The Department of Chemistry , The University of Memphis , 213 Smith Chemistry Building , Memphis , Tennessee 38152-3550 , United States.
J Chem Inf Model. 2019 Dec 23;59(12):5034-5044. doi: 10.1021/acs.jcim.9b00804. Epub 2019 Dec 6.
The validity and accuracy of protein modeling is dependent on constructing models that account for the inter-residue interactions crucial for protein structure and function. Residue interaction networks derived from interatomic van der Waals contacts have previously demonstrated usefulness toward designing protein models, but there has not yet been evidence of a connection between network-predicted interaction strength and quantitative interaction energies. This work evaluates the intraprotein contact networks of five proteins against ab initio interaction energies computed using symmetry-adapted perturbation theory. To more appropriately capture the local chemistry of the protein, we deviate from traditional protein network analysis to redefine the interacting nodes in terms of main chain and side chain functional groups rather than complete amino acids. While there is no simple correspondence between the features of the contact network and actual interaction strength, random forest models constructed from minimal structural, network, and chemical descriptors are capable of accurately predicting interaction energy. The results of this work serve as a foundation for the development and improvement of functional group-based contact networks.
蛋白质建模的有效性和准确性取决于构建能够解释蛋白质结构和功能至关重要的残基相互作用的模型。先前已经证明，基于原子间范德华接触的残基相互作用网络对于设计蛋白质模型非常有用，但尚未有证据表明网络预测的相互作用强度与定量相互作用能之间存在联系。这项工作评估了五个蛋白质的内蛋白接触网络与使用对称自适应微扰理论计算的从头计算相互作用能之间的关系。为了更恰当地捕捉蛋白质的局部化学性质，我们偏离传统的蛋白质网络分析，根据主链和侧链官能团而不是完整的氨基酸重新定义相互作用的节点。虽然接触网络的特征与实际相互作用强度之间没有简单的对应关系，但由最小结构、网络和化学描述符构建的随机森林模型能够准确预测相互作用能。这项工作的结果为基于官能团的接触网络的开发和改进奠定了基础。
