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BrainPepPass : a framework based on supervised dimensionality reduction for predicting blood-brain barrier-penetrating peptides

Ewerton Cristhian Limade Oliveira, Hannah Hirmz (UGent) , Evelien Wynendaele (UGent) , Juliana Auzier Seixas Feio, Igor Matheus Moreira, Kauê Santana da Costa, Anderson H. Lima, Bart De Spiegeleer (UGent) and Claudomiro de Souza de Sales Júnior
(2024) JOURNAL OF CHEMICAL INFORMATION AND MODELING.
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Abstract
Peptides that pass through the blood-brain barrier (BBB) not only are implicated in brain-related pathologies but also are promising therapeutic tools for treating brain diseases, e.g., as shuttles carrying active medicines across the BBB. Computational prediction of BBB-penetrating peptides (B3PPs) has emerged as an interesting approach because of its ability to screen large peptide libraries in a cost-effective manner. In this study, we present BrainPepPass, a machine learning (ML) framework that utilizes supervised manifold dimensionality reduction and extreme gradient boosting (XGB) algorithms to predict natural and chemically modified B3PPs. The results indicate that the proposed tool outperforms other classifiers, with average accuracies exceeding 94% and 98% in 10-fold cross-validation and leave-one-out cross-validation (LOOCV), respectively. In addition, accuracy values ranging from 45% to 97.05% were achieved in the independent tests. The BrainPepPass tool is available in a public repository for academic use (https://github.com/ewerton-cristhian/BrainPepPass).

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Citation

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MLA
Limade Oliveira, Ewerton Cristhian, et al. “BrainPepPass : A Framework Based on Supervised Dimensionality Reduction for Predicting Blood-Brain Barrier-Penetrating Peptides.” JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2024, doi:10.1021/acs.jcim.3c00951.
APA
Limade Oliveira, E. C., Hirmz, H., Wynendaele, E., Auzier Seixas Feio, J., Moreira, I. M., Santana da Costa, K., … de Souza de Sales Júnior, C. (2024). BrainPepPass : a framework based on supervised dimensionality reduction for predicting blood-brain barrier-penetrating peptides. JOURNAL OF CHEMICAL INFORMATION AND MODELING. https://doi.org/10.1021/acs.jcim.3c00951
Chicago author-date
Limade Oliveira, Ewerton Cristhian, Hannah Hirmz, Evelien Wynendaele, Juliana Auzier Seixas Feio, Igor Matheus Moreira, Kauê Santana da Costa, Anderson H. Lima, Bart De Spiegeleer, and Claudomiro de Souza de Sales Júnior. 2024. “BrainPepPass : A Framework Based on Supervised Dimensionality Reduction for Predicting Blood-Brain Barrier-Penetrating Peptides.” JOURNAL OF CHEMICAL INFORMATION AND MODELING. https://doi.org/10.1021/acs.jcim.3c00951.
Chicago author-date (all authors)
Limade Oliveira, Ewerton Cristhian, Hannah Hirmz, Evelien Wynendaele, Juliana Auzier Seixas Feio, Igor Matheus Moreira, Kauê Santana da Costa, Anderson H. Lima, Bart De Spiegeleer, and Claudomiro de Souza de Sales Júnior. 2024. “BrainPepPass : A Framework Based on Supervised Dimensionality Reduction for Predicting Blood-Brain Barrier-Penetrating Peptides.” JOURNAL OF CHEMICAL INFORMATION AND MODELING. doi:10.1021/acs.jcim.3c00951.
Vancouver
1.
Limade Oliveira EC, Hirmz H, Wynendaele E, Auzier Seixas Feio J, Moreira IM, Santana da Costa K, et al. BrainPepPass : a framework based on supervised dimensionality reduction for predicting blood-brain barrier-penetrating peptides. JOURNAL OF CHEMICAL INFORMATION AND MODELING. 2024;
IEEE
[1]
E. C. Limade Oliveira et al., “BrainPepPass : a framework based on supervised dimensionality reduction for predicting blood-brain barrier-penetrating peptides,” JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2024.
@article{01HFVTBVPHRM4954PJPGBK3ZHD,
  abstract     = {{Peptides that pass through the blood-brain barrier (BBB) not only are implicated in brain-related pathologies but also are promising therapeutic tools for treating brain diseases, e.g., as shuttles carrying active medicines across the BBB. Computational prediction of BBB-penetrating peptides (B3PPs) has emerged as an interesting approach because of its ability to screen large peptide libraries in a cost-effective manner. In this study, we present BrainPepPass, a machine learning (ML) framework that utilizes supervised manifold dimensionality reduction and extreme gradient boosting (XGB) algorithms to predict natural and chemically modified B3PPs. The results indicate that the proposed tool outperforms other classifiers, with average accuracies exceeding 94% and 98% in 10-fold cross-validation and leave-one-out cross-validation (LOOCV), respectively. In addition, accuracy values ranging from 45% to 97.05% were achieved in the independent tests. The BrainPepPass tool is available in a public repository for academic use (https://github.com/ewerton-cristhian/BrainPepPass).}},
  author       = {{Limade Oliveira, Ewerton Cristhian and Hirmz, Hannah and Wynendaele, Evelien and Auzier Seixas Feio, Juliana and Moreira, Igor Matheus and Santana da Costa, Kauê and Lima, Anderson H. and De Spiegeleer, Bart and de Souza de Sales Júnior, Claudomiro}},
  issn         = {{1549-9596}},
  journal      = {{JOURNAL OF CHEMICAL INFORMATION AND MODELING}},
  language     = {{eng}},
  pages        = {{15}},
  title        = {{BrainPepPass : a framework based on supervised dimensionality reduction for predicting blood-brain barrier-penetrating peptides}},
  url          = {{http://doi.org/10.1021/acs.jcim.3c00951}},
  year         = {{2024}},
}
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