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DEER-PREdict: Software for Efficient Calculation of Spin-Labeling EPR and NMR Data from Conformational Ensembles

By Giulio Tesei, Joao M Martins, Micha B.A. Kunze, Yong Wang, Ramon Crehuet, Kresten Lindorff-Larsen

Posted 10 Aug 2020
bioRxiv DOI: 10.1101/2020.08.09.243030

Owing to their plasticity, intrinsically disordered and multidomain proteins require descriptions based on multiple conformations, thus calling for techniques and analysis tools that are capable of dealing with conformational ensembles rather than a single protein structure. Here, we introduce DEER-PREdict, a software to predict Double Electron-Electron Resonance distance distributions as well as Paramagnetic Relaxation Enhancement rates from ensembles of protein conformations. DEER-PREdict uses an established rotamer library approach to describe the paramagnetic probes which are bound covalently to the protein. DEER-PREdict has been designed to operate efficiently on large conformational ensembles, such as those generated by molecular dynamics simulation, to facilitate the validation or refinement of molecular models as well as the interpretation of experimental data. The performance and accuracy of the software is demonstrated with experimentally characterized protein systems: HIV-1 protease, T4 Lysozyme and Acyl-CoA-binding protein. DEER-PREdict is open source (GPLv3) and available at https://github.com/KULL-Centre/DEERpredict and as a Python PyPI package https://pypi.org/project/DEERPREdict. ### Competing Interest Statement The authors have declared no competing interest.

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