Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning framework
Issued Date
2024-01-01
Resource Type
ISSN
07391102
eISSN
15380254
Scopus ID
2-s2.0-85186400918
Pubmed ID
38385478
Journal Title
Journal of Biomolecular Structure and Dynamics
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Biomolecular Structure and Dynamics (2024)
Suggested Citation
Charoenkwan P., Chumnanpuen P., Schaduangrat N., Shoombuatong W. Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning framework. Journal of Biomolecular Structure and Dynamics (2024). doi:10.1080/07391102.2024.2318482 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97534
Title
Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning framework
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Corresponding Author(s)
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Abstract
ABSTRACT: Plant-allergenic proteins (PAPs) have the potential to induce allergic reactions in certain individuals. While these proteins are generally innocuous for the majority of people, they can elicit an immune response in those with particular sensitivities. Thus, screening and prioritizing the allergenic potential of plant proteins is indispensable for the development of diagnostic tools, therapeutic interventions or medications to treat allergic reactions. However, investigating the allergenic potential of plant proteins based on experimental methods is costly and labour-intensive. Therefore, we develop StackPAP, a three-layer stacking ensemble framework for accurate large-scale identification of PAPs. In StackPAP, at the first layer, we conducted a comprehensive analysis of an extensive set of feature descriptors. Subsequently, we selected and fused five potential sequence-based feature descriptors, including amphiphilic pseudo-amino acid composition, dipeptide deviation from expected mean, amino acid composition, pseudo amino acid composition and dipeptide composition. Additionally, we applied an efficient genetic algorithm (GA-SAR) to determine informative feature sets. In the second layer, 12 powerful machine learning (ML) methods, in combination with all the informative feature sets, were employed to construct a pool of base classifiers. Finally, 13 potential base classifiers were selected using the GA-SAR method and combined to develop the final meta-classifier. Our experimental results revealed the promising prediction performance of StackPAP, with an accuracy, Matthew’s correlation coefficient and AUC of 0.984, 0.969 and 0.993, respectively, as judged by the independent test dataset. In conclusion, both cross-validation and independent test results indicated the superior performance of StackPAP compared with several ML-based classifiers. To accelerate the identification of the allergenicity of plant proteins, we developed a user-friendly web server for StackPAP (https://pmlabqsar.pythonanywhere.com/StackPAP). We anticipate that StackPAP will be an efficient and useful tool for rapidly screening PAPs from a vast number of plant proteins. Communicated by Ramaswamy H. Sarma.