A two-stage approach towards protein secondary structure classification.
Protein secondary structure (PSS) describes the local folded structures which get formed inside a polypeptide due to interactions among atoms of the backbone. Generally, globular proteins are divided into four classes, namely all-α, all-β, α + β, and α/β. As nearly 90% of proteins fall into the said...
| Published in: | Medical & Biological Engineering & Computing Vol. 58; no. 8; pp. 1723 - 1738 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Aug2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144423854&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144423854 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2020 vid: 58 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 144423854 143978895 144423854 NLM32472446 10.1007/s11517-020-02194-w NLM32472446 144423854 ppf: 1723 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A two-stage approach towards protein secondary structure classification. aug: au: Ghosh, Kushal Kanti Ghosh, Soulib Sen, Sagnik Sarkar, Ram Maulik, Ujjwal affil: Department of Computer Science and Engineering, Jadavpur University, Kolkata, India sug: subj: Proteins Resource Databases Peptides Scales ab: Protein secondary structure (PSS) describes the local folded structures which get formed inside a polypeptide due to interactions among atoms of the backbone. Generally, globular proteins are divided into four classes, namely all-α, all-β, α + β, and α/β. As nearly 90% of proteins fall into the said four classes, these are mostly considered for the purpose of computational classification of proteins. Classification of PSS is important for different biological functions that include protein fold recognition, tertiary structure prediction, prediction of DNA-binding sites, and reduction of the conformation search space among others. In this paper, we have proposed a machine learning-based model for secondary structure classification of proteins into four classes: all-α, all-β, α + β, and α/β. In doing so, we have considered both sequence-based and structure-based features. At first, mutual information (MI), a filter-based feature selection method, is used to remove the redundant features, and then these selected features are used to train three different classifiers-random forest, K-nearest neighbor (KNN), and multi-layer perceptron (MLP). After that, some standard classifier combination approaches are applied to integrate the decision made by the said classifiers and it has been found that weighted product rule performs the best among all. The overall accuracies obtained using the proposed model on the four standard datasets, namely 640, 1189, 25pdb, and fc699 are 86.89%, 92.93%, 91.38%, and 94.87% respectively. The proposed model outperforms some state-of-the-art methods considered here for comparison. Significantly high classification accuracy produced by our proposed model on four datasets is attributed to the development of a comprehensive feature set (by eliminating redundant features through feature selection technique) which is then passed through an ensemble consists of three different classifiers. Assigning different weights to the outcome of different classifiers thus proved to be useful in designing the model for predicting the secondary structure of proteins based on its sequence-based and structure-based features. Graphical abstract. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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