Structural Biology and NMR Laboratory & the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
CSIC-Institute for Advanced Chemistry of Catalonia (IQAC), Barcelona, Spain.
PLoS Comput Biol. 2021 Jan 22;17(1):e1008551. doi: 10.1371/journal.pcbi.1008551. eCollection 2021 Jan.
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.
由于其可塑性，固有无序和多域蛋白质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质结构的技术和分析工具。在这里，我们介绍了 DEER-PREdict，这是一个软件程序，可以从蛋白质构象的集合中预测双电子-电子共振距离分布以及顺磁弛豫增强率。DEER-PREdict 使用已建立的旋转体库方法来描述共价结合到蛋白质上的顺磁探针。DEER-PREdict 旨在有效地处理大型构象集合，例如通过分子动力学模拟生成的集合，以促进分子模型的验证或细化以及实验数据的解释。该软件的性能和准确性已通过经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 是开源的（GPLv3），可在 github.com/KULL-Centre/DEERpredict 和 Python PyPI 包 pypi.org/project/DEERPREdict 上获得。
