一种基于遗传算法的肽/蛋白质结合模式虚拟筛选建模技术

“合理”QSAR模型是指在了解配体与受体相互作用模式的前提下建立定量构效关系, 这样避开了传统做法仅仅依靠样本集分子自身信息来构建预测模型的诸多弊端. 本文将此思想应用于肽/蛋白质亲和活性的研究当中, 借助于遗传算法作为虚拟受体结合靶点及相互作用模式的筛选手段得到了一种新的建模技术: 肽/蛋白质结合模式遗传虚拟筛选(genetic virtual screening of combinative mode for peptide/protein, GVSC). 该法成功解决了“合理”QSAR研究中的难题, 即大多数情况下受体结构未知而难以了解配基与之发生的结合方式. 分别使用58个血管紧张素转化酶, 18个Camel抗体蛋白cAb-lys3双位点突变残基对GVSC加以检验, 其结果表明GVSC能够较好地阐明配基与受体之间的作用机理, 并能得到优于传统方法的QSAR模型.

Genetic Algorithm-Based Virtual Screening of Combinative Mode for Peptide/Protein

Reasonable QSAR model, i.e. constructing quantitative structure-activity relationship on the ba-sis of known ligand/receptor interaction mode, was proposed in this paper, which can avoid defections of traditional methods that build prediction model depending upon only structural features of molecules them-selves. When applying it to study on peptide/protein affinity activities, a new method of genetic virtual screening of combinative mode for peptide/protein (GVSC) was proposed with genetic algorithm as screen-ing tools for combinative targets and interaction mode of virtual receptors. GVSC technique successfully solved problems in reasonable QSAR study that it is hard to know ligand/receptor interaction mode under conditions of unknown structures. Further, 58 angiotension converting enzyme and 18 double site mutation residues in camel antibody protein cAb-Lys3 were employed respectively to examine the GVSC model. It was suggested that the GVSC model should outperform traditional QSAR model to interpret ligand/receptor interaction mode.