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SSIF-Affinity: Multimodal Deep Learning of Sequence-Structure Features for Precise Protein-Protein Binding Affinity

Xinyi Xu1,2, Haotian Zhang1,2, Qi Liu1,2

  • 1Center for Biomedical-photonics and Molecular Imaging, Advanced Diagnostic-Therapy Technology and Equipment Key Laboratory of Higher Education Institutions in Shaanxi Province, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

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|December 15, 2025
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Summary
This summary is machine-generated.

This study introduces SSIF-affinity, a deep learning model for predicting protein-protein binding affinity. It integrates structural and sequence data for high-precision predictions, advancing antibody drug discovery.

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Area of Science:

  • Computational biology
  • Structural biology
  • Drug discovery

Background:

  • Accurate prediction of protein-protein binding affinity is crucial for understanding biological processes and developing targeted therapies.
  • Experimental methods for binding affinity measurement are costly and time-consuming, necessitating computational approaches.
  • Deep learning presents a powerful alternative for high-throughput and precise binding affinity prediction.

Purpose of the Study:

  • To develop an innovative multimodal deep learning framework, SSIF-affinity, for high-precision prediction of protein-protein complex binding affinity.
  • To overcome the limitations of unimodal approaches by integrating both structural and sequence information.
  • To provide a new strategy for AI-driven antibody drug discovery.

Main Methods:

  • SSIF-affinity identifies binding interfaces and constructs geometrically constrained regions to extract atomic-level interaction features.
  • A structure-guided cross-modal attention module fuses structural and sequence features of key residues.
  • Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks extract full-length sequence features, capturing local and long-range dependencies.
  • Multilayer Perceptron (MLP) regression predicts binding affinity based on integrated multilevel features.

Main Results:

  • The framework effectively reduces redundant computations and noise by employing region selection strategies.
  • SSIF-affinity overcomes unimodal limitations through collaborative representation of sequence and structural features.
  • The model balances contributions from interface and long-range interactions for accurate predictions.
  • Case studies on antibody-antigen complexes demonstrate the model's strong generalization ability.

Conclusions:

  • SSIF-affinity offers a novel multimodal deep learning strategy for accurate protein-protein binding affinity prediction.
  • The framework provides a new paradigm for accelerating AI-driven antibody drug discovery.
  • This approach enhances the understanding of protein-protein interactions and facilitates therapeutic development.