ArticleBioinformatics (Oxford, England)2023
TT3D: Leveraging precomputed protein 3D sequence models to predict protein-protein interactions.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- 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
- Protein language models accurately predict polymorphic peptide-modulated NK cell receptor-HLA class I interaction strengths.Science advances · 2026Article
- pLM-Repeat: Exploiting the sequence representations of protein language models for sensitive repeat detection.Protein science : a publication of the Protein Society · 2026Article
- DSS-PPI: a self-supervised graph learning framework for protein-protein interaction prediction via multimodal sequence semantics.BMC genomics · 2026Article
- Learning the language of protein-protein interactions.Nature communications · 2026Article
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- PLM-interact: extending protein language models to predict protein-protein interactions.Nature communications · 2025Article
- Application of Protein Structure Encodings and Sequence Embeddings for Transporter Substrate Prediction.Molecules (Basel, Switzerland) · 2025Article
- Learning the language of protein-protein interactions.bioRxiv : the preprint server for biology · 2025Article
- SpatialPPIv2: Enhancing protein-protein interaction prediction through graph neural networks with protein language models.Computational and structural biotechnology journal · 2025Article
- MULAN: multimodal protein language model for sequence and structure encoding.Bioinformatics advances · 2025Article
- Democratizing protein language models with parameter-efficient fine-tuning.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Democratizing Protein Language Models with Parameter-Efficient Fine-Tuning.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
motivationHigh-quality computational structural models are now precomputed and available for nearly every protein in UniProt. However, the best way to leverage these models to predict which pairs of proteins interact in a high-throughput manner is not immediately clear. The recent Foldseek method of van Kempen et al. encodes the structural information of distances and angles along the protein backbone into a linear string of the same length as the protein string, using tokens from a 21-letter discretized structural alphabet (3Di).
resultsWe show that using both the amino acid sequence and the 3Di sequence generated by Foldseek as inputs to our recent deep-learning method, Topsy-Turvy, substantially improves the performance of predicting protein-protein interactions cross-species. Thus TT3D (Topsy-Turvy 3D) presents a way to reuse all the computational effort going into producing high-quality structural models from sequence, while being sufficiently lightweight so that high-quality binary protein-protein interaction predictions across all protein pairs can be made genome-wide. AVAILABILITY AND IMPLEMENTATION: TT3D is available at https://github.com/samsledje/D-SCRIPT. An archived version of the code at time of submission can be found at https://zenodo.org/records/10037674.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.