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Bringing Molecular Dynamics and Ion-Mobility Spectrometry Closer Together: Shape Correlations, Structure-Based Predictors, and Dissociation
journal contribution
posted on 2018-08-01, 00:00 authored by Alexander Kulesza, Erik G. Marklund, Luke MacAleese, Fabien Chirot, Philippe DugourdUnfolding
of proteins gives detailed information about their structure
and energetics and can be probed as a response to a change of experimental
conditions. Ion mobility coupled to native mass spectrometry is a
gas-phase technique that can observe such unfolding in the gas phase
by monitoring the collision cross section (CCS) after applying an
activation, for example, by collisions (collision-induced unfolding,
CIU). The structural assignments needed to interpret the experiments
can profit from dedicated modeling strategies. While predictions of
ion-mobility data for well-defined and structurally characterized
systems is straightforward, systematic free-energy calculations or
biased molecular dynamics simulations that employ IMS data are still
limited. The methods with which CCS values are calculated so far do
not allow for analytical gradients needed in biased molecular dynamics
(MD), and further, explicit CCS calculations still can pose computational
bottleneckwhen integrated into MD-bioinformatics workflows.
These limitations motivate one to revisit known correlations of the
CCS with the aim to find computationally cheap and versatile but still
at least semiquantitative descriptions of the CCS by pure structural
descriptors. We have therefore investigated the correlation of CCS
with the key structural parameter often used in computational unfolding
studiesthe gyration radiusfor several small monomeric
and dimeric proteins. We work out the challenges and caveats of the
combinations of the configurational sampling method and the CCS-calculation
algorithm. The correlations were found to be sensitive to the generation
conditions and additionally to the system topology. To reduce the
amount of fitting to be undertaken, we devise a simple structural
model for the CCS that shares some commonalities with the hard-sphere
model and the projection algorithm but is designed to take unfolding
into account. With this model, we suggest a two-point interpolating
function rather than fitting a large data set, at only little deterioration
of the predictive power. We further proceed to a model with composition
and structure dependence that builds only upon the gyration radius
and the chemical formula to apply the found CCS scaling behaviorthe
scaled macroscopic sphere (sMS) predictor. We demonstrate its applicability
to describe unfolding and also its transferability for a larger set
of structures from the RSCPDB. As we have found for the dimeric systems,
that shape correlations with one global descriptor qualitatively break
down, we finally suggest a recipe to switch between global and fragment-based
CCS prediction, that takes up the ideas of coarse-graining protein
complexes. The presented models and approaches might provide a basis
to boost the integration of structural modeling with multistage IMS
experiments, especially in the field of large-scale bioinformatics
or “on-the-fly” biasing of MD, where computational efficiency
is critical.
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Keywords
CCS-calculation algorithmRSCPDBgas-phase techniqueprojection algorithmdimeric systemsshape correlationsStructure-Based PredictorsCCS valuesBringing Molecular Dynamicsdimeric proteinsion mobilitymass spectrometrymodeling strategiesconfigurational sampling methodhard-sphere modelchemical formulaIMS experimentsShape Correlationstwo-point interpolating functiongyration radiusmacroscopic sphereIMS dataion-mobility datafree-energy calculationssystem topologystructure dependenceIon-Mobility SpectrometryCCS calculationsCIUgas phaseDissociation Unfoldingfragment-based CCS predictionMD-bioinformatics workflowsgeneration conditionscoarse-graining protein complexesdynamics simulations
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