ArticleCommunications biology2023
Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Structure-based virulence factor classification using a dual-driven graph transformer with a pretrained language model.Briefings in bioinformatics · 2026Article
- Shoebill: an interpretable AlphaFold2-informed predictor of protein crystallization propensity using XGBoost.Briefings in bioinformatics · 2026Article
- Probing the Role of Accessory FeS Clusters in Putative Sensory Group D [FeFe] Hydrogenase: Influence of the C-Terminal [4Fe-4S] Cluster on the Reactivity ofBiochemistry · 2026Article
- Multi-scale structural similarity embedding search across entire proteomes.Bioinformatics (Oxford, England) · 2026Article
- The Interaction between the Tyrosine Kinase Receptor EphA2 and RNF5: Structural Insights from anComputational and structural biotechnology journal · 2026Article
- Modeling Alternative Conformational States in CASP16.Proteins · 2026Article
- Multiple conformational states assembly of multidomain proteins using evolutionary algorithm based on structural analogues and sequential homologues.Fundamental research · 2026Article
- Mycoplasma penetrans methionyl-tRNA synthetase dimerizes via tandem N-terminal ancillary domains.PloS one · 2026Article
- Cheminformatic analysis of protein surfaces provides binding site insights and informs identification strategies.Future medicinal chemistry · 2025Article
- Functional (re)annotation ofCurrent research in structural biology · 2025Article
- Deep learning-assisted tools to understand the structural biology of the synapse.Biomedical engineering letters · 2025Review
- Using AlphaFold and Symmetrical Docking to Predict Protein-Protein Interactions for Exploring Potential Crystallization Conditions.Proteins · 2025Article
- Modeling Alternative Conformational States in CASP16.bioRxiv : the preprint server for biology · 2025Article
- Learning Universal Representations of Intermolecular Interactions with ATOMICA.bioRxiv : the preprint server for biology · 2025Article
- M-DeepAssembly: enhanced DeepAssembly based on multi-objective multi-domain protein conformation sampling.BMC bioinformatics · 2025Article
- Expanding kinetoplastid genome annotation through protein structure comparison.PLoS pathogens · 2025Article
- An outlook on structural biology after AlphaFold: tools, limits and perspectives.FEBS open bio · 2025Review
- Computational insights in CD58 binding to ASFV CD2v and in silico optimization of nanobody designs against the interface.Open veterinary journal · 2025Article
- Mass spectrometry-complemented molecular modeling predicts the interaction interface for a camelid single-domain antibody targeting theComputational and structural biotechnology journal · 2024Article
- Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.Molecules (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 13.1% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly successfully predicts the interface (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.
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