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

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

doi:10.1109/BIBM52615.2021.9669538

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 proceeding › Conference contribution

1 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 language	English (US)
Title of host publication	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Editors	Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2546-2552
Number of pages	7
ISBN (Electronic)	9781665401265
DOIs	https://doi.org/10.1109/BIBM52615.2021.9669538
State	Published - 2021
Externally published	Yes
Event	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States Duration: Dec 9 2021 → Dec 12 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/Territory	United States
City	Virtual, Online
Period	12/9/21 → 12/12/21

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

Access to Document

10.1109/BIBM52615.2021.9669538

Cite this

Nasr, K. A., Sekmen, A., Bilgin, B., Jones, C., & Koku, A. B. (2021). Deep Learning for Assignment of Protein Secondary Structure Elements from C Coordinates. In Y. Huang, L. Kurgan, F. Luo, X. T. Hu, Y. Chen, E. Dougherty, A. Kloczkowski, & Y. Li (Eds.), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 (pp. 2546-2552). (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM52615.2021.9669538

Deep Learning for Assignment of Protein Secondary Structure Elements from C Coordinates. / Nasr, Kamal Al; Sekmen, Ali; Bilgin, Bahadir et al.
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. ed. / Yufei Huang; Lukasz Kurgan; Feng Luo; Xiaohua Tony Hu; Yidong Chen; Edward Dougherty; Andrzej Kloczkowski; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2546-2552 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nasr, KA, Sekmen, A, Bilgin, B, Jones, C & Koku, AB 2021, Deep Learning for Assignment of Protein Secondary Structure Elements from C Coordinates. in Y Huang, L Kurgan, F Luo, XT Hu, Y Chen, E Dougherty, A Kloczkowski & Y Li (eds), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Institute of Electrical and Electronics Engineers Inc., pp. 2546-2552, 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Virtual, Online, United States, 12/9/21. https://doi.org/10.1109/BIBM52615.2021.9669538

Nasr KA, Sekmen A, Bilgin B, Jones C, Koku AB. Deep Learning for Assignment of Protein Secondary Structure Elements from C Coordinates. In Huang Y, Kurgan L, Luo F, Hu XT, Chen Y, Dougherty E, Kloczkowski A, Li Y, editors, Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 2546-2552. (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). doi: 10.1109/BIBM52615.2021.9669538

Nasr, Kamal Al ; Sekmen, Ali ; Bilgin, Bahadir et al. / Deep Learning for Assignment of Protein Secondary Structure Elements from C Coordinates. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. editor / Yufei Huang ; Lukasz Kurgan ; Feng Luo ; Xiaohua Tony Hu ; Yidong Chen ; Edward Dougherty ; Andrzej Kloczkowski ; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 2546-2552 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

@inproceedings{48f17fc96bf64269985b7d8528cfbc4b,

title = "Deep Learning for Assignment of Protein Secondary Structure Elements from C Coordinates",

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.",

keywords = "C backbone, deep neural networks, protein modeling, secondary structure classification",

author = "Nasr, {Kamal Al} and Ali Sekmen and Bahadir Bilgin and Christopher Jones and Koku, {Ahmet Bugra}",

note = "Funding Information: Ali Sekmen{\textquoteright}s research is supported by DOD grant W911NF-20-100284. Kamal Al Nasr{\textquoteright}s research is supported by NIH Academic Research Enhancement Award (R15 AREA: 1R15GM126509 01).* Corresponding author Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; Conference date: 09-12-2021 Through 12-12-2021",

year = "2021",

doi = "10.1109/BIBM52615.2021.9669538",

language = "English (US)",

series = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2546--2552",

editor = "Yufei Huang and Lukasz Kurgan and Feng Luo and Hu, {Xiaohua Tony} and Yidong Chen and Edward Dougherty and Andrzej Kloczkowski and Yaohang Li",

booktitle = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

}

TY - GEN

T1 - Deep Learning for Assignment of Protein Secondary Structure Elements from C Coordinates

AU - Nasr, Kamal Al

AU - Sekmen, Ali

AU - Bilgin, Bahadir

AU - Jones, Christopher

AU - Koku, Ahmet Bugra

N1 - Funding Information: Ali Sekmen’s research is supported by DOD grant W911NF-20-100284. Kamal Al Nasr’s research is supported by NIH Academic Research Enhancement Award (R15 AREA: 1R15GM126509 01).* Corresponding author Publisher Copyright: © 2021 IEEE.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - C backbone

KW - deep neural networks

KW - protein modeling

KW - secondary structure classification

UR - http://www.scopus.com/inward/record.url?scp=85125185618&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85125185618&partnerID=8YFLogxK

U2 - 10.1109/BIBM52615.2021.9669538

DO - 10.1109/BIBM52615.2021.9669538

M3 - Conference contribution

AN - SCOPUS:85125185618

T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

SP - 2546

EP - 2552

BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

A2 - Huang, Yufei

A2 - Kurgan, Lukasz

A2 - Luo, Feng

A2 - Hu, Xiaohua Tony

A2 - Chen, Yidong

A2 - Dougherty, Edward

A2 - Kloczkowski, Andrzej

A2 - Li, Yaohang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Y2 - 9 December 2021 through 12 December 2021

ER -

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this