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

Overview

Journal Biomolecules

Publisher MDPI

Specialties Biochemistry
Molecular Biology

Date 2023 Jun 28

PMID 37371503

Authors

Ali Sekmen

Kamal Al Nasr

Bahadir Bilgin

Ahmet Bugra Koku

Christopher Jones

Affiliations

Soon will be listed here.

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.

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