Deep learning-driven insights into super protein complexes for outer membrane protein biogenesis in bacteria.
Deep learning-driven insights into super protein complexes for outer membrane protein biogenesis in bacteria.
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DOI：
10.7554/elife.82885
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发表时间：
2022-12-28
期刊：
影响因子：
7.7
通讯作者：
Skolnick, Jeffrey
中科院分区：
文献类型：
--
作者：
Gao, Mu;An, Davi Nakajima;Skolnick, Jeffrey
关键词：
为了到达它们的最终目的地，革兰氏阴性细菌的外膜蛋白（OMP）从胞质溶胶开始进行一个多事的旅程。多个分子机器、分子伴侣、蛋白酶和其他酶促进外膜蛋白的移位和组装。这些辅助分子通常是短暂地结合在一起，形成大的蛋白质组装体。由于在捕获和表征蛋白质-蛋白质相互作用（PPI），特别是瞬时相互作用方面的实验挑战，它们还没有得到很好的理解。使用AF 2Complex，我们引入了一个高通量的深度学习管道来识别大肠杆菌细胞包膜内的PPI，并将其应用于OMP生物合成途径中的几种蛋白质。在从筛选~1500个包膜蛋白获得的最高置信命中中，我们不仅发现了预期的相互作用，而且发现了具有深远意义的意想不到的相互作用。随后，我们预测这些蛋白质复合物的原子结构。这些结构，通常具有高置信度，解释了实验观察结果，并导致了以下机制假设：伴侣蛋白如何帮助新生的前体OMP从易位子出现，另一种伴侣蛋白如何防止其聚集并停靠到β-桶组装端口，以及蛋白酶如何进行质量控制。这项工作提出了一种通过使用基于深度学习的预测获得的结构见解来研究生物途径的一般策略。所有活细胞都包含在脂肪细胞膜内，该细胞膜允许水和仅某些其他分子轻松通过。细菌仅由单个细胞组成，使其膜成为与周围环境的唯一界面。革兰氏阴性细菌-包括大肠杆菌，一种在所有人类肠道中发现的细菌-有一层额外的保护层，即“外膜”。这种膜中的蛋白质被称为“外膜蛋白”或OMP，并允许营养物质进入细胞。但是在细胞内产生的外膜蛋白需要被运送到外膜并正确折叠，然后才能发挥作用。这个涉及许多不同蛋白质之间相互作用的多步骤过程尚未完全了解。OMP从细胞中心到外膜的旅程是复杂的。首先，OMP需要穿过细胞内膜。要做到这一点，它必须与内膜中的“通道蛋白”相互作用，将OMP送入两层膜之间的空间，称为细菌包膜。此步骤需要展开OMP。一旦进入细菌包膜，OMP与蛋白质相互作用，帮助其正确折叠并整合到外膜中。细菌包膜中蛋白质之间的相互作用是短暂的，这使得它们很难通过实验室实验进行研究。另一种方法是从蛋白质的氨基酸序列预测蛋白质的结构，这是一个难以解决的计算问题。然而，在2020年，深度学习程序AlphaFold 2背后的开发人员能够使用一组组织在“神经网络”中的方程，该神经网络可以从已知蛋白质结构库中“学习”，以高精度预测未知结构。Gao等人使用AF 2Complex，一种基于AlphaFold 2的工具，专门用于预测蛋白质之间的相互作用，以研究OMP在通往外膜的途中可能涉及哪些相互作用。在橡树岭国家实验室的超级计算机的帮助下，Gao等人筛选了近1,500株E.大肠杆菌蛋白质的细菌包膜，看看他们如何与外膜蛋白相互作用。筛选鉴定了以前未知的蛋白质之间的相互作用，这表明细菌外膜的形成和蛋白质的整合涉及尚未表征的蛋白质复合物和分子机制。此外，屏幕还识别了之前描述的交互，证实了深度学习方法可以正确捕获真实的交互。总的来说，Gao et al.的工作启发了新的假设的机制，通过该机制外膜蛋白被运输到外膜，虽然进一步的工作将需要确认的作用，蛋白质相互作用的计算模型预测实验。此外，基于计算预测设计实验的能力令人兴奋。如果得到证实，新的蛋白质相互作用可以帮助科学家更好地了解OMP运输，这对细菌生物学至关重要。在未来，这可能会导致发现抗生素药物的新靶点。
To reach their final destinations, outer membrane proteins (OMPs) of gram-negative bacteria undertake an eventful journey beginning in the cytosol. Multiple molecular machines, chaperones, proteases, and other enzymes facilitate the translocation and assembly of OMPs. These helpers usually associate, often transiently, forming large protein assemblies. They are not well understood due to experimental challenges in capturing and characterizing protein-protein interactions (PPIs), especially transient ones. Using AF2Complex, we introduce a high-throughput, deep learning pipeline to identify PPIs within the Escherichia coli cell envelope and apply it to several proteins from an OMP biogenesis pathway. Among the top confident hits obtained from screening ~1500 envelope proteins, we find not only expected interactions but also unexpected ones with profound implications. Subsequently, we predict atomic structures for these protein complexes. These structures, typically of high confidence, explain experimental observations and lead to mechanistic hypotheses for how a chaperone assists a nascent, precursor OMP emerging from a translocon, how another chaperone prevents it from aggregating and docks to a β-barrel assembly port, and how a protease performs quality control. This work presents a general strategy for investigating biological pathways by using structural insights gained from deep learning-based predictions. All living cells are contained within a fatty cell membrane that allows water and only certain other molecules to pass through with ease. Bacteria only consist of a single cell, making their membrane the only interface with the surrounding environment. Gram-negative bacteria – which include Escherichia coli, a bacterium found in the gut of all humans – have an extra layer of protection, the ‘outer membrane’. Proteins in this membrane are called ‘outer membrane proteins’ or OMPs and allow nutrients to enter the cell. But OMPs, which are made inside the cell, need to be transported to the outer membrane and folded correctly before they can perform their role. This multistep process, which involves interactions between many different proteins, is not fully understood. The journey of an OMP from the center of the cell where it is made to the outer membrane is complicated. First, the OMP needs to pass through the cell’s inner membrane. To do this, it must interact with ‘channel proteins’ in the inner membrane that feed the OMP into the space between the two membranes, known as the bacterial envelope. This step requires the OMP to be unfolded. Once in the bacterial envelope the OMP interacts with proteins that help it fold correctly and integrate into the outer membrane. The interactions between proteins in the bacterial envelope are short-lived, making them difficult to study using lab-based experiments. An alternative approach is predicting a protein’s structure from its amino acid sequence which is a difficult computational problem to solve. However, in 2020 developers behind the AlphaFold2, a deep learning program, were able to use a set of equations organized in a ‘neural network’ that can ‘learn’ from a library of known protein structures to predict unknown structures with high accuracy. Gao et al. used AF2Complex, a tool based AlphaFold2, tailored to predicting interactions between proteins, to investigate what interactions OMPs could be involved with on their way to the outer membrane. With the help of a supercomputer at the Oakridge National Laboratory, Gao et al. screened nearly 1,500 E. coli proteins within the bacterial envelope to see how they might interact with OMPs. The screen identified previously unknown interactions between proteins that suggest that the formation of the bacterial outer membrane and the integration of proteins into it involve protein complexes and molecular mechanisms that have not yet been characterized. Additionally, the screen also identified interactions that had been previously described, confirming that the deep learning approach can correctly capture real interactions. Overall, Gao et al.’s work inspires new hypotheses about the mechanisms through which OMPs are transported to the outer membrane, although further work will be needed to confirm the roles of protein interactions predicted by the computational model experimentally. Furthermore, the ability to design experiments based on computational predictions is exciting. If confirmed, the new protein interactions could help scientists better understand OMP transport, which is essential for bacterial biology. In the future, this could lead to the discovery of new targets for antibiotic drugs.