Biomedical subjects

M Totrov

Publications and source records attributed to M Totrov.

12 recordsLinked to original sources

PubMed2001

Rapid boundary element solvation electrostatics calculations in folding simulations: successful folding of a 23-residue peptide.

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 beta beta alpha-peptide was then globally optimized with and without the solvation electrostatics contribution. An ensemble of beta beta alpha 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.

Algorithms↗

PubMed2001

High-throughput docking for lead generation.

Recent improvements in flexible docking technology may lead to a bigger role for computational methods in lead discovery. Although fast and accurate computational prediction of binding affinities for an arbitrary molecule is still beyond the limits of current methods, the docking and screening procedures can select small sets of likely lead candidates from large libraries of either commercially or synthetically available compounds.

Binding Sites↗

PubMed2000

Expression of the adenovirus receptor and its interaction with the fiber knob.

The coxsackievirus group B (CVB) and adenovirus (Ad) receptor (HCVADR, formerly HCAR) is a cell surface protein with two immunoglobulin-like regions (IG1 and IG2) that serves as a receptor for two structurally unrelated viruses. We have established the tissue distribution of the receptor in the rodent by immunohistochemistry and show that the receptor is broadly expressed during embryonic development in the central and peripheral nervous systems and in several types of epithelial cells. The tissue distribution is more restricted in the adult but remains high mainly in epithelial cells. Using site-directed mutagenesis, based on computer modeling of the IG1 region, Ad5 binding could be inhibited but CVB attachment was unaffected. A double amino acid substitution in a three-stranded anti-parallel beta sheet that may form a face of the receptor completely inhibited Ad5 binding. Therefore, we conclude that the molecular interactions critical for Ad5 binding to HCVADR do not overlap with those of CVB3. In fact a specific antibody interfering with only CVB binding recognizes the IG2 domain in the receptor, suggesting that the CVB interacts with this region or an overlap between the IG1 and the IG2 regions.

3T3 Cells↗

PubMed1997

Flexible protein-ligand docking by global energy optimization in internal coordinates.

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 [Abagyan, R.A., Totrov, M.M. J. Mol. Biol. 235: 983-1002, 1994]. The procedure uses two types of efficient random moves, a pseudobrownian positional move [Abagyan, R.A., Totrov, M.M., Kuznetsov, D.A. J. Comp. Chem. 15:488-506, 1994] and a Biased-Probability multitorsion move [Abagyan, R.A., Totrov, M.M. J. Mol. Biol. 235: 983-1002, 1994], 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.

Computer Simulation↗

PubMed1997

Homology modeling with internal coordinate mechanics: deformation zone mapping and improvements of models via conformational search.

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.

Models, Molecular↗

PubMed1996

The hydration of globular proteins as derived from volume and compressibility measurements: cross correlating thermodynamic and structural data.

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.

Crystallography, X-Ray↗

PubMed1996

Molecular docking programs successfully predict the binding of a beta-lactamase inhibitory protein to TEM-1 beta-lactamase.

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.

Amino Acid Sequence↗

PubMed1995

Homology modeling by the ICM method.

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.

Antibodies, Anti-Idiotypic↗

PubMed1994

Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins.

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.

Algorithms↗

PubMed1994

Detailed ab initio prediction of lysozyme-antibody complex with 1.6 A accuracy.

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-1) than any other solution.

Animals↗

PubMedDate not supplied

Prediction of the binding energy for small molecules, peptides and proteins.

A fast and reliable evaluation of the binding energy from a single conformation of a molecular complex is an important practical task. Knowledge-based scoring schemes may not be sufficiently general and transferable, while molecular dynamics or Monte Carlo calculations with explicit solvent are too computationally expensive for many applications. Recently, several empirical schemes using finite difference Poisson-Boltzmann electrostatics to predict energies for particular types of complexes were proposed. Here, an improved empirical binding energy function has been derived and validated on three different types of complexes: protein-small ligand, protein-peptide and protein-protein. The function uses the boundary element algorithm to evaluate the electrostatic solvation energy. We show that a single set of parameters can predict the relative binding energies of the heterogeneous validation set of complexes with 2.5 kcal/mol accuracy. We also demonstrate that global optimization of the ligand and of the flexible side-chains of the receptor improves the accuracy of the evaluation.

Computer Simulation↗

PubMedDate not supplied

The contour-buildup algorithm to calculate the analytical molecular surface.

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.

Algorithms↗