Biomedical subjects

A M Ruvinsky

Publications and source records attributed to A M Ruvinsky.

4 recordsLinked to original sources

PubMed2006

Novel statistical-thermodynamic methods to predict protein-ligand binding positions using probability distribution functions.

We present two novel methods to predict native protein-ligand binding positions. Both methods identify the native binding position as the most probable position corresponding to a maximum of a probability distribution function (PDF) of possible binding positions in a protein active site. Possible binding positions are the origins of clusters composed, on the basis of root-mean square deviations (RMSD), from the multiple ligand positions determined by a docking algorithm. The difference between the methods lies in the ways the PDF is derived. To validate the suggested methods, we compare the averaged RMSD of the predicted ligand docked positions relative to the experimentally determined positions for a set of 135 PDB protein-ligand complexes. We demonstrate that the suggested methods improve docking accuracy by as much as 21-24% in comparison with a method that simply identifies the binding position as the energy top-scored ligand position.

Binding Sites↗

PubMed2005

The key role of atom types, reference states, and interaction cutoff radii in the knowledge-based method: new variational approach.

We present a variational method to derive knowledge-based potentials. The method is based on an optimization procedure of objective variables: atom types, reference states, and interaction cutoff radii. We suggest and apply new unsymmetrical reference states. The cutoff radii and atom types are optimized to improve docking accuracy of the corresponding potentials. The atom types are varied along an atom type tree, with 6 root and 49 top atom types, and the set of 18 optimal atom types is obtained. We demonstrate strong dependence between the choice of atom types and the docking accuracy of the potentials derived with these atom types. The averaged root-mean square deviations (RMSDs) of the ligand docked positions relative to the experimentally determined positions decrease when the elements C, N, O are split into the optimal types.

Algorithms↗

PubMed2005

New and fast statistical-thermodynamic method for computation of protein-ligand binding entropy substantially improves docking accuracy.

We present a novel method to estimate the contributions of translational and rotational entropy to protein-ligand binding affinity. The method is based on estimates of the configurational integral through the sizes of clusters obtained from multiple docking positions. Cluster sizes are defined as the intervals of variation of center of ligand mass and Euler angles in the cluster. Then we suggest a method to consider the entropy of torsional motions. We validate the suggested methods on a set of 135 PDB protein-ligand complexes by comparing the averaged root-mean square deviations (RMSD) of the top-scored ligand docked positions, accounting and not accounting for entropy contributions, relative to the experimentally determined positions. We demonstrate that the method increases docking accuracy by 10-21% when used in conjunction with the AutoDock docking program, thus reducing the percent of incorrectly docked ligands by 1.4-fold to four-fold, so that in some cases the percent of ligands correctly docked to within an RMSD of 2 A is above 90%. We show that the suggested method to account for entropy of relative motions is identical to the method based on the Monte Carlo integration over intervals of variation of center of ligand mass and Euler angles in the cluster.

Algorithms↗

PubMed2004

Novel knowledge-based potentials for ligand docking: variational approach to the old problem.

The variational approach of evaluation for knowledge-based potentials is considered for the first time. In this approach, the problem to derive knowledge-based potentials is solved as the optimization task in the multiparametric model of atom types, reference states and interaction cutoff radii. Using analogy to liquid state theory we offered four new reference states and derived corresponding knowledge-based potentials. The cutoff radii and atom types are optimized to minimize averaged root-mean square deviations (RMSD) of the ligand docked positions regarding to the experimentally determined poses. The number of atom types is varied on the developed atom type tree with 6 root (C, N, O, S, P and the halogen type) and 49 apical atom types. We showed a pronounced effect of atom type choice on docking accuracy and proved that splitting of elements C, N and O of the periodic system up to the 18 optimal atom types essentially improves docking accuracy.

Ligands↗