ArticleNucleic acids research2018
ComplexContact: a web server for inter-protein contact prediction using deep learning.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
61 citing papers in PubMed.
- Improving protein and protein interactions using pseudo-dimers derived from monomeric proteins.Nature communications · 2026Article
- 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
- Mechanism-Driven Screening of Membrane-Targeting and Pore-Forming Antimicrobial Peptides.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Recent progress and future challenges in structure-based protein-protein interaction prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Uncovering the mechanism for polar sequestration of the major bacterial sugar regulator by high-throughput screens and 3D interaction modeling.Cell reports · 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
- The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction.Biomolecules · 2024Review
- Selenoprotein W modulates tau homeostasis in an Alzheimer's disease mouse model.Communications biology · 2024Article
- Pairing interacting protein sequences using masked language modeling.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure.Interdisciplinary sciences, computational life sciences · 2024Review
- AtHVA22a, a plant-specific homologue of Reep/DP1/Yop1 family proteins is involved in turnip mosaic virus propagation.Molecular plant pathology · 2024Article
- Protein language model-embedded geometric graphs power inter-protein contact prediction.eLife · 2024Article
- Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes.Nature communications · 2023Article
- DeepBindPPI: Protein-Protein Binding Site Prediction Using Attention Based Graph Convolutional Network.The protein journal · 2023Article
- N6-methyladenosine-modified DBT alleviates lipid accumulation and inhibits tumor progression in clear cell renal cell carcinoma through the ANXA2/YAP axis-regulated Hippo pathway.Cancer communications (London, England) · 2023Article
- Structural modeling of protein ensembles between E3 RING ligases and SARS-CoV-2: The role of zinc binding domains.Journal of trace elements in medicine and biology : organ of the Society for Minerals and Trace Elements (GMS) · 2023Article
- The Interactome of Protein, DNA, and RNA.Methods in molecular biology (Clifton, N.J.) · 2023Review
- PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries.Bioinformatics advances · 2023Article
1 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.
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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.