AlphaFold @ CASP13: “What just happened?”

Update: An updated version of this blogpost was published as a (peer-reviewed) Letter to the Editor at Bioinformatics, sans the “sociology” commentary.

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.

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CASP10, and the Future of Structure in Biology

I recently had the fortune of attending the 10th Community Assessment of protein Structure Prediction, or CASP, as it is affectionately known. CASP is a competition of sorts that happens once every two years to ascertain the progress made in computationally predicting protein structure. It is a blind experiment, where the structures to be predicted are unknown beforehand, and thus serves as a unbiased test of the predictive power of current computational methods. It is in many ways a model that the rest of computational biology ought (and is starting) to follow.

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