Advanced Analytics Institute and Center for Health Technologies, Faculty of Engineering and IT, University of Technology, Sydney, Australia.
BMC Bioinformatics. 2014 Feb 26;15:57. doi: 10.1186/1471-2105-15-57.
Binding free energy and binding hot spots at protein-protein interfaces are two important research areas for understanding protein interactions. Computational methods have been developed previously for accurate prediction of binding free energy change upon mutation for interfacial residues. However, a large number of interrupted and unimportant atomic contacts are used in the training phase which caused accuracy loss.
This work proposes a new method, βACVASA, to predict the change of binding free energy after alanine mutations. βACVASA integrates accessible surface area (ASA) and our newly defined β contacts together into an atomic contact vector (ACV). A β contact between two atoms is a direct contact without being interrupted by any other atom between them. A β contact's potential contribution to protein binding is also supposed to be inversely proportional to its ASA to follow the water exclusion hypothesis of binding hot spots. Tested on a dataset of 396 alanine mutations, our method is found to be superior in classification performance to many other methods, including Robetta, FoldX, HotPOINT, an ACV method of β contacts without ASA integration, and ACVASA methods (similar to βACVASA but based on distance-cutoff contacts). Based on our data analysis and results, we can draw conclusions that: (i) our method is powerful in the prediction of binding free energy change after alanine mutation; (ii) β contacts are better than distance-cutoff contacts for modeling the well-organized protein-binding interfaces; (iii) β contacts usually are only a small fraction number of the distance-based contacts; and (iv) water exclusion is a necessary condition for a residue to become a binding hot spot.
βACVASA is designed using the advantages of both β contacts and water exclusion. It is an excellent tool to predict binding free energy changes and binding hot spots after alanine mutation.
蛋白质-蛋白质界面的结合自由能和结合热点是理解蛋白质相互作用的两个重要研究领域。先前已经开发了计算方法来准确预测界面残基突变时的结合自由能变化。然而,在训练阶段使用了大量的中断和不重要的原子接触,导致准确性下降。
这项工作提出了一种新的方法βACVASA,用于预测丙氨酸突变后结合自由能的变化。βACVASA 将可及表面积(ASA)和我们新定义的β接触一起整合到一个原子接触向量(ACV)中。两个原子之间的β接触是直接接触,其间没有被任何其他原子中断。β接触对蛋白质结合的潜在贡献也应该与其 ASA 成反比,以遵循结合热点的水排除假设。在 396 个丙氨酸突变的数据集上进行测试,我们的方法在分类性能方面优于许多其他方法,包括 Robetta、FoldX、HotPOINT、不整合 ASA 的β接触的 ACV 方法以及 ACVASA 方法(类似于βACVASA,但基于距离截止接触)。基于我们的数据分析和结果,我们可以得出以下结论:(i)我们的方法在预测丙氨酸突变后结合自由能变化方面非常强大;(ii)β接触比距离截止接触更适合模拟组织良好的蛋白质结合界面;(iii)β接触通常只是基于距离的接触的一小部分;(iv)水排除是残基成为结合热点的必要条件。
βACVASA 利用了β接触和水排除的优势设计而成。它是预测丙氨酸突变后结合自由能变化和结合热点的优秀工具。