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Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα Coordinates

Authors :
Ali Sekmen
Kamal Al Nasr
Bahadir Bilgin
Ahmet Bugra Koku
Christopher Jones
Source :
Biomolecules, Vol 13, Iss 6, p 923 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information to locate hydrogen bonds to identify SSEs. When some spatial atomic details are missing, locating SSEs becomes a hinder. To address the problem, when some atomic information is missing, three approaches for classifying SSE types using Cα atoms in protein chains were developed: (1) a mathematical approach, (2) a deep learning approach, and (3) an ensemble of five machine learning models. The proposed methods were compared against each other and with a state-of-the-art approach, PCASSO.

Details

Language :
English
ISSN :
2218273X
Volume :
13
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Biomolecules
Publication Type :
Academic Journal
Accession number :
edsdoj.06d296bcd884e38adeaf295fac30c0b
Document Type :
article
Full Text :
https://doi.org/10.3390/biom13060923