of the solvating waters become influenced by neighboring polar groups. We discuss the implications of our aggregate results
in terms of various approaches currently being used to describe the hydration properties of globular proteins, particularly
focusing on the limitations of existing additive models based on small molecule data.
`39.pdf#{[PDF]}
>>>40{ref 40,Strynadka, 1996, Molecular docking programs, successfully predict the binding of a beta-lactamase inhibitory protein, TEM-1 beta-lactamase, Nature Struct Biol}
h4-- Strynadka, N.C.J., Eisenstein, M., Katchalski-Katzir, E., Shoichet, B.K., Kuntz, I.D., Abagyan, R., Totrov, M., Janin, J., Cherfils, J., Zimmerman, F., Olson, A., Duncan, B., Rao, M., Jackson, R., Sternberg, M., and. James, M.N.G. (1996). Molecular docking programs successfully predict the binding of a beta-lactamase inhibitory protein to TEM-1 beta-lactamase. Nature Struct. Biol., 3, 233-239
Crystallization of the 1:1 molecular complex between the beta-lactamase TEM-1 and the beta-lactamase inhibitory protein
BLIP has provided an opportunity to put a stringent test on current protein-docking algorithms. Prior to the successful
determination of the structure of the complex, nine laboratory groups were given the refined atomic coordinates of each of
the native molecules. Other than the fact that BLIP is an effective inhibitor of a number of beta-lactamase enzymes (KI for
TEM-1 approximately 100 pM) no other biochemical or structural data were available to assist the practitioners in their
molecular docking. In addition, it was not known whether the molecules underwent conformational changes upon
association or whether the inhibition was competitive or non-competitive. All six of the groups that accepted the challenge
correctly predicted the general mode of association of BLIP and TEM-1.
>>>41{ref 41,Goodman, 1996, Long Pentraxins, Emerging Group Proteins, Diverse Functions, Cytokine & Growth Factor Reviews}
h4-- Goodman, A.R., Cardozo, T., Abagyan, R.A., Altmeyer, A., Wisniewski, H.G., and Vilcek, J. (1996). Long Pentraxins: an Emerging Group of Proteins with Diverse Functions. Cytokine & Growth Factor Reviews, 7, 191-202
The earliest described pentraxins, C reactive protein (CRP) and serum amyloid P component (SAP), are cytokine-inducible acute phase
proteins implicated in innate immunity whose concentrations in the blood increase dramatically upon infection or trauma. The highly
conserved family of pentraxins was thought to consist solely of approximately 25 kDa proteins. Recently, several distinct larger proteins
have been identified in which only the C-terminal halves show characteristic features of the pentraxin family. One of the recently
described "long" pentraxins (TSG-14/PTX3) is inducible by TNF or IL-1 and is produced during the acute phase response. Other newly
identified long pentraxins are constitutively expressed proteins associated with sperm-egg fusion (apexin/p50), may function at the
neuronal synapse (neuronal pentraxin I, NPI), or may serve yet other, unknown functions (NPII and XL-PXN1). Evidence obtained by
molecular modeling and by direct physicochemical analysis suggests that TSG-14 protein retains some characteristic structural features
of the pentraxins, including the formation of pentameric complexes.
>>>42{ref 42,Thanki, 1997, Protein engineering, monomeric triosephosphate isomerase, monoTIM, structure verification, seven residue loop, Protein Eng}
h4-- Thanki, N., Zeelen, J.Ph., Mathieu, M., Jaenicke, R., Abagyan, R.A., Wierenga R.K., and Schliebs, W. (1997). Protein engineering with monomeric triosephosphate isomerase (monoTIM): the modelling and structure verification of a seven residue loop. Protein Eng., 10, 159-167
Protein engineering experiments have been carried out with loop-1 of monomeric triosephosphate isomerase (monoTIM).
Loop-1 of monoTIM is disordered in every crystal structure of liganded monoTIM, but in the wildtype TIM it is a very
rigid dimer interface loop. This loop connects the first beta-strand with the first alpha-helix of the TIM-barrel scaffold.
The first residue of this loop, Lys13, is a conserved catalytic residue. The protein design studies with loop-1 were aimed at
rigidifying this loop such that the Lys13 side chain points in the same direction as seen in wild type. The modelling suggested
that the loop should be made one residue shorter. With the modelling package ICM the optimal sequence of a new
seven-residue loop-1 was determined and its structure was predicted. The new variant could be expressed and purified and
has been characterized. The catalytic activity and stability are very similar to those of monoTIM. The crystal structure (at 2.6
A resolution) shows that the experimental loop-1 strucutre agrees well with the modelled loop-1 structure. The direct
superposition of the seven loop residues of the modelled and experimental structures results in an r.m.s. difference of 0.5 A
for the 28 main chain atoms. The good agreement between the predicted structure and the crystal structure shows that the
described modelling protocol can be used successfully for the reliable prediction of loop structure.
`42.pdf#{[PDF]}
>>>43{ref 43,Maiorov, 1997, new method modeling, large-scale rearrangements, protein domains, Proteins}
h4-- Maiorov, V.N., and Abagyan, R.A. (1997). A new method for modeling large-scale rearrangements of protein domains. Proteins, 27, 410-424
A method for modeling large-scale rearrangements of protein domains connected by a single- or a double-stranded linker
is proposed. Multidomain proteins may undergo substantial domain displacements while their intra-domain structure
remains essentially unchanged. The method allows automatic identification of an inter-domain linker and builds an
all-atom model of a protein structure in internal coordinates. Torsion angles belonging to the inter-domain linkers and
side-chains potentially able to form domain interfaces are set free while all remaining torsions, bond lengths and bond
angles are fixed. Large-scale sampling of the reduced torsion conformational subspace is effected with the "biased
probability Monte Carlo-minimization" method (Abagyan, R. A. & Totrov, M. M. (1994) J. Mol. Biol. 235, 983-1002).
Solvation and side-chain entropic contributions are added to the energy function. A special procedure has been developed
to generate concerted deformations of a double-stranded inter-domain linker in such a way that the polypeptide chain
continuity is preserved. The method was tested on Bence-Jones protein with a single-stranded linker and
lysine/arginine/ornithine-binding (LAO) protein with a double-stranded linker. For each protein, structurally diverse low
energy conformations with ideal covalent geometry were generated, and an overlap between two sets of conformations
generated starting from the crystallographically determined "closed" and "open" forms was found. One of the low energy
conformations generated in a run starting from the LAO "closed" form was only 2.2 A away from the structure of the
"open" form. The method can be useful in predicting the scope of possble domain rearrangements of a multidomain protein.
`43.pdf#{[PDF]}
>>>44{ref 44,Yu, 1997, Mapping, Active Site CD59, J Expt Medicine}
h4-- Yu, J., Abagyan, R., Dong, S., Gilbert, A., Nusenzweig, V., and Tomlinson, S. (1997). Mapping of the Active Site of CD59. J. Expt. Medicine, 185, 745-754
CD59 is a widely distributed membrane-bound inhibitor of the cytolic membrane attack complex (MAC) of complement.
This small (77 amino acid) glycopeptide is a member of the Ly6 superfamily of proteins and is important in protecting host
cells from the lytic and proinflammatory activity of the MAC. CD59 functions by binding to C8 and/or C9 in the nascent
MAC and interfering with C9 membranne insertion and polymerization. We present data obtained from a combination of
molecular modeling and mutagenesis techniques, which together indicate that the active site of CD59 is located in the vicinity
of a hydrophobic groove on the face of the molecule opposite to a "hydrophobic strip" suggestied earlier. In addition,
removal of the single N-linked glycosylation site at Asn18 of CD59 resulted in an enhancement of compliment inhibitory
activity.
`44.pdf#{[PDF]}
>>>45{ref 45,Abagyan, 1997, aligned sequences, share same fold, J Mol Biol}
h4-- Abagyan, R.A., and Batalov, S.V. (1997). Do aligned sequences share the same fold? J. Mol. Biol., 273, 355-368
Sequence comparison remains a powerful tool to assess the structural relatedness of two proteins. To improve reliability of
recognition in the twilight zone of sequence identities between 15 and 35%, we performed an exhaustive alignment of
sequences of protein domains with known