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Education: Visualization of AlphaFold2 Structure Prediction Breakthrough
Submitted by Eric Martz; posted on Friday, March 19, 2021
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As posted recently by Jeff Bizzaro, in 2020, AlphaFold2 predicted protein structures with truly astonishing accuracy, certified by the bi-annual double-blind competition, CASP 14. Its predictions were based on the amino acid sequences of the target proteins, using massive artificial intelligence machine learning from sequence and structure databases. Predictions were made "blind", without access to empirical structures of the targets, and were judged later in 2020 when empirical structures became public. The judges did not know who made which prediction.
AlphaFold2 was one of over 100 groups that submitted predictions for over 100 target single-chain domains. In most cases, AlphaFold2 made the best prediction, while the second best prediction was far less accurate. This was particularly impressive for "free modeling" targets, those for which no suitable homology modeling templates were available.
I have briefly summarized the breakthrough here:
https://proteopedia.org/w/Theoretical_modeling#Ab_Initio_Models
I have analyzed two free modeling cases in detail, with comparisons visualized in interactive 3D. One (92 amino acids) is the ORF8 virulence factor from SARS-CoV-2. Among the free modeling targets, it had the largest discrepancy between the best and 2nd best predictions. The second is a phage RNA polymerase, the longest free-modeling target domain (404 amino acids). See:
https://proteopedia.org/w/AlphaFold2_examples_from_CASP_14
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