Empowering NMR Utilization in Mechanistic Biology: Extending Size Limits and Catalyzing Automation

Abstract Nuclear Magnetic Resonance (NMR) has emerged as an essential tool for providing atomic-level insights into the structure, interactions, and dynamics of proteins, offering a unique perspective beyond what is achievable with de novo structure prediction programs, revealing domain orientations, alternate conformations, allosteric mechanisms, and minor states, thereby significantly enriching our understanding of how proteins function under near-native conditions. Despite its profound capabilities, NMR's broader application is limited by several factors including sensitivity issues, especially in large proteins. The complexity of resonance assignment which becomes increasingly challenging with larger proteins due to signal overlap, often requiring manual intervention despite advances in automation. The necessity of long measurement times to achieve adequate signal averaging and the significant expertise required to design and interpret NMR experiments, present a barrier that slows its adoption for specific biological inquiries. In response to these challenges, this research proposal aims to expand NMR's capabilities and streamline its workflow through a series of strategic innovations designed not only to enhance the traditional uses of NMR but also to make it more accessible and efficient for non-specialists. These innovations include the development of new methods that extend the size limits of observable systems, improve sensitivity through advanced noise reduction techniques, and automate and optimize experimental setups. Further, the introduction of innovative labels and probes and the development of new strategies for automated resonance assignment are intended to integrate seamlessly with existing methodologies, thus enabling rapid, reliable, and efficient NMR results. This approach includes leveraging machine learning for closed-loop optimization in pulse sequence design, developing a universal denoiser for cleaning up NMR spectra, creating adaptive, real-time sampling schedulers for multidimensional NMR experiments, reevaluating stochastic NMR approaches using modern machine learning frameworks, and employing graph neural networks to better predict chemical shifts by accurately capturing the local chemical environments of atoms. Additionally, the proposal introduces novel labeling techniques which leverage the 19F-13C TROSY effects, local deuteration that restores TROSY effect in eukaryotic expression systems and engineering specific bacterial gene knockouts to produce clearer labelling profiles that are economic, and relaxation optimized. The proposal engineers a new platform integrating NMR-based fragment screening with in silico screening methods to optimize drug discovery and development. By addressing the existing limitations and bringing forward these technological and methodological advances, the proposed research not only aims to democratize the use of NMR for a broader scientific community but also seeks to provide deeper, more actionable insights into the complex dynamics and interactions of proteins, thus potentially transforming our approach to mechanistic biomedical research and therapeutic development.