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bioRxiv · 10.1101/2025.07.01.662544

Accurate Protein-Protein Interactions Modeling through Physics-informed Geometric Invariant Learning

Rao, J.·Liu, D.·Zhou, X.·Yuan, Q.·Wei, W.·Lu, W.·Zhang, J.·Rong, Y.·Yang, Y.·Zheng, S.
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Abstract

AlphaFold has set a new standard for predicting protein structures from primary sequences; however, it faces challenges with protein complexes across species, engineered proteins, and antigen-antibody, where co-evolutionary signals may be sparse or missing. Herein, we present ProTact, a SE(3)-invariant geometric graph neural network that integrates physics-informed geometric complementarity and trigonometric constraints as robust inductive biases to enhance protein-protein contact predictions. ProTact is applicable to both experimental and predicted monomer structures and utilizes a modulated key point matching algorithm to approximate accurate docking poses. Experimental evaluations demonstrate that ProTact consistently outperforms state-of-the-art sequence-based and structure-based methods on benchmark datasets, achieving notable relative improvements of 31.63% in Precision@10 for CASP 13 and 14 targets, and 31.94% for DIPS-Plus datasets. Moreover, when combined with AlphaFold3 as re-scoring functions, ProTact surpasses its default confidence scores, offering over 30.48% improvements in the low-MSA contexts. We anticipate that the proposed framework will advance our understanding of protein interactions, functions, and design.

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Rao, J., Liu, D., Zhou, X., Yuan, Q., Wei, W., Lu, W., Zhang, J., Rong, Y., Yang, Y., Zheng, S.. 2025-07-04. Accurate Protein-Protein Interactions Modeling through Physics-informed Geometric Invariant Learning. https://doi.org/10.1101/2025.07.01.662544

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