Conformational Ensembles from Experimental Data and Computer Simulations

Conformational Ensembles from Experimental Data and Computer Simulations

Poster Abstracts

61-POS Board 21 Molecular Breakdown of DEER Data from Self-learning Atomistic Simulations Fabrizio Marinelli , Giacomo Fiorin, José Faraldo-Gómez. n/a, Bethesda, USA. Double Electron-Electron Resonance (DEER) has become a landmark technique to investigate bio-molecular structure and dynamics. DEER allows obtaining the distance distributions between spin-labels attached to a biomolecule and in contrast to X-ray crystallography and NMR spectroscopy, DEER is neither limited by the need of crystallization nor by the size of the biomolecule. This notwithstanding, it is often not straightforward to interpret DEER data as it reflects a plethora of molecular conformations and rotameric states of the spin-labels. Several strategies to disentangle this variability have been put forward recently, either based on approximate structural models or on atomistic simulations. Both kinds of approaches however rely on probability distributions that are inferred from the actual measured data and do not take into account the experimental noise. Building upon the maximum entropy principle, we present an adaptive simulation framework to minimally bias an atomistic simulation to sample a conformational ensemble that reproduces the DEER data within the experimental uncertainty. Our approach has been formulated either to target directly the DEER time signal within the experimental noise or to reproduce DEER distributions within the confidence intervals. We first test the performance of this approach for the spin-labeled T4 lysozyme. Then, we apply it to investigate the conformational dynamics of the apo VcSiaP binding protein, that undergoes an open to close conformational change upon substrate binding. The results indicate a wider opening of the VcSiaP apo state compared to both the X-ray structure and standard MD simulations, underlying that the proposed technique is a powerful tool to structurally characterize DEER experiments and to investigate the dynamics of biomolecules.

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