Recruiting machine learning methods for molecular simulations of proteins

Abstract

Molecular dynamics (MD) simulations are critical to understanding the movements of proteins in time. Yet, MD simulations are limited due to the availability of high-resolution protein structures, accuracy of the underlying force-field, computational expense, and difficulty in analysing big data-sets. Machine learning algorithms are now routinely used to circumvent many of these limitations and computational biophysicists are continuously making progress in developing novel applications. Here, we discuss some of these methods, varying from traditional dimensionality reduction approaches to more recent abstractions such as transfer learning and reinforcement learning, and how they have been used to deal with the challenges in MD. We conclude with the prospective issues in the application of machine learning methods in MD, to increase accuracy and efficiency of protein dynamics studies in general.

Keywords:

• Protein dynamics
• machine learning
• Markov state model
• reinforcement learning
• transfer learning

Acknowledgements

We thank the members of Shukla group for valuable discussions and suggestions.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

DS acknowledges support from the New Innovator award from the Foundation for Food and Agriculture Research [C2185]. SM acknowledges support from the Computational Science and Engineering Fellows Program at the University of Illinois, Urbana-Champaign, Urbana, IL.