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

使用未分配的核磁共振和质谱进行综合结构生物学的新贝叶斯统计机械方法

• 批准号: RGPIN-2022-03287
• 负责人: MacCallum, Justin
• 金额: $ 2.62万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=747989
• 关键词: New; Bayesian; statistical; mechanical; approaches

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.

蛋白质数据库中的 125,000 多个结构为我们提供了有关生物学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、在我们的神经系统中传导电信号等许多其他活动。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。这些结构有助于揭示生命如何在分子水平上发挥作用的非凡故事。然而，这个故事并不完整，因为我们仍然不知道许多蛋白质和蛋白质复合物（粘在一起并形成更大结构的蛋白质组）。这是因为并非所有蛋白质或复合物都适合结构生物学家常规使用的实验技术。但机器学习应用的最新进展有望改变这一现状。谷歌 DeepMind 的一个团队最近开发了一个名为 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，预测的一些细节可能是错误的，而这些细节对于某些任务（例如寻找新药）可能至关重要。更糟糕的是，部分预测根本就是错误的。因此，一项紧迫的挑战是开发能够快速验证预测的方法，即快速确定预测是否可信。另一个挑战是，虽然 AlphaFold2 擅长预测单个蛋白质的结构，但它对生物化学和细胞生物学中普遍存在的多蛋白质复合物并不总是准确。该提案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其组装成复合物来利用这种卓越的新预测工具。我们的方法侧重于核磁共振和交联质谱实验与复杂的计算机建模相结合。这些实验可应用于各种生化系统，但数据可能难以解释。我们正在开发建模工具，使用贝叶斯统计和统计力学的工具来理解这些具有挑战性的数据。解决这些挑战将为蛋白质结构测定开辟新途径。该项目的长期目标是实现结构生物学的一系列新方法，使我们能够揭示目前隐藏的蛋白质结构。通过提供新工具，我们将有助于更好地理解生命在分子水平上的运作方式，并为药物发现和蛋白质工程提供新途径。