ArticleNature medicine2023
Deep-learning-enabled protein-protein interaction analysis for prediction of SARS-CoV-2 infectivity and variant evolution.
Article in Nature medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed, 74 citations in OpenAlex.
- Structure-based virulence factor classification using a dual-driven graph transformer with a pretrained language model.Briefings in bioinformatics · 2026Article
- From sites to structure to serology: a roadmap for structure-aware molecular evolution of antigenically evolving viruses.Journal of virology · 2026Review
- Classification of SARS-CoV-2 Variants Through the Epistatic Circos Plots with Convolutional Neural Networks.Journal of molecular evolution · 2026Article
- USP-ddG: a unified structural paradigm with data efficacy and mixture-of-experts for predicting mutational effects on protein-protein interactions.Bioinformatics (Oxford, England) · 2026Article
- Article
- Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments.Journal of chemical information and modeling · 2026Article
- Genetic and Structural Analyses of Coronaviruses: Insights into SARS-CoV-2 and Beyond.Biomolecules · 2026Article
- Know your scientist: KYC as biosecurity infrastructure.Frontiers in microbiology · 2026Article
- Zoon0PredV: Potential Virus Species Crossover Prediction Using Convolutional Neural Networks and Viral Protein Sequence Patterns.Bioinformatics and biology insights · 2026Article
- Characterization of the heterogeneity in SARS-CoV-2 fitness dynamics via graph representation learning.PLoS computational biology · 2026Article
- Federated task-adaptive learning for personalized selection of human IVF-derived embryos.Communications medicine · 2025Article
- Modeling the Mutational Effects on Biochemical Phenotypes of SARS-CoV-2 Using Molecular Fields.Biomolecules · 2025Article
- Constrained Evolutionary Funnels Shape Viral Immune Escape.bioRxiv : the preprint server for biology · 2025Article
- Sequence-only prediction of binding affinity changes: a robust and interpretable model for antibody engineering.Bioinformatics (Oxford, England) · 2025Article
- ColdstartCPI: Induced-fit theory-guided DTI predictive model with improved generalization performance.Nature communications · 2025Article
- CATH-ddG: towards robust mutation effect prediction on protein-protein interactions out of CATH homologous superfamily.Bioinformatics (Oxford, England) · 2025Article
- SARS-CoV-2 keeps evolving, so must our research efforts.National science review · 2025Article
- Recent advances in antibody optimization based on deep learning methods.Journal of Zhejiang University. Science. B · 2025Review
- Generative prediction of real-world prevalent SARS-CoV-2 mutation with in silico virus evolution.Briefings in bioinformatics · 2025Article
- Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2.Nature biomedical engineering · 2025Article
Corrections and comments
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Authors and funding
17 authors at 7 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Host-pathogen interactions and pathogen evolution are underpinned by protein-protein interactions between viral and host proteins. An understanding of how viral variants affect protein-protein binding is important for predicting viral-host interactions, such as the emergence of new pathogenic SARS-CoV-2 variants. Here we propose an artificial intelligence-based framework called UniBind, in which proteins are represented as a graph at the residue and atom levels. UniBind integrates protein three-dimensional structure and binding affinity and is capable of multi-task learning for heterogeneous biological data integration. In systematic tests on benchmark datasets and further experimental validation, UniBind effectively and scalably predicted the effects of SARS-CoV-2 spike protein variants on their binding affinities to the human ACE2 receptor, as well as to SARS-CoV-2 neutralizing monoclonal antibodies. Furthermore, in a cross-species analysis, UniBind could be applied to predict host susceptibility to SARS-CoV-2 variants and to predict future viral variant evolutionary trends. This in silico approach has the potential to serve as an early warning system for problematic emerging SARS-CoV-2 variants, as well as to facilitate research on protein-protein interactions in general.
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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.