Protein folding with Alphafold 2

After a long hiatus, Papers We Love Montreal will be meeting again!

Max McCrea will present "Highly accurate protein structure prediction with AlphaFold" by Jumper, J., Evans, R., Pritzel, A. et al.

In 2018, Deepmind submitted a model to the Critical Assessment of protein Structure Prediction (CASP) competition whose results made headlines: achieving a median score of 59% on CASP's global distance score measure. This was over 6% better than the next two best results that year also used deep learning techniques and would have been the best results in the history of the competition. Two years later, Alphafold2 achieved a median score of 92, and many people now consider the protein folding problem "solved" for many practical purposes.

What exactly is the protein folding problem, and why was it so hard? Why is it considered so important in the field of biology? In the first part of my talk, I will take a look at the history of the problem, and explain how sequences of amino acid "residues" crumple up into the fundamental building blocks of nature, and why predicting the way they crumple up is such a central problem to understanding biological systems.

Next we'll do a quick primer on neural network architectures, and look more specifically at the ways Alphafold2 approached the problem a bit differently from past attempts.

We'd like to thank Monadical and Notman House for sponsoring this event.