RT Journal Article
SR Electronic
T1 Single-sequence protein structure prediction using supervised transformer protein language models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.01.15.476476
DO 10.1101/2022.01.15.476476
A1 Wenkai Wang
A1 Zhenling Peng
A1 Jianyi Yang
YR 2022
UL http://biorxiv.org/content/early/2022/01/18/2022.01.15.476476.abstract
AB It remains challenging for single-sequence protein structure prediction with AlphaFold2 and other deep learning methods. In this work, we introduce trRosettaX-Single, a novel algorithm for singlesequence protein structure prediction. It is built on sequence embedding from s-ESM-1b, a supervised transformer protein language model optimized from the pre-trained model ESM-1b. The sequence embedding is fed into a multi-scale network with knowledge distillation to predict inter-residue 2D geometry, including distance and orientations. The predicted 2D geometry is then used to reconstruct 3D structure models based on energy minimization. Benchmark tests show that trRosettaX-Single outperforms AlphaFold2 and RoseTTAFold on natural proteins. For instance, with single-sequence input, trRosettaX-Single generates structure models with an average TM-score ~0.5 on 77 CASP14 domains, significantly higher than AlphaFold2 (0.35) and RoseTTAFold (0.34). Further test on 101 human-designed proteins indicates that trRosettaX-Single works very well, with accuracy (average TM-score 0.77) approaching AlphaFold2 and higher than RoseTTAFold, but using much less computing resource. On 2000 designed proteins from network hallucination, trRosettaX-Single generates structure models highly consistent to the hallucinated ones. These data suggest that trRosettaX-Single may find immediate applications in de novo protein design and related studies. trRosettaX-Single is available through the trRosetta server at: http://yanglab.nankai.edu.cn/trRosetta/.Competing Interest StatementThe authors have declared no competing interest.