BBPpredict: A Web Service for Identifying Blood-Brain Barrier Penetrating Peptides

Abstract

Blood-brain barrier (BBB) is a major barrier to drug delivery into the brain in the treatment of central nervous system (CNS) diseases. Blood-brain barrier penetrating peptides (BBPs), a class of peptides that can cross BBB through various mechanisms without damaging BBB, are effective drug candidates for CNS diseases. However, identification of BBPs by experimental methods is time-consuming and laborious. To discover more BBPs as drugs for CNS disease, it is urgent to develop computational methods that can quickly and accurately identify BBPs and non-BBPs. In the present study, we created a training dataset that consists of 326 BBPs derived from previous databases and published manuscripts and 326 non-BBPs collected from UniProt, to construct a BBP predictor based on sequence information. We also constructed an independent testing dataset with 99 BBPs and 99 non-BBPs. Multiple machine learning methods were compared based on the training dataset via a nested cross-validation. The final BBP predictor was constructed based on the training dataset and the results showed that random forest (RF) method outperformed other classification algorithms on the training and independent testing dataset. Compared with previous BBP prediction tools, the RF-based predictor, named BBPpredict, performs considerably better than state-of-the-art BBP predictors. BBPpredict is expected to contribute to the discovery of novel BBPs, or at least can be a useful complement to the existing methods in this area. BBPpredict is freely available at http://i.uestc.edu.cn/BBPpredict/cgi-bin/BBPpredict.pl.

Molecular determinants for brain targeting by peptides: a meta-analysis approach with experimental validation

“…These BBB-crossing peptides do not simply engage in cell membrane interaction, via AMT or RMT, but can achieve transcytosis through other distinct pathways, such as paracellular, direct diffusion or carrier-mediated transport [ 4 , 13 , 28 – 31 ]. However, as the main physicochemical features that allow BBB transcytosis have not been investigated in sufficient detail, researchers may rely excessively—and controversially—on analogy in assuming that CPPs are likewise able to traverse cell barriers [ 4 , 32 , 33 ].…”

BrainPepPass: A Framework Based on Supervised Dimensionality Reduction for Predicting Blood-Brain Barrier-Penetrating Peptides

“…It is noteworthy to compare the performance of the BrainPepPass with previously developed techniques for predicting B3PPs. While some ML-based tools, such as BBPpred, 67 B3Pred, 68 BBPpredict, 36 and SCMB3PP, 69 have been developed to predict the BBB permeability of peptides using ML algorithms trained with properties extracted from the primary structure of natural peptides encoded in FASTA format, the proposed ML-based framework presented herein employs a distinct approach by incorporating the 3D structure of these molecules encoded in MOL format. Additionally, most peptides used for training and testing BrainPepPass contain chemical modifications, which further distinguish our tool from those that focus on natural peptides.…”

“…Owing to expensive, time-consuming, and labor-intensive experimental methods, there is an imminent need for efficient in silico methods to estimate the BBB permeability of peptides. Several computational methods for estimating the BBB permeability of small molecules (excluding peptides) have already been developed. − However, for estimating the BBB permeability of peptides, in silico methods have only been sparsely investigated . Dai et al presented a sequence-based prediction approach to identify whether a peptide can penetrate the BBB.…”

Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning

“…162 Alternative methods rely on generating comprehensive training datasets comprising sequences of blood–brain barrier penetrating linear peptides (BBPs) sourced from established databases and scientific literature, alongside non-BBPs peptides from UniProt to predict and explore novel BBPs with improved properties. 163 AbDiffuser introduced a diffusion model tailored for the generation of three-dimensional antibody structures and corresponding sequences for biotechnological applications. 164 Large protein families can be reliably mapped to a sequence ordinate using sequence alignment.…”