PT  - JOURNAL ARTICLE
AU  - Jae Hyeon Lee
AU  - Payman Yadollahpour
AU  - Andrew Watkins
AU  - Nathan C. Frey
AU  - Andrew Leaver-Fay
AU  - Stephen Ra
AU  - Kyunghyun Cho
AU  - Vladimir Gligorijevic
AU  - Aviv Regev
AU  - Richard Bonneau
TI  - EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation
AID  - 10.1101/2022.10.07.511322
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.10.07.511322
4099  - http://biorxiv.org/content/early/2022/10/08/2022.10.07.511322.short
4100  - http://biorxiv.org/content/early/2022/10/08/2022.10.07.511322.full
AB  - 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.Competing Interest StatementThe authors have declared no competing interest.