Secondary structure specific simpler prediction models for protein backbone angles

Abstract

Abstract Motivation Protein backbone angle prediction has achieved significant accuracy improvement with the development of deep learning methods. Usually the same deep learning model is used in making prediction for all residues regardless of the categories of secondary structures they belong to. In this paper, we propose to train separate deep learning models for each category of secondary structures. Machine learning methods strive to achieve generality over the training examples and consequently loose accuracy. In this work, we explicitly exploit classification knowledge to restrict generalisation within the specific class of training examples. This is to compensate the loss of generalisation by exploiting specialisation knowledge in an informed way. Results The new method named SAP4SS obtains mean absolute error (MAE) values of 15.59, 18.87, 6.03, and 21.71 respectively for four types of backbone angles $$\phi$$ ϕ , $$\psi$$ ψ , $$\theta$$ θ , and $$\tau$$ τ . Consequently, SAP4SS significantly outperforms existing state-of-the-art methods SAP, OPUS-TASS, and SPOT-1D: the differences in MAE for all four types of angles are from 1.5 to 4.1% compared to the best known results. Availability SAP4SS along with its data is available from https://gitlab.com/mahnewton/sap4ss .

Keywords

• Dihedral angle prediction
• Protein structure prediction
• Deep learning