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When artificial intelligence meets protein research.
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The 2024 Nobel Prizes in Chemistry and Physics mark a watershed moment in the convergence of artificial intelligence (AI) and molecular biology. This article explores how AI, particularly deep learning and neural networks, has revolutionized protein science through breakthroughs in structure prediction and computational design. It highlights the contributions of 2024 Nobel laureates John Hopfield, Geoffrey Hinton, David Baker, Demis Hassabis, and John Jumper, whose foundational work laid the groundwork for AI tools such as AlphaFold.
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Structural proteomics has undergone a profound transformation, driven by the convergence of advanced experimental methodologies and computational innovations. Cutting-edge mass spectrometry (MS)-based approaches, including cross-linking MS (XL-MS), hydrogen-deuterium exchange MS (HDX-MS), and limited proteolysis MS (LiP-MS), now enable unprecedented insights into protein topology, conformational dynamics, and protein-protein interactions. These methods, complemented by affinity purification (AP), co-immunoprecipitation (co-IP), proximity labeling (PL), and spatial proteomics techniques, have expanded our ability to characterize the structural proteome at a systems-wide scale.
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The application of artificial intelligence to structural biology has transformed protein design from a conceptual challenge into a practical approach for creating new-to-nature proteins. By leveraging machine learning, researchers can now computationally design proteins with tailored architectures and binding specificities. This has enabled the rapid in silico generation of high-affinity binders to diverse and previously intractable targets.
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