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Conformational Ensembles from Experimental Data
and Computer Simulations
Poster Abstracts
127
90-POS
Board 10
Guiding Stochastic Optimization Algorithms with Experimental Data to Model Protein
Energy Landscapes and Structural Transitions
Amarda Shehu
.
n/a, Fairfax, USA.
Some of the most complex human disorders are driven by DNA mutations that percolate to
protein dysfunction. While it is known that mutations percolate to dysfunction by changing the
energy landscape and in turn the structural dynamics of a protein, quantifying changes to the
landscape and dynamics of a protein in response to a mutation remains elusive. Even
reconstructing the energy landscape of the healthy, wildtype form of a protein is currently out of
reach. While the challenges to wet- and dry-laboratory techniques are different in nature, they all
relate to the fact that the dynamics of interest, corresponding to structural transitions on the
energy landscape, spans disparate spatio-temporal scales.
Recent work in our laboratory is exploiting the wealth of accumulated structural data on a
protein’s variants to address some of the outstanding challenges to in-silico models of
equilibrium dynamics. Stochastic opimization algorithms are developed in our laboratory to
build detailed, yet resource-aware maps of protein energy landscapes. These algorithms exploit
experimental data to make informed algorithmic decisions such as variable selection and
variation operators. The algorithms first construct unstructured, sample-based maps of a
protein’s energy landscape, and then enrich such maps with connectivity information to obtain
the connected landscape. The latter can provide information on any structural transitions of
interest, as well as yield summary statistics on dynamics. Studies on several proteins show this
approach is promising and can reconstruct landscapes that currently remain beyond the reach of
molecular dynamics and monte carlo-based approaches. Results on specific proteins of
importance to human disorders make the case that the computed, connected landscapes advance
our understanding of the role of dynamics on how mutations percolate to dysfunction and even
provide directions of relevance for novel therapeutics.