BindProfX: Assessing Mutation-Induced Binding Affinity Change by Protein Interface Profiles with Pseudo-Counts. Issue 3 (3rd February 2017)

• Record Type: Journal Article
• Authors: Xiong, Peng; Zhang, Chengxin; Zheng, Wei; Zhang, Yang
• Is Part Of: Journal of molecular biology. Volume 429:Issue 3(2017)
• Page Start: 426
• Page End: 434
• Publication Date: 2017-02-03
• Subjects: ΔΔG binding free-energy change -- NIL non-redundant interface library -- IS score interface similarity score -- iPTM interface probability transition matrix -- iMSA interface multiple structural alignment -- RMSE root mean square error; protein–protein binding interaction -- non-synonymous single nucleotide polymorphisms -- multiple-point mutations -- interface structure alignment -- profile score; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2016.11.022
• Languages: English
• ISSNs: 0022-2836

Abstract

Abstract: Understanding how gene-level mutations affect the binding affinity of protein–protein interactions is a key issue of protein engineering. Due to the complexity of the problem, using physical force field to predict the mutation-induced binding free-energy change remains challenging. In this work, we present a renewed approach to calculate the impact of gene mutations on the binding affinity through the structure-based profiling of protein–protein interfaces, where the binding free-energy change (ΔΔ G ) is counted as the logarithm of relative probability of mutant amino acids over wild-type ones in the interface alignment matrix; three pseudo-counts are introduced to alleviate the limit of the current interface library. Compared with a previous profile score that was based on the log-odds likelihood calculation, the correlation between predicted and experimental ΔΔ G of single-site mutations is increased in this approach from 0.33 to 0.68. The structure-based profile score is found complementary to the physical potentials, where a linear combination of the profile score with the FoldX potential could increase the ΔΔ G correlation from 0.46 to 0.74. It is also shown that the profile score is robust for counting the coupling effect of multiple individual mutations. For the mutations involving more than two mutation sites where the correlation between FoldX and experimental data vanishes, the profile-based calculation retains a strong correlation with the experimental measurements. Graphical abstract: Highlights: Renewed interface profile approach significantly improves binding affinity accuracy. Interface profile score is complementary to physics-based potentials. Interface profiles are robust when starting with low-resolution complex structures. Profile score is robust for calculating both single- and multiple-point mutations. The approach has strong potential to be used for protein interface engineering.