ArticleBriefings in bioinformatics2023
MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A Review and Experimental Analysis of Supervised Learning Systems and Methods for Protein-Protein Interaction Detection.International journal of molecular sciences · 2026Review
- Enhancing mutation impact prediction in protein-protein interactions through interpretable graph-based multi-level feature interactions.Bioinformatics (Oxford, England) · 2026Article
- MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.Briefings in bioinformatics · 2026Article
- A Unified Dataset for Antibody and Nanobody Design Including Sequence, Structure, and Binding Affinity Data.Scientific data · 2026Article
- DSSA-PPI: enhancing binding affinity change prediction upon protein mutations using disentangled structure-sequence aware attention.Chemical science · 2026Article
- AFToolkit: a framework for molecular modeling of proteins with AlphaFold-derived representations.Briefings in bioinformatics · 2025Article
- Recent advances in deep learning for protein-protein interaction: a review.BioData mining · 2025Review
- PackPPI: An integrated framework for protein-protein complex side-chain packing and ΔΔG prediction based on diffusion model.Protein science : a publication of the Protein Society · 2025Article
- Decoding the functional impact of the cancer genome through protein-protein interactions.Nature reviews. Cancer · 2025Review
- AlphaFold3 prediction of protein-protein complex: Is it ready for thermodynamic analysis?Computational and structural biotechnology journal · 2025Article
- Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction.Nature communications · 2024Article
- Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions.Communications biology · 2024Article
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6 authors.
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Abstract
The accurate prediction of the effect of amino acid mutations for protein-protein interactions (PPI $\Delta \Delta G$) is a crucial task in protein engineering, as it provides insight into the relevant biological processes underpinning protein binding and provides a basis for further drug discovery. In this study, we propose MpbPPI, a novel multi-task pre-training-based geometric equivariance-preserving framework to predict PPI $\Delta \Delta G$. Pre-training on a strictly screened pre-training dataset is employed to address the scarcity of protein-protein complex structures annotated with PPI $\Delta \Delta G$ values. MpbPPI employs a multi-task pre-training technique, forcing the framework to learn comprehensive backbone and side chain geometric regulations of protein-protein complexes at different scales. After pre-training, MpbPPI can generate high-quality representations capturing the effective geometric characteristics of labeled protein-protein complexes for downstream $\Delta \Delta G$ predictions. MpbPPI serves as a scalable framework supporting different sources of mutant-type (MT) protein-protein complexes for flexible application. Experimental results on four benchmark datasets demonstrate that MpbPPI is a state-of-the-art framework for PPI $\Delta \Delta G$ predictions. The data and source code are available at https://github.com/arantir123/MpbPPI.
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