Average assignment method for predicting the stability of protein mutants
Average assignment method for predicting the stability of protein mutants
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DOI：
10.1002/bip.20462
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发表时间：
2006-05-01
期刊：
影响因子：
2.9
通讯作者：
Ponnuswamy, MN
中科院分区：
文献类型：
--
作者：
Saraboji, K;Gromiha, MM;Ponnuswamy, MN
基于人工神经网络的蛋白质稳定性预测。无酸取代是分子生物学中的一个重要问题，它将有助于设计稳定的突变体。在这项工作中，我们使用了三个不同的数据集，分别为1791、1396和2204个突变体，分析了热稳定性(Delta T-m)、热致自由能变化(Delta G)和变性剂变性(Delta G(H2O))的稳定性。我们已经将突变归类为380个可能的替换，并使用从类似类型的突变中获得的信息来分配每个突变的稳定性。我们观察到，在不同的稳定性衡量标准下，这种分配可以区分稳定突变和不稳定突变，准确率为70%-80%。此外，我们还根据二级结构和溶剂可及性(ASA)对突变体进行了分类，观察到这种分类显著提高了预测的准确性。基于螺旋的突变体分类。链和线圈识别稳定/不稳定突变体的平均准确率为82%，相关系数为0.56；有关蛋白质内部、部分埋藏和表面区残基位置的信息正确识别稳定/不稳定残基的平均准确率为81%，相关系数为0.59。基于三个二级结构和溶剂可及性的九个子分类提高了对三个数据集分配稳定/不稳定突变的准确性，准确率为84-89%。此外，本方法能够在0.64千卡/摩尔的偏差内预测突变时的自由能变化(增量G)。我们认为该方法可用于预测蛋白质突变体的稳定性。(C)2006年威利期刊公司。
Prediction of protein stability upon ann. no acid substitutions is an important problem in molecular biology and it will be helpful for designing stable mutants. In this work we have analyzed the stability of protein mutants using three different data sets of 1791, 1396, and 2204 mutants, respectively, for thermal stability (Delta T-m), free energy change due to thermal (Delta Delta G), and denaturant denaturations (Delta Delta G(H2O)), obtained from the ProTherm database. We have classified the mutants into 380 possible substitutions and assigned the stability of each mutant using the information obtained with similar type of mutations. We observed that this assignment could distinguish the stabilizing and destabilizing Mutants to an accuracy of 70-80% at different measures of stability. Further, we have classified the mutants based on secondary structure and solvent accessibility (ASA) and observed that the classification significantly improved the accuracy of prediction. The classification of mutants based on helix. strand, and coil distinguished the stabilizing destabilizing mutants at an average accuracy of 82% and the correlation is 0.56; information about the location of residues at the interior, partially buried, and surface regions of a protein correctly identified the stabilizing/destabilizing residues at an average accuracy of 81% and the correlation is 0.59. The nine subclassifications based on three secondary structures and solvent accessibilities improved the accuracy of assigning stabilizing/destabilizing mutants to an accuracy of 84-89% for the three data sets. Further, the present method is able to predict the free energy change (Delta Delta G) upon mutations within a deviation of 0.64 kcal/mol. We suggest that this method could be used for predicting the stability of protein mutants. (c) 2006 Wiley Periodicals, Inc.