Evidence map›Paper›PMID 28012138›Full record

ArticleJournal of structural and functional genomics2016

Classification of ligand molecules in PDB with graph match-based structural superposition.

Clara Shionyu-Mitsuyama, Atsushi Hijikata, Toshiyuki Tsuji, Tsuyoshi Shirai

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Article in Journal of structural and functional genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Clara Shionyu-MitsuyamaDepartment of Bioscience, Nagahama Institute of Bio-science and Technology, 1266 Tamura, Nagahama, 526-0829, Japan.
Atsushi HijikataDepartment of Bioscience, Nagahama Institute of Bio-science and Technology, 1266 Tamura, Nagahama, 526-0829, Japan.
Toshiyuki TsujiDepartment of Bioscience, Nagahama Institute of Bio-science and Technology, 1266 Tamura, Nagahama, 526-0829, Japan.
Tsuyoshi ShiraiDepartment of Bioscience, Nagahama Institute of Bio-science and Technology, 1266 Tamura, Nagahama, 526-0829, Japan. t_shirai@nagahama-i-bio.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The fast heuristic graph match algorithm for small molecules, COMPLIG, was improved by adding a structural superposition process to verify the atom-atom matching. The modified method was used to classify the small molecule ligands in the Protein Data Bank (PDB) by their three-dimensional structures, and 16,660 types of ligands in the PDB were classified into 7561 clusters. In contrast, a classification by a previous method (without structure superposition) generated 3371 clusters from the same ligand set. The characteristic feature in the current classification system is the increased number of singleton clusters, which contained only one ligand molecule in a cluster. Inspections of the singletons in the current classification system but not in the previous one implied that the major factors for the isolation were differences in chirality, cyclic conformations, separation of substructures, and bond length. Comparisons between current and previous classification systems revealed that the superposition-based classification was effective in clustering functionally related ligands, such as drugs targeted to specific biological processes, owing to the strictness of the atom-atom matching.

Indexed as

AlgorithmsDatabases, ProteinProtein ConformationBinding SitesCluster AnalysisLigandsModels, MolecularProteinsLigandsProteinsBioinformaticsDrug designGraph matchProtein ligandStructural superposition

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.