ArticleBMC bioinformatics2013
A strategy to select suitable physicochemical attributes of amino acids for protein fold recognition.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Trial
- Exploring complexity of class-A Beta-lactamase family using physiochemical-based multiplex networks.Scientific reports · 2023Article
- Protein music of enhanced musicality by music style guided exploration of diverse amino acid properties.Heliyon · 2021Article
- Article
- GlyStruct: glycation prediction using structural properties of amino acid residues.BMC bioinformatics · 2019Article
- Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions.BMC bioinformatics · 2019Article
- PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids.Scientific reports · 2018Article
- Article
- HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.BioMed research international · 2017Article
- Predicting MoRFs in protein sequences using HMM profiles.BMC bioinformatics · 2016Article
- Gram-positive and Gram-negative subcellular localization using rotation forest and physicochemical-based features.BMC bioinformatics · 2015Article
- Proposing a highly accurate protein structural class predictor using segmentation-based features.BMC genomics · 2014Article
- Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information.BMC bioinformatics · 2014Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAssigning a protein into one of its folds is a transitional step for discovering three dimensional protein structure, which is a challenging task in bimolecular (biological) science. The present research focuses on: 1) the development of classifiers, and 2) the development of feature extraction techniques based on syntactic and/or physicochemical properties.
resultsApart from the above two main categories of research, we have shown that the selection of physicochemical attributes of the amino acids is an important step in protein fold recognition and has not been explored adequately. We have presented a multi-dimensional successive feature selection (MD-SFS) approach to systematically select attributes. The proposed method is applied on protein sequence data and an improvement of around 24% in fold recognition has been noted when selecting attributes appropriately.
conclusionThe MD-SFS has been applied successfully in selecting physicochemical attributes of the amino acids. The selected attributes show improved protein fold recognition performance.
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