Protein Folding and Structure Prediction from the Ground Up: The Atomistic Associative Memory, Water Mediated, Structure and Energy Model

被引:23
作者
Chen, Mingchen [1 ,3 ]
Lin, Xingcheng [1 ,2 ]
Zheng, Weihua [1 ,4 ]
Onuchic, Jose N. [1 ,2 ,4 ]
Wolynes, Peter G. [1 ,2 ,4 ]
机构
[1] Rice Univ, Ctr Theoret Biol Phys, Houston, TX 77005 USA
[2] Rice Univ, Dept Phys & Astron, Houston, TX 77005 USA
[3] Rice Univ, Dept Bioengn, Houston, TX 77030 USA
[4] Rice Univ, Dept Chem, POB 1892, Houston, TX 77251 USA
基金
美国国家科学基金会;
关键词
HELICAL PROTEINS; LANDSCAPES; DYNAMICS; HAMILTONIANS; BACKBONE;
D O I
10.1021/acs.jpcb.6b02451
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
The associative memory, water mediated, structure and energy model (AWSEM) is a coarse-grained force field with transferable tertiary interactions that incorporates local in sequence energetic biases using bioinformatically derived structural information about peptide fragments with locally similar sequences that we call memories. The memory information from the protein data bank (PDB) database guides proper protein folding. The structural information about available sequences in the database varies in quality and can sometimes lead to frustrated free energy landscapes locally. One way out of this difficulty is to construct the input fragment memory information from all-atom simulations of portions of the complete polypeptide chain. In this paper, we investigate this approach first put forward by Kwac and Wolynes in a more complete way by studying the structure prediction capabilities of this approach for six alpha-helical proteins. This scheme which we call the atomistic associative memory, water mediated, structure and energy model (AAWSEM) amounts to an ab initio protein structure prediction method that starts from the ground up without using bioinformatic input. The free energy profiles from AAWSEM show that atomistic fragment memories are sufficient to guide the correct folding when tertiary forces are included. AAWSEM combines the efficiency of coarse-grained simulations on the full protein level with the local structural accuracy achievable from all-atom simulations of only parts of a large protein. The results suggest that a hybrid use of atomistic fragment memory and database memory in structural predictions may well be optimal for many practical applications.
引用
收藏
页码:8557 / 8565
页数:9
相关论文
共 31 条
[11]   NEURONS WITH GRADED RESPONSE HAVE COLLECTIVE COMPUTATIONAL PROPERTIES LIKE THOSE OF 2-STATE NEURONS [J].
HOPFIELD, JJ .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES, 1984, 81 (10) :3088-3092
[12]   Protein secondary structure prediction based on position-specific scoring matrices [J].
Jones, DT .
JOURNAL OF MOLECULAR BIOLOGY, 1999, 292 (02) :195-202
[13]   Predictive energy landscapes for folding α-helical transmembrane proteins [J].
Kim, Bobby L. ;
Schafer, Nicholas P. ;
Wolynes, Peter G. .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2014, 111 (30) :11031-11036
[14]   Design of a novel globular protein fold with atomic-level accuracy [J].
Kuhlman, B ;
Dantas, G ;
Ireton, GC ;
Varani, G ;
Stoddard, BL ;
Baker, D .
SCIENCE, 2003, 302 (5649) :1364-1368
[15]   THE WEIGHTED HISTOGRAM ANALYSIS METHOD FOR FREE-ENERGY CALCULATIONS ON BIOMOLECULES .1. THE METHOD [J].
KUMAR, S ;
BOUZIDA, D ;
SWENDSEN, RH ;
KOLLMAN, PA ;
ROSENBERG, JM .
JOURNAL OF COMPUTATIONAL CHEMISTRY, 1992, 13 (08) :1011-1021
[16]  
Kwac K, 2008, B KOREAN CHEM SOC, V29, P2172
[17]   How Fast-Folding Proteins Fold [J].
Lindorff-Larsen, Kresten ;
Piana, Stefano ;
Dror, Ron O. ;
Shaw, David E. .
SCIENCE, 2011, 334 (6055) :517-520
[18]  
MacCammon J. A., 1989, DYNAMICS PROTEINS NU
[19]   Improved treatment of the protein backbone in empirical force fields [J].
MacKerell, AD ;
Feig, M ;
Brooks, CL .
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 2004, 126 (03) :698-699
[20]  
MacKerell AD, 2001, BIOPOLYMERS, V56, P257