RT Journal Article
SR Electronic
T1 Improved the Protein Complex Prediction with Protein Language Models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.09.15.508065
DO 10.1101/2022.09.15.508065
A1 Chen, Bo
A1 Xie, Ziwei
A1 Qiu, Jiezhong
A1 Ye, Zhaofeng
A1 Xu, Jinbo
A1 Tang, Jie
YR 2022
UL http://biorxiv.org/content/early/2022/11/22/2022.09.15.508065.abstract
AB AlphaFold-Multimer has greatly improved protein complex structure prediction, but its accuracy also depends on the quality of the multiple sequence alignment (MSA) formed by the interacting homologs (i.e., interologs) of the complex under prediction. Here we propose a novel method, denoted as ESMPair, that can identify interologs of a complex by making use of protein language models (PLMs). We show that ESMPair can generate better interologs than the default MSA generation method in AlphaFold-Multimer. Our method results in better complex structure prediction than AlphaFold-Multimer by a large margin (+10.7% in terms of the Top-5 best DockQ), especially when the predicted complex structures have low confidence. We further show that by combining several MSA generation methods, we may yield even better complex structure prediction accuracy than Alphafold-Multimer (+22% in terms of the Top-5 best DockQ). We systematically analyze the impact factors of our algorithm and find out the diversity of MSA of interologs significantly affects the prediction accuracy. Moreover, we show that ESMPair performs particularly well on complexes in eucaryotes.Competing Interest StatementThe authors have declared no competing interest.