SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction

A K M Mehedi Hasan; Ajmain Yasar Ahmed; Sazan Mahbub; M Saifur Rahman; Md Shamsuzzoha Bayzid

doi:10.1093/bioadv/vbad042

SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction

Bioinform Adv. 2023 Apr 5;3(1):vbad042. doi: 10.1093/bioadv/vbad042. eCollection 2023.

Authors

A K M Mehedi Hasan¹, Ajmain Yasar Ahmed¹, Sazan Mahbub^{1

2}, M Saifur Rahman¹, Md Shamsuzzoha Bayzid¹

Affiliations

¹ Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh.
² Department of Computer Science, University of Maryland, College Park, MD 20742, USA.

Abstract

Motivation: Protein structure provides insight into how proteins interact with one another as well as their functions in living organisms. Protein backbone torsion angles ( $ϕ$ and $ψ$ ) prediction is a key sub-problem in predicting protein structures. However, reliable determination of backbone torsion angles using conventional experimental methods is slow and expensive. Therefore, considerable effort is being put into developing computational methods for predicting backbone angles.

Results: We present SAINT-Angle, a highly accurate method for predicting protein backbone torsion angles using a self-attention-based deep learning network called SAINT, which was previously developed for the protein secondary structure prediction. We extended and improved the existing SAINT architecture as well as used transfer learning to predict backbone angles. We compared the performance of SAINT-Angle with the state-of-the-art methods through an extensive evaluation study on a collection of benchmark datasets, namely, TEST2016, TEST2018, TEST2020-HQ, CAMEO and CASP. The experimental results suggest that our proposed self-attention-based network, together with transfer learning, has achieved notable improvements over the best alternate methods.

Availability and implementation: SAINT-Angle is freely available as an open-source project at https://github.com/bayzidlab/SAINT-Angle.

Supplementary information: Supplementary data are available at Bioinformatics Advances online.