Department of Physics, University of Roma Tor Vergata, 00133 Rome, Italy.
Section of Roma Tor Vergata, National Institute of Nuclear Physics, 00133 Rome, Italy.
Int J Mol Sci. 2024 Mar 25;25(7):3663. doi: 10.3390/ijms25073663.
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 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.
深度学习算法在蛋白质折叠方面的应用开创了一个新纪元，使得一旦知道蛋白质的序列，就能够预测和优化其功能。当金属离子或小分子配体等辅助因子对于功能至关重要时，任务就变得更加复杂。在这种情况下，必须结合使用基于原子间力场的传统模拟方法和深度学习预测。我们以[FeFe]氢化酶为例来说明这种情况，[FeFe]氢化酶是单细胞藻类中的一种酶，对于生物技术应用具有很大的前景。[FeFe]氢化酶是一种铁硫蛋白，能够催化溶解在液态水中的质子化学还原为气体氢气。产氢效率和细胞对氧气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域内铁硫簇的组织有关。在这项工作中，我们提出了 211/11P [FeFe]氢化酶的可能三维结构，其序列是从最近公布的给定菌株基因组中提取的。初始结构模型是使用以下方法构建的：(i) 深度学习算法 AlphaFold；(ii) 同源建模服务器 SwissModel；(iii) 基于最知名的细菌晶体结构的手动构建。包含缺失的铁硫簇，并对嵌入水溶液环境的初始结构进行了微秒长的分子动力学模拟。还使用了多行走者元动力学方法来增强对结构的采样，这些结构包括铁硫簇的功能和非功能组织。由深度学习提供的结构模型与功能[FeFe]氢化酶一致，其特点是辅助因子与蛋白质基质之间存在特殊相互作用。
