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Conformational Ensembles from Experimental Data
and Computer Simulations
Sunday Speaker Abstracts
22
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.