DEER-PREdict: Software for efficient calculation of spin-labeling EPR and NMR data from conformational ensembles

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 and as a Python PyPI package . Author summary The accurate description of the structure of a protein is pivotal to fully understand its biological function. A large fraction of eukaryotic proteins is intrinsically disordered or consists of multiple folded domains connected by disordered regions. The structure of these proteins is highly flexible and can only be described by large ensembles of conformations. The characterization of these ensembles can be achieved by integrating in silico molecular modelling and simulations with experiments. Here, we present DEER-PREdict, an open-source software program to conveniently and efficiently calculate the observables of two biophysical methods, namely double electron-electron resonance (DEER) and paramagnetic relaxation enhancement (PRE) nuclear magnetic resonance. Both techniques provide distance information for highly dynamic systems and involve labelling proteins at one or more sites with flexible nitroxide molecules. The DEER-PREdict package combines previously developed and validated methods for placing multiple conformations of the nitroxide molecule at the protein sites with the rapid calculation of DEER and PRE observables from large ensembles of protein structures. Through examples, we illustrate the use of DEER-PREdict as a tool for interpreting experimental results, validating molecular models of flexible proteins as well as designing experiments.

Automated summary

DEER-PREdict is a software program designed to predict distance distributions and relaxation enhancement rates of proteins from ensembles of protein conformations. It efficiently operates on large conformational ensembles, with demonstrated performance and accuracy in experimentally characterized protein systems such as HIV-1 protease and T4 Lysozyme. The open-source program combines molecular modeling and simulations with experiments to provide useful tools for interpreting experimental results and validating molecular models of flexible proteins.