DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data

Md. Khaled Ben Islam; Md. Nazrul Islam Mondal; Julia Rahman; Md. Al Mehedi Hassan

doi:doi:10.11648/j.ajbls.20170506.15

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DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data

Md. Khaled Ben Islam, Md. Nazrul Islam Mondal, Julia Rahman, Md. Al Mehedi Hassan

Published in American Journal of Biomedical and Life Sciences (Volume 5, Issue 6)

Received: 8 September 2017 Accepted: 8 October 2017 Published: 30 November 2017

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Abstract

Protein Post Translation Modification identification is one of the important steps in conducting disease-associated mutation studies. Though multiple chemical alterations happen in a protein after translation, the addition of succinyl group to lysine residue plays a vital role in regulating cellular metabolism and thus disease. Use of a classification algorithm on some features, driven either from protein structural, physicochemical or even biochemical information becomes a common approach that can yield a satisfactory result up to a certain level. Although, researchers already developed many computational methods to identify whether a lysine residue modified with succinyl group after translation, most of them focused on the improvement either on a single decision using a single method or feature enrichment or even development of a benchmark dataset. Therefore, there still exists scope for further improvement to characterise lysine residues of a protein sequence by considering multiple predictors at a time. In this study, an ensemble based approach called DV-iSucLys has been designed to characterise the lysine residue by adapting three well known and conceptually different classifiers and ensembling their decisions. Also, a benchmark succinylation dataset was extracted from existing benchmark datasets and recently updated succinylation data from UniProt consortium to investigate the performance of the proposed approach as well as contribute to further research. Analysing rigorous cross-validation results show that DV-iSucLys can characterise succinyl lysine residue better than the existing predictors.

Published in	American Journal of Biomedical and Life Sciences (Volume 5, Issue 6)
DOI	10.11648/j.ajbls.20170506.15
Page(s)	135-143
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Lysine Succinylation, AAC, CKSAAP, Binary Encoding, PSAAP, AAindex, Ensemble Classifier

References

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APA Style

Md. Khaled Ben Islam, Md. Nazrul Islam Mondal, Julia Rahman, Md. Al Mehedi Hassan. (2017). DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data. American Journal of Biomedical and Life Sciences, 5(6), 135-143. https://doi.org/10.11648/j.ajbls.20170506.15

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ACS Style

Md. Khaled Ben Islam; Md. Nazrul Islam Mondal; Julia Rahman; Md. Al Mehedi Hassan. DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data. Am. J. Biomed. Life Sci. 2017, 5(6), 135-143. doi: 10.11648/j.ajbls.20170506.15

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AMA Style

Md. Khaled Ben Islam, Md. Nazrul Islam Mondal, Julia Rahman, Md. Al Mehedi Hassan. DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data. Am J Biomed Life Sci. 2017;5(6):135-143. doi: 10.11648/j.ajbls.20170506.15

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@article{10.11648/j.ajbls.20170506.15,
  author = {Md. Khaled Ben Islam and Md. Nazrul Islam Mondal and Julia Rahman and Md. Al Mehedi Hassan},
  title = {DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data},
  journal = {American Journal of Biomedical and Life Sciences},
  volume = {5},
  number = {6},
  pages = {135-143},
  doi = {10.11648/j.ajbls.20170506.15},
  url = {https://doi.org/10.11648/j.ajbls.20170506.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbls.20170506.15},
  abstract = {Protein Post Translation Modification identification is one of the important steps in conducting disease-associated mutation studies. Though multiple chemical alterations happen in a protein after translation, the addition of succinyl group to lysine residue plays a vital role in regulating cellular metabolism and thus disease. Use of a classification algorithm on some features, driven either from protein structural, physicochemical or even biochemical information becomes a common approach that can yield a satisfactory result up to a certain level. Although, researchers already developed many computational methods to identify whether a lysine residue modified with succinyl group after translation, most of them focused on the improvement either on a single decision using a single method or feature enrichment or even development of a benchmark dataset. Therefore, there still exists scope for further improvement to characterise lysine residues of a protein sequence by considering multiple predictors at a time. In this study, an ensemble based approach called DV-iSucLys has been designed to characterise the lysine residue by adapting three well known and conceptually different classifiers and ensembling their decisions. Also, a benchmark succinylation dataset was extracted from existing benchmark datasets and recently updated succinylation data from UniProt consortium to investigate the performance of the proposed approach as well as contribute to further research. Analysing rigorous cross-validation results show that DV-iSucLys can characterise succinyl lysine residue better than the existing predictors.},
 year = {2017}
}

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TY  - JOUR
T1  - DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data
AU  - Md. Khaled Ben Islam
AU  - Md. Nazrul Islam Mondal
AU  - Julia Rahman
AU  - Md. Al Mehedi Hassan
Y1  - 2017/11/30
PY  - 2017
N1  - https://doi.org/10.11648/j.ajbls.20170506.15
DO  - 10.11648/j.ajbls.20170506.15
T2  - American Journal of Biomedical and Life Sciences
JF  - American Journal of Biomedical and Life Sciences
JO  - American Journal of Biomedical and Life Sciences
SP  - 135
EP  - 143
PB  - Science Publishing Group
SN  - 2330-880X
UR  - https://doi.org/10.11648/j.ajbls.20170506.15
AB  - Protein Post Translation Modification identification is one of the important steps in conducting disease-associated mutation studies. Though multiple chemical alterations happen in a protein after translation, the addition of succinyl group to lysine residue plays a vital role in regulating cellular metabolism and thus disease. Use of a classification algorithm on some features, driven either from protein structural, physicochemical or even biochemical information becomes a common approach that can yield a satisfactory result up to a certain level. Although, researchers already developed many computational methods to identify whether a lysine residue modified with succinyl group after translation, most of them focused on the improvement either on a single decision using a single method or feature enrichment or even development of a benchmark dataset. Therefore, there still exists scope for further improvement to characterise lysine residues of a protein sequence by considering multiple predictors at a time. In this study, an ensemble based approach called DV-iSucLys has been designed to characterise the lysine residue by adapting three well known and conceptually different classifiers and ensembling their decisions. Also, a benchmark succinylation dataset was extracted from existing benchmark datasets and recently updated succinylation data from UniProt consortium to investigate the performance of the proposed approach as well as contribute to further research. Analysing rigorous cross-validation results show that DV-iSucLys can characterise succinyl lysine residue better than the existing predictors.
VL  - 5
IS  - 6
ER  -

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Author Information

Md. Khaled Ben Islam

Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh; Department of Computer Science & Engineering, Pabna University of Science & Technology, Pabna, Bangladesh
Md. Nazrul Islam Mondal

Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
Julia Rahman

Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
Md. Al Mehedi Hassan

Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh

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APA Style

Md. Khaled Ben Islam, Md. Nazrul Islam Mondal, Julia Rahman, Md. Al Mehedi Hassan. (2017). DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data. American Journal of Biomedical and Life Sciences, 5(6), 135-143. https://doi.org/10.11648/j.ajbls.20170506.15

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ACS Style

Md. Khaled Ben Islam; Md. Nazrul Islam Mondal; Julia Rahman; Md. Al Mehedi Hassan. DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data. Am. J. Biomed. Life Sci. 2017, 5(6), 135-143. doi: 10.11648/j.ajbls.20170506.15

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AMA Style

Md. Khaled Ben Islam, Md. Nazrul Islam Mondal, Julia Rahman, Md. Al Mehedi Hassan. DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data. Am J Biomed Life Sci. 2017;5(6):135-143. doi: 10.11648/j.ajbls.20170506.15

Copy | Download

@article{10.11648/j.ajbls.20170506.15,
  author = {Md. Khaled Ben Islam and Md. Nazrul Islam Mondal and Julia Rahman and Md. Al Mehedi Hassan},
  title = {DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data},
  journal = {American Journal of Biomedical and Life Sciences},
  volume = {5},
  number = {6},
  pages = {135-143},
  doi = {10.11648/j.ajbls.20170506.15},
  url = {https://doi.org/10.11648/j.ajbls.20170506.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbls.20170506.15},
  abstract = {Protein Post Translation Modification identification is one of the important steps in conducting disease-associated mutation studies. Though multiple chemical alterations happen in a protein after translation, the addition of succinyl group to lysine residue plays a vital role in regulating cellular metabolism and thus disease. Use of a classification algorithm on some features, driven either from protein structural, physicochemical or even biochemical information becomes a common approach that can yield a satisfactory result up to a certain level. Although, researchers already developed many computational methods to identify whether a lysine residue modified with succinyl group after translation, most of them focused on the improvement either on a single decision using a single method or feature enrichment or even development of a benchmark dataset. Therefore, there still exists scope for further improvement to characterise lysine residues of a protein sequence by considering multiple predictors at a time. In this study, an ensemble based approach called DV-iSucLys has been designed to characterise the lysine residue by adapting three well known and conceptually different classifiers and ensembling their decisions. Also, a benchmark succinylation dataset was extracted from existing benchmark datasets and recently updated succinylation data from UniProt consortium to investigate the performance of the proposed approach as well as contribute to further research. Analysing rigorous cross-validation results show that DV-iSucLys can characterise succinyl lysine residue better than the existing predictors.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - DV-iSucLys: Decision Voting to Improve Protein Lysine Succinylation Site Identification from Sequence Data
AU  - Md. Khaled Ben Islam
AU  - Md. Nazrul Islam Mondal
AU  - Julia Rahman
AU  - Md. Al Mehedi Hassan
Y1  - 2017/11/30
PY  - 2017
N1  - https://doi.org/10.11648/j.ajbls.20170506.15
DO  - 10.11648/j.ajbls.20170506.15
T2  - American Journal of Biomedical and Life Sciences
JF  - American Journal of Biomedical and Life Sciences
JO  - American Journal of Biomedical and Life Sciences
SP  - 135
EP  - 143
PB  - Science Publishing Group
SN  - 2330-880X
UR  - https://doi.org/10.11648/j.ajbls.20170506.15
AB  - Protein Post Translation Modification identification is one of the important steps in conducting disease-associated mutation studies. Though multiple chemical alterations happen in a protein after translation, the addition of succinyl group to lysine residue plays a vital role in regulating cellular metabolism and thus disease. Use of a classification algorithm on some features, driven either from protein structural, physicochemical or even biochemical information becomes a common approach that can yield a satisfactory result up to a certain level. Although, researchers already developed many computational methods to identify whether a lysine residue modified with succinyl group after translation, most of them focused on the improvement either on a single decision using a single method or feature enrichment or even development of a benchmark dataset. Therefore, there still exists scope for further improvement to characterise lysine residues of a protein sequence by considering multiple predictors at a time. In this study, an ensemble based approach called DV-iSucLys has been designed to characterise the lysine residue by adapting three well known and conceptually different classifiers and ensembling their decisions. Also, a benchmark succinylation dataset was extracted from existing benchmark datasets and recently updated succinylation data from UniProt consortium to investigate the performance of the proposed approach as well as contribute to further research. Analysing rigorous cross-validation results show that DV-iSucLys can characterise succinyl lysine residue better than the existing predictors.
VL  - 5
IS  - 6
ER  -

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