Chen Mingchen, Lin Xingcheng, Zheng Weihua, Onuchic José N, Wolynes Peter G
Department of Bioengineering, Rice University , Houston, Texas 77030, United States.
Department of Chemistry, Rice University Houston, Texas 77251, United States.
J Phys Chem B. 2016 Aug 25;120(33):8557-65. doi: 10.1021/acs.jpcb.6b02451. Epub 2016 May 13.
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 α-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.
关联记忆、水介导、结构与能量模型(AWSEM)是一种具有可转移三级相互作用的粗粒度力场,它利用生物信息学推导的关于具有局部相似序列的肽片段的结构信息(我们称之为记忆)来纳入序列中的局部能量偏差。来自蛋白质数据库(PDB)的记忆信息指导蛋白质正确折叠。数据库中可用序列的结构信息质量参差不齐,有时会导致局部自由能景观出现受挫情况。解决这一难题的一种方法是从完整多肽链部分的全原子模拟构建输入片段记忆信息。在本文中,我们通过研究该方法对六种α螺旋蛋白的结构预测能力,以更完整的方式研究了Kwac和Wolynes首次提出的这种方法。我们将这种方法称为原子关联记忆、水介导、结构与能量模型(AAWSEM),它相当于一种从头开始的蛋白质结构预测方法,无需使用生物信息学输入。AAWSEM的自由能分布表明,当包含三级作用力时,原子片段记忆足以指导正确折叠。AAWSEM将全蛋白水平上粗粒度模拟的效率与仅对大蛋白部分进行全原子模拟可实现的局部结构准确性相结合。结果表明,在结构预测中混合使用原子片段记忆和数据库记忆对于许多实际应用可能是最优的。