ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2019
DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- AFragmenter: schema-free, tuneable protein domain segmentation for AlphaFold protein structures.Bioinformatics (Oxford, England) · 2025Article
- A Structural Proteomics Exploration of Synphilin-1 and Alpha-Synuclein Interaction in Pathogenesis of Parkinson's Disease.Biomolecules · 2024Article
- Chainsaw: protein domain segmentation with fully convolutional neural networks.Bioinformatics (Oxford, England) · 2024Article
- Merizo: a rapid and accurate protein domain segmentation method using invariant point attention.Nature communications · 2023Article
- Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps.BMC bioinformatics · 2022Article
- Res-Dom: predicting protein domain boundary from sequence using deep residual network and Bi-LSTM.Bioinformatics advances · 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
- Identification of sub-Golgi protein localization by use of deep representation learning features.Bioinformatics (Oxford, England) · 2021Article
- MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation.Computational and structural biotechnology journal · 2021Article
- Protein domain identification methods and online resources.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Protein domain boundary prediction is usually an early step to understand protein function and structure. Most of the current computational domain boundary prediction methods suffer from low accuracy and limitation in handling multi-domain types, or even cannot be applied on certain targets such as proteins with discontinuous domain. We developed an ab-initio protein domain predictor using a stacked bidirectional LSTM model in deep learning. Our model is trained by a large amount of protein sequences without using feature engineering such as sequence profiles. Hence, the predictions using our method is much faster than others, and the trained model can be applied to any type of target proteins without constraint. We evaluated DeepDom by a 10-fold cross validation and also by applying it on targets in different categories from CASP 8 and CASP 9. The comparison with other methods has shown that DeepDom outperforms most of the current ab-initio methods and even achieves better results than the top-level template-based method in certain cases. The code of DeepDom and the test data we used in CASP 8, 9 can be accessed through GitHub at https://github.com/yuexujiang/DeepDom.
Indexed as
Identifiers
30864311PMC6417825What OpenQuestion holds
Registered trials
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