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-  2017 

同源建模方法预测蛋白质突变结构的适用性分析
The applicability of predicting protein structures with residue substitutions using homology modeling

Keywords: 生物信息 突变 同源建模 结构预测
Bioinformatics Mutation Homology Modeling Structure Prediction

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Abstract:

蛋白质的功能由其空间结构决定,其氨基酸突变有可能导致结构功能的巨大改变,因此研究蛋白质的突变三维结构有重要意义。蛋白质三维结构的模拟一般是从相似序列推知相似的新结构,它对突变蛋白质的三维结构是否有效,目前还缺少系统的研究。通过从Protein Data Bank(PDB)结构数据库中提取单氨基酸突变的晶体结构,构建了一组无冗余的测试数据集,对目前应用最广泛的两款同源建模预测软件(SWISS-MODEL和MODELLER)进行了测试分析,发现它们对蛋白质的整体结构预测效果良好,均方根偏差小于0.5埃(RMSD<0.5?),但在突变导致结构显著变化(RMSD>1.5?)的情况下却均不能得到准确结果。分类统计显示,发生在蛋白质结构内部和极性氨基酸之间的突变结构变化小,两款软件预测效果较好(RMSD<1.0?)。突变导致结构显著变化的可能性不高(<5%),但它对蛋白质功能的影响不可忽视,因此应用同源建模方法对于蛋白质突变的模拟并不完全适用,还需要开发新方法来提高准确性。
Protein biological function is determined by its three-dimensional structure. Any residue substitution may lead to dramatic changes in structure and result in functional alternations. Thus it is of great importance to study the relationship between substitution and structure. Usually, proteins three-dimensional structure prediction is based on the similarity of sequence and structure. Whether this method is valid to the protein with residue substitution is still an open question. To answer this question, single residue substitution crystal structures were extracted from Protein Data Bank (PDB) to construct a non-redundant test set, which was used to assess two homology modeling structure prediction tools, SWISS-MODEL and MODELLER. The results indicate that the two methods perform well in overall structure prediction (RMSD<0.5?), however, they fail to predict the mutants that own significant structural changes (A_RMSD>1.5?) upon residue substitutions. The two methods perform better in mutations of embedded residues and mutations between polar residues. Although it is a small fraction of residue substitutions (<5%) that lead to significant conformation changes, they may play important roles in loss or gain protein functions. Therefore traditional homologous modeling methods are not appropriate for predicting such protein structures. There is urgent need to developing more powerful prediction methods

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