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- W4376603265 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." @default.
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- W4376603265 date "2023-05-15" @default.
- W4376603265 modified "2023-10-18" @default.
- W4376603265 title "Chemical Features and Machine Learning Assisted Predictions of Protein-Ligand Short Hydrogen Bonds" @default.
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- W4376603265 doi "https://doi.org/10.21203/rs.3.rs-2895170/v1" @default.
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