CPPsite: a curated database of cell penetrating peptides

WERFE: A Gene Selection Algorithm Based on Recursive Feature Elimination and Ensemble Strategy

Gene selection algorithm in micro-array data classification problem finds a small set of genes which are most informative and distinctive. A well-performed gene selection algorithm should pick a set of genes that achieve high performance and the size of this gene set should be as small as possible. Many of the existing gene selection algorithms suffer from either low performance or large size. In this study, we propose a wrapper gene selection approach, named WERFE, within a recursive feature elimination (RFE) framework to make the classification more efficient. This WERFE employs an ensemble strategy, takes advantages of a variety of gene selection methods and assembles the top selected genes in each approach as the final gene subset. By integrating multiple gene selection algorithms, the optimal gene subset is determined through prioritizing the more important genes selected by each gene selection method and a more discriminative and compact gene subset can be selected. Experimental results show that the proposed method can achieve state-of-the-art performance.

G-DipC: An Improved Feature Representation Method for Short Sequences to Predict the Type of Cargo in Cell-Penetrating Peptides

Cell-penetrating peptides (CPPs) are functional short peptides with high carrying capacity. CPP sequences with targeting functions for the highly efficient delivery of drugs to target cells. In this paper, which is focused on the prediction of the cargo category of CPPs, a biocomputational model is constructed to efficiently distinguish the category of cargo carried by CPPs as macromolecular carriers among the seven known deliverable cargo categories. Based on dipeptide composition (DipC), an improved feature representation method, general dipeptide composition (G-DipC) is proposed for short peptide sequences and can effectively increase the abundance of features represented. Then linear discriminant analysis (LDA) is applied to mine some important low-dimensional features of G-DipC and a predictive model is built with the XGBoost algorithm. Experimental results with five-fold cross validation show that G-DipC improves accuracy by 25 and 5 percent compared with amino acid composition (AAC) and DipC, respectively. G-DipC is even found to be better than tripeptide composition (TipC). Thus, the proposed model provides a novel resource for the study of cell-penetrating peptides, and the improved dipeptide composition G-DipC can be widely adapted to determine the feature representation of other biological sequences.

Routing Nanomolar Protein Cargoes to Lipid Raft-Mediated/Caveolar Endocytosis through a Ganglioside GM1-Specific Recognition Tag

There is a pressing need to develop ways to deliver therapeutic macromolecules to their intracellular targets. Certain viral and bacterial proteins are readily internalized in functional form through lipid raft-mediated/caveolar endocytosis, but mimicking this process with protein cargoes at therapeutically relevant concentrations is a great challenge. Targeting ganglioside GM1 in the caveolar pits triggers endocytosis. A pentapeptide sequence WYKYW is presented, which specifically captures the glycan moiety of GM1 (K D = 24 nm). The WYKYW-tag facilitates the GM1-dependent endocytosis of proteins in which the cargo-loaded caveosomes do not fuse with lysosomes. A structurally intact immunoglobulin G complex (580 kDa) is successfully delivered into live HeLa cells at extracellular concentrations ranging from 20 to 160 nm, and escape of the cargo proteins to the cytosol is observed. The short peptidic WYKYW-tag is an advantageous endocytosis routing sequence for lipid raft-mediated/caveolar cell delivery of therapeutic macromolecules, especially for cancer cells that overexpress GM1.

BioDADPep: A Bioinformatics database for anti diabetic peptides

The increasing number of cases for diabetes worldwide is a concern. Therefore, it is of interest to design therapeutic peptides to overcome side effects caused by the available drugs. It should be noted that data on several known anti-diabetic peptides is available in the literature in an organized manner. Hence, it is of interest to collect, glean and store such data in form of a searchable database supported by RDBMS. Data on anti-diabetic peptides and their related data are collected from the literature using manual search. Data on related peptides from other databases (THPdb, ADP3, LAMP, AHTPDB, AVPdb, BioPepDB, CancerPPD, CPPsite, DRAMP, SATPdb, CAMPR3 and MBPDB) are also included after adequate curation. Thus, we describe the development and utility of BioDADPep, a Bioinformatics database for anti-diabetic peptides. The database has cross-reference for antidiabetic peptides. The database is enabled with a web-based GUI using a simple Google-like search function. Data presented in BioDADPep finds application in the design of an effective anti-diabetic peptide.

ADAPTABLE: a comprehensive web platform of antimicrobial peptides tailored to the user's research

Antimicrobial peptides (AMPs) are part of the innate immune response to pathogens in all of the kingdoms of life. They have received significant attention because of their extraordinary variety of activities, in particular, as candidate drugs against the threat of super-bacteria. A systematic study of the relation between the sequence and the mechanism of action is urgently needed, given the thousands of sequences already in multiple web resources. ADAPTABLE web platform (http://gec.u-picardie.fr/adaptable) introduces the concept of "property alignment" to create families of property and sequence-related peptides (SR families). This feature provides the researcher with a tool to select those AMPs meaningful to their research from among more than 40,000 nonredundant sequences. Selectable properties include the target organism and experimental activity concentration, allowing selection of peptides with multiple simultaneous actions. This is made possible by ADAPTABLE because it not only merges sequences of AMP databases but also merges their data, thereby standardizing values and handling non-proteinogenic amino acids. In this unified platform, SR families allow the creation of peptide scaffolds based on common traits in peptides with similar activity, independently of their source.

Stimuli-Sensitive Cell Penetrating Peptide-Modified Nanocarriers

The integration of drugs into nanocarriers favorably altered their pharmacodynamics and pharmacokinetics compared to free drugs, and increased their therapeutic index. However, selective cellular internalization in diseased tissues rather than normal tissues still presents a formidable challenge. In this chapter I will cover solutions involving environment-responsive cell-penetrating peptides (CPPs). I will discuss properties of CPPs as universal cellular uptake enhancers, and the modifications imparted to CPP-modified nanocarriers to confine CPP activation to diseased tissues.

Real-Time Quantification of Cell Internalization Kinetics by Functionalized Bioluminescent Nanoprobes

Cells transport mass dynamically, crossing cell membranes to maintain metabolism and systemic homeostasis, through which biomolecules are also delivered to cells for gene editing, cell reprograming, therapy, and other purposes. Quantifying the translocation kinetics is fundamentally and clinically essential, but remains limited by fluorescence-based technologies, which are semi-quantitative and only provide kinetics information at cellular level or in discrete time. Herein, a real-time method of quantifying cell internalization kinetics is reported using functionalized firefly-luciferase nanocapsules as the probe. This quantitative assay will facilitate the rational design of delivery vectors and enable high-throughput screening of peptides and other functional molecules, constituting an effective tool for broad applications, including drug development and cancer therapy.

The Development of Machine Learning Methods in Cell-Penetrating Peptides Identification: A Brief Review

Background:

Cell-penetrating Peptides (CPPs) are important short peptides that facilitate cellular intake or uptake of various molecules. CPPs can transport drug molecules through the plasma membrane and send these molecules to different cellular organelles. Thus, CPP identification and related mechanisms have been extensively explored. In order to reveal the penetration mechanisms of a large number of CPPs, it is necessary to develop convenient and fast methods for CPPs identification.

Methods:

Biochemical experiments can provide precise details for accurately identifying CPP, but these methods are expensive and laborious. To overcome these disadvantages, several computational methods have been developed to identify CPPs. We have performed review on the development of machine learning methods in CPP identification. This review provides an insight into CPP identification.

Results:

We summarized the machine learning-based CPP identification methods and compared the construction strategies of 11 different computational methods. Furthermore, we pointed out the limitations and difficulties in predicting CPPs.

Conclusion:

In this review, the last studies on CPP identification using machine learning method were reported. We also discussed the future development direction of CPP recognition with computational methods.

HEXIM1 Peptide Exhibits Antimicrobial Activity Against Antibiotic Resistant Bacteria Through Guidance of Cell Penetrating Peptide

The emergence of antibiotic resistant bacteria is one of the biggest threats to human health worldwide. In 2017, World Health Organization listed the world’s most dangerous antibiotic-resistant bacteria or “superbugs,” such as carbapenem-resistant Pseudomonas aeruginosa and Escherichia coli, indicating the highest priority needs for new antibiotics. The possibility that such infectious diseases may soon be untreatable, due to decreased antibiotic efficacy, creates an urgent need for novel and alternative antimicrobials. Antimicrobial peptides are naturally occurring small molecules found in the innate immunity of mammals, plants and bacteria, and are potentially therapeutic candidates against drug-resistant bacteria. In this study, we examine the antimicrobial activities of the cytotoxic peptides derived from the basic region (BR) of the human hexamethylene bisacetamide-inducible protein 1 (HEXIM1). We found that, when fused with a cell penetrating peptide, the HEXIM1 BR peptide and its derivative, BR-RRR12, exhibited inhibitory activities against selected “superbugs.” Negligible effects on the viability of human keratinocyte cell line were observed when the bactericidal dosages of HEXIM1 BR peptides were used. Different killing kinetics were observed between the membrane permeabilizing antimicrobial peptides and HEXIM1 BR peptides, suggesting that a different antimicrobial mechanism might be utilized by the HEXIM1 BR peptides. Using an in vitro translation system based on E. coli lysates, we found that HEXIM1 BR peptides blocked bacterial translation. Taken together, we identify the HEXIM1 BR peptide as a novel antimicrobial peptide with potent inhibitory activity against antibiotic-resistant “superbugs.”

Combination of Cell-Penetrating Peptides with Nanoparticles for Therapeutic Application: A Review

Cell-penetrating peptides (CPPs), also known as protein translocation domains, membrane translocating sequences or Trojan peptides, are small molecules of 6 to 30 amino acid residues capable of penetrating biological barriers and cellular membranes. Furthermore, CPP have become an alternative strategy to overcome some of the current drug limitations and combat resistant strains since CPPs are capable of delivering different therapeutic molecules against a wide range of diseases. In this review, we address the recent conjugation of CPPs with nanoparticles, which constitutes a new class of delivery vectors with high pharmaceutical potential in a variety of diseases.

ImmunoSPdb: an archive of immunosuppressive peptides

Immunosuppression proved as a captivating therapy in several autoimmune disorders, asthma as well as in organ transplantation. Immunosuppressive peptides are specific for reducing efficacy of immune system with wide range of therapeutic implementations. `ImmunoSPdb’ is a comprehensive, manually curated database of around 500 experimentally verified immunosuppressive peptides compiled from 79 research article and 32 patents. The current version comprises of 553 entries providing extensive information including peptide name, sequence, chirality, chemical modification, origin, nature of peptide, its target as well as mechanism of action, amino acid frequency and composition, etc. Data analysis revealed that most of the immunosuppressive peptides are linear (91%), are shorter in length i.e. up to 20 amino acids (62%) and have L form of amino acids (81%). About 30% peptide are either chemically modified or have end terminal modification. Most of the peptides either are derived from proteins (41%) or naturally (27%) exist. Blockage of potassium ion channel (24%) is one a major target for immunosuppressive peptides. In addition, we have annotated tertiary structure by using PEPstrMOD and I-TASSER. Many user-friendly, web-based tools have been integrated to facilitate searching, browsing and analyzing the data. We have developed a user-friendly responsive website to assist a wide range of users.

Inhibition of Inflammatory Changes in Human Myometrial Cells by Cell Penetrating Peptide and Small Molecule Inhibitors of NFκB

Complications arising from Preterm Birth are the leading causes of neonatal death globally. Current therapeutic strategies to prevent Preterm Birth are yet to demonstrate success in terms of reducing this neonatal disease burden. Upregulation of intracellular inflammatory pathways in uterine cells, including those involving nuclear factor kappa-B (NFκB), have been causally linked to both human term and preterm labor, but the barrier presented by the cell membrane presents an obstacle to interventions aimed at dampening these inflammatory responses. Cell penetrating peptides (CPPs) are novel vectors that can traverse cell membranes without the need for recognition by cell surface receptors and offer the ability to deliver therapeutic cargo internal to cell membranes. Using a human uterine cell culture inflammatory model, this study aimed to test the effectiveness of CPP-cargo delivery to inhibit inflammatory responses, comparing this effect with a small molecule inhibitor (Sc514) that has a similar intracellular target of action within the NFκB pathway (the IKK complex). The CPP Penetratin, conjugated to rhodamine, was able to enter uterine cells within a 60 min timeframe as assessed by live confocal microscopy, this phenomena was not observed with the use of a rhodamine-conjugated inert control peptide (GC(GS)4). Penetratin CPP conjugated to an IKK-inhibitory peptide (Pen-NBD) demonstrated ability to inhibit both the IL1β-induced expression of the inflammatory protein COX2 and dampen the expression of a bespoke array of inflammatory genes. Truncation of the CPP vector rendered the CPP-cargo conjugate much less effective, demonstrating the importance of careful vector selection. The small molecule inhibitor Sc514 also demonstrated ability to inhibit COX2 protein responses and a broad down-regulatory effect on uterine cell inflammatory gene expression. These results support the further exploration of either CPP-based or small molecular treatment strategies to dampen gestational cell inflammatory responses in the context of preterm birth. The work underlines both the importance of careful selection of CPP vector-cargo combinations and basic testing over a broad time and concentration range to ensure effective responses. Further work should demonstrate the effectiveness of CPP-linked cargos to dampen alternative pathways of inflammation linked to Preterm Birth such as MAP Kinase or AP1.

Breeding Cell Penetrating Peptides: Optimization of Cellular Uptake by a Function-Driven Evolutionary Process

In nature, building block-based biopolymers can adapt to functional and environmental demands by recombination and mutation of the monomer sequence. We present here an analogous, artificial evolutionary optimization process which we have applied to improve the functionality of cell-penetrating peptide molecules. The "evolution" consisted of repeated rounds of in silico peptide sequence alterations using a genetic algorithm followed by in vitro peptide synthesis, experimental analysis, and ranking according to their "fitness" (i.e., their ability to carry the cargo carboxyfluorescein into cultured cells). The genetic algorithm-based optimization method was customized and adapted from former successful applications in the lab to realize an early convergence and a minimum number of in vitro and in silico processing steps by configured settings derived from empirical in silico simulation. We started out with 20 "lead peptides" which we had previously identified as top performers regarding their ability to enter cultured cells. Ten breeding rounds comprising 240 peptides each yielded a peptide population of which the top 10 candidates displayed a 6-fold (median values) increase in its cell-penetration capability compared with the top 10 lead peptides, and two consensus sequences emerged which represent local fitness optima. In addition, the cell-penetrating potential could be proven independently of the carboxyfluorescein cargo in an alternative setting. Our results demonstrate that we have established a powerful optimization technology that can be used to further improve peptides with known functionality and adapt them to specific applications.

Development of a novel clustering tool for linear peptide sequences

Epitopes identified in large-scale screens of overlapping peptides often share significant levels of sequence identity, complicating the analysis of epitope-related data. Clustering algorithms are often used to facilitate these analyses, but available methods are generally insufficient in their capacity to define biologically meaningful epitope clusters in the context of the immune response. To fulfil this need we developed an algorithm that generates epitope clusters based on representative or consensus sequences. This tool allows the user to cluster peptide sequences on the basis of a specified level of identity by selecting among three different method options. These include the 'clique method', in which all members of the cluster must share the same minimal level of identity with each other, and the 'connected graph method', in which all members of a cluster must share a defined level of identity with at least one other member of the cluster. In cases where it is not possible to define a clear consensus sequence with the connected graph method, a third option provides a novel 'cluster-breaking algorithm' for consensus sequence driven sub-clustering. Herein we demonstrate the tool's clustering performance and applicability using (i) a selection of dengue virus epitopes for the 'clique method', (ii) sets of allergen-derived peptides from related species for the 'connected graph method' and (iii) large data sets of eluted ligand, major histocompatibility complex binding and T-cell recognition data captured within the Immune Epitope Database (IEDB) with the newly developed 'cluster-breaking algorithm'. This novel clustering tool is accessible at http://tools.iedb.org/cluster2/.

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/).

StraPep: a structure database of bioactive peptides

Bioactive peptides, with a variety of biological activities and wide distribution in nature, have attracted great research interest in biological and medical fields, especially in pharmaceutical industry. The structural information of bioactive peptide is important for the development of peptide-based drugs. Many databases have been developed cataloguing bioactive peptides. However, to our knowledge, database dedicated to collect all the bioactive peptides with known structure is not available yet. Thus, we developed StraPep, a structure database of bioactive peptides. StraPep holds 3791 bioactive peptide structures, which belong to 1312 unique bioactive peptide sequences. About 905 out of 1312 (68%) bioactive peptides in StraPep contain disulfide bonds, which is significantly higher than that (21%) of PDB. Interestingly, 150 out of 616 (24%) bioactive peptides with three or more disulfide bonds form a structural motif known as cystine knot, which confers considerable structural stability on proteins and is an attractive scaffold for drug design. Detailed information of each peptide, including the experimental structure, the location of disulfide bonds, secondary structure, classification, post-translational modification and so on, has been provided. A wide range of user-friendly tools, such as browsing, sequence and structure-based searching and so on, has been incorporated into StraPep. We hope that this database will be helpful for the research community.

Database URL: http://isyslab.info/StraPep

Cell penetrating peptides: a comparative transport analysis for 474 sequence motifs

Delivering reagents into cells is a key demand in molecular medicine. The vehicle of choice is often cell penetrating peptides (CPPs), which can ferry conjugated cargo across membranes. Although numerous peptides have been shown to promote such uptake events, there has been no comprehensive comparison of individual performance under standardized conditions. We have devised a method to rapidly analyze the ability of a multitude of CPP conjugates to carry a model cargo into HeLa cells. Sequence information for 474 CPPs was collected from literature sources, and the respective peptides were synthesized and modified with carboxyfluorescein (FAM) as model cargo. All candidates were evaluated in an identical uptake test, and transport was quantified using cellular fluorescence intensities. Substantial differences in the ability to carry the fluorophore into the cells were observed, with transport performance differing by a factor of 70 between the best CPP investigated and cargo alone. Strong correlations were observed between uptake efficiency and both sequence length and the presence of positive net charge. A compilation of the 20 top performers with regard to cargo delivery performance and cell compatibility is provided.

Cellular Uptake of Nanoparticles versus Small Molecules: A Matter of Size

The primary function of the cell membrane is to protect cells from their surroundings. This entails a strict regulation on controlling the exchange of matter between the cell and its environment. A key factor when considering potential biological applications of a particular chemical structure has to do with its ability to internalize into cells. Molecules that can readily cross cell membranes are frequently needed in biological research and medicine, since most therapeutic entities are designed to modulate intracellular components. However, the design of molecules that do not penetrate cells is also relevant toward, for example, extracellular contrast agents, which are most widely used in clinical diagnosis. Small molecules have occupied the forefront of biomedical research until recently, but the past few decades have seen an increasing use of larger chemical structures, such as proteins or nanoparticles, leading to unprecedented and often unexpectedly novel research. Great achievements have been made toward understanding the rules that govern cellular uptake, which show that cell internalization of molecules is largely affected by their size. For example, macromolecules such as proteins and nucleic acids are usually unable to internalize cells. Intriguingly, in the case of nanoparticles, larger sizes seem to facilitate internalization via endocytic pathways, through which the particles remain trapped in lysosomes and endosomes. In this Account, we aimed at presenting our personal view of how different chemical structures behave in terms of cell internalization due to their size, ranging from small drugs to large nanoparticles. We first introduce the properties of cell membranes and the main mechanisms involved in cellular uptake. We then discuss the cellular internalization of molecules, distinguishing between those with molecular weights below 1 kDa and biological macromolecules such as proteins and nucleic acids. In the last section, we review the biological behavior of nanoparticles, with a special emphasis on plasmonic nanoparticles, which feature a high potential in the biomedical field. For each group of chemical structures, we discuss the parameters affecting their cellular internalization but also strategies that can be applied to achieve the desired intracellular delivery. Particular attention is paid to approaches that allow conditional regulation of the cell internalization process using external triggers, such as activable cell penetrating peptides, due to the impact that these systems may have in drug delivery and sensing applications. The Account ends with a "Conclusions and Outlook" section, where general lessons and future directions toward further advancements are briefly presented.

Emerging Methods and Design Principles for Cell-Penetrant Peptides

Biomolecules such as antibodies, proteins, and peptides are important tools for chemical biology and leads for drug development. They have been used to inhibit a variety of extracellular proteins, but accessing intracellular proteins has been much more challenging. In this review, we discuss diverse chemical approaches that have yielded cell-penetrant peptides and identify three distinct strategies: masking backbone amides, guanidinium group patterning, and amphipathic patterning. We summarize a growing number of large data sets, which are starting to reveal more specific design guidelines for each strategy. We also discuss advantages and disadvantages of current methods for quantifying cell penetration. Finally, we provide an overview of best-odds approaches for applying these new methods and design principles to optimize cytosolic penetration for a given bioactive peptide.

Membrane Active Peptides and Their Biophysical Characterization

In the last 20 years, an increasing number of studies have been reported on membrane active peptides. These peptides exert their biological activity by interacting with the cell membrane, either to disrupt it and lead to cell lysis or to translocate through it to deliver cargos into the cell and reach their target. Membrane active peptides are attractive alternatives to currently used pharmaceuticals and the number of antimicrobial peptides (AMPs) and peptides designed for drug and gene delivery in the drug pipeline is increasing. Here, we focus on two most prominent classes of membrane active peptides; AMPs and cell-penetrating peptides (CPPs). Antimicrobial peptides are a group of membrane active peptides that disrupt the membrane integrity or inhibit the cellular functions of bacteria, virus, and fungi. Cell penetrating peptides are another group of membrane active peptides that mainly function as cargo-carriers even though they may also show antimicrobial activity. Biophysical techniques shed light on peptide–membrane interactions at higher resolution due to the advances in optics, image processing, and computational resources. Structural investigation of membrane active peptides in the presence of the membrane provides important clues on the effect of the membrane environment on peptide conformations. Live imaging techniques allow examination of peptide action at a single cell or single molecule level. In addition to these experimental biophysical techniques, molecular dynamics simulations provide clues on the peptide–lipid interactions and dynamics of the cell entry process at atomic detail. In this review, we summarize the recent advances in experimental and computational investigation of membrane active peptides with particular emphasis on two amphipathic membrane active peptides, the AMP melittin and the CPP pVEC.

Unraveling the bioactivity of anticancer peptides as deduced from machine learning

Cancer imposes a global health burden as it represents one of the leading causes of morbidity and mortality while also giving rise to significant economic burden owing to the associated expenditures for its monitoring and treatment. In spite of advancements in cancer therapy, the low success rate and recurrence of tumor has necessitated the ongoing search for new therapeutic agents. Aside from drugs based on small molecules and protein-based biopharmaceuticals, there has been an intense effort geared towards the development of peptide-based therapeutics owing to its favorable and intrinsic properties of being relatively small, highly selective, potent, safe and low in production costs. In spite of these advantages, there are several inherent weaknesses that are in need of attention in the design and development of therapeutic peptides. An abundance of data on bioactive and therapeutic peptides have been accumulated over the years and the burgeoning area of artificial intelligence has set the stage for the lucrative utilization of machine learning to make sense of these large and high-dimensional data. This review summarizes the current state-of-the-art on the application of machine learning for studying the bioactivity of anticancer peptides along with future outlook of the field. Data and R codes used in the analysis herein are available on GitHub at https://github.com/Shoombuatong2527/anticancer-peptides-review.

Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy

Cell-penetrating peptides (CPPs) can enter cells as a variety of biologically active conjugates and have various biomedical applications. To offset the cost and effort of designing novel CPPs in laboratories, computational methods are necessitated to identify candidate CPPs before in vitro experimental studies. We developed a two-layer prediction framework called machine-learning-based prediction of cell-penetrating peptides (MLCPPs). The first-layer predicts whether a given peptide is a CPP or non-CPP, whereas the second-layer predicts the uptake efficiency of the predicted CPPs. To construct a two-layer prediction framework, we employed four different machine-learning methods and five different compositions including amino acid composition (AAC), dipeptide composition, amino acid index, composition-transition-distribution, and physicochemical properties (PCPs). In the first layer, hybrid features (combination of AAC and PCP) and extremely randomized tree outperformed state-of-the-art predictors in CPP prediction with an accuracy of 0.896 when tested on independent data sets, whereas in the second layer, hybrid features obtained through feature selection protocol and random forest produced an accuracy of 0.725 that is better than state-of-the-art predictors. We anticipate that our method MLCPP will become a valuable tool for predicting CPPs and their uptake efficiency and might facilitate hypothesis-driven experimental design. The MLCPP server interface along with the benchmarking and independent data sets are freely accessible at www.thegleelab.org/MLCPP .

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.

Oral delivery of anti-diabetes therapeutics using cell penetrating and transcytosing peptide strategies

Oral delivery of insulin and other anti-diabetic peptides is inhibited by low intestinal absorption caused by the poor permeability across cellular membranes and the susceptibility to enzymatic degradation in the gastrointestinal tract. Cell-penetrating peptides (CPPs) have been investigated for a number of years as oral absorption enhancers for hydrophilic macromolecules by electrostatic or covalent conjugation on in conjunction with nanotechnology. Endogenous cellular uptake mechanisms present in the intestine can be exploited by engineering peptide conjugates that transcytose; entering cells by endocytosis and leaving by exocytosis. Efficiently delivering hydrophilic and sensitive peptide drugs to safely transverse the digestive barrier with no effect on gut physiology using remains a key driver for formulation research. Here we review the use of CPP and transcytosis peptide approaches, their modification and use in delivering anti-diabetic peptides (with the primary example of Insulin and engineered homologues) by direct oral administration to treat diabetes and associated metabolic disorders.

Cell-Penetrating Peptides: Design Strategies beyond Primary Structure and Amphipathicity

Efficient intracellular drug delivery and target specificity are often hampered by the presence of biological barriers. Thus, compounds that efficiently cross cell membranes are the key to improving the therapeutic value and on-target specificity of non-permeable drugs. The discovery of cell-penetrating peptides (CPPs) and the early design approaches through mimicking the natural penetration domains used by viruses have led to greater efficiency of intracellular delivery. Following these nature-inspired examples, a number of rationally designed CPPs has been developed. In this review, a variety of CPP designs will be described, including linear and flexible, positively charged and often amphipathic CPPs, and more rigid versions comprising cyclic, stapled, or dimeric and/or multivalent, self-assembled peptides or peptido-mimetics. The application of distinct design strategies to known physico-chemical properties of CPPs offers the opportunity to improve their penetration efficiency and/or internalization kinetics. This led to increased design complexity of new CPPs that does not always result in greater CPP activity. Therefore, the transition of CPPs to a clinical setting remains a challenge also due to the concomitant involvement of various internalization routes and heterogeneity of cells used in the in vitro studies.

SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides

Background

Cell-penetrating peptides (CPPs) are short peptides (5–30 amino acids) that can enter almost any cell without significant damage. On account of their high delivery efficiency, CPPs are promising candidates for gene therapy and cancer treatment. Accordingly, techniques that correctly predict CPPs are anticipated to accelerate CPP applications in future therapeutics. Recently, computational methods have been reportedly successful in predicting CPPs. Unfortunately, the predictive performance of existing methods is not satisfactory and reliable so as to accurately identify CPPs.

Results

In this study, we propose a novel computational predictor called SkipCPP-Pred to further improve the predictive performance. The novelty of the proposed predictor is that we present a sequence-based feature representation algorithm called adaptive k-skip-n-gram that sufficiently captures the intrinsic correlation information of residues. By fusing the proposed adaptive skip features with a random forest (RF) classifier, we successfully construct the prediction model of SkipCPP-Pred. The various jackknife results demonstrate that the proposed SkipCPP-Pred is 3.6% higher than state-of-the-art CPP predictors in terms of accuracy. Moreover, we construct a high-quality benchmark dataset by reducing the data redundancy and enhancing the similarity between the positive and negative classes. Using this dataset to build prediction models, we can successfully avoid the performance bias lying in existing methods and yield a promising predictive model.

Conclusions

The proposed SkipCPP-Pred is a simple and fast sequence-based predictor featured with the adaptive k-skip-n-gram model for the improved prediction of CPPs. Currently, SkipCPP-Pred is publicly available from an online webserver (http://server.malab.cn/SkipCPP-Pred/Index.html).

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4128-1) contains supplementary material, which is available to authorized users.

Synthesis and cellular characterization of various nano-assemblies of cell penetrating peptide-epirubicin-polyglutamate conjugates for the enhancement of antitumor activity

A new class of cell penetrating peptides (CPPs) named peptide amphiphile was designed to improve the intracellular uptake and the antitumor activity of epirubicin (EPR). Various amphiphilic CPPs were synthesized by solid phase peptide synthesis method and were chemically conjugated to EPR. Their corresponding nanoparticles (CPPs-E4 and CPPs-E8) were prepared via non-covalent binding of the peptides and polyanions. Cytotoxicity and anti-proliferative activity were evaluated by MTT assay. Cellular uptake was examined by flow cytometry and fluorescence microscopy. The CPPs exhibited slight cytotoxicity. Binding of polyglutamate to CPPs (CPPs-E4 and CPPs-E8 nanoparticles) decreased their cytotoxicity. CPPs-E8 nanoparticles showed lower cytotoxicity than CPPs-E4 nanoparticles. Cellular uptake of K3W4K3-E8, K2W4K2-E8 and W3K4W3-E8 reached 100% with no difference between each of the mentioned CPPs and its nanoparticles at 50 µM. The anti-proliferative activity of EPR was enhanced following conjugation to peptides and nanoparticles at 25 µM. CPPs-EPR-E4 and CPPs-E8-EPR nanoparticles displayed higher anti-proliferative activity than CPPs-EPR at 25 µM. CPPs-E8-EPR nanoparticles showed higher anti-proliferative activity than CPPs-E4-EPR. K3W4K3-E8-EPR nanoparticles exhibited the highest anti-proliferative activity at 25 µM. The synthesized peptide nanoparticles are proposed as suitable carriers for improving the intracellular delivery of EPR into tumor cells with low cytotoxicity and high antitumor activity.

Conformational Plasticity of the Cell-Penetrating Peptide SAP As Revealed by Solid-State 19F-NMR and Circular Dichroism Spectroscopies

The cell-penetrating peptide SAP, which was designed as an amphipathic poly-l-proline helix II (PPII), was suggested to self-assemble into regular fibrils that are relevant for its internalization. Herein we have analyzed the structure of SAP in the membrane-bound state by solid-state ¹⁹F-NMR, which revealed other structural states, in addition to the expected surface-aligned PPII. Trifluoromethyl-bicyclopentyl-glycine (CF₃-Bpg) and two rigid isomers of trifluoromethyl-4,5-methanoprolines (CF₃-MePro) were used as labels for ¹⁹F-NMR analysis. The equilibria between different conformations of SAP were studied and were found to be shifted by the substituents at Pro-11. Synchrotron-CD results suggested that substituting Pro-11 by CF₃-MePro governed the coil-to-PPII equilibrium in solution and in the presence of a lipid bilayer. Using CD and ¹⁹F-NMR, we examined the slow kinetics of the association of SAP with membranes and the dependence of the SAP conformational dynamics on the lipid composition. The peptide did not bind to lipids in the solid ordered phase and aggregated only in the liquid ordered "raft"-like bilayers. Self-association could not be detected in solution or in the presence of liquid disordered membranes. Surface-bound amphipathic SAP in a nonaggregated state was structured as a mixture of nonideal extended conformations reflecting the equilibrium already present in solution, i.e., before binding to the membrane.

iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space

Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers.

A versatile cell-penetrating peptide-adaptor system for efficient delivery of molecular cargos to subcellular destinations

Cell penetrating peptides have long held great potential for delivery of biomolecular cargos for research, therapeutic and diagnostic purposes. They allow rapid, relatively nontoxic passage of a wide variety of biomolecules through the plasma membranes of living cells. However, CPP-based research tools and therapeutics have been stymied by poor efficiency in release from endosomes and a great deal of effort has been made to solve this ‘endosomal escape problem.’ Previously, we showed that use of a reversible, noncovalent coupling between CPP and cargo using calmodulin and a calmodulin binding motif allowed efficient delivery of cargo proteins to the cytoplasm in baby hamster kidney and other mammalian cell lines. The present report demonstrates the efficacy of our CPP-adaptor scheme for efficient delivery of model cargos to the cytoplasm using a variety of CPPs and adaptors. Effective overcoming of the endosomal escape problem is further demonstrated by the delivery of cargo to the nucleus, endoplasmic reticulum and peroxisomes by addition of appropriate subcellular localization signals to the cargos. CPP-adaptors were also used to deliver cargo to myotubes, demonstrating the feasibility of the system as an alternative to transfection for the manipulation of hard-to-transfect cells.

Novel Peptides Derived from Dengue Virus Capsid Protein Translocate Reversibly the Blood-Brain Barrier through a Receptor-Free Mechanism

The delivery of therapeutic molecules to the central nervous system is hampered by poor delivery across the blood-brain barrier (BBB). Several strategies have been proposed to enhance transport into the brain, including invasive techniques and receptor-mediated transport (RMT). Both approaches have several drawbacks, such as BBB disruption, receptor saturation, and off-target effects, raising safety issues. Herein, we show that specific domains of Dengue virus type 2 capsid protein (DEN2C) can be used as trans-BBB peptide vectors. Their mechanism of translocation is receptor-independent and consistent with adsorptive-mediated transport (AMT). One peptide in particular, named PepH3, reaches equilibrium distribution concentrations across the BBB in less than 24 h in a cellular in vitro assay. Importantly, in vivo biodistribution data with radiolabeled peptide derivatives show high brain penetration. In addition, there is fast clearance from the brain and high levels of excretion, showing that PepH3 is a very good candidate to be used as a peptide shuttle taking cargo in and out of the brain.

Combinatorial Library Screening with Liposomes for Discovery of Membrane Active Peptides

Membrane active peptides (MAPs) represent a class of short biomolecules that have shown great promise in facilitating intracellular delivery without disrupting cellular plasma membranes. Yet their clinical application has been stalled by numerous factors: off-target delivery, a requirement for high local concentration near cells of interest, degradation en route to the target site, and in the case of cell-penetrating peptides, eventual entrapment in endolysosomal compartments. The current method of deriving MAPs from naturally occurring proteins has restricted the discovery of new peptides that may overcome these limitations. Here, we describe a new branch of assays featuring high-throughput functional screening capable of discovering new peptides with tailored cell uptake and endosomal escape capabilities. The one-bead-one-compound (OBOC) combinatorial method is used to screen libraries containing millions of potential MAPs for binding to synthetic liposomes, which can be adapted to mimic various aspects of limiting membranes. By incorporating unnatural and d-amino acids in the library, in addition to varying buffer conditions and liposome compositions, we have identified several new highly potent MAPs that improve on current standards and introduce motifs that were previously unknown or considered unsuitable. Since small variations in pH and lipid composition can be controlled during screening, peptides discovered using this methodology could aid researchers building drug delivery platforms with unique requirements, such as targeted intracellular localization.

Pathogenicity-associated protein domains: The fiercely-conserved evolutionary signatures

Proteins have highly conserved domains that determine their functionality. Out of the thousands of domains discovered so far across all living forms, some of the predominant clinically-relevant domains include IENR1, HNHc, HELICc, Pro-kuma_activ, Tryp_SPc, Lactamase_B, PbH1, ChtBD3, CBM49, acidPPc, G3P_acyltransf, RPOL8c, KbaA, HAMP, HisKA, Hr1, Dak2, APC2, Citrate_ly_lig, DALR, VKc, YARHG, WR1, PWI, ZnF_BED, TUDOR, MHC_II_beta, Integrin_B_tail, Excalibur, DISIN, Cadherin, ACTIN, PROF, Robl_LC7, MIT, Kelch, GAS2, B41, Cyclin_C, Connexin_CCC, OmpH, Bac_rhodopsin, AAA, Knot1, NH, Galanin, IB, Elicitin, ACTH, Cache_2, CHASE, AgrB, PRP, IGR, and Antimicrobial21. These domains are distributed in nucleases/helicases, proteases, esterases, lipases, glycosylase, GTPases, phosphatases, methyltransferases, acyltransferase, acetyltransferase, polymerase, kinase, ligase, synthetase, oxidoreductase, protease inhibitors, nucleic acid binding proteins, adhesion and immunity-related proteins, cytoskeletal component-manipulating proteins, lipid biosynthesis and metabolism proteins, membrane-associated proteins, hormone-like and signaling proteins, etc. These domains are ubiquitous stretches or folds of the proteins in pathogens and allergens. Pathogenesis alleviation efforts can benefit enormously if the characteristics of these domains are known. Hence, this review catalogs and discusses the role of such pivotal domains, suggesting hypotheses for better understanding of pathogenesis at molecular level.

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Proteins have highly conserved regions or domains across pathogens and allergens.

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Knowledge on these critical domains can facilitate our understanding of pathogenesis mechanisms.

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Such immune manipulation-related domains include IENR1, HNHc, HELICc, ACTIN, PROF, Robl_LC7, OmpH etc.

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These domains are presnt in enzyme, transcription regulators, adhesion proteins, and hormones.

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This review discusses and hypothesizes on these domains.

Macrocycles as protein-protein interaction inhibitors

Macrocyclic compounds such as cyclic peptides have emerged as a new and exciting class of drug candidates for inhibition of intracellular protein-protein interactions, which are challenging targets for conventional drug modalities (i.e. small molecules and proteins). Over the past decade, several complementary technologies have been developed to synthesize macrocycle libraries and screen them for binding to therapeutically relevant targets. Two different approaches have also been explored to increase the membrane permeability of cyclic peptides. In this review, we discuss these methods and their applications in the discovery of macrocyclic compounds against protein-protein interactions.

Heparin-Promoted Cellular Uptake of the Cell-Penetrating Glycosaminoglycan Binding Peptide, GBPECP, Depends on a Single Tryptophan

A 10-residue, glycosaminoglycan-binding peptide, GBP_ECP, derived from human eosinophil cationic protein has been recently designated as a potent cell-penetrating peptide. A model system containing peptide, glycan, and lipid was monitored by nuclear magnetic resonance (NMR) spectroscopy to determine the cell-penetrating mechanism. Heparin octasaccharide with dodecylphosphocholine (DPC) lipid micelle was titrated into the GBP_ECP solution. Our data revealed substantial roles for the charged residues Arg5 and Lys7 in recognizing heparin, whereas Arg3 had less effect. The aromatic residue Trp4 acted as an irreplaceable moiety for membrane insertion, as the replacement of Trp4 with Arg4 abolished cell penetration, although it significantly improved the heparin-binding ability. GBP_ECP bound either heparin or lipid in the presence or absence of the other ligand indicating that the peptide has two alternative binding sites: Trp4 is responsible for lipid insertion, and Arg5 and Lys7 are for GAG binding. We developed a molecular model showing that the two effects synergistically promote the penetration. The loss of either effect would abolish the penetration. GBP_ECP has been proven to enter cells through macropinocytosis. The GBP_ECP treatment inhibited A549 lung cancer cell migration and invasion, implying that the cellular microenvironment would be modulated by GBP_ECP internalization. The intracellular penetration of GBP_ECP leading to inhibition of epithelial cell migration and invasion depends on the presence of the tryptophan residue in its sequence compared with similar derivative peptides. Therefore, GBP_ECP shows substantial potential as a novel delivery therapeutic through rapid and effective internalization and interference with cell mobility.

Short peptides interfering with signaling pathways as new therapeutic tools for cancer treatment

Short peptides have many advantages, such as low molecular weight, selectivity for a specific target, organelles or cells with minimal toxicity. We describe properties of short peptides, which interfere with communication networks in tumor cells and within microenvironment of malignant gliomas, the most common brain tumors. We focus on ligand/receptor axes and intracellular signaling pathways critical for gliomagenesis that could be targeted with interfering peptides. We review structures and efficacy of organelle-specific and cell-penetrating peptides and describe diverse chemical modifications increasing proteolytic stability and protecting synthetic peptides against degradation. We report results of application of short peptides in glioma therapy clinical trials, their rises and falls. The most advanced examples of therapeutics such as short interfering peptides combined with cell-penetrating peptides that show good effectiveness in disease models are presented. It is foreseen that identification of peptides with better clinical properties may improve their success rates in clinical trials.

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.

Mitochondrial transit peptide exhibits cell penetration ability and efficiently delivers macromolecules to mitochondria

Mitochondrial malfunction under various circumstances can lead to a variety of disorders. Effective targeting of macromolecules (drugs) is important for restoration of mitochondrial function and treatment of related disorders. We have designed a novel cell-penetrating mitochondrial transit peptide (CpMTP) for delivery of macromolecules to mitochondria. Comparison between properties of cell-penetrating peptides (CPPs) and mitochondrial signal sequences enabled prediction of peptides with dual ability for cellular translocation and mitochondrial localization. Among the predicted peptides, CpMTP translocates across HeLa cells and shows successful delivery of noncovalently conjugated cargo molecules to mitochondria. CpMTP may have applications in transduction and transfection of mitochondria for therapeutics.

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.

Expression, purification and production of antisera against recombinant truncated VP22 protein

Cell-penetrating peptides (CPPs) are non-invasive vectors that can efficiently transport bioactive cargo across the cell membrane. Naturally occurring CPPs, such as the tegument protein VP22 of the Herpes simplex virus type 1, can potentiate protein-drug delivery into living cells. The aim of the present study was to construct anti-VP22 antibodies that can be used to detect VP22-fusion drugs. Therefore, 60- and 45-amino acid peptides corresponding to the N-terminus and C-terminus of VP22, respectively, were cloned, expressed and purified. Subsequently, polyclonal antisera against them were generated. The DNA sequence, cloned into the pGEX-5X-1 vector, was transformed into E. coli BL21 (DE3). After inducing expression with 1 mM isopropyl-β-d-thiogalactopyranoside (IPTG) at 25°C for 4 h, the recombinant VP22 proteins were purified by electroelution. The high titers of polyclonal antisera obtained subsequent to immunization of mice with the purified recombinant truncated VP22 was confirmed by ELISA. Western blot and immunofluorescence analysis showed that the antisera detected both the truncated and full-length VP22 protein. Therefore, the polyclonal antisera against VP22 may be used in the detection of the intracellular location of VP22-fusion protein drugs.

Effective Design of Multifunctional Peptides by Combining Compatible Functions

Multifunctionality is a common trait of many natural proteins and peptides, yet the rules to generate such multifunctionality remain unclear. We propose that the rules defining some protein/peptide functions are compatible. To explore this hypothesis, we trained a computational method to predict cell-penetrating peptides at the sequence level and learned that antimicrobial peptides and DNA-binding proteins are compatible with the rules of our predictor. Based on this finding, we expected that designing peptides for CPP activity may render AMP and DNA-binding activities. To test this prediction, we designed peptides that embedded two independent functional domains (nuclear localization and yeast pheromone activity), linked by optimizing their composition to fit the rules characterizing cell-penetrating peptides. These peptides presented effective cell penetration, DNA-binding, pheromone and antimicrobial activities, thus confirming the effectiveness of our computational approach to design multifunctional peptides with potential therapeutic uses. Our computational implementation is available at http://bis.ifc.unam.mx/en/software/dcf.

Most proteins and peptides in nature display multiple activities either by fusing different domains (with different activities) or by evolving multiple activities in a single domain. Understanding which activities may be combined to render multifunctional proteins remains an open question relevant to understanding the organization of living organisms and to improve the design of pharmacological peptides. To address this problem, we introduce the concept of compatible activities, that is, activities that may combine without losing any of these in a single polypeptide chain. To identify compatible activities in peptide sequences, we used a machine-learning approach and discovered that a penetrating activity should be compatible with DNA-binding and antimicrobial activities. To test if these activities may combine without any functional loss, we designed peptide sequences that harbor two independent activities (nuclear localization and pheromone) and experimentally showed that all our designed peptides display penetrability, pheromone, antimicrobial and DNA-binding activities, supporting the idea that multifunctionality may be achieved combining compatible activities.

Intracellular Delivery of Proteins with Cell-Penetrating Peptides for Therapeutic Uses in Human Disease

Protein therapy exhibits several advantages over small molecule drugs and is increasingly being developed for the treatment of disorders ranging from single enzyme deficiencies to cancer. Cell-penetrating peptides (CPPs), a group of small peptides capable of promoting transport of molecular cargo across the plasma membrane, have become important tools in promoting the cellular uptake of exogenously delivered proteins. Although the molecular mechanisms of uptake are not firmly established, CPPs have been empirically shown to promote uptake of various molecules, including large proteins over 100 kiloDaltons (kDa). Recombinant proteins that include a CPP tag to promote intracellular delivery show promise as therapeutic agents with encouraging success rates in both animal and human trials. This review highlights recent advances in protein-CPP therapy and discusses optimization strategies and potential detrimental effects.