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EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation
Jae Hyeon Lee · Payman Yadollahpour · Andrew Watkins · Nathan Frey · Andrew Leaver-Fay · Stephen Ra · Vladimir Gligorijevic · Kyunghyun Cho · Aviv Regev · Richard Bonneau

Sat Dec 03 11:25 AM -- 11:40 AM (PST) @
in Machine Learning in Structural Biology Workshop »

Designing proteins to achieve specific functions often requires in silico modeling of their properties at high throughput scale and can significantly benefit from fast and accurate protein structure prediction. We introduce EquiFold, a new end-to-end differentiable, SE(3)-equivariant, all-atom protein structure prediction model. EquiFold uses a novel coarse-grained representation of protein structures that does not require multiple sequence alignments or protein language model embeddings, inputs that are commonly used in other state-of-the-art structure prediction models. Our method relies on geometrical structure representation and is substantially smaller than prior state-of-the-art models. In preliminary studies, EquiFold achieved comparable accuracy to AlphaFold but was orders of magnitude faster. The combination of high speed and accuracy make EquiFold suitable for a number of downstream tasks, including protein property prediction and design.

Author Information

Jae Hyeon Lee (Prescient / Genentech)
Payman Yadollahpour (Genentech)
Andrew Watkins (Prescient Design, Genentech)
Nathan Frey (Genentech)
Andrew Leaver-Fay (Genentech)
Stephen Ra (Prescient Design / Genentech)
Vladimir Gligorijevic (Prescient Design/Genentech)
Kyunghyun Cho (Genentech / NYU)
Aviv Regev (Genentech)
Richard Bonneau (Genentech)

Richard Bonneau is on leave from NYU and currently at Prescient Design.

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