Andrew Senior explains how DeepMind's AlphaFold uses deep dilated convolutional residual networks to predict protein structures.
Andrew Senior is a research scientist at Google DeepMind and team lead on the AlphaFold project. This talk was recorded at the University of Washington on August 19, 2019.
— Protein structure prediction at DeepMind
— Protein folding problem (overview)
— CASP13 (overview)
— CASP13 results
— AlphaFold system (overview)
— Key aspects of AlphaFold
— Deep learning (overview)
— Why machine learning for protein structure modelling?
— Predicting inter-residue distances
— Data used by AlphaFold
— Deep Dilated Convolutional Residual network
— Data cropping
— Example of an AlphaFold prediction
— Distogram performance on contact metrics
— Secondary structure and Torsion angle prediction
— Using deep learning to construct a reference state
— Accuracy vs computational cost
— Conclusions
— What’s next
— Q&A
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u/Hornobsterin r/programming • Recommends
On “AlphaFold: a solution to a 50-year-old grand challenge in biology”
From what I remember from a presentation I watched a while ago, they use distances between each combination of locations in the sequence.
For example, with the sequence ABCD, you can encode the 3d structure with a list of (idx_1, idx_2, distance) tuples. If ABCD forms a "closed" loop, you would have a very small (A, D) distance. If it forms a straight line, the (A, D) distance would be greater. If I remember correctly they do an initial pass with this encoding and then optimise with another loss function based on torsion angles.
link to this video
EDIT: link to this video inter-residue distance prediction
