Abstract not found

This article describes and reviews our e#orts using Hex 3.1 to predict the docking modes of the seven target protein-protein complexes presented in the CAPRI (Critical Assessment of Predicted Interactions) blind docking trial. For each target, the structure of at least one of the docking partners was given in its unbound form, and several of the targets involved large multimeric structures (e.g. Lactobacillus HPr kinase, hemagglutinin, bovine rotavirus VP6). Here, we describe several enhancements to our original spherical polar Fourier docking correlation algorithm. For example, a novel surface sphere smothering algorithm is introduced to generate multiple local coordinate systems around the surface of a large receptor molecule which may be used to define a small number of initial ligand docking orientations distributed over the receptor surface. High resolution spherical polar docking correlations are performed over the resulting receptor surface patches, and candidate docking solutions are refined using a novel soft molecular mechanics energy minimisation procedure. Overall, this approach identified two good solutions at rank 5 or less for two of the seven CAPRI complexes. Subsequent analysis of our results shows that Hex 3.1 is able to place good solutions within a list of 20 or less for four of the seven targets. This demonstrates that useful in silico protein-protein docking predictions can now be made with increasing confidence, even for very large macromolecular complexes

This article gives an overview of recent progress in protein-protein docking and it identifies several directions for future research. Recent results from the CAPRI blind docking experiments show that docking algorithms are steadily improving in both reliability and accuracy. Current docking algorithms employ a range of efficient search and scoring strategies, including e.g. fast Fourier transform correlations, geometric hashing, and Monte Carlo techniques. These approaches can often produce a relatively small list of up to a few thousand orientations, amongst which a near-native binding mode is often observed. However, despite the use of improved scoring functions which typically include models of desolvation, hydrophobicity, and electrostatics, current algorithms still have difficulty in identifying the correct solution from the list of false positives, or decoys. Nonetheless, significant progress is being made through better use of bioinformatics, biochemical, and biophysical information such as e.g. sequence conservation analysis, protein interaction databases, alanine scanning, and NMR residual dipolar coupling restraints to help identify key binding residues. Promising new approaches to incorporate models of protein flexibility during docking are being developed, including the use of molecular dynamics snapshots, rotameric and off-rotamer searches, internal coordinate mechanics, and principal component analysis based techniques. Some investigators now use explicit solvent models in their docking protocols. Many of these approaches

Analysis of the distributions of physicochemical properties mapped onto molecular surfaces can highlight important similarities or differences between compound classes, contributing to rational drug design efforts. Here we present an approach that uses maximal common subgraph comparison and harmonic shape image matching to detect locally similar regions between two molecular surfaces augmented with properties such as the electrostatic potential or lipophilicity. The complexity of the problem is reduced by a set of filters that implement various geometric and physicochemical heuristics. The approach was tested on dihydrofolate reductase and thermolysin inhibitors and was shown to recover the correct alignments of the compounds bound in the active sites. 1.

Motivation: Predicting how proteins interact at the molecular level is a computationally intensive task. Many protein docking algorithms begin by using FFT correlation techniques to find putative rigid body docking orientations. Most such approaches use 3D Cartesian grids and are therefore limited to computing 3D translational correlations. However, translational FFTs can speed up the calculation in only three of the six rigid body degrees of freedom, and they cannot easily incorporate prior knowledge about a complex to focus and hence further accelerate the calculation. Furthemore, several groups have developed multi-term interaction potentials and others use multicopy approaches to simulate protein flexibility, which both add to the computational cost of FFT-based docking algorithms. Hence there is a need to develop more powerful and more versatile FFT docking techniques.

This article describes our attempts to dock the targets in CAPRI Rounds 3–5 using Hex 4.2, and it introduces a novel essential dynamics approach to generate multiple feasible conformations for docking. In the blind trial, the basic Hex algorithm found one high accuracy solution for CAPRI target 12, and several further medium and low accuracy solutions for targets 11, 12, 13, and 14. Subsequent a posteriori docking of the targets using essential dynamics “eigenstructures ” was found to give consistently better predic-tions than rigidly docking only the unbound or model-built starting structures. Some suggestions to improve this promising new approach are presented.

In recent years, many virtual screening (VS) tools have been developed that employ different molecular representations and have different speed and accuracy characteristics. In this paper, we compare ten popular ligand-based VS tools using the publicly available Directory of Useful Decoys (DUD) dataset comprising over 100,000 compounds distributed across 40 protein targets. The DUD was developed initially to evaluate docking algorithms, but our results from an operational correlation analysis show that it is also well suited for comparing ligand-based VS tools. Although it is conventional wisdom that 3D molecular shape is an important determinant of biological activity, our results based on permutational significance tests of several commonly used VS metrics show that the 2D fingerprint-based methods generally give better VS performance than the 3D shape-based approaches for surprisingly many of the DUD targets. In order to help understand this finding, we have analysed the nature of the scoring functions used and the composition of the DUD dataset itself. We propose that in order to To whom correspondence should be addressed

We are investigating the use of spherical harmonic (SH) expansions as an efficient way to represent and compare small ligand molecules for high throughput virtual screening (HTVS). It has been shown previously that SH expansions are well suited to calculating rotational correlations to superpose similar molecular surfaces 1 and dock complementary protein shapes 2. It has also been shown that SHs provide a compact way to encode quantum-mechanically calculated

docking with multiple residue

Protein–protein docking with multiple residue conformations and