![]() ![]() Models can now be generated for proteins that have been intractable to current methods for experimental structure determination. AlphaFold predictions have already demonstrated sustained accuracy over a range of targets, with insights into where the predictions can be improved 2, 3, 4, 8.ĪlphaFold’s structure prediction method also independently generates a quantitative estimate of reliability for every residue of a predicted structure, as well as of the reliability of the relative position and orientation of different parts of it. Iteration of prediction and experimental validation will now become the process that defines the discipline of structural biology. ![]() Nevertheless, the availability of the predictions means that across all biological disciplines, studies involving proteins can begin with a structural model, with the focus of experiments being the testing of a series of predictions that can validate, repudiate or refine the model and the structural hypothesis. These predictions are thus not yet complete structural models in the classical sense of an atomic model obtained by X-ray crystallography: that is, they do not include description of atomic positions for cofactors, metal ions, water molecules and any other ordered, bound ligands. An important point to appreciate is that AlphaFold and RoseTTAFold predict structures of only the polypeptide components. Structure determination itself is only the first step in the science of structural biology, which seeks to use structure as a basis to derive insights and hypotheses regarding biological functions and mechanisms but the barriers to entry have now been lowered. The arrival of AlphaFold and RoseTTAFold heralds a paradigm shift 7 in structural biology. This database is expected to grow to >100 million models in 2022. DeepMind’s collaboration with the European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI) to make structure predictions available at proteome scale has resulted in the AlphaFold Protein Structure Database resource (AlphaFold DB ), which in its initial release contained a set of 365,000 predicted structures for unique UniProt entries, covering most of the human proteome and those of 20 other model organisms, and which now contains 800,000 predicted structures, covering most entries in SwissProt 4, 5. The astonishing accuracy with which protein folds can now be predicted by the programs AlphaFold (developed at DeepMind 2, a subsidiary of Alphabet Inc.) and RoseTTAFold (developed at the University of Washington in Seattle 3) represents a dramatic advance in structural biology. These efforts have steadily gathered momentum over the years, fueled by the biannual CASP community challenge 1 to correctly predict protein folds and structures with accuracy measured against structures independently determined by X-ray crystallography, NMR spectroscopy or cryo-electron microscopy (cryo-EM) methods. It has long been a goal of computational biology to bypass the need to determine structures experimentally by predicting accurate 3D structures directly from amino acid sequences. ![]()
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