New Bayesian statistical mechanical approaches to integrative structural biology using unassigned NMR and mass spectrometry

蛋白质数据库中的125,000+结构为我们提供了一些关于生物如何在分子水平上发挥作用的最丰富的见解。蛋白质是生命的小机器。这些微小的分子负责复制我们的DNA，将食物转化为能量，收缩我们的肌肉，并在我们的神经系统中传导电子信号--以及许多其他活动。每种蛋白质的功能取决于其结构：每种蛋白质折叠成的特定三维形状，由其氨基酸序列编码。这些结构帮助揭示了生命如何在分子水平上发挥作用的非凡故事。然而，这个故事是不完整的，因为我们仍然不知道有许多蛋白质和蛋白质复合体(粘在一起并形成更大结构的蛋白质组)。这是因为并不是所有的蛋白质或复合体都适用于结构生物学家经常使用的实验技术。但最近在机器学习应用方面的进展正准备改变这一点。谷歌DeepMind的一个团队最近开发了一种名为AlphaFold2的计算机程序，可以非常准确地预测蛋白质结构。在许多情况下，预测的结构非常准确，有时甚至与通过黄金标准实验方法确定的结构一样好。然而，在其他一些情况下，预测的一些细节是错误的，这些细节对于一些任务可能至关重要，比如搜索新药。更糟糕的是，一些预测根本就是错误的。因此，一个紧迫的挑战是开发能够快速验证预测的方法，即快速确定它们是否可信。另一个挑战是，虽然AlphaFold2擅长预测单个蛋白质的结构，但对于生物化学和细胞生物学中普遍存在的多蛋白质复合体，它并不总是准确的。这项提议的主要目的是开发新的计算和实验方法，通过快速验证结构并将它们组装成复合体来利用这一非凡的新预测工具。我们的方法侧重于核磁共振和交联质谱学实验，并结合复杂的计算机建模。这些实验可以应用于各种生化系统，但数据可能很难解释。我们正在开发建模工具，这些工具使用贝叶斯统计学和统计力学的工具来理解这些具有挑战性的数据。解决这些挑战将开启蛋白质结构测定的新途径。该项目的长期目标是实现一系列结构生物学的新方法，使我们能够揭示目前隐藏的蛋白质结构。通过提供新的工具，我们将有助于更好地了解生命在分子水平上的功能，并为药物发现和蛋白质工程提供新的途径。

The 125,000+ structures in the protein databank provide some of our richest insights into how biology functions at the molecular level. Proteins are life's little machines. These tiny molecules are responsible for replicating our DNA, turning food into energy, contracting our muscles, and conducting electrical signals in our nervous systems-among many other activities. The function of each protein depends on its structure: the specific three-dimensional shape that each protein folds into, which is encoded by its amino acid sequence. These structures have helped to reveal the extraordinary story of how life functions at the molecular level. This story, however, is incomplete because there are many proteins and protein complexes (groups of proteins that stick together and form larger structures) that we still do not know. This is because not all proteins or complexes are amenable to the experimental techniques that structural biologists routinely use. But recent advances in the application of machine learning are poised to change this. A team at Google's DeepMind recently developed a computer program called AlphaFold2 that can predict the protein structures with remarkable accuracy. In many cases predicted structures are very accurate, sometimes even as good as structures determined through gold-standard experimental approaches. However, in some other cases, the predictions get some details wrong, and these details can be critical important for some tasks, like searching for new drugs. Even worse, some fraction of predictions is simply wrong. Thus, one pressing challenge is to develop methods that can rapidly validate predictions, that is, to determine quickly if they can be trusted or not. Another challenge is that while AlphaFold2 excels at predicting the structures of individual proteins, it is not always accurate on multi-protein complexes, which are ubiquitous in biochemistry and cell biology. The main aim of this proposal is to develop new computational and experimental approaches that can leverage this remarkable new prediction tool by rapidly validating structures and assembling them into complexes. Our approach is focused on nuclear magnetic resonance and cross-linking mass spectrometry experiments in combination with sophisticated computer modeling. These experiments can be applied to a variety of biochemical systems, but the data can be difficult to interpret. We are developing modeling tools that use tools from Bayesian statistics and statistical mechanics to make sense of this challenging data. Solving these challenges will unlock new avenues for protein structure determination. The long-term goal of this project is to enable a range of new approaches to structural biology that will allow us to reveal protein structures that are currently hidden. By providing new tools, we will contribute to a better understanding of how life functions at the molecular level and provide new avenues for drug discovery and protein engineering.