ArticleBMC bioinformatics2014
Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information.
Article in BMC bioinformatics, 2014. 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.
- DeepFrag-k: a fragment-based deep learning approach for protein fold recognition.BMC bioinformatics · 2020Article
- HseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues.BMC genomics · 2019Article
- SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure.Molecules (Basel, Switzerland) · 2018Article
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- InCoB2014: bioinformatics to tackle the data to knowledge challenge. Introduction.BMC bioinformatics · 2014Article
- ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier.BioMed research internationalArticle
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4 authors.
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Abstract
Deciphering three dimensional structure of a protein sequence is a challenging task in biological science. Protein fold recognition and protein secondary structure prediction are transitional steps in identifying the three dimensional structure of a protein. For protein fold recognition, evolutionary-based information of amino acid sequences from the position specific scoring matrix (PSSM) has been recently applied with improved results. On the other hand, the SPINE-X predictor has been developed and applied for protein secondary structure prediction. Several reported methods for protein fold recognition have only limited accuracy. In this paper, we have developed a strategy of combining evolutionary-based information (from PSSM) and predicted secondary structure using SPINE-X to improve protein fold recognition. The strategy is based on finding the probabilities of amino acid pairs (AAP). The proposed method has been tested on several protein benchmark datasets and an improvement of 8.9% recognition accuracy has been achieved. We have achieved, for the first time over 90% and 75% prediction accuracies for sequence similarity values below 40% and 25%, respectively. We also obtain 90.6% and 77.0% prediction accuracies, respectively, for the Extended Ding and Dubchak and Taguchi and Gromiha benchmark protein fold recognition datasets widely used for in the literature.
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