Bridging the Coordination Chemistry of Small Compounds and Metalloproteins Using Machine Learning

Metalloproteins require metal ions as cofactors to catalyze specific reactions with remarkable efficiency and specificity. In various electron transfer reactions, metals in the active sites change their oxidation states to facilitate the biochemical reactions. Cryogenic electron microscopy, X-ray, and X-ray free electron laser (XFEL) crystallography are used to image metalloproteins to understand the reaction mechanisms. However, radiation damage in cryoEM and X-ray crystallography, and the challenge of generating homogeneous crystals and keeping the appropriate experimental conditions for all the crystals in XFEL crystallography, may alter the oxidation states. Here, we build machine learning models trained on a large data set from the Cambridge Crystallographic Data Center to evaluate the metal oxidation states. The models yield high accuracy scores (from 82% to 94%) for all metals in the small molecules. Then, they were used to predict the oxidation states of more than 30 000 metal clusters in metalloproteins with Fe, Mn, Co, and Cu in their active sites. We found that most of the metals exist in the lower oxidation states (Fe2+ 77%, Mn2+ 85%, Co2+ 65%, and Cu+ 64%), and these populations correlate with the standard reduction potentials of the metal ions. Furthermore, we found no clear correlation between these populations and the resolution of the structures, which suggests no significant dependence of these predictions on the resolution. Our models represent a valuable tool for evaluating the oxidation states of the metals in metalloproteins imaged with different techniques. The data files and the machine learning code are available in a public GitHub repository: https://github.com/mamin03/OxitationStatesMetalloprotein.git.金属蛋白(Metalloproteins)需以金属离子作为辅因子，以极高的效率与特异性催化特定化学反应。在各类电子转移反应中，活性位点内的金属会通过改变自身氧化态来助力生化反应进行。研究人员常采用冷冻电子显微镜(Cryogenic Electron Microscopy)、X射线衍射以及X射线自由电子激光(XFEL)晶体学对金属蛋白进行成像，以解析其反应机制。然而，冷冻电镜与X射线晶体学中的辐射损伤，以及X射线自由电子激光晶体学中制备均一晶体、维持所有晶体实验条件一致性的技术难题，均可能改变金属的氧化态。本研究基于剑桥晶体学数据中心(Cambridge Crystallographic Data Centre)的大型数据集训练机器学习模型，用于评估金属的氧化态。该模型对小分子中的各类金属均取得了82%至94%的高准确率。随后，研究人员利用模型对活性位点含铁、锰、钴与铜的金属蛋白中超过30000个金属团簇的氧化态进行预测。研究发现，绝大多数金属以较低氧化态存在：Fe²⁺占比77%、Mn²⁺占比85%、Co²⁺占比65%、Cu⁺占比64%，且该占比与金属离子的标准还原电势显著相关。此外，本研究未发现上述金属氧化态占比与蛋白结构分辨率之间存在明确关联，表明模型预测结果不受分辨率的显著影响。本研究构建的机器学习模型可为通过不同成像技术获取的金属蛋白中金属氧化态的评估提供极具价值的工具。本研究的数据集文件与机器学习代码已公开至GitHub仓库：https://github.com/mamin03/OxitationStatesMetalloprotein.git。