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

• Main Authors: Vinod Kumar (Author), Sumeet Patiyal (Author), Anjali Dhall (Author), Neelam Sharma (Author), Gajendra Pal Singh Raghava (Author)
• Format: Book
• Published: MDPI AG, 2021-08-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.3390/pharmaceutics13081237; 1999-4923
• Keywords: blood-brain barrier; penetrating peptides; machine learning techniques; drug delivery; prediction server; Pharmacy and materia medica; RS1-441
• Source: Pharmaceutics, Vol 13, Iss 8, p 1237 (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.