The Silent Revolution
how AlphaFold redefined Structural Biology and opened new frontiers in science
DOI:
https://doi.org/10.47385/cadunifoa.v20.n55.5859Keywords:
AlphaFold, Structural Biology, Protein Folding, Artificial IntelligenceAbstract
For half a century, determining the three-dimensional structure of a protein from its amino acid sequence has represented one of the greatest challenges in biology. The understanding of molecular function, drug development, and protein engineering depended on overcoming this obstacle. In 2020, AlphaFold, an artificial intelligence system developed by DeepMind, emerged as a revolutionary solution, predicting structures with atomic accuracy previously unattainable by computational methods. This review outlines the significance of the protein folding problem, the conceptual foundations of AlphaFold, and its transformative impact. The release of hundreds of millions of structures via the AlphaFold DB has democratized access to structural information, accelerating research in drug development, biotechnology, and fundamental biology. However, despite its revolutionary nature, the method possesses intrinsic limitations, such as the prediction of static conformations and the inability to directly model the effect of ligands, which demand a critical and conscientious analysis of its results. AlphaFold is not an endpoint, but the beginning of a new era of "digital biology," in which the synergistic integration of computational prediction and experimental validation promises to unveil the mechanisms of life at an unprecedented level of detail.
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