Literature DB >> 33129066

The role of local versus nonlocal physicochemical restraints in determining protein native structure.

Jeffrey Skolnick1, Mu Gao2.   

Abstract

The tertiary structure of a native protein is dictated by the interplay of local secondary structure propensities, hydrogen bonding, and tertiary interactions. It is argued that the space of known protein topologies covers all single domain folds and results from the compactness of the native structure and excluded volume. Protein compactness combined with the chirality of the protein's side chains also yields native-like Ramachandran plots. It is the many-body, tertiary interactions among residues that collectively select for the global structure that a particular protein sequence adopts. This explains why the recent advances in deep-learning approaches that predict protein side-chain contacts, the distance matrix between residues, and sequence alignments are successful. They succeed because they implicitly learned the many-body interactions among protein residues.
Copyright © 2020 Elsevier Ltd. All rights reserved.

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Year:  2020        PMID: 33129066      PMCID: PMC8079554          DOI: 10.1016/j.sbi.2020.10.008

Source DB:  PubMed          Journal:  Curr Opin Struct Biol        ISSN: 0959-440X            Impact factor:   7.786


  57 in total

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Journal:  Curr Opin Struct Biol       Date:  2008-04-22       Impact factor: 6.809

Review 2.  Deep learning.

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Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

Review 3.  ChSeq: A database of chameleon sequences.

Authors:  Wenlin Li; Lisa N Kinch; P Andrew Karplus; Nick V Grishin
Journal:  Protein Sci       Date:  2015-06-16       Impact factor: 6.725

4.  Respective roles of short- and long-range interactions in protein folding.

Authors:  N Go; H Taketomi
Journal:  Proc Natl Acad Sci U S A       Date:  1978-02       Impact factor: 11.205

5.  Improved protein structure prediction using potentials from deep learning.

Authors:  Andrew W Senior; Richard Evans; John Jumper; James Kirkpatrick; Laurent Sifre; Tim Green; Chongli Qin; Augustin Žídek; Alexander W R Nelson; Alex Bridgland; Hugo Penedones; Stig Petersen; Karen Simonyan; Steve Crossan; Pushmeet Kohli; David T Jones; David Silver; Koray Kavukcuoglu; Demis Hassabis
Journal:  Nature       Date:  2020-01-15       Impact factor: 49.962

6.  CCMpred--fast and precise prediction of protein residue-residue contacts from correlated mutations.

Authors:  Stefan Seemayer; Markus Gruber; Johannes Söding
Journal:  Bioinformatics       Date:  2014-07-26       Impact factor: 6.937

7.  ECOD: an evolutionary classification of protein domains.

Authors:  Hua Cheng; R Dustin Schaeffer; Yuxing Liao; Lisa N Kinch; Jimin Pei; Shuoyong Shi; Bong-Hyun Kim; Nick V Grishin
Journal:  PLoS Comput Biol       Date:  2014-12-04       Impact factor: 4.475

8.  Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.

Authors:  Sheng Wang; Siqi Sun; Zhen Li; Renyu Zhang; Jinbo Xu
Journal:  PLoS Comput Biol       Date:  2017-01-05       Impact factor: 4.475

9.  DESTINI: A deep-learning approach to contact-driven protein structure prediction.

Authors:  Mu Gao; Hongyi Zhou; Jeffrey Skolnick
Journal:  Sci Rep       Date:  2019-03-05       Impact factor: 4.379

Review 10.  The history of the CATH structural classification of protein domains.

Authors:  Ian Sillitoe; Natalie Dawson; Janet Thornton; Christine Orengo
Journal:  Biochimie       Date:  2015-08-04       Impact factor: 4.079

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  4 in total

1.  rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation.

Authors:  Ya-Lan Tan; Xunxun Wang; Ya-Zhou Shi; Wenbing Zhang; Zhi-Jie Tan
Journal:  Biophys J       Date:  2021-11-17       Impact factor: 4.033

2.  High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function.

Authors:  Mu Gao; Peik Lund-Andersen; Alex Morehead; Sajid Mahmud; Chen Chen; Xiao Chen; Nabin Giri; Raj S Roy; Farhan Quadir; T Chad Effler; Ryan Prout; Subil Abraham; Wael Elwasif; N Quentin Haas; Jeffrey Skolnick; Jianlin Cheng; Ada Sedova
Journal:  Workshop Mach Learn HPC Environ       Date:  2021-12-27

3.  Local Backbone Geometry Plays a Critical Role in Determining Conformational Preferences of Amino Acid Residues in Proteins.

Authors:  Nicole Balasco; Luciana Esposito; Alfonso De Simone; Luigi Vitagliano
Journal:  Biomolecules       Date:  2022-08-26

4.  On the emergence of homochirality and life itself.

Authors:  Jeffrey Skolnick; Mu Gao
Journal:  Biochem (Lond)       Date:  2021-01-20
  4 in total

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