PDF(Pubmed)
Abstract：
The advent of deep learning algorithms for protein folding opened a new era in the ability of predicting and optimizing the function of proteins once the sequence is known. The task is more intricate when cofactors like metal ions or small ligands are essential to functioning. In this case, the combined use of traditional simulation methods based on interatomic force fields and deep learning predictions is mandatory. We use the example of [FeFe] hydrogenases, enzymes of unicellular algae promising for biotechnology applications to illustrate this situation. [FeFe] hydrogenase is an iron-sulfur protein that catalyzes the chemical reduction of protons dissolved in liquid water into molecular hydrogen as a gas. Hydrogen production efficiency and cell sensitivity to dioxygen are important parameters to optimize the industrial applications of biological hydrogen production. Both parameters are related to the organization of iron-sulfur clusters within protein domains. In this work, we propose possible three-dimensional structures of Chlorella vulgaris 211/11P [FeFe] hydrogenase, the sequence of which was extracted from the recently published genome of the given strain. Initial structural models are built using: (i) the deep learning algorithm AlphaFold; (ii) the homology modeling server SwissModel; (iii) a manual construction based on the best known bacterial crystal structure. Missing iron-sulfur clusters are included and microsecond-long molecular dynamics of initial structures embedded into the water solution environment were performed. Multiple-walkers metadynamics was also used to enhance the sampling of structures encompassing both functional and non-functional organizations of iron-sulfur clusters. The resulting structural model provided by deep learning is consistent with functional [FeFe] hydrogenase characterized by peculiar interactions between cofactors and the protein matrix.
摘要：
蛋白质折叠深度学习算法的出现开启了一个新时代，一旦序列已知，就可以预测和优化蛋白质的功能。当辅因子如金属离子或小配体对功能至关重要时，任务更加复杂。在这种情况下,基于原子间力场和深度学习预测的传统模拟方法的结合使用是强制性的。我们使用[FeFe]氢化酶的例子，单细胞藻类的酶有望用于生物技术应用，以说明这种情况。[FeFe]氢化酶是铁-硫蛋白,其催化溶解在液态水中的质子化学还原为作为气体的分子氢。制氢效率和细胞对双氧的敏感性是优化生物制氢工业应用的重要参数。这两个参数都与蛋白质结构域内铁-硫簇的组织有关。在这项工作中,我们提出了小球藻211/11P[FeFe]氢化酶的可能的三维结构，其序列是从最近发表的给定菌株的基因组中提取的。初始结构模型使用:(i)深度学习算法AlphaFold;(ii)同源性建模服务器SwissModel;(iii)基于最著名的细菌晶体结构的手动构建。包括缺失的铁硫簇，并进行了嵌入水溶液环境中的初始结构的微秒长的分子动力学。多步行者元动力学还用于增强包含铁硫簇的功能性和非功能性组织的结构的采样。由深度学习提供的所得结构模型与功能性[FeFe]氢化酶一致，其特征在于辅因子与蛋白质基质之间的特殊相互作用。
