60 citations · 87 across the 5 of their papers we have counts for
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Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning
Diego E. Kleiman, Hassan Nadeem, Diwakar Shukla
Molecular Dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes. However, sampling protein conformational changes…
Billion-years old proteins show the importance of N-lobe orientation in Imatinib-kinase selectivity
Zahra Shamsi, Diwakar Shukla
The molecular origins of proteins' functions are a combinatorial search problem in the proteins' sequence space, which requires enormous resources to solve. However, evolution has…
Thirty years of molecular dynamics simulations on posttranslational modifications of proteins
Austin T. Weigle, Jiangyan Feng, Diwakar Shukla
Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabili…
Automatic Feature Selection in Markov State Models Using Genetic Algorithm
Qihua Chen, Jiangyan Feng, Shriyaa Mittal +1
Markov State Models (MSMs) are a powerful framework to reproduce the long-time conformational dynamics of biomolecules using a set of short Molecular Dynamics (MD) simulations. How…
REinforcement learning based Adaptive samPling: REAPing Rewards by Exploring Protein Conformational Landscapes
Zahra Shamsi, Kevin J. Cheng, Diwakar Shukla
One of the key limitations of Molecular Dynamics simulations is the computational intractability of sampling protein conformational landscapes associated with either large system s…