Predicting the Impacts of Mutations on Protein-Ligand Affinity Based on Molecular Dynamics Simulations and Machine Learning Methods

Debby D. WANG, Ou-Yang LE, Haoran XIE, Mengxu ZHU, Hong YAN

Research output: Journal PublicationsJournal Article (refereed)peer-review

48 Citations (Scopus)


Purpose: Mutation-induced variation of protein-ligand 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 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.
Original languageEnglish
Pages (from-to)439-454
Number of pages16
JournalComputational and Structural Biotechnology Journal
Publication statusPublished - Mar 2020

Bibliographical note

This work was support by the Hong Kong Research Grants Council [Project CityU 11200818]; City University ofHong Kong [Projects 9610034 and 9610460]; the Shenzhen Fundamental Research Program [Grant JCYJ20170817095450210760]; National Natural Science Foundation of China [Grant 61602309]; the Interdisciplinary Research Schemeof the Dean’s Research Fund 2018-19 [FLASS/DRF/IDS-3] and Departmental Collaborative Research Fund 2019[MIT/DCRF-R2/18-19] of The Education University of Hong Kong.


  • Missense mutation
  • Mutation impact
  • Protein-ligand affinity
  • Molecular dynamics (MD) simulations
  • Local geometrical features
  • Time series features
  • Deep learning
  • Protein-ligand binding affinity


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