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Updated: Jul 25, 2025
A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα
Ali Sekmen1, Kamal Al Nasr1, Bahadir Bilgin1,2
1Department of Computer Science, Tennessee State University, Nashville, TN 37209, USA.
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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