2-AIN-506 a 2-AIN-252: Seminár z bioinformatiky (2) a (4)
Leto 2020

Mohammed AlQuraishi. AlphaFold at CASP13. Bioinformatics, 35(22):4862-4865. 2019.

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SUMMARY: Computational prediction of protein structure from sequence is broadly
viewed as a foundational problem of biochemistry and one of the most difficult
challenges in bioinformatics. Once every two years the Critical Assessment of
protein Structure Prediction (CASP) experiments are held to assess the state of
the art in the field in a blind fashion, by presenting predictor groups with
protein sequences whose structures have been solved but have not yet been made
publicly available. The first CASP was organized in 1994, and the latest, CASP13,
took place last December, when for the first time the industrial laboratory
DeepMind entered the competition. DeepMind's entry, AlphaFold, placed first in
the Free Modeling (FM) category, which assesses methods on their ability to
predict novel protein folds (the Zhang group placed first in the Template-Based
Modeling (TBM) category, which assess methods on predicting proteins whose folds 
are related to ones already in the Protein Data Bank.) DeepMind's success
generated significant public interest. Their approach builds on two ideas
developed in the academic community during the preceding decade: (i) the use of
co-evolutionary analysis to map residue co-variation in protein sequence to
physical contact in protein structure, and (ii) the application of deep neural
networks to robustly identify patterns in protein sequence and co-evolutionary
couplings and convert them into contact maps. In this Letter, we contextualize
the significance of DeepMind's entry within the broader history of CASP, relate
AlphaFold's methodological advances to prior work, and speculate on the future of
this important problem.