TSCC: Two-Stage Combinatorial Clustering for virtual screening using protein-ligand interactions and physicochemical features

Abstract

BackgroundThe increasing numbers of 3D compounds and protein complexes stored in databases contribute greatly to current advances in biotechnology, being employed in several pharmaceutical and industrial applications. However, screening and retrieving appropriate candidates as well as handling false positives presents a challenge for all post-screening analysis methods employed in retrieving therapeutic and industrial targets.ResultsUsing the TSCC method, virtually screened compounds were clustered based on their protein-ligand interactions, followed by structure clustering employing physicochemical features, to retrieve the final compounds. Based on the protein-ligand interaction profile (first stage), docked compounds can be clustered into groups with distinct binding interactions. Structure clustering (second stage) grouped similar compounds obtained from the first stage into clusters of similar structures; the lowest energy compound from each cluster being selected as a final candidate.ConclusionBy representing interactions at the atomic-level and including measures of interaction strength, better descriptions of protein-ligand interactions and a more specific analysis of virtual screening was achieved. The two-stage clustering approach enhanced our post-screening analysis resulting in accurate performances in clustering, mining and visualizing compound candidates, thus, improving virtual screening enrichment.

Uncovering Flexible Active Site Conformations of SARS-CoV-2 3CL Proteases through Protease Pharmacophore Clusters and COVID-19 Drug Repurposing

“…To quantify and summarize the diversity of active site flexible conformations in our SARS-CoV-2 3CL protease dataset, we used a TSCC method, successfully applied to study protein− ligand interactions and previously for virtual screening 15 (Protease Dataset Collection and Clustering by TSCC section in Materials and Methods). In the first stage of TSCC, the 42 protease−ligand complexes were clustered by the similarity of ligand−residue interactions using interaction similarity (IS) scores to obtain blue and orange clusters (Figure S5A) confirmed by cluster IS scores of 0.7 and 0.77, respectively, within the clusters.…”

Medicinal Chemistry for 2020

“…Only 18 of these are identified by more than one of the three pharmacophores, and only two by all three. None of the 150 hit structures have ever actually been synthesized;A similarity cluster analysis [118] is carried out on the 150 hit structures, from which 70 relatively disparate structures are selected;The 70 structures are subjected to rigorous docking analysis, again based on the pseudoreceptor model, involving consensus-scoring methodology, and provisions for limited target (pseudo-receptor) flexibility [119,120] as deemed appropriate based on the PPI model. Approximately 30 of the structures cannot be docked under these constraints.…”

“…The 70 structures are subjected to rigorous docking analysis, again based on the pseudoreceptor model, involving consensus-scoring methodology, and provisions for limited target (pseudo-receptor) flexibility [119,120] as deemed appropriate based on the PPI model. Approximately 30 of the structures cannot be docked under these constraints.…”

Challenges of the next decade for the Asia Pacific region: 2010 International Conference in Bioinformatics (InCoB 2010)

“…Clinchiu et al . [ 40 ] propose TSCC: a Two-Stage Combinative Clustering for virtual screening using protein-ligand interactions and physical-chemical features, to assist drug design.…”