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Article

B3Pred: A Random-Forest-Based Method for Predicting and Designing Blood–Brain Barrier Penetrating Peptides

1
Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla 110020, India
2
Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector-39A, Chandigarh 160036, India
*
Author to whom correspondence should be addressed.
Academic Editors: Prisca Boisguérin and Sébastien Deshayes
Pharmaceutics 2021, 13(8), 1237; https://doi.org/10.3390/pharmaceutics13081237
Received: 7 June 2021 / Revised: 7 July 2021 / Accepted: 14 July 2021 / Published: 11 August 2021
The blood–brain barrier is a major obstacle in treating brain-related disorders, as it does not allow the delivery of drugs into the brain. We developed a method for predicting blood–brain barrier penetrating peptides to facilitate drug delivery into the brain. These blood–brain barrier penetrating peptides (B3PPs) can act as therapeutics, as well as drug delivery agents. We trained, tested, and evaluated our models on blood–brain barrier peptides obtained from the B3Pdb database. First, we computed a wide range of peptide features. Then, we selected relevant peptide features. Finally, we developed numerous machine-learning-based models for predicting blood–brain barrier peptides using the selected features. The random-forest-based model performed the best with respect to the top 80 selected features and achieved a maximal 85.08% accuracy with an AUROC of 0.93. We also developed a webserver, B3pred, that implements our best models. It has three major modules that allow users to predict/design B3PPs and scan B3PPs in a protein sequence. View Full-Text
Keywords: blood–brain barrier; penetrating peptides; machine learning techniques; drug delivery; prediction server blood–brain barrier; penetrating peptides; machine learning techniques; drug delivery; prediction server
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MDPI and ACS Style

Kumar, V.; Patiyal, S.; Dhall, A.; Sharma, N.; Raghava, G.P.S. B3Pred: A Random-Forest-Based Method for Predicting and Designing Blood–Brain Barrier Penetrating Peptides. Pharmaceutics 2021, 13, 1237. https://doi.org/10.3390/pharmaceutics13081237

AMA Style

Kumar V, Patiyal S, Dhall A, Sharma N, Raghava GPS. B3Pred: A Random-Forest-Based Method for Predicting and Designing Blood–Brain Barrier Penetrating Peptides. Pharmaceutics. 2021; 13(8):1237. https://doi.org/10.3390/pharmaceutics13081237

Chicago/Turabian Style

Kumar, Vinod, Sumeet Patiyal, Anjali Dhall, Neelam Sharma, and Gajendra Pal Singh Raghava. 2021. "B3Pred: A Random-Forest-Based Method for Predicting and Designing Blood–Brain Barrier Penetrating Peptides" Pharmaceutics 13, no. 8: 1237. https://doi.org/10.3390/pharmaceutics13081237

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