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

Kamal Al Nasr, Ali Sekmen, Bahadir Bilgin, Christopher Jones, Ahmet Bugra Koku

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
EditorsYufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2546-2552
Number of pages7
ISBN (Electronic)9781665401265
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States
Duration: Dec 9 2021Dec 12 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/9/2112/12/21

Keywords

  • C backbone
  • deep neural networks
  • protein modeling
  • secondary structure classification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Biomedical Engineering
  • Health Informatics
  • Information Systems and Management

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