The AlphaFold (AF) initiative profoundly impacted structural biology, evidenced by its 2024 Nobel Prize. AlphaFold progressed from AF1 to AF2, which achieved near-experimental accuracy in single-chain protein folding, and then to AF3, expanding predictions to protein-ligand, protein-nucleic acid, and protein–protein complexes. This evolution led to the widespread adoption of AF tools, expanded structural coverage, and greater accessibility through the AlphaFold Database (AFDB), accelerating translational research, especially in structure-based drug discovery (SBDD) and the study of complex macromolecular assemblies. AF1 uses deep neural networks (DNNs), AF2 employs the Evoformer to model evolutionarily related sequences, and AF3 applies the Pairformer for pairwise amino acid interactions. The main differences between AF versions are architectural. Remaining challenges include predicting protein dynamics and multiple conformational states. This review first outlines AlphaFold’s architectural evolution, then explores the post-AlphaFold landscape and its global impact, discusses translational research applications, and addresses limitations and future directions. Despite challenges, AlphaFold is poised to further advance structural biology, particularly in biotechnology and medicine.
Chakraborty et al. (Wed,) studied this question.
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