title: B3Pred: A Random-Forest-Based Method for Predicting and Designing Blood-Brain Barrier Penetrating Peptides
creator: Kumar, Vinod
creator: Patiyal, Sumeet
creator: Dhall, Anjali
creator: Sharma, Neelam
creator: Raghava, Gajendra Pal Singh
subject: QR Microbiology
description: 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
publisher: MDPI
date: 2021-07-11
type: Article
type: PeerReviewed
relation: https://www.mdpi.com/1999-4923/13/8/1237#abstractc
identifier:   Kumar, Vinod and Patiyal, Sumeet and Dhall, Anjali and Sharma, Neelam and Raghava, Gajendra Pal Singh  (2021) B3Pred: A Random-Forest-Based Method for Predicting and Designing Blood-Brain Barrier Penetrating Peptides.  PHARMACEUTICS, 13 (8).       
relation: http://crdd.osdd.net/open/2722/