PNBACE: an ensemble algorithm to predict the effects of mutations on protein-nucleic acid binding affinity

• Published in: BMC biology Vol. 22; no. 1; pp. 203 - 15
• Main Authors: Xiao, Si-Rui, Zhang, Yao-Kun, Liu, Kai-Yu, Huang, Yu-Xiang, Liu, Rong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 11.09.2024; BioMed Central; BMC
• Subjects: Acids; Affinity; Algorithms; Binding; Binding affinity; Binding energy; Computational Biology - methods; Datasets; Deoxyribonucleic acid; Differential evolution; DNA; DNA - metabolism; DNA mutation; DNA-Binding Proteins - genetics; DNA-Binding Proteins - metabolism; Energy; Energy network; Evolutionary algorithms; Evolutionary computation; Gene mutations; Impact prediction; Machine learning; Methodology; Mutation; Nucleic acids; Optimization techniques; Point mutation; Prediction models; Protein Binding; Protein mutation; Proteins; Regression analysis; Task complexity; China; DNA mutation; Differential evolution; Protein mutation; Binding affinity; Energy network
• ISSN: 1741-7007; 1741-7007
• DOI: 10.1186/s12915-024-02006-9

Mutations occurring in nucleic acids or proteins may affect the binding affinities of protein-nucleic acid interactions. Although many efforts have been devoted to the impact of protein mutations, few computational studies have addressed the effect of nucleic acid mutations and explored whether the identical methodology could be applied to the prediction of binding affinity changes caused by these two mutation types. Here, we developed a generalized algorithm named PNBACE for both DNA and protein mutations. We first demonstrated that DNA mutations could induce varying degrees of changes in binding affinity from multiple perspectives. We then designed a group of energy-based topological features based on different energy networks, which were combined with our previous partition-based energy features to construct individual prediction models through feature selections. Furthermore, we created an ensemble model by integrating the outputs of individual models using a differential evolution algorithm. In addition to predicting the impact of single-point mutations, PNBACE could predict the influence of multiple-point mutations and identify mutations significantly reducing binding affinities. Extensive comparisons indicated that PNBACE largely performed better than existing methods on both regression and classification tasks. PNBACE is an effective method for estimating the binding affinity changes of protein-nucleic acid complexes induced by DNA or protein mutations, therefore improving our understanding of the interactions between proteins and DNA/RNA.