ArticleCell systems2021
D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions.
Article in Cell systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.
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105 citing papers in PubMed.
- Article
- NERINE reveals rare variant associations in gene networks across phenotypes and implicates an SNCA-PRL-LRRK2 subnetwork in Parkinson's disease.Cell genomics · 2026Article
- Machine Learning and Deep Learning Frameworks for Human-Virus Protein-Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and Challenges.International journal of molecular sciences · 2026Review
- Linear-time prediction of proteome-scale microbial protein interactions.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Protein language models are accidental taxonomists.BMC bioinformatics · 2026Article
- eSIG-Net: an interaction language model that decodes the protein code of single mutations.Nature methods · 2026Article
- BindPred: a framework for predicting protein-protein binding affinity from language model embeddings.Bioinformatics (Oxford, England) · 2026Article
- Improving protein and protein interactions using pseudo-dimers derived from monomeric proteins.Nature communications · 2026Article
- ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Rapid Proteome-Wide Discovery of Protein-Protein Interactions With ppIRIS.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SARS-CoV-2 Nsp1 suppresses the canonical NF-κB pathway by promoting ubiquitin-dependent degradation of TAK1 kinase.PLoS pathogens · 2026Article
- Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks.Nature communications · 2026Article
- eSIG-Net: Accurate prediction of single-mutation induced perturbations on protein interactions using a language model.bioRxiv : the preprint server for biology · 2026Article
- DeepISO: deep learning-powered prediction of protein-protein interaction rewiring generated by alternative splicing.Genome biology · 2026Article
- DSS-PPI: a self-supervised graph learning framework for protein-protein interaction prediction via multimodal sequence semantics.BMC genomics · 2026Article
- Identifying microbial protease allergens through protein language model-guided homology.Cell systems · 2026Article
- A paired sequence language model for protein-protein interaction modeling.Nature communications · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Quantum-augmented graph differential geometry enhances accuracy in protein-protein interaction prediction.Scientific reports · 2026Article
- Splicing regulation and intron evolution in the short-intron ciliate model of endosymbiosis Paramecium bursaria.Nucleic acids research · 2026Article
45 more citing papers are in PubMed but not listed here.
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Abstract
We combine advances in neural language modeling and structurally motivated design to develop D-SCRIPT, an interpretable and generalizable deep-learning model, which predicts interaction between two proteins using only their sequence and maintains high accuracy with limited training data and across species. We show that a D-SCRIPT model trained on 38,345 human PPIs enables significantly improved functional characterization of fly proteins compared with the state-of-the-art approach. Evaluating the same D-SCRIPT model on protein complexes with known 3D structure, we find that the inter-protein contact map output by D-SCRIPT has significant overlap with the ground truth. We apply D-SCRIPT to screen for PPIs in cow (Bos taurus) at a genome-wide scale and focusing on rumen physiology, identify functional gene modules related to metabolism and immune response. The predicted interactions can then be leveraged for function prediction at scale, addressing the genome-to-phenome challenge, especially in species where little data are available.
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