ArticleNature communications2022
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Accurate protein-protein interactions modeling through physics-informed geometric invariant learning.Communications biology · 2026Article
- A paired sequence language model for protein-protein interaction modeling.Nature communications · 2026Article
- The power and limits of predicting inter-protein exon-exon interactions using protein 3D structures.Bioinformatics advances · 2026Article
- Deciphering RNA modification and post-transcriptional regulation with NetRNApan.Briefings in bioinformatics · 2025Article
- Pyriproxyfen Disrupts the Ongoing Spermatogenesis Wave in Danio rerio Potentially Mediated by Voltage-Dependent Calcium Channels and Protein Kinase C.Journal of applied toxicology : JAT · 2025Article
- A deep learning method for predicting interactions for intrinsically disordered regions of proteins.bioRxiv : the preprint server for biology · 2025Article
- Leveraging protein structural information to improve variant effect prediction.Current opinion in structural biology · 2025Review
- AI protocol for retrieving protein dynamic structures from two-dimensional infrared spectra.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- AI-integrated network for RNA complex structure and dynamic prediction.Biophysics reviews · 2024Review
- A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions.Nature communications · 2024Article
- Protein language model-embedded geometric graphs power inter-protein contact prediction.eLife · 2024Article
- MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics.Briefings in bioinformatics · 2024Article
- Article
- Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning.Communications biology · 2023Article
- Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15.Communications biology · 2023Article
- Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15.Communications chemistry · 2023Article
- Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes.Nature communications · 2023Article
- Thermoresponsive Polypeptide Fused L-Asparaginase with Mitigated Immunogenicity and Enhanced Efficacy in Treating Hematologic Malignancies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- A gated graph transformer for protein complex structure quality assessment and its performance in CASP15.Bioinformatics (Oxford, England) · 2023Article
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4 authors.
Funding
Abstract
Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo's 37.40% and 23.08% and GLINTER's 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER's 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.
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