{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Zhou S"],"funding":["NHGRI NIH HHS"],"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."],"journal":["Research square"],"pagination":["rs.3.rs-2895170"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10246099"],"repository":["biostudies-literature"],"pubmed_title":["Chemical Features and Machine Learning Assisted Predictions of Protein-Ligand Short Hydrogen Bonds."],"pmcid":["PMC10246099"],"funding_grant_id":["R01 HG007377"],"pubmed_authors":["Liu Y","Zhou S","Wang S","Wang L"],"additional_accession":[]},"is_claimable":false,"name":"Chemical Features and Machine Learning Assisted Predictions of Protein-Ligand Short Hydrogen Bonds.","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.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 May","modification":"2025-04-04T10:21:48.847Z","creation":"2025-02-19T02:38:03.182Z"},"accession":"S-EPMC10246099","cross_references":{"pubmed":["37292822"],"doi":["10.21203/rs.3.rs-2895170/v1"]}}