期刊
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
卷 18, 期 -, 页码 439-454出版社
ELSEVIER
DOI: 10.1016/j.csbj.2020.02.007
关键词
Missense mutation; Mutation impact; Protein-ligand binding affinity; Molecular dynamics (MD) simulations; Local geometrical features; Time series features; Deep learning
资金
- Hong Kong Research Grants Council [CityU 11200818]
- City University of Hong Kong [9610034, 9610460]
- Shenzhen Fundamental Research Program [JCYJ20170817095210760]
- National Natural Science Foundation of China [61602309]
- Guangdong Basic and Applied Basic Research Foundation [2019A1515011384]
- Interdisciplinary Research Scheme of the Dean's Research Fund 2018-19 [MIT/DCRF-R2/18-19]
- Departmental Collaborative Research Fund 2019 of The Education University of Hong Kong [MIT/DCRF-R2/18-19]
Purpose: 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. 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. Results: 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. Conclusion: 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. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
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