ArticlePloS one2015
Identify Beta-Hairpin Motifs with Quadratic Discriminant Algorithm Based on the Chemical Shifts.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- MultiToxPred 1.0: a novel comprehensive tool for predicting 27 classes of protein toxins using an ensemble machine learning approach.BMC bioinformatics · 2024Article
- Linear discriminant analysis reveals hidden patterns in NMR chemical shifts of intrinsically disordered proteins.PLoS computational biology · 2022Article
- Current Approaches in Supersecondary Structures Investigation.International journal of molecular sciences · 2021Review
- Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences.Medical & biological engineering & computing · 2021Article
- iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition.Molecular genetics and genomics : MGG · 2019Article
- Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks.Scientific reports · 2018Article
- Using feature optimization-based support vector machine method to recognize the β-hairpin motifs in enzymes.Saudi journal of biological sciences · 2017Article
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2 authors.
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Abstract
Successful prediction of the beta-hairpin motif will be helpful for understanding the of the fold recognition. Some algorithms have been proposed for the prediction of beta-hairpin motifs. However, the parameters used by these methods were primarily based on the amino acid sequences. Here, we proposed a novel model for predicting beta-hairpin structure based on the chemical shift. Firstly, we analyzed the statistical distribution of chemical shifts of six nuclei in not beta-hairpin and beta-hairpin motifs. Secondly, we used these chemical shifts as features combined with three algorithms to predict beta-hairpin structure. Finally, we achieved the best prediction, namely sensitivity of 92%, the specificity of 94% with 0.85 of Mathew's correlation coefficient using quadratic discriminant analysis algorithm, which is clearly superior to the same method for the prediction of beta-hairpin structure from 20 amino acid compositions in the three-fold cross-validation. Our finding showed that the chemical shift is an effective parameter for beta-hairpin prediction, suggesting the quadratic discriminant analysis is a powerful algorithm for the prediction of beta-hairpin.
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