A Comprehensive Analysis of Protein Secondary Structure Prediction Models

Proteins are vital for numerous biological functions, and understanding their secondary structure is essential for unravelling their function. Predicting secondary protein's structure has been the focus of extensive study throughout the years. This article brings forth an rigorously overview of the latest advanced methods and approaches used for protein secondary structure prediction also aims to come up with researchers and practitioners in the field with a valuable resource for advancing the creation of dependable and precise contraption for anticipating protein secondary structures. This paper presents a benchmarking study of different secondary structure prediction methods, evaluating their accuracy, strengths, and limitations. We discuss widely used techniques such as the Chou-Fasman method, GOR method, PHD method, and more recent approaches based on machine learning and deep learning. This detailed summary is designed to assist researchers in determining the most suitable method for their specific needs research requirements and further the field of protein structure prediction.