Deep Learning for Assignment of Protein Secondary Structure Elements from C Coordinates


Nasr K. A. , Sekmen A., Bilgin B., Jones C., KOKU A. B.

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Virtual, Online, United States Of America, 9 - 12 December 2021, pp.2546-2552 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/bibm52615.2021.9669538
  • City: Virtual, Online
  • Country: United States Of America
  • Page Numbers: pp.2546-2552
  • Keywords: C backbone, deep neural networks, protein modeling, secondary structure classification

Abstract

© 2021 IEEE.This paper presents a Deep Neural network (DNN) system that uses a large set of geometric and categorical features for classification of secondary structure elements (SSEs) in the protein's trace that consists of Calpha atoms on the backbone. A systematical approach is implemented for classification of protein SSE problem. This approach consists of two network architecture search (NAS) algorithms for selecting (1) network architecture and layer connectivity, and (2) regularization parameters. Each algorithm uses a different search space and they are used in succession to develop a DNN. The DNN system generates over 93% classification rate on average for multiple test sets without any post processing for amino acid configurations.