ArticlePloS one2013
DomHR: accurately identifying domain boundaries in proteins using a hinge region strategy.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps.BMC bioinformatics · 2022Article
- Protein domain identification methods and online resources.Computational and structural biotechnology journal · 2021Review
- PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach.Journal of molecular modeling · 2016Article
- Analysis and prediction of the critical regions of antimicrobial peptides based on conditional random fields.PloS one · 2015Article
- Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes.PloS one · 2014Article
- NMRDSP: an accurate prediction of protein shape strings from NMR chemical shifts and sequence data.PloS one · 2013Article
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Authors and funding
8 authors.
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Abstract
motivationThe precise prediction of protein domains, which are the structural, functional and evolutionary units of proteins, has been a research focus in recent years. Although many methods have been presented for predicting protein domains and boundaries, the accuracy of predictions could be improved.
resultsIn this study we present a novel approach, DomHR, which is an accurate predictor of protein domain boundaries based on a creative hinge region strategy. A hinge region was defined as a segment of amino acids that covers part of a domain region and a boundary region. We developed a strategy to construct profiles of domain-hinge-boundary (DHB) features generated by sequence-domain/hinge/boundary alignment against a database of known domain structures. The DHB features had three elements: normalized domain, hinge, and boundary probabilities. The DHB features were used as input to identify domain boundaries in a sequence. DomHR used a nonredundant dataset as the training set, the DHB and predicted shape string as features, and a conditional random field as the classification algorithm. In predicted hinge regions, a residue was determined to be a domain or a boundary according to a decision threshold. After decision thresholds were optimized, DomHR was evaluated by cross-validation, large-scale prediction, independent test and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DomHR outperformed other well-established, publicly available domain boundary predictors for prediction accuracy. AVAILABILITY: The DomHR is available at http://cal.tongji.edu.cn/domain/.
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