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Review
. 2023 Apr 25;19(8):2135-2148.
doi: 10.1021/acs.jctc.2c01085. Epub 2023 Mar 29.

Predicting Biomolecular Binding Kinetics: A Review

Jinan Wang  1 , Hung N Do  1 , Kushal Koirala  1 , Yinglong Miao  1
Affiliations
Review

Predicting Biomolecular Binding Kinetics: A Review

Jinan Wang et al. J Chem Theory Comput. .

Abstract

Biomolecular binding kinetics including the association (kon) and dissociation (koff) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.

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Figures

Figure 1.
Figure 1.
The number (A) and accuracy (B) of predictions of biomolecular binding kinetic rates obtained from MD simulations plotted over the years.
Figure 2.
Figure 2.
The number (A) and accuracy (B) of predicted biomolecular binding kinetic rates using different MD techniques, including Metadynamics (MetaD), Markov State Models (MSM), Gaussian accelerated MD (GaMD), conventional MD (cMD), Weighted Ensemble (WE), simulation enabled estimation of kinetic rates (SEEKR), coarse-grained MD (CGMD) and combination of Metadynamics and Machine Learning (MetaD+ML).
See this image and copyright information in PMC

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