Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.01.07.523121v1?rss=1
Authors: Si, Y., Yan, C.
Abstract:
Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions (PPIs). Although significant improvement has been made in inter-protein contact prediction recently, there is still large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets shows that PLMGraph-Inter significantly outperforms three top inter-protein contact prediction methods, including DRN-1D2D_Inter, DeepHomo and GLINTER. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein-protein docking can dramatically improve its performance for protein complex structure prediction.
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