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, Amerika Birleşik Devletleri, 9 - 12 Aralık 2021, ss.2546-2552 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/bibm52615.2021.9669538
  • Basıldığı Şehir: Virtual, Online
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.2546-2552
  • Anahtar Kelimeler: C backbone, deep neural networks, protein modeling, secondary structure classification
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

© 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.