TSCC: Two-Stage Combinatorial Clustering for virtual screening using protein-ligand interactions and physicochemical features
| Autor: | Yen Fu Chen, Cheng Neng Ko, Chi Chun Lo, Jinn-Moon Yang, Daniel L. Clinciu |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Genetics
Virtual screening Chemical Phenomena Databases Factual Combinatorial Chemistry Techniques Proteins Computational biology Biology Ligands High-Throughput Screening Assays Structure-Activity Relationship Proceedings False positive paradox Cluster (physics) Cluster Analysis Humans Computer Simulation DNA microarray Cluster analysis Protein Binding Biotechnology Protein ligand |
| Zdroj: | BMC Genomics |
| ISSN: | 1471-2164 |
| DOI: | 10.1186/1471-2164-11-s4-s26 |
| Popis: | Background The 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. Results Using 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. Conclusion By 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. |
| Databáze: | OpenAIRE |
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