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The three-dimensional architecture of biomolecules often creates specialized structural elements, notably short hydrogen bonds that have donor–acceptor separations below 2.7 Å. In this work, we statistically analyze 1663 high-resolution biomolecular structures from the Protein Data Bank and demonstrate that short hydrogen bonds are prevalent in proteins, protein–ligand complexes and nucleic acids. From these biological macromolecules, we characterize the preferred location, connectivity and amino acid composition in short hydrogen bonds and hydrogen bond networks, and assess their possible functional importance. Using electronic structure calculations, we further uncover how the interplay of the structural and chemical features determines the proton potential energy surfaces and proton sharing conditions in biological short hydrogen bonds.
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Effective prediction of short hydrogen bonds in proteins via machine learning method
Abstract Short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms lie within 2.7 Å, exhibit prominent quantum mechanical characters and are connected to a wide range of essential biomolecular processes. However, exact determination of the geometry and functional roles of SHBs requires a protein to be at atomic resolution. In this work, we analyze 1260 high-resolution peptide and protein structures from the Protein Data Bank and develop a boosting based machine learning model to predict the formation of SHBs between amino acids. This model, which we name as machine learning assisted prediction of short hydrogen bonds (MAPSHB), takes into account 21 structural, chemical and sequence features and their interaction effects and effectively categorizes each hydrogen bond in a protein to a short or normal hydrogen bond. The MAPSHB model reveals that the type of the donor amino acid plays a major role in determining the class of a hydrogen bond and that the side chain Tyr-Asp pair demonstrates a significant probability of forming a SHB. Combining electronic structure calculations and energy decomposition analysis, we elucidate how the interplay of competing intermolecular interactions stabilizes the Tyr-Asp SHBs more than other commonly observed combinations of amino acid side chains. The MAPSHB model, which is freely available on our web server, allows one to accurately and efficiently predict the presence of SHBs given a protein structure with moderate or low resolution and will facilitate the experimental and computational refinement of protein structures.
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- Award ID(s):
- 1904800
- PAR ID:
- 10361616
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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