Universität Hamburg, ZBH - Center for Bioinformatics, Hamburg, Germany.
Deutsches Elektronen-Synchrotron DESY, Center for Free-Electron Laser Science, Hamburg, Germany.
Proteins. 2022 Aug;90(8):1521-1537. doi: 10.1002/prot.26337. Epub 2022 Apr 5.
Protein adaptations to extreme environmental conditions are drivers in biotechnological process optimization and essential to unravel the molecular limits of life. Most proteins with such desirable adaptations are found in extremophilic organisms inhabiting extreme environments. The deep sea is such an environment and a promising resource that poses multiple extremes on its inhabitants. Conditions like high hydrostatic pressure and high or low temperature are prevalent and many deep-sea organisms tolerate multiple of these extremes. While molecular adaptations to high temperature are comparatively good described, adaptations to other extremes like high pressure are not well-understood yet. To fully unravel the molecular mechanisms of individual adaptations it is probably necessary to disentangle multifactorial adaptations. In this study, we evaluate differences of protein structures from deep-sea organisms and their respective related proteins from nondeep-sea organisms. We created a data collection of 1281 experimental protein structures from 25 deep-sea organisms and paired them with orthologous proteins. We exhaustively evaluate differences between the protein pairs with machine learning and Shapley values to determine characteristic differences in sequence and structure. The results show a reasonable discrimination of deep-sea and nondeep-sea proteins from which we distinguish correlations previously attributed to thermal stability from other signals potentially describing adaptions to high pressure. While some distinct correlations can be observed the overall picture appears intricate.
蛋白质对极端环境条件的适应是生物技术过程优化的驱动因素,也是揭示生命分子极限的关键。大多数具有这种理想适应性的蛋白质存在于栖息在极端环境中的极端微生物中。深海就是这样一种环境,是一种有前途的资源,对其居民来说存在多种极端情况。普遍存在高静水压力和高温或低温等条件,许多深海生物能够耐受多种极端情况。虽然对高温的分子适应已有较好的描述,但对其他极端情况(如高压)的适应仍知之甚少。要全面揭示个别适应的分子机制,可能需要将多因素适应分开。在这项研究中,我们评估了深海生物与非深海生物的蛋白质结构差异。我们创建了一个包含 1281 个来自 25 种深海生物的实验蛋白质结构的数据集,并将它们与直系同源蛋白进行配对。我们使用机器学习和 Shapley 值来详尽地评估蛋白质对之间的差异,以确定序列和结构上的特征差异。结果表明,可以合理地区分深海和非深海蛋白质,从中我们可以区分以前归因于热稳定性的相关性与可能描述高压适应的其他信号。虽然可以观察到一些明显的相关性,但总体情况似乎很复杂。