<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Nunez-Vivanco G</submitter><funding>Fondo Nacional de Deasarrollo Cientifico y Tecnologico</funding><pagination>19</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC4834829</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>8</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Since the structure of proteins is more conserved than the sequence, the identification of conserved three-dimensional (3D) patterns among a set of proteins, can be important for protein function prediction, protein clustering, drug discovery and the establishment of evolutionary relationships. Thus, several computational applications to identify, describe and compare 3D patterns (or motifs) have been developed. Often, these tools consider a 3D pattern as that described by the residues surrounding co-crystallized/docked ligands available from X-ray crystal structures or homology models. Nevertheless, many of the protein structures stored in public databases do not provide information about the location and characteristics of ligand binding sites and/or other important 3D</pubmed_abstract><journal>Journal of cheminformatics</journal><pubmed_title>Geomfinder: a multi-feature identifier of similar three-dimensional protein patterns: a ligand-independent approach.</pubmed_title><pmcid>PMC4834829</pmcid><funding_grant_id>1130185</funding_grant_id><pubmed_authors>Valdes-Jimenez A</pubmed_authors><pubmed_authors>Besoain F</pubmed_authors><pubmed_authors>Reyes-Parada M</pubmed_authors><pubmed_authors>Nunez-Vivanco G</pubmed_authors></additional><is_claimable>false</is_claimable><name>Geomfinder: a multi-feature identifier of similar three-dimensional protein patterns: a ligand-independent approach.</name><description>&lt;h4>Background&lt;/h4>Since the structure of proteins is more conserved than the sequence, the identification of conserved three-dimensional (3D) patterns among a set of proteins, can be important for protein function prediction, protein clustering, drug discovery and the establishment of evolutionary relationships. Thus, several computational applications to identify, describe and compare 3D patterns (or motifs) have been developed. Often, these tools consider a 3D pattern as that described by the residues surrounding co-crystallized/docked ligands available from X-ray crystal structures or homology models. Nevertheless, many of the protein structures stored in public databases do not provide information about the location and characteristics of ligand binding sites and/or other important 3D</description><dates><release>2016-01-01T00:00:00Z</release><publication>2016</publication><modification>2026-06-12T05:35:31.418Z</modification><creation>2019-03-26T22:35:20Z</creation></dates><accession>S-EPMC4834829</accession><cross_references><pubmed>27092185</pubmed><doi>10.1186/s13321-016-0131-9</doi></cross_references></HashMap>