Literature DB >> 16581371

Potential functions for hydrogen bonds in protein structure prediction and design.

Alexandre V Morozov1, Tanja Kortemme.   

Abstract

Hydrogen bonds are an important contributor to free energies of biological macromolecules and macromolecular complexes, and hence an accurate description of these interactions is important for progress in biomolecular modeling. A simple description of the hydrogen bond is based on an electrostatic dipole-dipole interaction involving hydrogen-donor and acceptor-acceptor base dipoles, but the physical nature of hydrogen bond formation is more complex. At the most fundamental level, hydrogen bonding is a quantum mechanical phenomenon with contributions from covalent effects, polarization, and charge transfer. Recent experiments and theoretical calculations suggest that both electrostatic and covalent components determine the properties of hydrogen bonds. Likely, the level of rigor required to describe hydrogen bonding will depend on the problem posed. Current approaches to modeling hydrogen bonds include knowledge-based descriptions based on surveys of hydrogen bond geometries in structural databases of proteins and small molecules, empirical molecular mechanics models, and quantum mechanics-based electronic structure calculations. Ab initio calculations of hydrogen bonding energies and geometries accurately reproduce energy landscapes obtained from the distributions of hydrogen bond geometries observed in protein structures. Orientation-dependent hydrogen bonding potentials were found to improve the quality of protein structure prediction and refinement, protein-protein docking, and protein design.

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Year:  2005        PMID: 16581371     DOI: 10.1016/S0065-3233(05)72001-5

Source DB:  PubMed          Journal:  Adv Protein Chem        ISSN: 0065-3233


  18 in total

1.  Improvement of structure-based potentials for protein folding by native and nonnative hydrogen bonds.

Authors:  Marta Enciso; Antonio Rey
Journal:  Biophys J       Date:  2011-09-20       Impact factor: 4.033

Review 2.  A backbone-based theory of protein folding.

Authors:  George D Rose; Patrick J Fleming; Jayanth R Banavar; Amos Maritan
Journal:  Proc Natl Acad Sci U S A       Date:  2006-10-30       Impact factor: 11.205

3.  Structure prediction of domain insertion proteins from structures of individual domains.

Authors:  Monica Berrondo; Marc Ostermeier; Jeffrey J Gray
Journal:  Structure       Date:  2008-04       Impact factor: 5.006

Review 4.  Biomolecular force fields: where have we been, where are we now, where do we need to go and how do we get there?

Authors:  Pnina Dauber-Osguthorpe; A T Hagler
Journal:  J Comput Aided Mol Des       Date:  2018-11-30       Impact factor: 3.686

5.  Charting Hydrogen Bond Anisotropy.

Authors:  Diogo Santos-Martins; Stefano Forli
Journal:  J Chem Theory Comput       Date:  2020-03-10       Impact factor: 6.006

6.  OPUS-DOSP: A Distance- and Orientation-Dependent All-Atom Potential Derived from Side-Chain Packing.

Authors:  Gang Xu; Tianqi Ma; Tianwu Zang; Weitao Sun; Qinghua Wang; Jianpeng Ma
Journal:  J Mol Biol       Date:  2017-08-31       Impact factor: 5.469

Review 7.  Chemical shift-based methods in NMR structure determination.

Authors:  Santrupti Nerli; Andrew C McShan; Nikolaos G Sgourakis
Journal:  Prog Nucl Magn Reson Spectrosc       Date:  2018-03-11       Impact factor: 9.795

8.  Scientific benchmarks for guiding macromolecular energy function improvement.

Authors:  Andrew Leaver-Fay; Matthew J O'Meara; Mike Tyka; Ron Jacak; Yifan Song; Elizabeth H Kellogg; James Thompson; Ian W Davis; Roland A Pache; Sergey Lyskov; Jeffrey J Gray; Tanja Kortemme; Jane S Richardson; James J Havranek; Jack Snoeyink; David Baker; Brian Kuhlman
Journal:  Methods Enzymol       Date:  2013       Impact factor: 1.600

9.  Modeling proteins using a super-secondary structure library and NMR chemical shift information.

Authors:  Vilas Menon; Brinda K Vallat; Joseph M Dybas; Andras Fiser
Journal:  Structure       Date:  2013-05-16       Impact factor: 5.006

10.  Four distances between pairs of amino acids provide a precise description of their interaction.

Authors:  Mati Cohen; Vladimir Potapov; Gideon Schreiber
Journal:  PLoS Comput Biol       Date:  2009-08-14       Impact factor: 4.475

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