<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><submitter>Zhou S</submitter><funding>NHGRI NIH HHS</funding><pubmed_abstract>There are continuous efforts to elucidate the structure and biological functions of short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms reside more than 0.3 A closer than the sum of their van der Waals radii. In this work, we evaluate 1070 atomic-resolution protein structures and characterize the common chemical features of SHBs formed between the side chains of amino acids and small molecule ligands. We then develop a machine learning assisted prediction of protein-ligand SHBs (MAPSHB-Ligand) model and reveal that the types of amino acids and ligand functional groups as well as the sequence of neighboring residues are essential factors that determine the class of protein-ligand hydrogen bonds. The MAPSHB-Ligand model and its implementation on our web server enable the effective identification of protein-ligand SHBs in proteins, which will facilitate the design of biomolecules and ligands that exploit these close contacts for enhanced functions.</pubmed_abstract><journal>Research square</journal><pagination>rs.3.rs-2895170</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10246099</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Chemical Features and Machine Learning Assisted Predictions of Protein-Ligand Short Hydrogen Bonds.</pubmed_title><pmcid>PMC10246099</pmcid><funding_grant_id>R01 HG007377</funding_grant_id><pubmed_authors>Liu Y</pubmed_authors><pubmed_authors>Zhou S</pubmed_authors><pubmed_authors>Wang S</pubmed_authors><pubmed_authors>Wang L</pubmed_authors></additional><is_claimable>false</is_claimable><name>Chemical Features and Machine Learning Assisted Predictions of Protein-Ligand Short Hydrogen Bonds.</name><description>There are continuous efforts to elucidate the structure and biological functions of short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms reside more than 0.3 A closer than the sum of their van der Waals radii. In this work, we evaluate 1070 atomic-resolution protein structures and characterize the common chemical features of SHBs formed between the side chains of amino acids and small molecule ligands. We then develop a machine learning assisted prediction of protein-ligand SHBs (MAPSHB-Ligand) model and reveal that the types of amino acids and ligand functional groups as well as the sequence of neighboring residues are essential factors that determine the class of protein-ligand hydrogen bonds. The MAPSHB-Ligand model and its implementation on our web server enable the effective identification of protein-ligand SHBs in proteins, which will facilitate the design of biomolecules and ligands that exploit these close contacts for enhanced functions.</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 May</publication><modification>2025-04-04T10:21:48.847Z</modification><creation>2025-02-19T02:38:03.182Z</creation></dates><accession>S-EPMC10246099</accession><cross_references><pubmed>37292822</pubmed><doi>10.21203/rs.3.rs-2895170/v1</doi></cross_references></HashMap>