Webinar: Clustering free energy landscapes from molecular dynamics simulations (2020-05-12)

BioExcel’s webinar series continue with a presentation by Lucie Delemotte and Annie Westerlund

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Abstract

Free energy landscapes provide insights into conformational ensembles of biomolecules. In order to analyze these landscapes and elucidate mechanisms underlying conformational changes, there is a need to extract metastable states with limited noise. This has remained a difficult task, despite a multitude of existing clustering methods. We present and discuss a few well-known clustering methods as well as InfleCS, a novel method for extracting well-defined core states from free energy landscapes. InfleCS identifies core states by exploiting a Gaussian mixture density estimator and the shape of the estimated density landscape. Finally, we apply the clustering on a molecular simulation of the Ca2+/CaM conformational ensemble and go through each step using a jupyter notebook tutorial.

Presenters


Lucie Delemotte

Lucie Delemotte is an associate professor in Biophysics at KTH Royal institute of technology and SciLifeLab fellow. She joined KTH in 2016 after a postdoc at Temple University, Philadelphia, USA and at EPFL, Lausanne, Switzerland. Her main research focus is on mechanisms of membrane transport and of allosteric regulation, as well as the development of protocols that enable to describe these phenomena in a quantitative manner.

www.github.com/delemottelab
Twitter: @DelemotteLab

 

 

 

Annie Westerlund

Annie has a background in engineering mathematics and complex adaptive systems. She joined the Delemotte lab in 2016, starting her PhD in computational biophysics. By developing and applying data analysis methods, she is aiming to understand protein conformational dynamics, ion channel gating and allosteric pathways.

www.github.com/anniewesterlund

Register for webinar

Title: Clustering free energy landscapes from molecular dynamics simulations

Date: 12th May, 2020
Time: 15:00 BST / 16:00 CEST

Registration URL: https://attendee.gotowebinar.com/register/6492088802384483339

Webinar ID: 146-579-707

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