pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments.

Computational Biology / methods Cysteine / metabolism Disulfides / chemistry Neural Networks, Computer Proteins / chemistry Supervised Machine Learning
10-fold Cross Validation 5-step rule Neural networks Position relative features

Journal

Journal of theoretical biology
ISSN: 1095-8541
Titre abrégé: J Theor Biol
Pays: England
ID NLM: 0376342

Informations de publication

Date de publication:
21 02 2019
Historique:
received: 26 10 2018
revised: 05 12 2018
accepted: 11 12 2018
pubmed: 15 12 2018
medline: 21 3 2020
entrez: 15 12 2018
Statut: ppublish

Résumé

The structure of protein gains additional stability against various detrimental effects by the presence of disulfide bonds. The formation of correct disulfide bonds between cysteine residues ensures proper in vivo and in vitro folding of the protein. Many cysteine residues can be present in the polypeptide chain of a protein, however, not all cysteine residues are involved in the formation of a disulfide bond, and therefore, accurate prediction of these bonds is crucial for identifying biophysical characteristics of a protein. In the present study, a novel method is proposed for the prediction of intramolecular disulfide bonds accurately using statistical moments and PseAAC. The pSSbond-PseAAC uses PseAAC along with position and composition relative features to calculate statistical moments. Statistical moments are important as they are very sensitive regarding the position of data sequences and for prediction of intramolecular disulfide bonds, moments are combined together to train neural networks. The overall accuracy of the pSSbond-PseAAC is 98.97% to sensitivity value 98.92%, specificity 98.99% and 0.98 MCC; and it outperforms various previously reported studies.

Identifiants

DOI: 10.1016/j.jtbi.2018.12.015 PMID: 30550863
pubmed: 30550863
pii: S0022-5193(18)30604-0
doi: 10.1016/j.jtbi.2018.12.015
pii:
doi:

Substances chimiques

Disulfides 0
Proteins 0
Cysteine K848JZ4886

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

47-55

Informations de copyright

Copyright © 2018 Elsevier Ltd. All rights reserved.

Auteurs

Yaser Daanial Khan (YD)

Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore 54770, Pakistan. Electronic address: yaser.khan@umt.edu.pk.

Mehreen Jamil (M)

Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore 54770, Pakistan.

Waqar Hussain (W)

Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore 54770, Pakistan.

Nouman Rasool (N)

Department of Life Sciences, School of Science, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore 54770, Pakistan; Dr Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan.

Sher Afzal Khan (SA)

Faculty of Computing and Information Technology in Rabigh, Jeddah, 21577, KSA; Abdul Wali Khan University, Department of Computer Sciences, Mardan, Pakistan.

Kuo-Chen Chou (KC)

Gordon Life Science Institute, Boston, MA 02478, USA.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Informatique › Biologie informatique › pSSbond-PseAAC: Prediction of disulfide...
questionsmedicales.fr › Acides aminés, peptides et protéines › Acides aminés › Acides aminés soufrés › Cystéine › pSSbond-PseAAC: Prediction of disulfide...
questionsmedicales.fr › Composés chimiques organiques › Composés du soufre › Sulfures › Disulfures › pSSbond-PseAAC: Prediction of disulfide...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Réseaux neuronaux (ordinateur) › pSSbond-PseAAC: Prediction of disulfide...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › pSSbond-PseAAC: Prediction of disulfide...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Apprentissage machine supervisé › pSSbond-PseAAC: Prediction of disulfide...