Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Unité Mixte de Recherche 8601, Centre National de la Recherche Scientifique (CNRS), Université Paris Descartes, 45 rue des Saints-Pères, 75006 Paris, France.
Bioinformatics. 2010 Jan 1;26(1):53-60. doi: 10.1093/bioinformatics/btp623. Epub 2009 Nov 12.
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.
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.
AuPosSOM is available at http://www.aupossom.com.
配体 docking 软件提供的打分函数仍然是虚拟筛选 (VS) 过程中化合物分类的主要限制因素。对接的打分分析无法找出所有的活性化合物。这是由于配体结合能的估计不佳。假设活性化合物应该与它们的靶标具有特定的接触来显示活性，那么通过仔细分析分子与靶标之间的原子间接触，可以将活性化合物与非活性化合物区分开来。然而，由于从 docking 实验提出的不同构象中提取了大量的接触，化合物聚类非常繁琐。
对接结构的结构分析分三个步骤进行：（i）使用药物-蛋白质接触描述符进行 Kohonen 自组织映射 (SOM) 训练阶段，然后进行（ii）无监督聚类分析和（iii）Newick 文件生成，以便以树状形式可视化结果。然后使用 AuPosSOM（使用 SOM 进行自动分析）快速自动分析对接构象。AuPosSOM 可以集成到当前使用的 VS 策略中。我们证明，仅使用对接软件提供的多个构象中的平均蛋白质接触“足迹”计算，就可以将活性化合物与非活性化合物区分开来。不需要化合物的化学结构和关键结合残基信息来找出活性分子。因此，可以将接触-活性关系用作新的 VS 过程。
AuPosSOM 可在 http://www.aupossom.com 上获得。
