Brainpeps: the blood-brain barrier peptide database

Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur

BACKGROUND

The blood-brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain. In response to this challenge, many researchers have devoted themselves to developing drug delivery systems capable of breaching the blood-brain barrier. Among these, blood-brain barrier penetrating peptides have emerged as promising candidates. These peptides had the advantages of high biosafety, ease of synthesis, and exceptional penetration efficiency, making them an effective drug delivery solution. While previous studies have developed a few prediction models for blood-brain barrier penetrating peptides, their performance has often been hampered by issue of limited positive data.

RESULTS

In this study, we present Augur, a novel prediction model using borderline-SMOTE-based data augmentation and machine learning. we extract highly interpretable physicochemical properties of blood-brain barrier penetrating peptides while solving the issues of small sample size and imbalance of positive and negative samples. Experimental results demonstrate the superior prediction performance of Augur with an AUC value of 0.932 on the training set and 0.931 on the independent test set.

CONCLUSIONS

This newly developed Augur model demonstrates superior performance in predicting blood-brain barrier penetrating peptides, offering valuable insights for drug development targeting neurological disorders. This breakthrough may enhance the efficiency of peptide-based drug discovery and pave the way for innovative treatment strategies for central nervous system diseases.

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).

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

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.

A prediction model for blood-brain barrier penetrating peptides based on masked peptide transformers with dynamic routing

Blood-brain barrier penetrating peptides (BBBPs) are short peptide sequences that possess the ability to traverse the selective blood-brain interface, making them valuable drug candidates or carriers for various payloads. However, the in vivo or in vitro validation of BBBPs is resource-intensive and time-consuming, driving the need for accurate in silico prediction methods. Unfortunately, the scarcity of experimentally validated BBBPs hinders the efficacy of current machine-learning approaches in generating reliable predictions. In this paper, we present DeepB3P3, a novel framework for BBBPs prediction. Our contribution encompasses four key aspects. Firstly, we propose a novel deep learning model consisting of a transformer encoder layer, a convolutional network backbone, and a capsule network classification head. This integrated architecture effectively learns representative features from peptide sequences. Secondly, we introduce masked peptides as a powerful data augmentation technique to compensate for small training set sizes in BBBP prediction. Thirdly, we develop a novel threshold-tuning method to handle imbalanced data by approximating the optimal decision threshold using the training set. Lastly, DeepB3P3 provides an accurate estimation of the uncertainty level associated with each prediction. Through extensive experiments, we demonstrate that DeepB3P3 achieves state-of-the-art accuracy of up to 98.31% on a benchmarking dataset, solidifying its potential as a promising computational tool for the prediction and discovery of BBBPs.

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.

Rational Discovery of Antimicrobial Peptides by Means of Artificial Intelligence

Antibiotic resistance is a worldwide public health problem due to the costs and mortality rates it generates. However, the large pharmaceutical industries have stopped searching for new antibiotics because of their low profitability, given the rapid replacement rates imposed by the increasingly observed resistance acquired by microorganisms. Alternatively, antimicrobial peptides (AMPs) have emerged as potent molecules with a much lower rate of resistance generation. The discovery of these peptides is carried out through extensive in vitro screenings of either rational or non-rational libraries. These processes are tedious and expensive and generate only a few AMP candidates, most of which fail to show the required activity and physicochemical properties for practical applications. This work proposes implementing an artificial intelligence algorithm to reduce the required experimentation and increase the efficiency of high-activity AMP discovery. Our deep learning (DL) model, called AMPs-Net, outperforms the state-of-the-art method by 8.8% in average precision. Furthermore, it is highly accurate to predict the antibacterial and antiviral capacity of a large number of AMPs. Our search led to identifying two unreported antimicrobial motifs and two novel antimicrobial peptides related to them. Moreover, by coupling DL with molecular dynamics (MD) simulations, we were able to find a multifunctional peptide with promising therapeutic effects. Our work validates our previously proposed pipeline for a more efficient rational discovery of novel AMPs.

Brain physiome: A concept bridging in vitro 3D brain models and in silico models for predicting drug toxicity in the brain

In the last few decades, adverse reactions to pharmaceuticals have been evaluated using 2D in vitro models and animal models. However, with increasing computational power, and as the key drivers of cellular behavior have been identified, in silico models have emerged. These models are time-efficient and cost-effective, but the prediction of adverse reactions to unknown drugs using these models requires relevant experimental input. Accordingly, the physiome concept has emerged to bridge experimental datasets with in silico models. The brain physiome describes the systemic interactions of its components, which are organized into a multilevel hierarchy. Because of the limitations in obtaining experimental data corresponding to each physiome component from 2D in vitro models and animal models, 3D in vitro brain models, including brain organoids and brain-on-a-chip, have been developed. In this review, we present the concept of the brain physiome and its hierarchical organization, including cell- and tissue-level organizations. We also summarize recently developed 3D in vitro brain models and link them with the elements of the brain physiome as a guideline for dataset collection. The connection between in vitro 3D brain models and in silico modeling will lead to the establishment of cost-effective and time-efficient in silico models for the prediction of the safety of unknown drugs.

Mass Spectrometry Approaches Empowering Neuropeptide Discovery and Therapeutics

The discovery of insulin in the early 1900s ushered in the era of research related to peptides acting as hormones and neuromodulators, among other regulatory roles. These essential gene products are found in all organisms, from the most primitive to the most evolved, and carry important biologic information that coordinates complex physiology and behavior; their misregulation has been implicated in a variety of diseases. The evolutionary origins of at least 30 neuropeptide signaling systems have been traced to the common ancestor of protostomes and deuterostomes. With the use of relevant animal models and modern technologies, we can gain mechanistic insight into orthologous and paralogous endogenous peptides and translate that knowledge into medically relevant insights and new treatments. Groundbreaking advances in medicine and basic science influence how signaling peptides are defined today. The precise mechanistic pathways for over 100 endogenous peptides in mammals are now known and have laid the foundation for multiple drug development pipelines. Peptide biologics have become valuable drugs due to their unique specificity and biologic activity, lack of toxic metabolites, and minimal undesirable interactions. This review outlines modern technologies that enable neuropeptide discovery and characterization, and highlights lessons from nature made possible by neuropeptide research in relevant animal models that is being adopted by the pharmaceutical industry. We conclude with a brief overview of approaches/strategies for effective development of peptides as drugs. SIGNIFICANCE STATEMENT: Neuropeptides, an important class of cell-cell signaling molecules, are involved in maintaining a range of physiological functions. Since the discovery of insulin's activity, over 100 bioactive peptides and peptide analogs have been used as therapeutics. Because these are complex molecules not easily predicted from a genome and their activity can change with subtle chemical modifications, mass spectrometry (MS) has significantly empowered peptide discovery and characterization. This review highlights contributions of MS-based research towards the development of therapeutic peptides.

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

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.

Biological Membrane-Penetrating Peptides: Computational Prediction and Applications

Peptides comprise a versatile class of biomolecules that present a unique chemical space with diverse physicochemical and structural properties. Some classes of peptides are able to naturally cross the biological membranes, such as cell membrane and blood-brain barrier (BBB). Cell-penetrating peptides (CPPs) and blood-brain barrier-penetrating peptides (B3PPs) have been explored by the biotechnological and pharmaceutical industries to develop new therapeutic molecules and carrier systems. The computational prediction of peptides’ penetration into biological membranes has been emerged as an interesting strategy due to their high throughput and low-cost screening of large chemical libraries. Structure- and sequence-based information of peptides, as well as atomistic biophysical models, have been explored in computer-assisted discovery strategies to classify and identify new structures with pharmacokinetic properties related to the translocation through biomembranes. Computational strategies to predict the permeability into biomembranes include cheminformatic filters, molecular dynamics simulations, artificial intelligence algorithms, and statistical models, and the choice of the most adequate method depends on the purposes of the computational investigation. Here, we exhibit and discuss some principles and applications of these computational methods widely used to predict the permeability of peptides into biomembranes, exhibiting some of their pharmaceutical and biotechnological applications.

Antioxidant and Antimicrobial Peptides Derived from Food Proteins

Recently, the demand for food proteins in the market has increased due to a rise in degenerative illnesses that are associated with the excessive production of free radicals and the unwanted side effects of various drugs, for which researchers have suggested diets rich in bioactive compounds. Some of the functional compounds present in foods are antioxidant and antimicrobial peptides, which are used to produce foods that promote health and to reduce the consumption of antibiotics. These peptides have been obtained from various sources of proteins, such as foods and agri-food by-products, via enzymatic hydrolysis and microbial fermentation. Peptides with antioxidant properties exert effective metal ion (Fe2+/Cu2+) chelating activity and lipid peroxidation inhibition, which may lead to notably beneficial effects in promoting human health and food processing. Antimicrobial peptides are small oligo-peptides generally containing from 10 to 100 amino acids, with a net positive charge and an amphipathic structure; they are the most important components of the antibacterial defense of organisms at almost all levels of life-bacteria, fungi, plants, amphibians, insects, birds and mammals-and have been suggested as natural compounds that neutralize the toxicity of reactive oxygen species generated by antibiotics and the stress generated by various exogenous sources. This review discusses what antioxidant and antimicrobial peptides are, their source, production, some bioinformatics tools used for their obtainment, emerging technologies, and health benefits.

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

The blood-brain barrier poses major hurdles in the treatment of brain-related ailments. Over the past decade, interest in peptides-based therapeutics has thrived a lot because of their higher benefit to risk ratio. However, a complete knowledgebase providing a well-annotated picture of the peptide as a therapeutic molecule to cure brain-related ailments is lacking. We have built up a knowledgebase B3Pdb on blood-brain barrier (BBB)-penetrating peptides in the present study. The B3Pdb holds clinically relevant experimental information on 1225 BBB-penetrating peptides, including mode of delivery, animal model, in vitro/in vivo experiments, chemical modifications, length. Hoping that drug delivery systems can improve central nervous system disorder-related therapeutics. In this regard, B3Pdb is an important resource to support the rational design of therapeutics peptides for CNS-related disorders. The complete ready-to-use and updated database with a user-friendly web interface is available to the scientific community at https://webs.iiitd.edu.in/raghava/b3pdb/ .

BBPpred: Sequence-Based Prediction of Blood-Brain Barrier Peptides with Feature Representation Learning and Logistic Regression

Blood-brain barrier peptides (BBPs) have a large range of biomedical applications since they can cross the blood-brain barrier based on different mechanisms. As experimental methods for the identification of BBPs are laborious and expensive, computational approaches are necessary to be developed for predicting BBPs. In this work, we describe a computational method, BBPpred (blood-brain barrier peptides prediction), that can efficiently identify BBPs using logistic regression. We investigate a wide variety of features from amino acid sequence information, and then a feature learning method is adopted to represent the informative features. To improve the prediction performance, seven informative features are selected for classification by eliminating redundant and irrelevant features. In addition, we specifically create two benchmark data sets (training and independent test), which contain a total of 119 BBPs from public databases and the literature. On the training data set, BBPpred shows promising performances with an AUC score of 0.8764 and an AUPR score of 0.8757 using the 10-fold cross-validation. We also test our new method on the independent test data set and obtain a favorable performance. We envision that BBPpred will be a useful tool for identifying, annotating, and characterizing BBPs. BBPpred is freely available at http://BBPpred.xialab.info.

BIOPEP-UWM Database of Bioactive Peptides: Current Opportunities

The BIOPEP-UWM™ database of bioactive peptides (formerly BIOPEP) has recently become a popular tool in the research on bioactive peptides, especially on these derived from foods and being constituents of diets that prevent development of chronic diseases. The database is continuously updated and modified. The addition of new peptides and the introduction of new information about the existing ones (e.g., chemical codes and references to other databases) is in progress. New opportunities include the possibility of annotating peptides containing D-enantiomers of amino acids, batch processing option, converting amino acid sequences into SMILES code, new quantitative parameters characterizing the presence of bioactive fragments in protein sequences, and finding proteinases that release particular peptides.

In Vitro Potency and Preclinical Pharmacokinetic Comparison of All-D-Enantiomeric Peptides Developed for the Treatment of Alzheimer's Disease

Diffusible amyloid-β (Aβ) oligomers are currently presumed to be the most cytotoxic Aβ assembly and held responsible to trigger the pathogenesis of Alzheimer’s disease (AD). Thus, Aβ oligomers are a prominent target in AD drug development. Previously, we reported on our solely D-enantiomeric peptide D3 and its derivatives as AD drug candidates. Here, we compare one of the most promising D3 derivatives, ANK6, with its tandem version (tANK6), and its head-to-tail cyclized isoform (cANK6r). In vitro tests investigating the D-peptides’ potencies to inhibit Aβ aggregation, eliminate Aβ oligomers, and reduce Aβ-induced cytotoxicity revealed that all three D-peptides efficiently target Aβ. Subsequent preclinical pharmacokinetic studies of the three all-D-peptides in wildtype mice showed promising blood-brain barrier permeability with cANK6r yielding the highest levels in brain. The peptides’ potencies to lower Aβ toxicity and their remarkable brain/plasma ratios make them promising AD drug candidates.

Prediction of Antimicrobial Potential of a Chemically Modified Peptide From Its Tertiary Structure

Designing novel antimicrobial peptides is a hot area of research in the field of therapeutics especially after the emergence of resistant strains against the conventional antibiotics. In the past number of in silico methods have been developed for predicting the antimicrobial property of the peptide containing natural residues. This study describes models developed for predicting the antimicrobial property of a chemically modified peptide. Our models have been trained, tested and evaluated on a dataset that contains 948 antimicrobial and 931 non-antimicrobial peptides, containing chemically modified and natural residues. Firstly, the tertiary structure of all peptides has been predicted using software PEPstrMOD. Structure analysis indicates that certain type of modifications enhance the antimicrobial property of peptides. Secondly, a wide range of features was computed from the structure of these peptides using software PaDEL. Finally, models were developed for predicting the antimicrobial potential of chemically modified peptides using a wide range of structural features of these peptides. Our best model based on support vector machine achieve maximum MCC of 0.84 with an accuracy of 91.62% on training dataset and MCC of 0.80 with an accuracy of 89.89% on validation dataset. To assist the scientific community, we have developed a web server called "AntiMPmod" which predicts the antimicrobial property of the chemically modified peptide. The web server is present at the following link (http://webs.iiitd.edu.in/raghava/antimpmod/).

In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs

The prolonged conventional approaches of drug screening and vaccine designing prerequisite patience, vigorous effort, outrageous cost as well as additional manpower. Screening and experimentally validating thousands of molecules for a specific therapeutic property never proved to be an easy task. Similarly, traditional way of vaccination includes administration of either whole or attenuated pathogen, which raises toxicity and safety issues. Emergence of sequencing and recombinant DNA technology led to the epitope-based advanced vaccination concept, i.e., small peptides (epitope) can stimulate specific immune response. Advent of bioinformatics proved to be an adjunct in vaccine and drug designing. Genomic study of pathogens aid to identify and analyze the protective epitope. A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades. These tools proved to be a catalyst in drug and vaccine designing. This review solicits therapeutic peptide databases as well as in silico tools developed for designing peptide-based vaccine and drugs.

Comparison of blood-brain barrier penetration efficiencies between linear and cyclic all-d-enantiomeric peptides developed for the treatment of Alzheimer's disease

Alzheimer's disease (AD), until now, is an incurable progressive neurodegenerative disease. To target toxic amyloid β oligomers in AD patients' brains and to convert them into non-toxic aggregation-incompetent species, we designed peptides consisting solely of d-enantiomeric amino acid residues. The original lead compound was named D3 and several D3 derivatives were designed to enhance beneficial properties. Here, we compare four d-peptides concerning their efficiencies to pass the blood-brain barrier (BBB). We demonstrate that the d-peptides' concentrations in murine brain directly correlate with concentrations in cerebrospinal fluid. The cyclic d-enantiomeric peptide cRD2D3 is characterized by the highest efficiency to pass the BBB. For in total three cyclic peptides we show that administration of cyclic peptides resulted in up to tenfold higher peak concentrations in brain as compared to their linear equivalents which have partially been characterized before (Jiang et al., 2015; Leithold et al., 2016a). These results suggest that cyclic peptides pass the murine BBB more efficiently than their linear equivalents. cRD2D3's proteolytic stability, oral bioavailability, long duration of action and its favorable brain/plasma ratio reveal that it may become a suitable drug for long-term AD-treatment from a pharmacokinetic point of view.

Annotation of Peptide Structures Using SMILES and Other Chemical Codes-Practical Solutions

Contemporary peptide science exploits methods and tools of bioinformatics, and cheminformatics. These approaches use different languages to describe peptide structures—amino acid sequences and chemical codes (especially SMILES), respectively. The latter may be applied, e.g., in comparative studies involving structures and properties of peptides and peptidomimetics. Progress in peptide science “in silico” may be achieved via better communication between biologists and chemists, involving the translation of peptide representation from amino acid sequence into SMILES code. Recent recommendations concerning good practice in chemical information include careful verification of data and their annotation. This publication discusses the generation of SMILES representations of peptides using existing software. Construction of peptide structures containing unnatural and modified amino acids (with special attention paid on glycosylated peptides) is also included. Special attention is paid to the detection and correction of typical errors occurring in SMILES representations of peptides and their correction using molecular editors. Brief recommendations for training of staff working on peptide annotations, are discussed as well.

d-Retroenantiomer of Quorum-Sensing Peptide-Modified Polymeric Micelles for Brain Tumor-Targeted Drug Delivery

Compared to that of other tumors, various barriers, such as the blood-brain barrier (BBB), enzymatic barriers, and the blood-brain tumor barrier, severely impede the successful treatment of gliomas. Peptide ligands were frequently used as targeting moieties to mediate brain tumor-targeted drug delivery. ^LWSW (SYPGWSW) is a recently reported quorum-sensing (QS) peptide that is able to efficiently cross the BBB. Even though linear ^LWSW traverses the BBB in vitro, its in vivo targeting ability has been greatly impaired due to proteolysis. Here, we developed a stable peptide, ^DWSW (^DW^DS^DW^DG^DP^DY^DS), using the retro-inverso isomerization technique to achieve an enhanced antiglioma effect. In vitro studies have demonstrated that both the ^LWSW and ^DWSW peptides possessed excellent tumor-homing properties and barrier-penetration abilities, whereas ^DWSW exhibited exceptional stability in serum and maintained its targeting ability after serum preincubation. In vivo, ^DWSW-modified probes and micelles accumulated more efficiently in the glioma region in comparison with ^LWSW-modified probes and micelles because of full resistance to proteolysis in blood circulation. As expected, ^DWSW-modified paclitaxel (PTX)-loaded micelles (^DWSW Micelle/PTX) exhibited the longest median survival time among glioma-bearing nude mice. Our results suggested that the QS peptide appears to be a promising targeting moiety, with potential applications in glioma-targeted drug delivery.

THPdb: Database of FDA-approved peptide and protein therapeutics

THPdb (http://crdd.osdd.net/raghava/thpdb/) is a manually curated repository of Food and Drug Administration (FDA) approved therapeutic peptides and proteins. The information in THPdb has been compiled from 985 research publications, 70 patents and other resources like DrugBank. The current version of the database holds a total of 852 entries, providing comprehensive information on 239 US-FDA approved therapeutic peptides and proteins and their 380 drug variants. The information on each peptide and protein includes their sequences, chemical properties, composition, disease area, mode of activity, physical appearance, category or pharmacological class, pharmacodynamics, route of administration, toxicity, target of activity, etc. In addition, we have annotated the structure of most of the protein and peptides. A number of user-friendly tools have been integrated to facilitate easy browsing and data analysis. To assist scientific community, a web interface and mobile App have also been developed.

Generation of bioactive peptides during food processing

Large amounts of peptides are naturally generated in foods through the proteolysis phenomena taking place during processing. Such proteolysis is carried out either by endogenous enzymes in ripened foods or by the combined action of endogenous and microbial enzymes when fermented. Food proteins can also be isolated and hydrolysed by peptidases to produce hydrolysates. endo-peptidases act first followed by the successive action of exo-peptidases (mainly, tri- and di-peptidylpeptidases, aminopeptidases and carboxypeptidases). The generated peptides may be further hydrolysed through the gastrointestinal digestion resulting in a pool of peptides with different sequences and lengths, some of them with relevant bioactivity. However, these peptides should be absorbed intact through the intestinal barrier and reach the blood stream to exert their physiological action. This manuscript is reporting the enzymatic routes and strategies followed for the generation of bioactive peptides.

PEPlife: A Repository of the Half-life of Peptides

Short half-life is one of the key challenges in the field of therapeutic peptides. Various studies have reported enhancement in the stability of peptides using methods like chemical modifications, D-amino acid substitution, cyclization, replacement of labile aminos acids, etc. In order to study this scattered data, there is a pressing need for a repository dedicated to the half-life of peptides. To fill this lacuna, we have developed PEPlife (http://crdd.osdd.net/raghava/peplife), a manually curated resource of experimentally determined half-life of peptides. PEPlife contains 2229 entries covering 1193 unique peptides. Each entry provides detailed information of the peptide, like its name, sequence, half-life, modifications, the experimental assay for determining half-life, biological nature and activity of the peptide. We also maintain SMILES and structures of peptides. We have incorporated web-based modules to offer user-friendly data searching and browsing in the database. PEPlife integrates numerous tools to perform various types of analysis such as BLAST, Smith-Waterman algorithm, GGSEARCH, Jalview and MUSTANG. PEPlife would augment the understanding of different factors that affect the half-life of peptides like modifications, sequence, length, route of delivery of the peptide, etc. We anticipate that PEPlife will be useful for the researchers working in the area of peptide-based therapeutics.

N-Cadherin is Involved in Neuronal Activity-Dependent Regulation of Myelinating Capacity of Zebrafish Individual Oligodendrocytes In Vivo

Stimulating neuronal activity increases myelin sheath formation by individual oligodendrocytes, but how myelination is regulated by neuronal activity in vivo is still not fully understood. While in vitro studies have revealed the important role of N-cadherin in myelination, our understanding in vivo remains quite limited. To obtain the role of N-cadherin during activity-dependent regulation of myelinating capacity of individual oligodendrocytes, we successfully built an in vivo dynamic imaging model of the Mauthner cell at the subcellular structure level in the zebrafish central nervous system. Enhanced green fluorescent protein (EGFP)-tagged N-cadherin was used to visualize the stable accumulations and mobile transports of N-cadherin by single-cell electroporation at the single-cell level. We found that pentylenetetrazol (PTZ) significantly enhanced the accumulation of N-cadherin in Mauthner axons, a response that was paralleled by enhanced sheath number per oligodendrocytes. By offsetting this phenotype using oligopeptide (AHAVD) which blocks the function of N-cadherin, we showed that PTZ regulates myelination in an N-cadherin-dependent manner. What is more, we further suggested that PTZ influences N-cadherin and myelination via a cAMP pathway. Consequently, our data indicated that N-cadherin is involved in neuronal activity-dependent regulation of myelinating capacity of zebrafish individual oligodendrocytes in vivo.

Quantitative In Vitro and In Vivo Evaluation of Intestinal and Blood-Brain Barrier Transport Kinetics of the Plant N-Alkylamide Pellitorine

Objective. To evaluate the gut mucosa and blood-brain barrier (BBB) pharmacokinetic permeability properties of the plant N-alkylamide pellitorine. Methods. Pure pellitorine and an Anacyclus pyrethrum extract were used to investigate the permeation of pellitorine through (1) a Caco-2 cell monolayer, (2) the rat gut after oral administration, and (3) the BBB in mice after intravenous and intracerebroventricular administration. A validated bioanalytical UPLC-MS(2) method was used to quantify pellitorine. Results. Pellitorine was able to cross the Caco-2 cell monolayer from the apical-to-basolateral and from the basolateral-to-apical side with apparent permeability coefficients between 0.6 · 10(-5) and 4.8 · 10(-5) cm/h and between 0.3 · 10(-5) and 5.8 · 10(-5) cm/h, respectively. In rats, a serum elimination rate constant of 0.3 h(-1) was obtained. Intravenous injection of pellitorine in mice resulted in a rapid and high permeation of pellitorine through the BBB with a unidirectional influx rate constant of 153 μL/(g·min). In particular, 97% of pellitorine reached the brain tissue, while only 3% remained in the brain capillaries. An efflux transfer constant of 0.05 min(-1) was obtained. Conclusion. Pellitorine shows a good gut permeation and rapidly permeates the BBB once in the blood, indicating a possible role in the treatment of central nervous system diseases.

Mucosal and blood-brain barrier transport kinetics of the plant N-alkylamide spilanthol using in vitro and in vivo models

Background

N-alkylamides (NAAs) are a large group of secondary metabolites occurring in more than 25 plant families which are often used in traditional medicine. A prominent active NAA is spilanthol. The general goal was to quantitatively investigate the gut mucosa and blood-brain barrier (BBB) permeability pharmacokinetic properties of spilanthol.

Methods

Spilanthes acmella (L.) L. extracts, as well as purified spilanthol were used to investigate (1) the permeation of spilanthol through a Caco-2 cell monolayer in vitro, (2) the absorption from the intestinal lumen after oral administration to rats, and (3) the permeation through the BBB in mice after intravenous injection. Quantification of spilanthol was performed using a validated bio-analytical UPLC-MS² method.

Results

Spilanthol was able to cross the Caco-2 cell monolayer in vitro from the apical-to-basolateral side and from the basolateral-to-apical side with apparent permeability coefficients P_app between 5.2 · 10⁻⁵ and 10.2 · 10⁻⁵ cm/h. This in vitro permeability was confirmed by the in vivo intestinal absorption in rats after oral administration, where an elimination rate constant k_e of 0.6 h⁻¹ was obtained. Furthermore, once present in the systemic circulation, spilanthol rapidly penetrated the blood-brain barrier: a highly significant influx of spilanthol into the brains was observed with a unidirectional influx rate constant K₁ of 796 μl/(g · min).

Conclusions

Spilanthol shows a high intestinal absorption from the gut into the systemic circulation, as well as a high BBB permeation rate from the blood into the brain.

Electronic supplementary material

The online version of this article (doi:10.1186/s12906-016-1159-0) contains supplementary material, which is available to authorized users.

Topical Delivery of Protein and Peptide Using Novel Cell Penetrating Peptide IMT-P8

Skin, being the largest organ of the body, is an important site for drug administration. However, most of the drugs have poor permeability and thus drug delivery through the skin is very challenging. In this study, we examined the transdermal delivery capability of IMT-P8, a novel cell-penetrating peptide. We generated IMT-P8-GFP and IMT-P8-KLA fusion constructs and evaluated their internalization into mouse skin after topical application. Our results demonstrate that IMT-P8 is capable of transporting green fluorescent protein (GFP) and proapoptotic peptide, KLA into the skin and also in different cell lines. Interestingly, uptake of IMT-P8-GFP was considerably higher than TAT-GFP in HeLa cells. After internalization, IMT-P8-KLA got localized to the mitochondria and caused significant cell death in HeLa cells signifying an intact biological activity. Further in vivo skin penetration experiments revealed that after topical application, IMT-P8 penetrated the stratum corneum, entered into the viable epidermis and accumulated inside the hair follicles. In addition, both IMT-P8-KLA and IMT-P8-GFP internalized into the hair follicles and dermal tissue of the skin following topical application. These results suggested that IMT-P8 could be a potential candidate to be used as a topical delivery vehicle for various cosmetic and skin disease applications.

Blood-brain barrier transport kinetics of the neuromedin peptides NMU, NMN, NMB and NT

The neuromedin peptides are peripherally and centrally produced, but until now, it is generally believed that they only function as locally acting compounds without any quantitative knowledge about their blood-brain barrier (BBB) passage. Here, we characterize the transport kinetics of four neuromedins (NMU, NMN, NMB and NT) across the BBB, as well as their metabolization profile, and evaluate if they can act as endocrine hormones. Using the in vivo mouse model, multiple time regression (MTR), capillary depletion (CD) and brain efflux studies were performed. Data was fitted using linear (NMU, NT and NMB) or biphasic modeling (NMU and NMN). Three of the four investigated peptides, i.e. NMU, NT and NMN, showed a significant influx into the brain with unidirectional influx rate constants of 1.31 and 0.75 μL/(g × min) for NMU and NT respectively and initial influx constants (K1) of 72.14 and 7.55 μL/(g × min) and net influx constants (K) of 1.28 and 1.36 × 10(-16) μL/(g×min) for NMU and NMN respectively. The influx of NMB was negligible. Only NMN and NT showed a significant efflux out of the brain with an efflux constant (kout) of 0.042 min(-1) and 0.053 min(-1) respectively. Our results indicate that locally produced neuromedin peptides and/or fragments can be transported through the whole body, including passing the BBB, and taken up by different organs/tissues, supporting the idea that the neuromedins could have a much bigger role in the regulation of biological processes than currently assumed.

SATPdb: a database of structurally annotated therapeutic peptides

SATPdb (http://crdd.osdd.net/raghava/satpdb/) is a database of structurally annotated therapeutic peptides, curated from 22 public domain peptide databases/datasets including 9 of our own. The current version holds 19192 unique experimentally validated therapeutic peptide sequences having length between 2 and 50 amino acids. It covers peptides having natural, non-natural and modified residues. These peptides were systematically grouped into 10 categories based on their major function or therapeutic property like 1099 anticancer, 10585 antimicrobial, 1642 drug delivery and 1698 antihypertensive peptides. We assigned or annotated structure of these therapeutic peptides using structural databases (Protein Data Bank) and state-of-the-art structure prediction methods like I-TASSER, HHsearch and PEPstrMOD. In addition, SATPdb facilitates users in performing various tasks that include: (i) structure and sequence similarity search, (ii) peptide browsing based on their function and properties, (iii) identification of moonlighting peptides and (iv) searching of peptides having desired structure and therapeutic activities. We hope this database will be useful for researchers working in the field of peptide-based therapeutics.

A tetrapeptide from maize protects a transgenic Caenorhabditis elegans Aβ1-42model from Aβ-induced toxicity

A food-derived bioactive peptide that works as an important antioxidantin vivocould be used to remedy oxidative stress-related diseases.

PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues

Background

In the past, many methods have been developed for peptide tertiary structure prediction but they are limited to peptides having natural amino acids. This study describes a method PEPstrMOD, which is an updated version of PEPstr, developed specifically for predicting the structure of peptides containing natural and non-natural/modified residues.

Results

PEPstrMOD integrates Forcefield_NCAA and Forcefield_PTM force field libraries to handle 147 non-natural residues and 32 types of post-translational modifications respectively by performing molecular dynamics using AMBER. AMBER was also used to handle other modifications like peptide cyclization, use of D-amino acids and capping of terminal residues. In addition, GROMACS was used to implement 210 non-natural side-chains in peptides using SwissSideChain force field library. We evaluated the performance of PEPstrMOD on three datasets generated from Protein Data Bank; i) ModPep dataset contains 501 non-natural peptides, ii) ModPep16, a subset of ModPep, and iii) CyclicPep contains 34 cyclic peptides. We achieved backbone Root Mean Square Deviation between the actual and predicted structure of peptides in the range of 3.81–4.05 Å.

Conclusions

In summary, the method PEPstrMOD has been developed that predicts the structure of modified peptide from the sequence/structure given as input. We validated the PEPstrMOD application using a dataset of peptides having non-natural/modified residues. PEPstrMOD offers unique advantages that allow the users to predict the structures of peptides having i) natural residues, ii) non-naturally modified residues, iii) terminal modifications, iv) post-translational modifications, v) D-amino acids, and also allows extended simulation of predicted peptides. This will help the researchers to have prior structural information of modified peptides to further design the peptides for desired therapeutic property. PEPstrMOD is freely available at http://osddlinux.osdd.net/raghava/pepstrmod/.

Reviewers

This article was reviewed by Prof Michael Gromiha, Dr. Bojan Zagrovic and Dr. Zoltan Gaspari.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-015-0103-4) contains supplementary material, which is available to authorized users.

Quorum Sensing Peptides Selectively Penetrate the Blood-Brain Barrier

Bacteria communicate with each other by the use of signaling molecules, a process called 'quorum sensing'. One group of quorum sensing molecules includes the oligopeptides, which are mainly produced by Gram-positive bacteria. Recently, these quorum sensing peptides were found to biologically influence mammalian cells, promoting i.a. metastasis of cancer cells. Moreover, it was found that bacteria can influence different central nervous system related disorders as well, e.g. anxiety, depression and autism. Research currently focuses on the role of bacterial metabolites in this bacteria-brain interaction, with the role of the quorum sensing peptides not yet known. Here, three chemically diverse quorum sensing peptides were investigated for their brain influx (multiple time regression technique) and efflux properties in an in vivo mouse model (ICR-CD-1) to determine blood-brain transfer properties: PhrCACET1 demonstrated comparatively a very high initial influx into the mouse brain (Kin = 20.87 μl/(g×min)), while brain penetrabilities of BIP-2 and PhrANTH2 were found to be low (Kin = 2.68 μl/(g×min)) and very low (Kin = 0.18 μl/(g×min)), respectively. All three quorum sensing peptides were metabolically stable in plasma (in vitro) during the experimental time frame and no significant brain efflux was observed. Initial tissue distribution data showed remarkably high liver accumulation of BIP-2 as well. Our results thus support the potential role of some quorum sensing peptides in different neurological disorders, thereby enlarging our knowledge about the microbiome-brain axis.

Rational, computer-enabled peptide drug design: principles, methods, applications and future directions

Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.

S R, Venkatesan S. AntiAngioPred: A Server for Prediction of Anti-Angiogenic Peptides

The process of angiogenesis is a vital step towards the formation of malignant tumors. Anti-angiogenic peptides are therefore promising candidates in the treatment of cancer. In this study, we have collected anti-angiogenic peptides from the literature and analyzed the residue preference in these peptides. Residues like Cys, Pro, Ser, Arg, Trp, Thr and Gly are preferred while Ala, Asp, Ile, Leu, Val and Phe are not preferred in these peptides. There is a positional preference of Ser, Pro, Trp and Cys in the N terminal region and Cys, Gly and Arg in the C terminal region of anti-angiogenic peptides. Motif analysis suggests the motifs "CG-G", "TC", "SC", "SP-S", etc., which are highly prominent in anti-angiogenic peptides. Based on the primary analysis, we developed prediction models using different machine learning based methods. The maximum accuracy and MCC for amino acid composition based model is 80.9% and 0.62 respectively. The performance of the models on independent dataset is also reasonable. Based on the above study, we have developed a user-friendly web server named "AntiAngioPred" for the prediction of anti-angiogenic peptides. AntiAngioPred web server is freely accessible at http://clri.res.in/subramanian/tools/antiangiopred/index.html (mirror site: http://crdd.osdd.net/raghava/antiangiopred/).

Immunogenicity and pharmacokinetics of short, proline-rich antimicrobial peptides

Aim: The potential of proline-rich antimicrobial peptides (PrAMPs) to treat multidrug-resistant Gram-negative pathogens has been intensively investigated. They are efficacious at low doses in infection models and well tolerated in healthy mice at high doses. Methods & results: PrAMPs Onc72 and Api88 were nonimmunogenic in mice unless conjugated to a carrier protein. Monoclonal IgG1/IgG2b antibodies produced by hybridoma cells were mapped to different Onc72 regions and combined in a sandwich-ELISA in a pharmacokinetic study. Onc72 was detected at concentrations up to 32 µg/ml in murine blood after administering 20 mg/kg and reached several organs within 10 min. Conclusion: Both PrAMPs were not immunogenic and Onc72 concentrations in blood were well above the minimal inhibitory concentrations for Enterobacteriaceae further confirming their potential as novel antibiotics.

BBB-targeting, protein-based nanomedicines for drug and nucleic acid delivery to the CNS

The increasing incidence of diseases affecting the central nervous system (CNS) demands the urgent development of efficient drugs. While many of these medicines are already available, the Blood Brain Barrier and to a lesser extent, the Blood Spinal Cord Barrier pose physical and biological limitations to their diffusion to reach target tissues. Therefore, efforts are needed not only to address drug development but specially to design suitable vehicles for delivery into the CNS through systemic administration. In the context of the functional and structural versatility of proteins, recent advances in their biological fabrication and a better comprehension of the physiology of the CNS offer a plethora of opportunities for the construction and tailoring of plain nanoconjugates and of more complex nanosized vehicles able to cross these barriers. We revise here how the engineering of functional proteins offers drug delivery tools for specific CNS diseases and more transversally, how proteins can be engineered into smart nanoparticles or 'artificial viruses' to afford therapeutic requirements through alternative administration routes.

Blood-brain transfer and antinociception of linear and cyclic N-methyl-guanidine and thiourea-enkephalins

Enkephalins are active in regulation of nociception in the body and are key in development of new synthetic peptide analogs that target centrally located opioid receptors. In this study, we investigated the in vivo blood-brain barrier (BBB) penetration behavior and antinociceptive activity of two cyclic enkephalin analogs with a thiourea (CycS) or a N-methyl-guanidine bridge (CycNMe), and their linear counterparts (LinS and LinNMe) in mice, as well as their in vitro metabolic stability. (125)I-LinS had the highest blood-brain clearance (K1=3.46μL/gmin), followed by (125)I-LinNMe, (125)I-CycNMe, and (125)I-CycS (K1=1.64, 0.31, and 0.11μL/gmin, respectively). Also, these peptides had a high metabolic stability (t1/2>1h) in mouse serum and brain homogenate, and half-inhibition constant (Ki) values in the nanomolar range with predominantly μ-opioid receptor selectivity. The positively charged NMe-enkephalins showed a higher antinociceptive activity (LinNMe: 298% and CycNMe: 205%), expressed as molar-dose normalized area under the curve (AUC) relative to morphine, than the neutral S-enkephalins (CycS: 122% and LinS: 130%).

Computer-Aided Virtual Screening and Designing of Cell-Penetrating Peptides

Cell-penetrating peptides (CPPs) have proven their potential as versatile drug delivery vehicles. Last decade has witnessed an unprecedented growth in CPP-based research, demonstrating the potential of CPPs as therapeutic candidates. In the past, many in silico algorithms have been developed for the prediction and screening of CPPs, which expedites the CPP-based research. In silico screening/prediction of CPPs followed by experimental validation seems to be a reliable, less time-consuming, and cost-effective approach. This chapter describes the prediction, screening, and designing of novel efficient CPPs using "CellPPD," an in silico tool.

CancerPPD: a database of anticancer peptides and proteins

CancerPPD (http://crdd.osdd.net/raghava/cancerppd/) is a repository of experimentally verified anticancer peptides (ACPs) and anticancer proteins. Data were manually collected from published research articles, patents and from other databases. The current release of CancerPPD consists of 3491 ACP and 121 anticancer protein entries. Each entry provides comprehensive information related to a peptide like its source of origin, nature of the peptide, anticancer activity, N- and C-terminal modifications, conformation, etc. Additionally, CancerPPD provides the information of around 249 types of cancer cell lines and 16 different assays used for testing the ACPs. In addition to natural peptides, CancerPPD contains peptides having non-natural, chemically modified residues and D-amino acids. Besides this primary information, CancerPPD stores predicted tertiary structures as well as peptide sequences in SMILES format. Tertiary structures of peptides were predicted using the state-of-art method, PEPstr and secondary structural states were assigned using DSSP. In order to assist users, a number of web-based tools have been integrated, these include keyword search, data browsing, sequence and structural similarity search. We believe that CancerPPD will be very useful in designing peptide-based anticancer therapeutics.

Designing of peptides with desired half-life in intestine-like environment

BACKGROUND

In past, a number of peptides have been reported to possess highly diverse properties ranging from cell penetrating, tumor homing, anticancer, anti-hypertensive, antiviral to antimicrobials. Owing to their excellent specificity, low-toxicity, rich chemical diversity and availability from natural sources, FDA has successfully approved a number of peptide-based drugs and several are in various stages of drug development. Though peptides are proven good drug candidates, their usage is still hindered mainly because of their high susceptibility towards proteases degradation. We have developed an in silico method to predict the half-life of peptides in intestine-like environment and to design better peptides having optimized physicochemical properties and half-life.

RESULTS

In this study, we have used 10mer (HL10) and 16mer (HL16) peptides dataset to develop prediction models for peptide half-life in intestine-like environment. First, SVM based models were developed on HL10 dataset which achieved maximum correlation R/R2 of 0.57/0.32, 0.68/0.46, and 0.69/0.47 using amino acid, dipeptide and tripeptide composition, respectively. Secondly, models developed on HL16 dataset showed maximum R/R2 of 0.91/0.82, 0.90/0.39, and 0.90/0.31 using amino acid, dipeptide and tripeptide composition, respectively. Furthermore, models that were developed on selected features, achieved a correlation (R) of 0.70 and 0.98 on HL10 and HL16 dataset, respectively. Preliminary analysis suggests the role of charged residue and amino acid size in peptide half-life/stability. Based on above models, we have developed a web server named HLP (Half Life Prediction), for predicting and designing peptides with desired half-life. The web server provides three facilities; i) half-life prediction, ii) physicochemical properties calculation and iii) designing mutant peptides.

CONCLUSION

In summary, this study describes a web server 'HLP' that has been developed for assisting scientific community for predicting intestinal half-life of peptides and to design mutant peptides with better half-life and physicochemical properties. HLP models were trained using a dataset of peptides whose half-lives have been determined experimentally in crude intestinal proteases preparation. Thus, HLP server will help in designing peptides possessing the potential to be administered via oral route (http://www.imtech.res.in/raghava/hlp/).