ArticleFrontiers in molecular biosciences2021
DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-chain Contact Prediction and Distance-Based Modelling.
Article in Frontiers in molecular biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- Machine Learning Methods in Protein-Protein Docking.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Article
- 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
- Enhancing AlphaFold-Multimer-based Protein Complex Structure Prediction with MULTICOM in CASP15.bioRxiv : the preprint server for biology · 2023Article
- Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15.bioRxiv : the preprint server for biology · 2023Article
- Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks.Nature communications · 2022Article
- Distance-based reconstruction of protein quaternary structures from inter-chain contacts.Proteins · 2022Article
- High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function.Workshop on Machine Learning in HPC Environments. Workshop on Machine Learning in HPC Environments · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
Proteins interact to form complexes. Predicting the quaternary structure of protein complexes is useful for protein function analysis, protein engineering, and drug design. However, few user-friendly tools leveraging the latest deep learning technology for inter-chain contact prediction and the distance-based modelling to predict protein quaternary structures are available. To address this gap, we develop DeepComplex, a web server for predicting structures of dimeric protein complexes. It uses deep learning to predict inter-chain contacts in a homodimer or heterodimer. The predicted contacts are then used to construct a quaternary structure of the dimer by the distance-based modelling, which can be interactively viewed and analysed. The web server is freely accessible and requires no registration. It can be easily used by providing a job name and an email address along with the tertiary structure for one chain of a homodimer or two chains of a heterodimer. The output webpage provides the multiple sequence alignment, predicted inter-chain residue-residue contact map, and predicted quaternary structure of the dimer. DeepComplex web server is freely available at http://tulip.rnet.missouri.edu/deepcomplex/web_index.html.
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