MD Simulations Analysis

Molecular simulations of proteins give rise to a large amount of data which makes identifying important details by mere visual inspection difficult. By using a wide range of data-driven tools such as machine learning, bayesian inference, clustering and Gaussian mixture density estimation, we are not only able to identify important details in the simulations but we also reduce the risk of introducing bias to the analysis.

We develop tools for estimating free energy landscapes, as well as clustering well-defined core-states. We also adapt and apply machine learning methods for characterizing intermediate states or differences between conditions, e.g. ligand-bound vs ligand-unbound. In addition to this, we use graph theory and network analysis to understand allosteric communication in membrane proteins. We also use bayesian inference techniques synergically with experimental data like NMR.

Featured Code

The rest of our code, tools and tutorials can be found at

Sergio Pérez-Conesa
Oliver Fleetwood
Lucie Delemotte

Recent publications

Informing NMR experiments with molecular dynamics simulations to characterize the dominant activated state of the KcsA ion channel  Pérez-Conesa, S., Keeler, E. G., Zhang, D., Delemotte, L., & McDermott, A. E. (2021). The Journal of Chemical Physics154(16), 165102.

Identification of ligand-specific G protein-coupled receptor states and prediction of downstream efficacy via data-driven modeling Fleetwood, O., Carlsson, J., & Delemotte, L. (2021). Elife10, e60715

Network analysis reveals how lipids and other cofactors influence membrane protein allostery Westerlund, A. M., Fleetwood, O., Perez-Conesa, S., & Delemotte, L. (2020).  The Journal of Chemical Physics153(14), 141103.

Molecular Insights From Conformational Ensembles Using Machine Learning, Fleetwood, O., Kasimova, M. A., Westerlund, A. M., & Delemotte, L. (2020).  Biophysical journal118(3), 765-780.

InfleCS: Clustering Free Energy Landscapes with Gaussian Mixtures, Westerlund, A. M., Harpole, T. J., Blau, C., & Delemotte, L. (2018). Journal of chemical theory and computation14(1), 63-71. 

Effect of Ca2+ on the promiscuous target-protein binding mechanism of calmodulin Westerlund, A. M., & Delemotte, L. (2018). PLoS computational biology14(4), e1006072.

Inference of Calmodulin’s Ca2+-Dependent Free Energy Landscapes via Gaussian Mixture Model Validation Westerlund, A. M., Harpole, T. J., Blau, C., & Delemotte, L. (2018). Journal of chemical theory and computation14(1), 63-71

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