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

Date

2021-01-01

Author

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

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© 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.

Subject Keywords

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

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85125185618&origin=inward
https://hdl.handle.net/11511/99039

DOI

https://doi.org/10.1109/bibm52615.2021.9669538

Conference Name

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Collections

Department of Mechanical Engineering, Conference / Seminar

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Citation Formats

K. A. Nasr, A. Sekmen, B. Bilgin, C. Jones, and A. B. Koku, “Deep Learning for Assignment of Protein Secondary Structure Elements from C Coordinates,” presented at the 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Virtual, Online, Amerika Birleşik Devletleri, 2021, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85125185618&origin=inward.