Conformational Ensembles from Experimental Data and Computer Simulations

Conformational Ensembles from Experimental Data and Computer Simulations

Sunday Speaker Abstracts

What Does It Mean for a Protein to be Disordered? Insights from Experiment and Molecular Simulations

Collin Stultz Massachusetts Institute of Technology, Cambridge, MA, USA

No Abstract

Bridging the Gap Between Stationary and Dynamic Data Through Augmented Markov Models Simon Olsson , Frank Noé. Freie Universität Berlin, Berlin, Germany. Structural biology is rapidly moving towards a paradigm characterized by data from a broad range of experimental and computational data. Each of these are potentially sensitive to structural changes across multiple time and length scales. However, a major open problem remains: devise inference methods which optimally combine all of these different sources of information into models amenable to human analysis. There has been a considerable number of contributions to achieve this, however, reconciling information which is dynamic in nature - that is, time-series, correlation functions etc - with stationary information, remains difficult. To this end, we introduce augmented Markov models (AMM). The approach marries concepts from probability theory and information theory to optimally balance multiple sources of data - and since these models are mathematically equivalent to Markov state models, a broad suite of techniques is already available to facilitate their analysis. Through a number of examples we show how the use of AMMs results in accurate models of thermodynamics and kinetics of a number of protein systems. We therefore consider AMMs to constitute an important first step towards developing truly mechanistic, data-driven models in integrative structural biology.

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