Blood-brain barrier peptides (BBPs) can cross the blood-brain barrier based on various mechanisms and have a variety of biomedical applications. As experimental methods for the prediction of BBPs are laborious and expensive, the development of computational methods are necessary for identifying BBPs on a large scale.
BBPpred is a web application used to identify BBPs. More specifically, we have developed a logistic regression classifier based on feature representation learning scheme that learns the most discriminative features from existing feature descriptors in a supervised way. It shows superior results in independent test set.
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*The sequences must be in FASTA format.The sequence length should be between 5 and 150.

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Ruyu Dai, Wei Zhang, Wending Tang, Evelien Wynendaele, Qizhi Zhu, Yannan Bin, Bart De Spiegeleer, and Junfeng Xia*, BBPpred: Sequence-Based Prediction of Blood-Brain Barrier Peptides with Feature Representation Learning and Logistic Regression[J]. Journal of Chemical Information and Modeling, 2021, 61(1): 525-534.