Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods

• Published in: Computational and structural biotechnology journal 2020-01, Vol.18, p.439-454
• Main Authors: Wang, Debby D., Ou-Yang, Le, Xie, Haoran, Zhu, Mengxu, Yan, Hong
• Format: Article
• Language: English
• Subjects: artificial intelligence; binding capacity; Deep learning; drug design; drug resistance; energy; genetic disorders; induced mutation; Local geometrical features; Missense mutation; molecular dynamics; Molecular dynamics (MD) simulations; Mutation impact; prediction; Protein-ligand binding affinity; simulation models; time series analysis; Time series features
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2020.02.007

[Display omitted] Mutation-induced variation of protein-ligand binding affinity is the key to many genetic diseases and the emergence of drug resistance, and therefore predicting such mutation impacts is of great importance. In this work, we aim to predict the mutation impacts on protein-ligand binding affinity using efficient structure-based, computational methods. Relying on consolidated databases of experimentally determined data we characterize the affinity change upon mutation based on a number of local geometrical features and monitor such feature differences upon mutation during molecular dynamics (MD) simulations. The differences are quantified according to average difference, trajectory-wise distance or time-vary differences. Machine-learning methods are employed to predict the mutation impacts using the resulting conventional or time-series features. Predictions based on estimation of energy and based on investigation of molecular descriptors were conducted as benchmarks. Our method (machine-learning techniques using time-series features) outperformed the benchmark methods, especially in terms of the balanced F1 score. Particularly, deep-learning models led to the best prediction performance with distinct improvements in balanced F1 score and a sustained accuracy. Our work highlights the effectiveness of the characterization of affinity change upon mutations. Furthermore, deep-learning techniques are well designed for handling the extracted time-series features. This study can lead to a deeper understanding of mutation-induced diseases and resistance, and further guide the development of innovative drug design.