DEER-PREdict: Software for efficient calculation of spin-labeling EPR and NMR data from conformational ensembles
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DEER-PREdict : Software for efficient calculation of spin-labeling EPR and NMR data from conformational ensembles. / Tesei, Giulio; Martins, João M.; Kunze, Micha B. A.; Wang, Yong; Crehuet, Ramon; Lindorff-Larsen, Kresten.
In: PLOS Computational Biology, Vol. 17, No. 1, e1008551, 2021.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - DEER-PREdict
T2 - Software for efficient calculation of spin-labeling EPR and NMR data from conformational ensembles
AU - Tesei, Giulio
AU - Martins, João M.
AU - Kunze, Micha B. A.
AU - Wang, Yong
AU - Crehuet, Ramon
AU - Lindorff-Larsen, Kresten
PY - 2021
Y1 - 2021
N2 - 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 program 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 github.com/KULL-Centre/ DEERpredict and as a Python PyPI package pypi.org/project/DEERPREdict.
AB - 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 program 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 github.com/KULL-Centre/ DEERpredict and as a Python PyPI package pypi.org/project/DEERPREdict.
U2 - 10.1371/journal.pcbi.1008551
DO - 10.1371/journal.pcbi.1008551
M3 - Journal article
C2 - 33481784
AN - SCOPUS:85100020212
VL - 17
JO - P L o S Computational Biology (Online)
JF - P L o S Computational Biology (Online)
SN - 1553-734X
IS - 1
M1 - e1008551
ER -
ID: 257603739