Inferring linear-B cell epitopes using 2-step metaheuristic variant-feature selection using genetic algorithm.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
05 09 2023
05 09 2023
Historique:
received:
26
03
2023
accepted:
23
08
2023
medline:
7
9
2023
pubmed:
6
9
2023
entrez:
5
9
2023
Statut:
epublish
Résumé
Linear-B cell epitopes (LBCE) play a vital role in vaccine design; thus, efficiently detecting them from protein sequences is of primary importance. These epitopes consist of amino acids arranged in continuous or discontinuous patterns. Vaccines employ attenuated viruses and purified antigens. LBCE stimulate humoral immunity in the body, where B and T cells target circulating infections. To predict LBCE, the underlying protein sequences undergo a process of feature extraction, feature selection, and classification. Various system models have been proposed for this purpose, but their classification accuracy is only moderate. In order to enhance the accuracy of LBCE classification, this paper presents a novel 2-step metaheuristic variant-feature selection method that combines a linear support vector classifier (LSVC) with a Modified Genetic Algorithm (MGA). The feature selection model employs mono-peptide, dipeptide, and tripeptide features, focusing on the most diverse ones. These selected features are fed into a machine learning (ML)-based parallel ensemble classifier. The ensemble classifier combines correctly classified instances from various classifiers, including k-Nearest Neighbor (kNN), random forest (RF), logistic regression (LR), and support vector machine (SVM). The ensemble classifier came up with an impressively high accuracy of 99.3% as a result of its work. This accuracy is superior to the most recent models that are considered to be state-of-the-art for linear B-cell classification. As a direct consequence of this, the entire system model can now be utilised effectively in real-time clinical settings.
Identifiants
pubmed: 37670007
doi: 10.1038/s41598-023-41179-1
pii: 10.1038/s41598-023-41179-1
pmc: PMC10480427
doi:
Substances chimiques
Epitopes, B-Lymphocyte
0
Amino Acids
0
Antifibrinolytic Agents
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
14593Informations de copyright
© 2023. Springer Nature Limited.
Références
El-Manzalawy, Y. & Honavar, V. Building classifier ensembles for B-cell epitope prediction. Methods Mol. Biol. 1184, 285–294. https://doi.org/10.1007/978-1-4939-1115-8_15 (2014).
doi: 10.1007/978-1-4939-1115-8_15
pubmed: 25048130
pmcid: 4385709
Rostami, M., Berahmand, K., Nasiri, E. & Forouzandeh, S. Review of swarm intelligence-based feature selection methods. Eng. Appl. Artif. Intell. 100, 104210 (2021).
doi: 10.1016/j.engappai.2021.104210
Azadifar, S., Rostami, M., Berahmand, K., Moradi, P. & Oussalah, M. Graph-based relevancy-redundancy gene selection method for cancer diagnosis. Comput. Biol. Med. 147, 105766 (2022).
doi: 10.1016/j.compbiomed.2022.105766
pubmed: 35779479
El-Manzalawy, Y. & Honavar, V. Recent advances in B-cell epitope prediction methods. Immunome Res. 6(2), S2. https://doi.org/10.1186/1745-7580-6-S2-S2 (2010).
doi: 10.1186/1745-7580-6-S2-S2
pubmed: 21067544
pmcid: 2981878
Hu, Y.-J., Lin, S.-C., Lin, Y.-L., Lin, K.-H. & You, S.-N. A meta-learning approach for B-cell conformational epitope prediction. BMC Bioinform. 15, 378. https://doi.org/10.1186/s12859-014-0378-y (2014).
doi: 10.1186/s12859-014-0378-y
Liu, T., Shi, K. & Li, W. Deep learning methods improve linear B-cell epitope prediction. BioData Min. 13, 85. https://doi.org/10.1186/s13040-020-00211-0 (2020).
doi: 10.1186/s13040-020-00211-0
Raoufi, E. et al. Epitope prediction by novel immunoinformatics approach: A state-of-the-art review. Int. J. Pept. Res. Ther. 26, 1155–1163. https://doi.org/10.1007/s10989-019-09918-z (2020).
doi: 10.1007/s10989-019-09918-z
pubmed: 32435171
Çınar, A. & Tuncer, S. A. Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM. SN Appl. Sci. 3, 503. https://doi.org/10.1007/s42452-021-04485-9 (2021).
doi: 10.1007/s42452-021-04485-9
Talaat, A., Kollmannsberger, P. & Ewees, A. Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Sci. Rep. 2020, 10. https://doi.org/10.1038/s41598-020-59215-9 (2020).
doi: 10.1038/s41598-020-59215-9
Hasan, M. M., Shamima, K. & Kurata, H. iLBE for computational identification of linear B-cell epitopes by integrating sequence and evolutionary features. Genom. Proteom. Bioinform. https://doi.org/10.1016/j.gpb.2019.04.004 (2020).
doi: 10.1016/j.gpb.2019.04.004
Niikura, M. et al. Analysis of linear B-cell epitopes of the nucleoprotein of ebola virus that distinguish ebola virus subtypes. Clin. Diagn. Lab. Immunol. 10, 83–87. https://doi.org/10.1128/CDLI.10.1.83-87.2003 (2003).
doi: 10.1128/CDLI.10.1.83-87.2003
pubmed: 12522044
pmcid: 145268
Chen, Z. et al. T and B cell Epitope analysis of SARS-CoV-2 S protein based on immunoinformatics and experimental research. J. Cell. Mol. Med. 2020, 25. https://doi.org/10.1111/jcmm.16200 (2020).
doi: 10.1111/jcmm.16200
Zhao, M. et al. Hematologist-level classification of mature B-cell neoplasm using deep learning on multiparameter flow cytometry data. Cytometry 97, 1073–1080. https://doi.org/10.1002/cyto.a.24159 (2020).
doi: 10.1002/cyto.a.24159
pubmed: 32519455
Khan, S., Sajjad, M., Hussain, T., Ullah, A. & Imran, A. S. A review on traditional machine learning and deep learning models for WBCs classification in blood smear images. IEEE Access 9, 10657–10673. https://doi.org/10.1109/ACCESS.2020.3048172 (2021).
doi: 10.1109/ACCESS.2020.3048172
Hsin-Wei, W., Ya-Chi, L., Tun-Wen, P. & Hao-Teng, C. Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification. BioMed. Res. Int. 2011, 12. https://doi.org/10.1155/2011/432830 (2011).
doi: 10.1155/2011/432830
Galanis, K. A. et al. Linear B-cell epitope prediction for in silico vaccine design: A performance review of methods available via command-line interface. Int. J. Mol. Sci. 22(6), 3210. https://doi.org/10.3390/ijms22063210 (2021).
doi: 10.3390/ijms22063210
pubmed: 33809918
pmcid: 8004178
Hooshmand, N., Fayazi, J., Tabatabaei, S. & Ghaleh Golab Behbahan, N. Prediction of B cell and T-helper cell epitopes candidates of bovine leukaemia virus (BLV) by in silico approach. Vet. Med. Sci. 6, 730–739. https://doi.org/10.1002/vms3.307 (2020).
doi: 10.1002/vms3.307
pubmed: 32592322
pmcid: 7738742
Marsh-Wakefield, F. et al. IgG3+ B cells are associated with the development of multiple sclerosis. Clin. Transl. Immunol. 2020, 9. https://doi.org/10.1002/cti2.1133 (2020).
doi: 10.1002/cti2.1133
Manavalan, B., Govindaraj, R. G., Shin, T.-H., Kim, M. & Lee, G. iBCE-EL: A new ensemble learning framework for improved linear B-cell epitope prediction. Front. Immunol. 2018, 9. https://doi.org/10.3389/fimmu.2018.01695 (2018).
doi: 10.3389/fimmu.2018.01695
Huang, J.-H. et al. Using random forest to classify T-cell epitopes based on amino acid properties and molecular features. Anal. Chim. Acta 804C, 70–75. https://doi.org/10.1016/j.aca.2013.10.003 (2013).
doi: 10.1016/j.aca.2013.10.003
Jain, N. et al. Prediction modelling of COVID using machine learning methods from B-cell dataset. Results Phys. 21, 103813. https://doi.org/10.1016/j.rinp.2021.103813 (2021).
doi: 10.1016/j.rinp.2021.103813
pubmed: 33495725
pmcid: 7816944
Amrun, S. N. et al. Novel differential linear B-cell epitopes to identify Zika and dengue virus infections in patients. Clin. Transl. Immunol. 8(7), e1066. https://doi.org/10.1002/cti2.1066 (2019).
doi: 10.1002/cti2.1066
Crooke, S. N., Ovsyannikova, I. G., Kennedy, R. B. & Poland, G. A. Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome. Sci. Rep. 10(1), 14179. https://doi.org/10.1038/s41598-020-70864-8 (2020).
doi: 10.1038/s41598-020-70864-8
pubmed: 32843695
pmcid: 7447814
Identification of a novel B-cell epitope in the spike protein of porcine epidemic diarrhea virus, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7119268/ (2020).
Bi, Y. et al. Identification of two distinct linear B cell epitopes of the matrix protein of the newcastle disease virus vaccine strain LaSota. Viral. Immunol. 32(5), 221–229. https://doi.org/10.1089/vim.2019.0007 (2019).
doi: 10.1089/vim.2019.0007
pubmed: 31094659
Guedes, R. L. M. et al. A comparative in silico linear B-cell epitope prediction and characterization for South American and African Trypanosoma vivax strains. Genomics 111(3), 407–417. https://doi.org/10.1016/j.ygeno.2018.02.017 (2019).
doi: 10.1016/j.ygeno.2018.02.017
pubmed: 29499243
Jespersen, M. C., Mahajan, S., Peters, B., Nielsen, M. & Marcatili, P. Antibody specific B-cell epitope predictions: leveraging information from antibody-antigen protein complexes. Front. Immunol. 10, 298. https://doi.org/10.3389/fimmu.2019.00298 (2019).
doi: 10.3389/fimmu.2019.00298
pubmed: 30863406
pmcid: 6399414
Amrun, S. N. et al. Linear B-cell epitopes in the spike and nucleocapsid proteins as markers of SARS-CoV-2 exposure and disease severity. EBioMedicine 58, 102911. https://doi.org/10.1016/j.ebiom.2020.102911 (2020).
doi: 10.1016/j.ebiom.2020.102911
pubmed: 32711254
pmcid: 7375792
Wright, G. W. et al. A probabilistic classification tool for genetic subtypes of diffuse large B cell lymphoma with therapeutic implications. Cancer Cell 37(4), 551-568.e14. https://doi.org/10.1016/j.ccell.2020.03.015 (2020).
doi: 10.1016/j.ccell.2020.03.015
pubmed: 32289277
pmcid: 8459709
Hartley, G. et al. Rapid generation of durable B cell memory to SARS-CoV-2 spike and nucleocapsid proteins in COVID-19 and convalescence. Sci. Immunol. 2020, 5. https://doi.org/10.1126/sciimmunol.abf8891 (2020).
doi: 10.1126/sciimmunol.abf8891
Glass, D. et al. An integrated multi-omic single-cell atlas of human B cell identity. Immunity 53, 217-232.e5. https://doi.org/10.1016/j.immuni.2020.06.013 (2020).
doi: 10.1016/j.immuni.2020.06.013
pubmed: 32668225
pmcid: 7369630
Holmes, A. et al. Single-cell analysis of germinal-center B cells informs on lymphoma cell of origin and outcome. J. Exp. Med. 2020, 217 (2020).
Zivkovic, M. et al. Hybrid genetic algorithm and machine learning method for COVID-19 cases prediction. In Proceedings of International Conference on Sustainable Expert Systems. Lecture Notes in Networks and Systems, vol 176 (eds. Shakya, S. et al.) (Springer, 2021). https://doi.org/10.1007/978-981-33-4355-9_14 .
Doewes, R. I., Nair, R. & Sharma, T. Diagnosis of COVID-19 through blood sample using ensemble genetic algorithms and machine learning classifier. World J. Eng. 19(2), 175–182. https://doi.org/10.1108/WJE-03-2021-0174 (2022).
doi: 10.1108/WJE-03-2021-0174
Seyed, M. J. J. et al. X-ray image based COVID-19 detection using evolutionary deep learning approach. Expert Syst. Appl. 201, 116942. https://doi.org/10.1016/j.eswa.2022.116942 (2022).
doi: 10.1016/j.eswa.2022.116942
Aleksa, C. et al. Feedforward Multi-Layer Perceptron Training by Hybridized Method between Genetic Algorithm and Artificial Bee Colony (Chapman and Hall/CRC, 2021).