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

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

Blood-brain barrier (BBB) peptide-shuttles (BBBpS) are able to translocate the BBB and reach the brain. Despite the importance of brain targeting in pharmacology, BBBpS are poorly characterized. Currently, their development relies on the empiric assumption that cell-penetrating peptides (CPPs), with proven ability to traverse lipid membranes, will likewise behave as a BBBpS. The relationship between CPPs/BBBpS remains elusive and, to the best of our knowledge, has not hitherto been subject to thorough experimental scrutiny. In this work, we have identified/quantified the main physicochemical properties of BBBpS and then searched for CPPs with these properties, hence potential BBBpS. The specific features found for BBBpS are: (i) small size, (ii) none or few aromatic residues, (iii) hydrophobic, and (iv) slight cationic nature. Then, we selected the 10 scoring best in an ordinary least squares analysis, and tested them in vitro and in vivo. Overall, we identified the molecular determinants for brain targeting by peptides, devised a methodology that can be used to assist in the design of peptides with potential brain penetration from amino acid residue sequences, and found four new BBBpS within the CPP library.

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

Peptides that pass through the blood-brain barrier (BBB) not only are implicated in brain-related pathologies but also are promising therapeutic tools for treating brain diseases, e.g., as shuttles carrying active medicines across the BBB. Computational prediction of BBB-penetrating peptides (B3PPs) has emerged as an interesting approach because of its ability to screen large peptide libraries in a cost-effective manner. In this study, we present BrainPepPass, a machine learning (ML) framework that utilizes supervised manifold dimensionality reduction and extreme gradient boosting (XGB) algorithms to predict natural and chemically modified B3PPs. The results indicate that the proposed tool outperforms other classifiers, with average accuracies exceeding 94% and 98% in 10-fold cross-validation and leave-one-out cross-validation (LOOCV), respectively. In addition, accuracy values ranging from 45% to 97.05% were achieved in the independent tests. The BrainPepPass tool is available in a public repository for academic use (https://github.com/ewerton-cristhian/BrainPepPass).

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

Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties.

BBB-PEP-prediction: improved computational model for identification of blood-brain barrier peptides using blending position relative composition specific features and ensemble modeling

BBPs have the potential to facilitate the delivery of drugs to the brain, opening up new avenues for the development of treatments targeting diseases of the central nervous system (CNS). The obstacle faced in central nervous system disorders stems from the formidable task of traversing the blood-brain barrier (BBB) for pharmaceutical agents. Nearly 98% of small molecule-based drugs and nearly 100% of large molecule-based drugs encounter difficulties in successfully penetrating the BBB. This importance leads to identification of these peptides, can help in healthcare systems. In this study, we proposed an improved intelligent computational model BBB-PEP-Prediction for identification of BBB peptides. Position and statistical moments based features have been computed for acquired benchmark dataset. Four types of ensembles such as bagging, boosting, stacking and blending have been utilized in the methodology section. Bagging employed Random Forest (RF) and Extra Trees (ET), Boosting utilizes XGBoost (XGB) and Light Gradient Boosting Machine (LGBM). Stacking uses ET and XGB as base learners, blending exploited LGBM and RF as base learners, while Logistic Regression (LR) has been applied as Meta learner for stacking and blending. Three classifiers such as LGBM, XGB and ET have been optimized by using Randomized search CV. Four types of testing such as self-consistency, independent set, cross-validation with 5 and 10 folds and jackknife test have been employed. Evaluation metrics such as Accuracy (ACC), Specificity (SPE), Sensitivity (SEN), Mathew's correlation coefficient (MCC) have been utilized. The stacking of classifiers has shown best results in almost each testing. The stacking results for independent set testing exhibits accuracy, specificity, sensitivity and MCC score of 0.824, 0.911, 0.831 and 0.663 respectively. The proposed model BBB-PEP-Prediction shown superlative performance as compared to previous benchmark studies. The proposed system helps in future research and research community for in-silico identification of BBB peptides.

Aggregation Limiting Cell-Penetrating Peptides Derived from Protein Signal Sequences

Alzheimer's disease (AD) is the most common neurodegenerative disease (ND) and the leading cause of dementia. It is characterized by non-linear, genetic-driven pathophysiological dynamics with high heterogeneity in the biological alterations and the causes of the disease. One of the hallmarks of the AD is the progression of plaques of aggregated amyloid-β (Aβ) or neurofibrillary tangles of Tau. Currently there is no efficient treatment for the AD. Nevertheless, several breakthroughs in revealing the mechanisms behind progression of the AD have led to the discovery of possible therapeutic targets. Some of these include the reduction in inflammation in the brain, and, although highly debated, limiting of the aggregation of the Aβ. In this work we show that similarly to the Neural cell adhesion molecule 1 (NCAM1) signal sequence, other Aβ interacting protein sequences, especially derived from Transthyretin, can be used successfully to reduce or target the amyloid aggregation/aggregates in vitro. The modified signal peptides with cell-penetrating properties reduce the Aβ aggregation and are predicted to have anti-inflammatory properties. Furthermore, we show that by expressing the Aβ-EGFP fusion protein, we can efficiently assess the potential for reduction in aggregation, and the CPP properties of peptides in mammalian cells.

Crotalphine Modulates Microglia M1/M2 Phenotypes and Induces Spinal Analgesia Mediated by Opioid-Cannabinoid Systems

Pain is a worldwide public health problem and its treatment is still a challenge since clinically available drugs do not completely reverse chronic painful states or induce undesirable effects. Crotalphine is a 14 amino acids synthetic peptide that induces a potent and long-lasting analgesic effect on acute and chronic pain models, peripherally mediated by the endogenous release of dynorphin A and the desensitization of the transient receptor potential ankyrin 1 (TRPA1) receptor. However, the effects of crotalphine on the central nervous system (CNS) and the signaling pathway have not been investigated. Thus, the central effect of crotalphine was evaluated on the partial sciatic nerve ligation (PSNL)-induced chronic neuropathic pain model. Crotalphine (100 µg/kg, p.o.)-induced analgesia on the 14th day after surgery lasting up to 24 h after administration. This effect was prevented by intrathecal administration of CB1 (AM251) or CB2 (AM630) cannabinoid receptor antagonists. Besides that, crotalphine-induced analgesia was reversed by CTOP, nor-BNI, and naltrindole, antagonists of mu, kappa, and delta-opioid receptors, respectively, and also by the specific antibodies for β-endorphin, dynorphin-A, and met-enkephalin. Likewise, the analgesic effect of crotalphine was blocked by the intrathecal administration of minocycline, an inhibitor of microglial activation and proliferation. Additionally, crotalphine decreased the PSNL-induced IL-6 release in the spinal cord. Importantly, in vitro, crotalphine inhibited LPS-induced CD86 expression and upregulated CD206 expression in BV-2 cells, demonstrating a polarization of microglial cells towards the M2 phenotype. These results demonstrated that crotalphine, besides activating opioid and cannabinoid analgesic systems, impairs central neuroinflammation, confirming the neuromodulatory mechanism involved in the crotalphine analgesic effect.

Peptide Shuttles for Blood–Brain Barrier Drug Delivery

The blood–brain barrier (BBB) limits the delivery of therapeutics to the brain but also represents the main gate for nutrient entrance. Targeting the natural transport mechanisms of the BBB offers an attractive route for brain drug delivery. Peptide shuttles are able to use these mechanisms to increase the transport of compounds that cannot cross the BBB unaided. As peptides are a group of biomolecules with unique physicochemical and structural properties, the field of peptide shuttles has substantially evolved in the last few years. In this review, we analyze the main classifications of BBB–peptide shuttles and the leading sources used to discover them.

AntiDMPpred: a web service for identifying anti-diabetic peptides

Diabetes mellitus (DM) is a chronic metabolic disease that has been a major threat to human health globally, causing great economic and social adversities. The oral administration of anti-diabetic peptide drugs has become a novel route for diabetes therapy. Numerous bioactive peptides have demonstrated potential anti-diabetic properties and are promising as alternative treatment measures to prevent and manage diabetes. The computational prediction of anti-diabetic peptides can help promote peptide-based drug discovery in the process of searching newly effective therapeutic peptide agents for diabetes treatment. Here, we resorted to random forest to develop a computational model, named AntiDMPpred, for predicting anti-diabetic peptides. A benchmark dataset with 236 anti-diabetic and 236 non-anti-diabetic peptides was first constructed. Four types of sequence-derived descriptors were used to represent the peptide sequences. We then combined four machine learning methods and six feature scoring methods to select the non-redundant features, which were fed into diverse machine learning classifiers to train the models. Experimental results show that AntiDMPpred reached an accuracy of 77.12% and area under the receiver operating curve (AUCROC) of 0.8193 in the nested five-fold cross-validation, yielding a satisfactory performance and surpassing other classifiers implemented in the study. The web service is freely accessible at http://i.uestc.edu.cn/AntiDMPpred/cgi-bin/AntiDMPpred.pl. We hope AntiDMPpred could improve the discovery of anti-diabetic bioactive peptides.