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

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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.