BBPpredict: A Web Service for Identifying Blood-Brain Barrier Penetrating Peptides - Chen, Xue; Zhang, Qianyue; Li, Bowen; Lu, Chunying; Yang, Shanshan; Long, Jinjin; He, Bifang; Chen, Heng; Huang, Jian (Frontiers in Genetics, 2022)
Blood-brain barrier (BBB) is a major barrier to drug delivery to the brain in the treatment of central nervous system (CNS) diseases. Blood-brain barrier penetrating peptides (BBPs) are a class of peptides that can cross the BBB through various mechanisms without damaging the BBB.  However, the experimental identification of BBPs is time-consuming and laborious. To discover and design more BBPs as CNS drugs, it is urgent to develop computational methods that can quickly and accurately identify BBPs and non-BBPs. In the present study, 326 BBPs, which were derived from previous databases and published manuscripts, and 326 non-BBPs were utilized as the training dataset to construct a BBPs predictor based on sequence information. We also constructed an independent testing dataset with 105 BBPs and 105 non-BBPs. Multiple machine learning methods were compared based on the training dataset via a nested cross-validation. Then the final BBPs predictor was constructed based on the whole training dataset and compared with previous BBPs predictor tools. The results showed that random forest (RF) method outperformed other methods on the training and independent testing dataset. The RF-based predictor, named BBPpredict, performed considerably better than state-of-the-art BBPs predictors. Our proposed predictor is expected to contribute to the discovery and design of more novel BBPs, or at least can play a complementary role to the existing methods in this area. BBPpredict is freely available at http://i.uestc.edu.cn/BBPpredict/cgi-bin/BBPpredict.pl.