Systems Biology of Infection Lab, Department of Biochemistry and Molecular Biology, Biosciences Faculty, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.
Elife. 2024 Jan 16;13:e94919. doi: 10.7554/eLife.94919.
The study of protein interactions in living organisms is fundamental for understanding biological processes and central metabolic pathways. Yet, our knowledge of the bacterial interactome remains limited. Here, we combined gene deletion mutant analysis with deep-learning protein folding using AlphaFold2 to predict the core bacterial essential interactome. We predicted and modeled 1402 interactions between essential proteins in bacteria and generated 146 high-accuracy models. Our analysis reveals previously unknown details about the assembly mechanisms of these complexes, highlighting the importance of specific structural features in their stability and function. Our work provides a framework for predicting the essential interactomes of bacteria and highlight the potential of deep-learning algorithms in advancing our understanding of the complex biology of living organisms. Also, the results presented here offer a promising approach to identify novel antibiotic targets.
在活生物体中研究蛋白质相互作用对于理解生物过程和中心代谢途径至关重要。然而，我们对细菌相互作用组的了解仍然有限。在这里，我们将基因缺失突变分析与使用 AlphaFold2 的深度学习蛋白质折叠相结合，以预测核心细菌必需相互作用组。我们预测和模拟了细菌中必需蛋白质之间的 1402 个相互作用，并生成了 146 个高精度模型。我们的分析揭示了这些复合物组装机制的先前未知细节，强调了特定结构特征在其稳定性和功能中的重要性。我们的工作为预测细菌的必需相互作用组提供了一个框架，并强调了深度学习算法在推进我们对生物体复杂生物学的理解方面的潜力。此外，这里呈现的结果为鉴定新型抗生素靶标提供了一种很有前途的方法。
