MD Simulations Analysis

Molecular dynamics trajectories of proteins give rise to a large amount of data which makes identifying properties by mere visual inspection difficult. Unsupervised learning and data analysis allows to automatically extract important features from simulations. We apply protocols to identify and characterize intermediate states of a conformational ensemble [1, 2], we develop accurate free energy landscape estimation methods based on Gaussian mixture models [3], we graph theoretical concepts to describe the molecular mechanisms underpinning protein function. We also resort to machine learning methods (neural networks, random forrest…) trained to distinguish between different conformational states and to identify the most important residues for classification [4].

Recent publications:

[1] Effect of Ca2+ on the promiscuous target-protein binding mechanism of calmodulin AM Westerlund, L Delemotte, PloS Comp Biol, DOI: 10.1371/journal.pcbi.1006072 2018

[2] Parameter-free Clustering of Free Energy Landscapes with Gaussian Mixtures AM Westerlund, L Delemotte – arXiv preprint arXiv:1905.03110, 2019

[3] Inference of Calmodulin’s Ca2+-Dependent Free Energy Landscapes via Gaussian Mixture Model Validation AM Westerlund, TJ Harpole, C Blau and L Delemotte, J. Chem. Theor. Comp. DOI: 10.1021/acs.jctc.7b00346 2018

[4] Extracting molecular insights from conformational ensembles using Machine Learning O Fleetwood, MA Kasimova, AM Westerlund, L Delemotte, BioRxiv, 695254 2019


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