Improved inter-protein contact prediction using dimensional hybrid residual networks and protein language models

Si, Y.; Yan, C.

doi:10.1101/2022.08.04.502748

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bioRxiv · 10.1101/2022.08.04.502748

Improved inter-protein contact prediction using dimensional hybrid residual networks and protein language models

Si, Y.·Yan, C.

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Abstract

The knowledge of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions (PPIs). However, accurately identifying the tens of contacting ones from hundreds of thousands of inter-protein residue pairs is extremely challenging, and performances of the state-of-the-art inter-protein contact prediction methods are still quite limited. In this study, we developed a deep learning method for inter-protein contact prediction, referred to as DRN-1D2D_Inter. Specifically, we employed pretrained protein language models to generate structural information enriched input features to residual networks formed by dimensional hybrid residual blocks to perform inter-protein contact prediction. Extensively benchmarked DRN-1D2D_Inter on multiple datasets including both heteromeric PPIs and homomeric PPIs, we show DRN-1D2D_Inter consistently and significantly outperformed two state-of-the-art inter-protein contact prediction methods including GLINTER and DeepHomo, although both the latter two methods leveraged native structures of interacting proteins in the prediction, and DRN-1D2D_Inter made the prediction purely from sequences.

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Si, Y., Yan, C.. 2022-08-05. Improved inter-protein contact prediction using dimensional hybrid residual networks and protein language models. https://doi.org/10.1101/2022.08.04.502748

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