ArticleBioinformatics (Oxford, England)2023
Deep learning of protein sequence design of 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 9 papers.
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Who cites it
9 citing papers in PubMed.
- Neural network conditioned to produce thermophilic protein sequences can increase thermal stability.Scientific reports · 2025Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences.Briefings in bioinformatics · 2024Article
- Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.Bioengineering (Basel, Switzerland) · 2024Review
- SPIDER: constructing cell-type-specific protein-protein interaction networks.Bioinformatics advances · 2024Article
- Prospection of Peptide Inhibitors of Thrombin from Diverse Origins Using a Machine Learning Pipeline.Bioengineering (Basel, Switzerland) · 2023Article
- Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe.Scientific data · 2023Article
- Deep learning of protein sequence design of protein-protein interactions.Bioinformatics (Oxford, England) · 2023Article
- Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
motivationAs more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence-structure relationships become more feasible. Within this space, the amino acid sequence design of protein-protein interactions is still a rather challenging subproblem with very low success rates-yet, it is central to most biological processes.
resultsWe developed an attention-based deep learning model inspired by algorithms used for image-caption assignments to design peptides or protein fragment sequences. Our trained model can be applied for the redesign of natural protein interfaces or the designed protein interaction fragments. Here, we validate the potential by recapitulating naturally occurring protein-protein interactions including antibody-antigen complexes. The designed interfaces accurately capture essential native interactions and have comparable native-like binding affinities in silico. Furthermore, our model does not need a precise backbone location, making it an attractive tool for working with de novo design of protein-protein interactions. AVAILABILITY AND IMPLEMENTATION: The source code of the method is available at https://github.com/strauchlab/iNNterfaceDesign. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
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.