Literature DB >> 28799172

Protein structure prediction: making AWSEM AWSEM-ER by adding evolutionary restraints.

Brian J Sirovetz1,2, Nicholas P Schafer1, Peter G Wolynes1,2,3,4.   

Abstract

Protein sequences have evolved to fold into functional structures, resulting in families of diverse protein sequences that all share the same overall fold. One can harness protein family sequence data to infer likely contacts between pairs of residues. In the current study, we combine this kind of inference from coevolutionary information with a coarse-grained protein force field ordinarily used with single sequence input, the Associative memory, Water mediated, Structure and Energy Model (AWSEM), to achieve improved structure prediction. The resulting Associative memory, Water mediated, Structure and Energy Model with Evolutionary Restraints (AWSEM-ER) yields a significant improvement in the quality of protein structure prediction over the single sequence prediction from AWSEM when a sufficiently large number of homologous sequences are available. Free energy landscape analysis shows that the addition of the evolutionary term shifts the free energy minimum to more native-like structures, which explains the improvement in the quality of structures when performing predictions using simulated annealing. Simulations using AWSEM without coevolutionary information have proved useful in elucidating not only protein folding behavior, but also mechanisms of protein function. The success of AWSEM-ER in de novo structure prediction suggests that the enhanced model opens the door to functional studies of proteins even when no experimentally solved structures are available.
© 2017 Wiley Periodicals, Inc.

Entities:  

Keywords:  coevolution; contact prediction; energy landscape theory; hybrid model; knowledge-based model; physically motivated potential; sequence covariation

Mesh:

Substances:

Year:  2017        PMID: 28799172      PMCID: PMC5807005          DOI: 10.1002/prot.25367

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


  48 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Twilight zone of protein sequence alignments.

Authors:  B Rost
Journal:  Protein Eng       Date:  1999-02

3.  Protein structure prediction and structural genomics.

Authors:  D Baker; A Sali
Journal:  Science       Date:  2001-10-05       Impact factor: 47.728

4.  Scoring function for automated assessment of protein structure template quality.

Authors:  Yang Zhang; Jeffrey Skolnick
Journal:  Proteins       Date:  2004-12-01

5.  How fast-folding proteins fold.

Authors:  Kresten Lindorff-Larsen; Stefano Piana; Ron O Dror; David E Shaw
Journal:  Science       Date:  2011-10-28       Impact factor: 47.728

6.  HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment.

Authors:  Michael Remmert; Andreas Biegert; Andreas Hauser; Johannes Söding
Journal:  Nat Methods       Date:  2011-12-25       Impact factor: 28.547

7.  Genomics-aided structure prediction.

Authors:  Joanna I Sułkowska; Faruck Morcos; Martin Weigt; Terence Hwa; José N Onuchic
Journal:  Proc Natl Acad Sci U S A       Date:  2012-06-12       Impact factor: 11.205

8.  Learning generative models for protein fold families.

Authors:  Sivaraman Balakrishnan; Hetunandan Kamisetty; Jaime G Carbonell; Su-In Lee; Christopher James Langmead
Journal:  Proteins       Date:  2011-01-25

Review 9.  Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models.

Authors:  Richard R Stein; Debora S Marks; Chris Sander
Journal:  PLoS Comput Biol       Date:  2015-07-30       Impact factor: 4.475

10.  Learning To Fold Proteins Using Energy Landscape Theory.

Authors:  N P Schafer; B L Kim; W Zheng; P G Wolynes
Journal:  Isr J Chem       Date:  2014-08       Impact factor: 3.333

View more
  7 in total

1.  Forging tools for refining predicted protein structures.

Authors:  Xingcheng Lin; Nicholas P Schafer; Wei Lu; Shikai Jin; Xun Chen; Mingchen Chen; José N Onuchic; Peter G Wolynes
Journal:  Proc Natl Acad Sci U S A       Date:  2019-04-18       Impact factor: 11.205

2.  Exploring the F-actin/CPEB3 interaction and its possible role in the molecular mechanism of long-term memory.

Authors:  Xinyu Gu; Nicholas P Schafer; Qian Wang; Sarah S Song; Mingchen Chen; M Neal Waxham; Peter G Wolynes
Journal:  Proc Natl Acad Sci U S A       Date:  2020-08-26       Impact factor: 11.205

3.  Fibril Surface-Dependent Amyloid Precursors Revealed by Coarse-Grained Molecular Dynamics Simulation.

Authors:  Yuan-Wei Ma; Tong-You Lin; Min-Yeh Tsai
Journal:  Front Mol Biosci       Date:  2021-08-06

4.  AWSEM-Suite: a protein structure prediction server based on template-guided, coevolutionary-enhanced optimized folding landscapes.

Authors:  Shikai Jin; Vinicius G Contessoto; Mingchen Chen; Nicholas P Schafer; Wei Lu; Xun Chen; Carlos Bueno; Arya Hajitaheri; Brian J Sirovetz; Aram Davtyan; Garegin A Papoian; Min-Yeh Tsai; Peter G Wolynes
Journal:  Nucleic Acids Res       Date:  2020-07-02       Impact factor: 16.971

5.  Structural and Dynamical Order of a Disordered Protein: Molecular Insights into Conformational Switching of PAGE4 at the Systems Level.

Authors:  Xingcheng Lin; Prakash Kulkarni; Federico Bocci; Nicholas P Schafer; Susmita Roy; Min-Yeh Tsai; Yanan He; Yihong Chen; Krithika Rajagopalan; Steven M Mooney; Yu Zeng; Keith Weninger; Alex Grishaev; José N Onuchic; Herbert Levine; Peter G Wolynes; Ravi Salgia; Govindan Rangarajan; Vladimir Uversky; John Orban; Mohit Kumar Jolly
Journal:  Biomolecules       Date:  2019-02-22

6.  OpenAWSEM with Open3SPN2: A fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations.

Authors:  Wei Lu; Carlos Bueno; Nicholas P Schafer; Joshua Moller; Shikai Jin; Xun Chen; Mingchen Chen; Xinyu Gu; Aram Davtyan; Juan J de Pablo; Peter G Wolynes
Journal:  PLoS Comput Biol       Date:  2021-02-12       Impact factor: 4.475

7.  The N-terminal domain of RfaH plays an active role in protein fold-switching.

Authors:  Pablo Galaz-Davison; Ernesto A Román; César A Ramírez-Sarmiento
Journal:  PLoS Comput Biol       Date:  2021-09-03       Impact factor: 4.475

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.