RT Journal Article
SR Electronic
T1 EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.10.07.511322
DO 10.1101/2022.10.07.511322
A1 Lee, Jae Hyeon
A1 Yadollahpour, Payman
A1 Watkins, Andrew
A1 Frey, Nathan C.
A1 Leaver-Fay, Andrew
A1 Ra, Stephen
A1 Cho, Kyunghyun
A1 Gligorijevic, Vladimir
A1 Regev, Aviv
A1 Bonneau, Richard
YR 2022
UL http://biorxiv.org/content/early/2022/10/08/2022.10.07.511322.abstract
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.