Mahalingam Rajasekaran, Peng Hung-Pin, Yang An-Suei
Genomics Research Center, Academia Sinica, Taipei 115, Taiwan.
Genomics Research Center, Academia Sinica, Taipei 115, Taiwan; Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan; Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.
Biophys Chem. 2014 Aug;192:10-9. doi: 10.1016/j.bpc.2014.05.002. Epub 2014 May 29.
Protein-fatty acid interaction is vital for many cellular processes and understanding this interaction is important for functional annotation as well as drug discovery. In this work, we present a method for predicting the fatty acid (FA)-binding residues by using three-dimensional probability density distributions of interacting atoms of FAs on protein surfaces which are derived from the known protein-FA complex structures. A machine learning algorithm was established to learn the characteristic patterns of the probability density maps specific to the FA-binding sites. The predictor was trained with five-fold cross validation on a non-redundant training set and then evaluated with an independent test set as well as on holo-apo pair's dataset. The results showed good accuracy in predicting the FA-binding residues. Further, the predictor developed in this study is implemented as an online server which is freely accessible at the following website, http://ismblab.genomics.sinica.edu.tw/.
蛋白质-脂肪酸相互作用对许多细胞过程至关重要，理解这种相互作用对于功能注释以及药物发现都很重要。在这项工作中，我们提出了一种通过使用蛋白质表面脂肪酸（FA）相互作用原子的三维概率密度分布来预测FA结合残基的方法，这些分布来自已知的蛋白质-FA复合物结构。建立了一种机器学习算法来学习FA结合位点特有的概率密度图的特征模式。该预测器在一个非冗余训练集上进行了五折交叉验证训练，然后在一个独立测试集以及全蛋白-无配体对数据集上进行了评估。结果表明在预测FA结合残基方面具有良好的准确性。此外，本研究开发的预测器作为一个在线服务器实现，可通过以下网站免费访问：http://ismblab.genomics.sinica.edu.tw/ 。
