B3Pdb: an archive of blood–brain barrier-penetrating peptides

Kumar, Vinod; Patiyal, Sumeet; Kumar, Rajesh; Sahai, Sukriti; Kaur, Dilraj; Lathwal, Anjali; Raghava, Gajendra P. S.

doi:10.1007/s00429-021-02341-5

Cited by 22 publications

(15 citation statements)

References 36 publications

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“…In this work, we selected experimentally validated BBPs as candidate positive samples that were collected from Brainpeps ( Van Dorpe et al, 2012 ), B3Pdb( Kumar et al, 2021b ), public datasets of BBPpred ( Dai et al, 2021 ) and B3Pred ( Kumar et al, 2021a ), and other published literatures from PubMed with query “(((Brain [Title/Abstract]) OR (blood–brain barrier [Title/Abstract])) AND peptide [Title/Abstract]) AND (transport [Title/Abstract] OR transfer [Title/Abstract] OR permeation [Title/Abstract] OR permeability [Title/Abstract])”, covering the period 2011–2021. BBPs were then preprocessed as follows: 1) the repetitive sequences were eliminated; 2) peptide sequences with ambiguous residues (“X”, “B” and “Z”, etc.)…”

Section: Methodsmentioning

confidence: 99%

“…There have been two BBP databases published to date, Brainpeps ( Van Dorpe et al, 2012 ) and B3Pdb ( Kumar et al, 2021b ), since BBPs became candidates for developing peptide agents for managing CNS disorders. These studies are undoubtedly a strong boost to the development of medications for CNS diseases.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we collected more BBPs from existing databases ( Van Dorpe et al, 2012 ; Kumar et al, 2021b ) and published literatures to construct a new BBP predictor named BBPpredict, which is an online web service and freely available at http://i.uestc.edu.cn/BBPpredict/cgi-bin/BBPpredict.pl . By comparing the results of the nested five-fold cross-validation and independent testing dataset of various machine learning predictors, the RF-based model showed the best prediction performance.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Zhang

et al. 2022

Front. Genet.

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chen

Zhang

et al. 2022

Front. Genet.

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“…The resulting RVG-9 R CPP was shown to bind and deliver an siRNA cargo to Neuro 2a cells in vitro . Moreover intravenously administered RVG-9 R-siRNA showed enhanced delivery of the compound across the BBB in mice and silencing of superoxide dismutase 1 (SOD1) in the mouse brain [ 63 , 64 ].…”

Section: Delivery Of Rna Drugs To the Cnsmentioning

confidence: 99%

Clinical advances of RNA therapeutics for treatment of neurological and neuromuscular diseases

Holm

Hansen²,

Klitgaard³

et al. 2022

RNA Biology

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RNA therapeutics comprise a diverse group of oligonucleotide-based drugs such as antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), and short hairpin RNAs (shRNAs) that can be designed to selectively interact with drug targets currently undruggable with small molecule-based drugs or monoclonal antibodies. Furthermore, RNA-based therapeutics have the potential to modulate entire disease pathways, and thereby represent a new modality with unprecedented potential for generating disease-modifying drugs for a wide variety of human diseases, including central nervous system (CNS) disorders. Here, we describe different strategies for delivering RNA drugs to the CNS and review recent advances in clinical development of ASO drugs and siRNA-based therapeutics for the treatment of neurological diseases and neuromuscular disorders. Abbreviations 2’-MOE: 2’- O -(2-methoxyethyl); 2’- O -Me: 2’- O -methyl; 2’-F: 2’-fluoro; AD: Alzheimer's disease; ALS: Amyotrophic lateral sclerosis; ALSFRS-R: Revised Amyotrophic Lateral Sclerosis Functional Rating Scale; ARC: Antibody siRNA Conjugate; AS: Angelman Syndrome; ASGRP: Asialoglycoprotein receptor; ASO: Antisense oligonucleotide; AxD: Alexander Disease; BBB: Blood brain barrier; Bp: Basepair; CNM: Centronuclear myopathies; CNS: Central Nervous System; CPP: Cell-penetrating Peptide; CSF: Cerebrospinal fluid; DMD: Duchenne muscular dystrophy; DNA: Deoxyribonucleic acid; FAP: Familial amyloid polyneuropathy; FALS: Familial amyotrophic lateral sclerosis; FDA: The United States Food and Drug Administration; GalNAc: N-acetylgalactosamine; GoF: Gain of function; hATTR: Hereditary transthyretin amyloidosis; HD: Huntington's disease; HRQOL: health-related quality of life; ICV: Intracerebroventricular; IT: Intrathecal; LNA: Locked nucleic acid; LoF: Loss of function; mRNA: Messenger RNA; MS: Multiple Sclerosis; MSA: Multiple System Atrophy; NBE: New Biological Entity; NCE: New Chemical Entity; NHP: Nonhuman primate; nt: Nucleotide; PD: Parkinson's disease; PNP: Polyneuropathy; PNS: Peripheral nervous system; PS: Phosphorothioate; RISC: RNA-Induced Silencing Complex; RNA: Ribonucleic acid; RNAi: RNA interference; s.c.: Subcutaneous; siRNA: Small interfering RNA; SMA: Spinal muscular atrophy; SMN: Survival motor neuron; TTR: Transthyretin

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“…In this study, we collected 465 blood-brain barrier penetrating peptides (B3PPs) from the B3Pdb database (https://webs.iiitd.edu.in/raghava/b3pdb/, accessed on 22 July 2020) [50]. We considered B3PPs having a length between 6 and 30 amino acid (AA) residues, inclusive.…”

Section: Dataset Collectionmentioning

confidence: 99%

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

et al. 2021

Self Cite

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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.

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B3Pdb: an archive of blood–brain barrier-penetrating peptides

Cited by 22 publications

References 36 publications

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

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

Clinical advances of RNA therapeutics for treatment of neurological and neuromuscular diseases

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

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