topics ranging from sequence-structure relationships to molecular simulations of biomolecules http://www.citeulike.org/groupfunc/122 CiteULike.org en-gb Copyright © 2004-2007 Oversity Limited http://www.citeulike.org/group/122/article/6420819 Motivation: Scoring functions provided by the docking software are still a major limiting factor in virtual screening (VS) process to classify compounds. Score analysis of the docking is not able to find out all active compounds. This is due to a bad estimation of the ligand binding energies. Making the assumption that active compounds should have specific contacts with their target to display activity, it would be possible to discriminate active compounds from inactive ones with careful analysis of interatomic contacts between the molecule and the target. However, compounds clustering is very tedious due to the large number of contacts extracted from the different conformations proposed by docking experiments. Results: Structural analysis of docked structures is processed in three steps: (i) a Kohonen self-organizing map (SOM) training phase using drug-protein contact descriptors followed by (ii) an unsupervised cluster analysis and (iii) a Newick file generation for results visualization as a tree. The docking poses are then analysed and classified quickly and automatically by AuPosSOM (Automatic analysis of Poses using SOM). AuPosSOM can be integrated into strategies for VS currently employed. We demonstrate that it is possible to discriminate active compounds from inactive ones using only mean protein contacts' footprints calculation from the multiple conformations given by the docking software. Chemical structure of the compound and key binding residues information are not necessary to find out active molecules. Thus, contact-activity relationship can be employed as a new VS process. Availability: AuPosSOM is available at http://www.aupossom.com. Contact: contact@aupossom.com; gildas.bertho@parisdescartes.fr Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btp623 2009-12-22T12:47:11+00:00 http://www.citeulike.org/group/122/article/6420719 Motivation: Prediction of ligand binding sites of proteins is significant as it can provide insight into biological functions and reaction mechanisms of proteins. It is also a prerequisite for protein-ligand docking and an important step in structure-based drug design. Results: We present a new algorithm, Roll, implemented in a program named POCASA, which can predict binding sites by detecting pockets and cavities of proteins with a rolling sphere. To evaluate the performance of POCASA, a test with the same data set as used in several existing methods was carried out. POCASA achieved a high success rate of 77%. In addition, the test results indicated that POCASA can predict good shapes of ligand binding sites. Availability:A web version of POCASA is freely available at http://altair.sci.hokudai.ac.jp/g6/Research/POCASA_e.html Contact: yao@castor.sci.hokudai.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btp599 2009-12-22T12:16:58+00:00