The Future of Protein Science will not be Supervised

But it may well be semi-supervised.

For some time now I have thought that building a latent representation of protein sequence space is a really good idea, both because we have far more sequences than any form of labelled data, and because, once built, such a representation can inform a broad range of downstream tasks. This is why I jumped at the opportunity last year when Surge Biswas, from the Church Lab, approached me about collaborating on exactly such a project. Last week we posted a preprint on bioRxiv describing this effort. It was led by Ethan Alley, Grigory Khimulya, and Surge. All I did was to enthusiastically cheer them on, and so the bulk of the credit goes to them and George Church for his mentorship.

AlphaFold @ CASP13: “What just happened?”

I just came back from CASP13, the biennial assessment of protein structure prediction methods (I previously blogged about CASP10.) I participated in a panel on deep learning methods in protein structure prediction, as well as a predictor (more on that later.) If you keep tabs on science news, you may have heard that DeepMind’s debut went rather well. So well in fact that not only did they take first place, but put a comfortable distance between them and the second place predictor (the Zhang group) in the free modeling (FM) category, which focuses on modeling novel protein folds. Is the news real or overhyped? What is AlphaFold’s key methodological advance, and does it represent a fundamentally new approach? Is DeepMind forthcoming in sharing the details? And what was the community’s reaction? I will summarize my thoughts on these questions and more below. At the end I will also briefly discuss how RGNs, my end-to-end differentiable model for structure prediction, did on CASP13.

It is tempting to assume that with the appropriate choice of weights for the edges connecting the second and third layers of the NN discussed in this post, it would be possible to create classifiers that output $1$ over any composite region defined by unions and intersections of the 7 regions shown below.