Journal of Computer Aided Molecular Design

A new peptide docking strategy using a mean field technique with mutually orthogonal Latin square sampling.

Publication Date: 2008 May 9 PMID: 18465087
Authors: Arun Prasad, P. - Gautham, N.
Journal: J Comput Aided Mol Des

The theoretical prediction of the association of a flexible ligand with a protein receptor requires efficient sampling of the conformational space of the ligand. Several docking methodologies are currently available. We propose a new docking technique that performs well at low computational cost. The method uses mutually orthogonal Latin squares to efficiently sample the docking space. A variant of the mean field technique is used to analyze this sample to arrive at the optimum. The method has been previously applied to explore the conformational space of peptides and identify structures with low values for the potential energy. Here we extend this method to simultaneously identify both the low energy conformation as well as a 'high-scoring' docking mode. Application of the method to 56 protein-peptide complexes, in which the length of the peptide ligand ranges from three to seven residues, and comparisons with Autodock 3.05, showed that the method works well.

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Editorial: contributions by wendy warr.

Publication Date: 2008 May PMID: 18338231
Authors: Stouch, T. R.
Journal: J Comput Aided Mol Des

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Customizing scoring functions for docking.

Publication Date: 2008 May PMID: 18273558
Authors: Pham, T. A. - Jain, A. N.
Journal: J Comput Aided Mol Des

Empirical scoring functions used in protein-ligand docking calculations are typically trained on a dataset of complexes with known affinities with the aim of generalizing across different docking applications. We report a novel method of scoring-function optimization that supports the use of additional information to constrain scoring function parameters, which can be used to focus a scoring function's training towards a particular application, such as screening enrichment. The approach combines multiple instance learning, positive data in the form of ligands of protein binding sites of known and unknown affinity and binding geometry, and negative (decoy) data of ligands thought not to bind particular protein binding sites or known not to bind in particular geometries. Performance of the method for the Surflex-Dock scoring function is shown in cross-validation studies and in eight blind test cases. Tuned functions optimized with a sufficient amount of data exhibited either improved or undiminished screening performance relative to the original function across all eight complexes. Analysis of the changes to the scoring function suggest that modifications can be learned that are related to protein-specific features such as active-site mobility.

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Docking and multivariate methods to explore HIV-1 drug-resistance: a comparative analysis.

Publication Date: 2008 May PMID: 18273557
Authors: Almerico, A. M. - Tutone, M. - Lauria, A.
Journal: J Comput Aided Mol Des

In this paper we describe a comparative analysis between multivariate and docking methods in the study of the drug resistance to the reverse transcriptase and the protease inhibitors. In our early papers we developed a simple but efficient method to evaluate the features of compounds that are less likely to trigger resistance or are effective against mutant HIV strains, using the multivariate statistical procedures PCA and DA. In the attempt to create a more solid background for the prediction of susceptibility or resistance, we carried out a comparative analysis between our previous multivariate approach and molecular docking study. The intent of this paper is not only to find further support to the results obtained by the combined use of PCA and DA, but also to evidence the structural features, in terms of molecular descriptors, similarity, and energetic contributions, derived from docking, which can account for the arising of drug-resistance against mutant strains.

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A new method for ligand docking to flexible receptors by dual alanine scanning and refinement (SCARE).

Publication Date: 2008 May PMID: 18273556
Authors: Bottegoni, G. - Kufareva, I. - Totrov, M. - Abagyan, R.
Journal: J Comput Aided Mol Des

Protein binding sites undergo ligand specific conformational changes upon ligand binding. However, most docking protocols rely on a fixed conformation of the receptor, or on the prior knowledge of multiple conformations representing the variation of the pocket, or on a known bounding box for the ligand. Here we described a general induced fit docking protocol that requires only one initial pocket conformation and identifies most of the correct ligand positions as the lowest score. We expanded a previously used diverse "cross-docking" benchmark to thirty ligand-protein pairs extracted from different crystal structures. The algorithm systematically scans pairs of neighbouring side chains, replaces them by alanines, and docks the ligand to each 'gapped' version of the pocket. All docked positions are scored, refined with original side chains and flexible backbone and re-scored. In the optimal version of the protocol pairs of residues were replaced by alanines and only one best scoring conformation was selected from each 'gapped' pocket for refinement. The optimal SCARE (SCan Alanines and REfine) protocol identifies a near native conformation (under 2 A RMSD) as the lowest rank for 80% of pairs if the docking bounding box is defined by the predicted pocket envelope, and for as many as 90% of the pairs if the bounding box is derived from the known answer with approximately 5 A margin as used in most previous publications. The presented fully automated algorithm takes about 2 h per pose of a single processor time, requires only one pocket structure and no prior knowledge about the binding site location. Furthermore, the results for conformationally conserved pockets do not deteriorate due to substantial increase of the pocket variability.

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RIP MDL information systems. Is this really the beginning of a new era?

Publication Date: 2008 May PMID: 18256891
Authors: Warr, W. A.
Journal: J Comput Aided Mol Des

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Quantitative structure-activity relationship studies of mushroom tyrosinase inhibitors.

Publication Date: 2008 May PMID: 18256890
Authors: Xue, C. B. - Luo, W. C. - Ding, Q. - Liu, S. Z. - Gao, X. X.
Journal: J Comput Aided Mol Des

Here, we report our results from quantitative structure-activity relationship studies on tyrosinase inhibitors. Interactions between benzoic acid derivatives and tyrosinase active sites were also studied using a molecular docking method. These studies indicated that one possible mechanism for the interaction between benzoic acid derivatives and the tyrosinase active site is the formation of a hydrogen-bond between the hydroxyl ((a)OH) and carbonyl oxygen atoms of Tyr(98), which stabilized the position of Tyr(98) and prevented Tyr(98 )from participating in the interaction between tyrosinase and ORF378. Tyrosinase, also known as phenoloxidase, is a key enzyme in animals, plants and insects that is responsible for catalyzing the hydroxylation of tyrosine into o-diphenols and the oxidation of o-diphenols into o-quinones. In the present study, the bioactivities of 48 derivatives of benzaldehyde, benzoic acid, and cinnamic acid compounds were used to construct three-dimensional quantitative structure-activity relationship (3D-QSAR) models using comparative molecular field (CoMFA) and comparative molecular similarity indices (CoMSIA) analyses. After superimposition using common substructure-based alignments, robust and predictive 3D-QSAR models were obtained from CoMFA (q (2) = 0.855, r (2) = 0.978) and CoMSIA (q (2) = 0.841, r (2) = 0.946), with 6 optimum components. Chemical descriptors, including electronic (Hammett sigma), hydrophobic (pi), and steric (MR) parameters, hydrogen bond acceptor (H-acc), and indicator variable (I), were used to construct a 2D-QSAR model. The results of this QSAR indicated that pi, MR, and H-acc account for 34.9, 31.6, and 26.7% of the calculated biological variance, respectively. The molecular interactions between ligand and target were studied using a flexible docking method (FlexX). The best scored candidates were docked flexibly, and the interaction between the benzoic acid derivatives and the tyrosinase active site was elucidated in detail. We believe that the QSAR models built here provide important information necessary for the design of novel tyrosinase inhibitors.

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