Literature DB >> 31197841

Driven to near-experimental accuracy by refinement via molecular dynamics simulations.

Lim Heo1, Collin F Arbour1, Michael Feig1.   

Abstract

Protein model refinement has been an essential part of successful protein structure prediction. Molecular dynamics simulation-based refinement methods have shown consistent improvement of protein models. There had been progress in the extent of refinement for a few years since the idea of ensemble averaging of sampled conformations emerged. There was little progress in CASP12 because conformational sampling was not sufficiently diverse due to harmonic restraints. During CASP13, a new refinement method was tested that achieved significant improvements over CASP12. The new method intended to address previous bottlenecks in the refinement problem by introducing new features. Flat-bottom harmonic restraints replaced harmonic restraints, sampling was performed iteratively, and a new scoring function and selection criteria were used. The new protocol expanded conformational sampling at reduced computational costs. In addition to overall improvements, some models were refined significantly to near-experimental accuracy.
© 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  CASP; Markov-state modeling; model refinement; molecular dynamics simulation; protein structure prediction

Mesh:

Substances:

Year:  2019        PMID: 31197841      PMCID: PMC6851469          DOI: 10.1002/prot.25759

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  54 in total

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Journal:  Bioinformatics       Date:  2004-11-05       Impact factor: 6.937

4.  The other 90% of the protein: assessment beyond the Calphas for CASP8 template-based and high-accuracy models.

Authors:  Daniel A Keedy; Christopher J Williams; Jeffrey J Headd; W Bryan Arendall; Vincent B Chen; Gary J Kapral; Robert A Gillespie; Jeremy N Block; Adam Zemla; David C Richardson; Jane S Richardson
Journal:  Proteins       Date:  2009

5.  Simultaneous refinement of inaccurate local regions and overall structure in the CASP12 protein model refinement experiment.

Authors:  Gyu Rie Lee; Lim Heo; Chaok Seok
Journal:  Proteins       Date:  2017-10-26

6.  Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.

Authors:  Badri Adhikari; Jie Hou; Jianlin Cheng
Journal:  Proteins       Date:  2017-10-31

7.  Computational protein structure refinement: Almost there, yet still so far to go.

Authors:  Michael Feig
Journal:  Wiley Interdiscip Rev Comput Mol Sci       Date:  2017-03-28

8.  Evaluation of the template-based modeling in CASP12.

Authors:  Andriy Kryshtafovych; Bohdan Monastyrskyy; Krzysztof Fidelis; John Moult; Torsten Schwede; Anna Tramontano
Journal:  Proteins       Date:  2017-12-04

9.  Improved protein contact predictions with the MetaPSICOV2 server in CASP12.

Authors:  Daniel W A Buchan; David T Jones
Journal:  Proteins       Date:  2017-09-29

10.  GalaxyRefine: Protein structure refinement driven by side-chain repacking.

Authors:  Lim Heo; Hahnbeom Park; Chaok Seok
Journal:  Nucleic Acids Res       Date:  2013-06-03       Impact factor: 16.971

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  16 in total

1.  High-accuracy protein structures by combining machine-learning with physics-based refinement.

Authors:  Lim Heo; Michael Feig
Journal:  Proteins       Date:  2019-11-15

2.  Improved protein structure prediction using predicted interresidue orientations.

Authors:  Jianyi Yang; Ivan Anishchenko; Hahnbeom Park; Zhenling Peng; Sergey Ovchinnikov; David Baker
Journal:  Proc Natl Acad Sci U S A       Date:  2020-01-02       Impact factor: 11.205

3.  Antiviral Strategies Against SARS-CoV-2: A Systems Biology Approach.

Authors:  Erica T Prates; Michael R Garvin; Piet Jones; J Izaak Miller; Kyle A Sullivan; Ashley Cliff; Joao Gabriel Felipe Machado Gazolla; Manesh B Shah; Angelica M Walker; Matthew Lane; Christopher T Rentsch; Amy Justice; Mirko Pavicic; Jonathon Romero; Daniel Jacobson
Journal:  Methods Mol Biol       Date:  2022

4.  ClusPro in rounds 38 to 45 of CAPRI: Toward combining template-based methods with free docking.

Authors:  Dzmitry Padhorny; Kathryn A Porter; Mikhail Ignatov; Andrey Alekseenko; Dmitri Beglov; Sergei Kotelnikov; Ryota Ashizawa; Israel Desta; Nawsad Alam; Zhuyezi Sun; Emiliano Brini; Ken Dill; Ora Schueler-Furman; Sandor Vajda; Dima Kozakov
Journal:  Proteins       Date:  2020-03-23

5.  Critical assessment of methods of protein structure prediction (CASP)-Round XIII.

Authors:  Andriy Kryshtafovych; Torsten Schwede; Maya Topf; Krzysztof Fidelis; John Moult
Journal:  Proteins       Date:  2019-10-23

6.  Physics-based protein structure refinement in the era of artificial intelligence.

Authors:  Lim Heo; Giacomo Janson; Michael Feig
Journal:  Proteins       Date:  2021-06-29

7.  Improved Protein Model Quality Assessment By Integrating Sequential And Pairwise Features Using Deep Learning.

Authors:  Xiaoyang Jing; Jinbo Xu
Journal:  Bioinformatics       Date:  2020-12-16       Impact factor: 6.937

8.  Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy.

Authors:  Cheng-Peng Zhou; Di Wang; Xiaoyong Pan; Hong-Bin Shen
Journal:  Int J Mol Sci       Date:  2021-04-23       Impact factor: 5.923

9.  High-accuracy refinement using Rosetta in CASP13.

Authors:  Hahnbeom Park; Gyu Rie Lee; David E Kim; Ivan Anishchenko; Qian Cong; David Baker
Journal:  Proteins       Date:  2019-08-05

10.  Improved Sampling Strategies for Protein Model Refinement Based on Molecular Dynamics Simulation.

Authors:  Lim Heo; Collin F Arbour; Giacomo Janson; Michael Feig
Journal:  J Chem Theory Comput       Date:  2021-02-09       Impact factor: 6.006

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