Title: IRNN-SS: deep learning for optimised protein secondary structure prediction through PROMOTIF and DSSP annotation fusion

Authors: Mukhtar Ahmad Sofi; M. Arif Wani

Addresses: Department of Information Technology, BVRIT HYDERABAD College of Engineering for Women, Hyderabad, India ' Department of Computer Science, School of Applied Science and Technology, University of Kashmir, Jammu and Kashmir, India

Abstract: DSSP stands as a foundational tool in the domain of protein secondary structure prediction, yet it encounters notable challenges in accurately annotating irregular structures, such as β-turns and γ-turns, which constitute approximately 25%-30% and 10%-15% of protein turns, respectively. This limitation arises from DSSP's reliance on hydrogen-bond analysis, resulting in annotation gaps and reduced consensus on irregular structures. Alternatively, PROMOTIF excels at identifying these irregular structure annotations using phi-psi information. Despite their complementary strengths, previous methodologies utilised DSSP and PROMOTIF separately, leading to disparate prediction methods for protein secondary structures, hampering comprehensive structure analysis crucial for drug development. In this work, we bridge this gap using an annotation fusion approach, combining DSSP structures with beta, and gamma turns. We introduce IRNN-SS, a model employing deep inception and bidirectional gated recurrent neural networks, achieving 77.4% prediction accuracy on benchmark datasets, outpacing current models.

Keywords: protein secondary structure; PSS; beta-turns; gamma-turns; prediction; deep learning.

DOI: 10.1504/IJBRA.2024.142546

International Journal of Bioinformatics Research and Applications, 2024 Vol.20 No.6, pp.608 - 626

Received: 27 Sep 2023
Accepted: 29 Mar 2024
Published online: 08 Nov 2024 *

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