S-PDB: Analysis and Classification of SARS-CoV-2 Spike Protein Structures
2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
; : 2259-2265, 2022.
Article
in English
| Scopus | ID: covidwho-2233703
ABSTRACT
This paper proposes a novel and efficient method, called S-PDB, for the analysis and classification of Spike (S) protein structures of SARS-CoV-2 and other viruses/organisms in the Protein Data Bank (PDB). The method first finds and identifies protein structures in PDB that are similar to a protein structure of interest (SARS-CoV-2 S) via a protein structure comparison tool. The amino acid (AA) sequences of identified protein structures, downloaded from PDB, and their aligned amino acids (AAA) and secondary structure elements (ASSE), that are stored in three separate datasets, are then used for the reliable detection/classification of SARS-CoV-2 S protein structures. Three classifiers are used and their performance is compared by using six evaluation metrics. Obtained results show that two classifiers for text data (Multinomial Naive Bayes and Stochastic Gradient Descent) performed better and achieved high accuracy on the dataset that contains AAA of protein structures compared to the datasets for AA and ASSE, respectively. © 2022 IEEE.
Classification; DALI; PDB; SARS-CoV-2; Spike; Amino acids; Ascorbic acid; Classification (of information); Gradient methods; Proteins; Stochastic systems; Amino acid sequence; Amino-acids; Protein data bank; Protein structure comparison; Proteins structures; Reliable detection; Secondary structure elements; Spike protein; Coronavirus
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Year:
2022
Document Type:
Article
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