Polymer Research Center, Bogazici University, Bebek, Istanbul, Turkey.
PLoS Comput Biol. 2010 Jul 8;6(7):e1000845. doi: 10.1371/journal.pcbi.1000845.
A statistical thermodynamics approach is proposed to determine structurally and functionally important residues in native proteins that are involved in energy exchange with a ligand and other residues along an interaction pathway. The structure-function relationships, ligand binding and allosteric activities of ten structures of HLA Class I proteins of the immune system are studied by the Gaussian Network Model. Five of these models are associated with inflammatory rheumatic disease and the remaining five are properly functioning. In the Gaussian Network Model, the protein structures are modeled as an elastic network where the inter-residue interactions are harmonic. Important residues and the interaction pathways in the proteins are identified by focusing on the largest eigenvalue of the residue interaction matrix. Predicted important residues match those known from previous experimental and clinical work. Graph perturbation is used to determine the response of the important residues along the interaction pathway. Differences in response patterns of the two sets of proteins are identified and their relations to disease are discussed.
提出了一种统计热力学方法，用于确定与配体和相互作用途径中的其他残基进行能量交换的天然蛋白质中结构和功能重要的残基。通过高斯网络模型研究了免疫系统中 10 种 HLA Ⅰ类蛋白结构的结构-功能关系、配体结合和变构活性。其中五个模型与炎症性风湿性疾病有关，其余五个模型则功能正常。在高斯网络模型中，蛋白质结构被建模为弹性网络，其中残基间相互作用是调和的。通过关注残基相互作用矩阵的最大特征值，确定蛋白质中的重要残基和相互作用途径。预测的重要残基与先前实验和临床工作中已知的残基相匹配。图扰动用于确定相互作用途径中重要残基的响应。确定了两组蛋白质的响应模式差异，并讨论了它们与疾病的关系。
