Optimized Elastic Network Models With Direct Characterization of Inter-Residue Cooperativity for Protein Dynamics
Optimized Elastic Network Models With Direct Characterization of Inter-Residue Cooperativity for Protein Dynamics
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DOI：
10.1109/tcbb.2020.3023147
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Hua Zhang;G. Shan;Bailin Yang
中科院分区：
文献类型：
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作者：
Hua Zhang;G. Shan;Bailin Yang
弹性网络模型被称为具有代表性的粗粒度模型，用来描述蛋白质的本质动力学。由于以前的许多研究中将力常数简单地设计为随残基对的空间距离的衰减，所以ENM仍有很大的改进空间。在这篇文章中，我们使用脊型算子对一组大规模的核磁共振系综进行精确矩阵估计(ROPE)，直接用逆协方差估计计算力常数。进一步根据几种配对类型的序列和结构信息，包括二级结构、相对溶剂可及性、序列距离和末端，对力常数进行了距离相关的统计分析。各种不同的平均力常数分布突出了结构和顺序特征，以及超越空间距离的残基之间的协作性。最后，我们结合这些结构和序列特征，使用粒子群优化算法进行参数估计，构建了新的ENM变异。在均方起伏和模式重叠的相关系数方面，本文提出的变分方法与传统的变分方法相比有了显著的改善。这项研究为开发更精确的蛋白质动力学弹性网络模型开辟了一条新的途径。
The elastic network models (ENMs)are known as representative coarse-grained models to capture essential dynamics of proteins. Due to simple designs of the force constants as a decay with spatial distances of residue pairs in many previous studies, there is still much room for the improvement of ENMs. In this article, we directly computed the force constants with the inverse covariance estimation using a ridge-type operater for the precision matrix estimation (ROPE)on a large-scale set of NMR ensembles. Distance-dependent statistical analyses on the force constants were further comprehensively performed in terms of several paired types of sequence and structural information, including secondary structure, relative solvent accessibility, sequence distance and terminal. Various distinguished distributions of the mean force constants highlight the structural and sequential characteristics coupled with the inter-residue cooperativity beyond the spatial distances. We finally integrated these structural and sequential characteristics to build novel ENM variations using the particle swarm optimization for the parameter estimation. The considerable improvements on the correlation coefficient of the mean-square fluctuation and the mode overlap were achieved by the proposed variations when compared with traditional ENMs. This study opens a novel way to develop more accurate elastic network models for protein dynamics.