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The University of Southampton

Selection and scoring of simulated conformational ensembles in agreement with HDX-MS data Seminar

14:00 - 15:00
18 February 2019
Building 34, Room 3001 University of Southampton SO17 1BJ

For more information regarding this seminar, please email Prof. Jon Essex at .

Event details

Richard T. Bradshaw presents a seminar as part of the computational systems chemistry section’s seminar series.

Hydrogen-deuterium exchange coupled to mass spectrometry (HDX-MS) is an attractive, rapid, label-free method for investigating dynamics of biomolecules in realistic environments, and is increasingly combined with molecular simulation approaches to structurally interpret HDX-MS measurements at the atomistic level. Empirical models of exchange, combined with structures generated by molecular dynamics (MD) simulations, allow the direct correlation of experimental and predicted deuterium uptake. However, sampling limitations and uncertainty in the experimental measurements can lead to difficulties in meaningfully interpreting differences between predicted and observed exchange rates.

To evaluate the reliability of HDX-MS predictions for membrane proteins, we used HDX-MS to measure backbone deuteration of the bacterial amino acid transporter LeuT as a model system. HDX-MS experiments and MD simulations compared LeuT in two distinct conformational ensembles, suggested to represent extracellular- or intracellular-open states in the transport cycle. Predicted deuterium exchange rates correlated well with the experimental data, but were unable to unambiguously discern between the two states.

Reliable interpretation of the HDX-MS measurements instead requires techniques to rigorously select the conformational ensemble that best represents the experimental data. Here we propose a maximum entropy approach to robustly reweight MD simulations with HDX-MS experiments, in which a minimal bias is applied to a simulation ensemble such that the predicted deuteration agrees with a target (experimental) value, within a given level of uncertainty. The bias applied to reweight the simulation provides a direct measure to rank the ability of different ensembles to describe the underlying experimental data. We demonstrate the implementation of this technique with example data and with LeuT, underscoring the capabilities of the approach for scoring and ranking MD structural ensembles and for rationalizing discrepancies between simulated and measured HDX-MS exchange.

Speaker information

Richard T. Bradshaw, NINDS, National Institutes of Health, Bethesda, MD, USA. Computational biophysicist

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