An Effective Approach for Clustering InhA Molecular Dynamics Trajectory Using Substrate-Binding Cavity Features.
An Effective Approach for Clustering InhA Molecular Dynamics Trajectory Using Substrate-Binding Cavity Features.
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DOI：
10.1371/journal.pone.0133172
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Norberto de Souza O
中科院分区：
文献类型：
--
作者：
De Paris R;Quevedo CV;Ruiz DD;Norberto de Souza O
从分子动力学（MD）模拟中获得的蛋白质受体构象，由于其明确的灵活性，在应用于药物发现和开发的分子对接实验中已成为一种有前途的治疗方法。然而，在对接实验中整合整个MD构象集合来筛选大型候选化合物库目前是一项不可行的任务。聚类算法已被广泛用作一种手段，以减少这样的集成到一个可管理的规模。大多数研究对所有或部分MD构象使用两两均方根偏差（RMSD）值来研究不同的算法。然而，当目标受体具有塑性活性位点时，仅RMSD可能不是最合适的聚类构象测量标准，因为它们受到结构其他部分发生的变化的影响。因此，我们应用了两种划分方法（k-means和k- medidoids）和四种聚集分层方法（完全链接，Ward 's，未加权对组方法和加权对组方法）来分析和比较由酶受体底物结合腔的属性组成的数据集和使用不同RMSD方法创建的两个数据集之间的划分质量。通过从分析的所有分区中选择每组的一个介质，生成具有代表性的MD构象集合。我们通过20 ns MD轨迹与20种不同配体之间的交叉对接实验，研究了评估候选药物与InhA酶结合构象的新方法的性能。统计分析表明，在对接实验中，仅占0.48%的MD构象的新型系综能够再现75%的结合腔内动态行为。此外，这种新方法不仅优于其他两种rmsd聚类解决方案，而且还显示出从MD轨迹中提取生物学相关信息的有前途的策略，特别是用于对接目的。
Protein receptor conformations, obtained from molecular dynamics (MD) simulations, have become a promising treatment of its explicit flexibility in molecular docking experiments applied to drug discovery and development. However, incorporating the entire ensemble of MD conformations in docking experiments to screen large candidate compound libraries is currently an unfeasible task. Clustering algorithms have been widely used as a means to reduce such ensembles to a manageable size. Most studies investigate different algorithms using pairwise Root-Mean Square Deviation (RMSD) values for all, or part of the MD conformations. Nevertheless, the RMSD only may not be the most appropriate gauge to cluster conformations when the target receptor has a plastic active site, since they are influenced by changes that occur on other parts of the structure. Hence, we have applied two partitioning methods (k-means and k-medoids) and four agglomerative hierarchical methods (Complete linkage, Ward’s, Unweighted Pair Group Method and Weighted Pair Group Method) to analyze and compare the quality of partitions between a data set composed of properties from an enzyme receptor substrate-binding cavity and two data sets created using different RMSD approaches. Ensembles of representative MD conformations were generated by selecting a medoid of each group from all partitions analyzed. We investigated the performance of our new method for evaluating binding conformation of drug candidates to the InhA enzyme, which were performed by cross-docking experiments between a 20 ns MD trajectory and 20 different ligands. Statistical analyses showed that the novel ensemble, which is represented by only 0.48% of the MD conformations, was able to reproduce 75% of all dynamic behaviors within the binding cavity for the docking experiments performed. Moreover, this new approach not only outperforms the other two RMSD-clustering solutions, but it also shows to be a promising strategy to distill biologically relevant information from MD trajectories, especially for docking purposes.