Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα Coordinates
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 31, 2023
- Source ID
- 10.3390/biom13060923
Entities
People
- Ahmet Bugra Koku
- Ali Sekmen
- Bahadir Bilgin
- Christopher K. R. T. Jones
- Kamal Al Nasr
Organizations
- Middle East Technical University
- National Science Foundation
- Tennessee State University
- United States Department of Defense