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.

中文翻译：

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DEER-PREdict：从构象整合体高效计算自旋标记EPR和NMR数据的软件

由于它们的可塑性，本质上无序的多域蛋白需要基于多种构象的描述，因此需要能够处理构象集合而不是单个蛋白结构的技术和分析工具。在这里，我们介绍DEER-PREdict，这是一种从蛋白质构象集合预测双电子-电子共振距离分布以及顺磁弛豫增强率的软件。DEER-PREdict使用已建立的旋转异构体文库方法来描述与蛋白质共价结合的顺磁探针。DEER-PREdict经过精心设计，可以在大型构象的集成体上高效运行，例如通过分子动力学模拟生成的组合，有助于验证或完善分子模型以及解释实验数据。该软件的性能和准确性已通过实验表征的蛋白质系统得到证明：HIV-1蛋白酶，T4溶菌酶和酰基辅酶A结合蛋白。DEER-PREdict是开源（GPLv3），可在https://github.com/KULL-Centre/DEERpredict和Python PyPI软件包https://pypi.org/project/DEERPREdict获得。