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Abstract

The innovation in antibody and protein design highlights the transformation from empirical approaches to sophisticated strategies integrating computational methods and artificial intelligence (AI). Key principles, such as combinatorial, structure-based, consensus, and computational designs, have been pivotal in predicting structures from sequences ( design). Advances in tools, like AlphaFold and Rosetta suite, enable accurate structure prediction, facilitating the development of functional proteins and antibodies. However, challenges remain, including improving prediction accuracy, modeling flexible regions, understanding structural dynamics, and designing catalytic and binding sites. Despite these, the field promises groundbreaking advancements in biomedical sciences, enriching our understanding and serving human health and scientific discovery.

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2025-02-20
2025-09-01

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  • Article Type:     Editorial
Keywords: data-driven development ; machine-learning methods ; Combinatorial protein design ; sequence-based (consensus) design ; structure-based design ; computational design guided by physical principles

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