Pretoria: An effective computational approach for accurate and high-throughput identification of CD8<sup>+</sup> t-cell epitopes of eukaryotic pathogens
Issued Date
2023-05-31
Resource Type
ISSN
01418130
eISSN
18790003
Scopus ID
2-s2.0-85154056025
Pubmed ID
36996953
Journal Title
International Journal of Biological Macromolecules
Volume
238
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Journal of Biological Macromolecules Vol.238 (2023)
Suggested Citation
Charoenkwan P., Schaduangrat N., Pham N.T., Manavalan B., Shoombuatong W. Pretoria: An effective computational approach for accurate and high-throughput identification of CD8<sup>+</sup> t-cell epitopes of eukaryotic pathogens. International Journal of Biological Macromolecules Vol.238 (2023). doi:10.1016/j.ijbiomac.2023.124228 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/82899
Title
Pretoria: An effective computational approach for accurate and high-throughput identification of CD8<sup>+</sup> t-cell epitopes of eukaryotic pathogens
Author's Affiliation
Other Contributor(s)
Abstract
T-cells recognize antigenic epitopes present on major histocompatibility complex (MHC) molecules, triggering an adaptive immune response in the host. T-cell epitope (TCE) identification is challenging because of the extensive number of undetermined proteins found in eukaryotic pathogens, as well as MHC polymorphisms. In addition, conventional experimental approaches for TCE identification are time-consuming and expensive. Thus, computational approaches that can accurately and rapidly identify CD8+ T-cell epitopes (TCEs) of eukaryotic pathogens based solely on sequence information may facilitate the discovery of novel CD8+ TCEs in a cost-effective manner. Here, Pretoria (Predictor of CD8+ TCEs of eukaryotic pathogens) is proposed as the first stack-based approach for accurate and large-scale identification of CD8+ TCEs of eukaryotic pathogens. In particular, Pretoria enabled the extraction and exploration of crucial information embedded in CD8+ TCEs by employing a comprehensive set of 12 well-known feature descriptors extracted from multiple groups, including physicochemical properties, composition-transition-distribution, pseudo-amino acid composition, and amino acid composition. These feature descriptors were then utilized to construct a pool of 144 different machine learning (ML)-based classifiers based on 12 popular ML algorithms. Finally, the feature selection method was used to effectively determine the important ML classifiers for the construction of our stacked model. The experimental results indicated that Pretoria is an accurate and effective computational approach for CD8+ TCE prediction; it was superior to several conventional ML classifiers and the existing method in terms of the independent test, with an accuracy of 0.866, MCC of 0.732, and AUC of 0.921. Additionally, to maximize user convenience for high-throughput identification of CD8+ TCEs of eukaryotic pathogens, a user-friendly web server of Pretoria (http://pmlabstack.pythonanywhere.com/Pretoria) was developed and made freely available.