An Effective Approach for Clustering InhA Molecular Dynamics Trajectory Using Substrate-Binding Cavity Features

Abstract

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.

Clustering algorithms to analyze molecular dynamics simulation trajectories for complex chemical and biological systems

“…As reported by Shao et al [23], the two algorithms produced clusters of varying sizes and performed well in clustering MD datasets. In the work by De Paris et al [78], the authors showed that, the performances of the two algorithms were generally good when using cavity attributes as the clustering metric.…”

“…torsional angles, inter-residue distances, etc.) [22,45], substrate-binding cavity [78], and collective coordinates [80] can also be applied to compare the similarities between different MD conformations of biological macromolecules.…”

“…3. In this section, we will introduce the basic principles and discuss the performances of some widely used hierarchical algorithms [22,23,[68][69][70]78].…”

“…From the clustering results, they found that unlike the singlelinkage algorithm, which tends to generate singletons, the complete-linkage algorithm is more likely to produce meaningful sets of clusters. De Paris et al [78] implemented the complete-linkage algorithm to classify the MD trajectories of the InhA enzyme for docking purposes using the cavity attributes as the clustering metric. The authors found that in their cases, completelinkage algorithm was among the best choices.…”

The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix

“…The use of clustering algorithms to group similar conformations is the most appropriate data mining technique to distill the structural information from properties of an MD trajectory [7–10]. Therefore, the selection of representative conformers is valuable and very important in the 3D-QSAR model, pharmacophore model, protein–ligand docking [11], and Bayesian classification model from 3D fingerprints.…”