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首页> 外文期刊>Acta crystallographica. Section D, Structural biology >A scoring function for the prediction of protein complex interfaces based on the neighborhood preferences of amino acids
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A scoring function for the prediction of protein complex interfaces based on the neighborhood preferences of amino acids

机译：基于氨基酸邻域偏好的蛋白质复合物界面预测评分函数

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摘要

Proteins often assemble into functional complexes, the structures of which are more difficult to obtain than those of the individual protein molecules. Given the structures of the subunits, it is possible to predict plausible complex models via computational methods such as molecular docking. Assessing the quality of the predicted models is crucial to obtain correct complex structures. Here, an energy-scoring function was developed based on the interfacial residues of structures in the Protein Data Bank. The statistically derived energy function (Nepre) imitates the neighborhood preferences of amino acids, including the types and relative positions of neighboring residues. Based on the preference statistics, a program iNepre was implemented and its performance was evaluated with several benchmarking decoy data sets. The results show that iNepre scores are powerful in model ranking to select the best protein complex structures.
机译：通常蛋白质组装成功能性配合物,结构更加困难获得比单独的蛋白质分子。可以预测的复杂模型通过计算方法等分子对接。预测模型获得正确是至关重要的复杂的结构。函数是基于界面开发的残留的结构蛋白质数据银行。统计得到的能量函数(Nepre)模仿的社区偏好氨基酸酸,包括类型和相对位置邻近的残留物。统计数据,程序iNepre实施其性能与几个评估基准测试假数据集。排名iNepre分数是强大的模型选择最好的蛋白质复杂的结构。

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