Comparison of Sampling Methods via Robust Free Energy Inference: Application to Calmodulin

Comparison of Sampling Methods via Robust Free Energy Inference: Application to Calmodulin

Abstract

A free energy landscape estimation-method based on Bayesian inference is presented and used for comparing the efficiency of thermally enhanced sampling methods with respect to regular molecular dynamics, where the simulations are carried out on two binding states of calmodulin. The proposed free energy estimation method (the GM method) is compared to other estimators using a toy model showing that the GM method provides a robust estimate not subject to overfitting. The continuous nature of the GM method, as well as predictive inference on the number of basis functions, provide better estimates on sparse data. We find that the free energy diffusion properties determine sampling method effectiveness, such that the diffusion dominated apo-calmodulin is most efficiently sampled by regular molecular dynamics, while the holo with its rugged free energy landscape is better sampled by enhanced methods.

Free energy estimation, REST, temperature replica exchange
\externaldocument

supplementary_material KTH]Science for Life Laboratory, Department of Physics, KTH Royal Institute of Technology, Box 1031, SE-171 21 Solna KTH]Science for Life Laboratory, Department of Physics, KTH Royal Institute of Technology, Box 1031, SE-171 21 Solna SU]Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, SE-171 21 Solna KTH]Science for Life Laboratory, Department of Physics, KTH Royal Institute of Technology, Box 1031, SE-171 21 Solna \abbreviationsFE,GM,KDE,kNN, REST

\subfile

introduction.tex \subfilemethod.tex \subfileresults.tex

{acknowledgement}

The simulations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC Centre for High Performance Computing (PDC-HPC). CB acknowledges the Knut and Alice Wallenberg foundation (1484505) and the Carl Trygger foundation (CTS-15:298) for funding. Furthermore, the authors thank Berk Hess for insightful comments during the writing process.

\suppinfo

The code for estimating FE landscapes with the GM method is available free of charge at http://delemottelab.theophys.kth.se/index.php/resources/. The supplementary information contains tables with the number of estimated basis functions in the GM method (Tables LABEL:table:inferred_bf-LABEL:tab:nGaussApo) and figures comparing the end results from using the GM method and Bayesian inference on a step function (Figures LABEL:fig:supp:1D-LABEL:fig:supp:DRID50).

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