Daniel R. Fox, Cyntia Taveneau, Janik Clement, Rhys Grinter, Gavin J. Knott
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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. This approach dramatically reduces binder development time and resource requirements, compared to traditional experimental approaches, while improving hit rates and designability. Recent successes include the creation of binding proteins that neutralize toxins, modulate immune pathways, and engage disordered targets with high affinity and specificity. Improvements in model accuracy are expanding the scope of what can be designed, while characterization in preclinical models is paving the way for therapeutic development. De novo binder design represents a paradigm shift in protein engineering, where custom binders can now be programmed to meet specific biological challenges.
期刊介绍:
Structure aims to publish papers of exceptional interest in the field of structural biology. The journal strives to be essential reading for structural biologists, as well as biologists and biochemists that are interested in macromolecular structure and function. Structure strongly encourages the submission of manuscripts that present structural and molecular insights into biological function and mechanism. Other reports that address fundamental questions in structural biology, such as structure-based examinations of protein evolution, folding, and/or design, will also be considered. We will consider the application of any method, experimental or computational, at high or low resolution, to conduct structural investigations, as long as the method is appropriate for the biological, functional, and mechanistic question(s) being addressed. Likewise, reports describing single-molecule analysis of biological mechanisms are welcome.
In general, the editors encourage submission of experimental structural studies that are enriched by an analysis of structure-activity relationships and will not consider studies that solely report structural information unless the structure or analysis is of exceptional and broad interest. Studies reporting only homology models, de novo models, or molecular dynamics simulations are also discouraged unless the models are informed by or validated by novel experimental data; rationalization of a large body of existing experimental evidence and making testable predictions based on a model or simulation is often not considered sufficient.