Department of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK.
Department of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK.
Structure. 2023 Nov 2;31(11):1360-1374. doi: 10.1016/j.str.2023.09.011. Epub 2023 Oct 16.
Biomolecular nuclear magnetic resonance (NMR) spectroscopy and artificial intelligence (AI) have a burgeoning synergy. Deep learning-based structural predictors have forever changed structural biology, yet these tools currently face limitations in accurately characterizing protein dynamics, allostery, and conformational heterogeneity. We begin by highlighting the unique abilities of biomolecular NMR spectroscopy to complement AI-based structural predictions toward addressing these knowledge gaps. We then highlight the direct integration of deep learning approaches into biomolecular NMR methods. AI-based tools can dramatically improve the acquisition and analysis of NMR spectra, enhancing the accuracy and reliability of NMR measurements, thus streamlining experimental processes. Additionally, deep learning enables the development of novel types of NMR experiments that were previously unattainable, expanding the scope and potential of biomolecular NMR spectroscopy. Ultimately, a combination of AI and NMR promises to further revolutionize structural biology on several levels, advance our understanding of complex biomolecular systems, and accelerate drug discovery efforts.
生物分子核磁共振（NMR）光谱学和人工智能（AI）之间存在着蓬勃发展的协同作用。基于深度学习的结构预测器已经彻底改变了结构生物学，但这些工具目前在准确描述蛋白质动力学、变构和构象异质性方面仍面临着限制。我们首先强调生物分子 NMR 光谱学的独特能力，这些能力可以补充基于 AI 的结构预测，以解决这些知识空白。然后，我们强调将深度学习方法直接集成到生物分子 NMR 方法中。基于 AI 的工具可以极大地改善 NMR 光谱的获取和分析，提高 NMR 测量的准确性和可靠性，从而简化实验过程。此外，深度学习还可以开发以前无法实现的新型 NMR 实验类型，扩大生物分子 NMR 光谱学的范围和潜力。最终，AI 和 NMR 的结合有望在多个层面上进一步推动结构生物学的发展，加深我们对复杂生物分子系统的理解，并加速药物发现的进程。
