CPPsite: a curated database of cell penetrating peptides

Cholesterol Attenuates the Pore-Forming Capacity of CARC-Containing Amphipathic Peptides

Artificial peptides P4, A1 and A4 are homologous to amphipathic α-helical fragments of the influenza virus M1 protein. P4 and A4 contain the cholesterol recognition sequence CARC, which is absent in A1. As shown previously, P4 and A4 but not A1 have cytotoxic effects on some eukaryotic and bacterial cells. This might be caused by the dysfunction of cholesterol-dependent cellular structures, inhibition of the respiratory chain, or disruption of the membrane. Here, we analyzed the latter hypothesis by studying the uncoupling effect of the peptides on asolectin membranes. The influence of A4 on Δψ pre-formed either by the valinomycin-dependent K+ diffusion or by the activity of membrane-built cytochrome c oxidase (CcO) was studied on (proteo)liposomes. Also, we investigated the effect of P4, A1 and A4 on liposomes loaded with calcein. It is found that A4 in a submicromolar range causes an immediate and complete dissipation of diffusion Δψ across the liposomal membrane. Uncoupling of the CcO-containing proteoliposomes requires an order of magnitude of higher peptide concentration, which may indicate the sorption of A4 on the enzyme. The presence of cholesterol in the membrane significantly weakens the uncoupling. Submicromolar A4 and P4 cause the release of calcein from liposomes, indicating the formation of membrane pores. The process develops in minutes and is significantly decelerated by cholesterol. Micromolar A1 induces pore formation in a cholesterol-independent manner. We conclude that the peptides P4, A4 and, in higher concentrations, A1 form pores in the asolectin membrane. The CARC-mediated interaction of A4 and P4 with cholesterol impedes the peptide oligomerization necessary for pore formation. The rapid uncoupling effect of A4 is apparently caused by an increase in the proton conductivity of the membrane without pore formation.

The Passage of Chaperonins to Extracellular Locations in Legionella pneumophila Requires a Functional Dot/Icm System

HtpB, the chaperonin of the bacterial pathogen L. pneumophila, is found in extracellular locations, even the cytoplasm of host cells. Although chaperonins have an essential cytoplasmic function in protein folding, HtpB exits the cytoplasm to perform extracellular virulence-related functions that support L. pneumophila's lifestyle. The mechanism by which HtpB reaches extracellular locations is not currently understood. To address this experimental gap, immunoelectron microscopy, trypsin-accessibility assays, and cell fractionation were used to localize HtpB in various L. pneumophila secretion mutants. Dot/Icm type IV secretion mutants displayed less surface-exposed HtpB and more periplasmic HtpB than parent strains. The analysis of periplasmic extracts and outer membrane vesicles of these mutants, where HtpB co-localized with bona fide periplasmic proteins, confirmed the elevated levels of periplasmic HtpB. Genetic complementation of the mutants recovered parent strain levels of surface-exposed and periplasmic HtpB. The export of GSK-tagged HtpB into the cytoplasm of infected cells was also Dot/Icm-dependent. The translocating role of the Dot/Icm system was not specific for HtpB because GroEL, the chaperonin of Escherichia coli, was found at the cell surface and accumulated in the periplasm of Dot mutants when expressed in L. pneumophila. These findings establish that a functional Dot/Icm system is required for HtpB to reach extracellular locations, but the mechanism by which cytoplasmic HtpB reaches the periplasm remains partially unidentified.

A bird's-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides

Cell-penetrating peptides (CPPs) are highly effective at passing through eukaryotic membranes with various cargo molecules, like drugs, proteins, nucleic acids, and nanoparticles, without causing significant harm. Creating drug delivery systems with CPP is associated with cancer, genetic disorders, and diabetes due to their unique chemical properties. Wet lab experiments in drug discovery methodologies are time-consuming and expensive. Machine learning (ML) techniques can enhance and accelerate the drug discovery process with accurate and intricate data quality. ML classifiers, such as support vector machine (SVM), random forest (RF), gradient-boosted decision trees (GBDT), and different types of artificial neural networks (ANN), are commonly used for CPP prediction with cross-validation performance evaluation. Functional CPP prediction is improved by using these ML strategies by using CPP datasets produced by high-throughput sequencing and computational methods. This review focuses on several ML-based CPP prediction tools. We discussed the CPP mechanism to understand the basic functioning of CPPs through cells. A comparative analysis of diverse CPP prediction methods was conducted based on their algorithms, dataset size, feature encoding, software utilities, assessment metrics, and prediction scores. The performance of the CPP prediction was evaluated based on accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) on independent datasets. In conclusion, this review will encourage the use of ML algorithms for finding effective CPPs, which will have a positive impact on future research on drug delivery and therapeutics.

Designing microplastic-binding peptides with a variational quantum circuit-based hybrid quantum-classical approach

De novo peptide design exhibits great potential in materials engineering, particularly for the use of plastic-binding peptides to help remediate microplastic pollution. There are no known peptide binders for many plastics-a gap that can be filled with de novo design. Current computational methods for peptide design exhibit limitations in sampling and scaling that could be addressed with quantum computing. Hybrid quantum-classical methods can leverage complementary strengths of near-term quantum algorithms and classical techniques for complex tasks like peptide design. This work introduces a hybrid quantum-classical generative framework for designing plastic-binding peptides combining variational quantum circuits with a variational autoencoder network. We demonstrate the framework's effectiveness in generating peptide candidates, evaluate its efficiency for property-oriented design, and validate the candidates with molecular dynamics simulations. This quantum computing-based approach could accelerate the development of biomolecular tools for environmental and biomedical applications while advancing the study of biomolecular systems through quantum technologies.

A high hydrophobic moment arginine-rich peptide screened by a machine learning algorithm enhanced ADC antitumor activity

Cell-penetrating peptides (CPPs) with better biomolecule delivery properties will expand their clinical applications. Using the MLCPP2.0 machine algorithm, we screened multiple candidate sequences with potential cellular uptake ability from the nuclear localization signal/nuclear export signal database and verified them through cell-penetrating fluorescent tracing experiments. A peptide (NCR) derived from the Rev protein of the caprine arthritis-encephalitis virus exhibited efficient cell-penetrating activity, delivering over four times more EGFP than the classical CPP TAT, allowing it to accumulate in lysosomes. Structural and property analysis revealed that a high hydrophobic moment and an appropriate hydrophobic region contribute to the high delivery activity of NCR. Trastuzumab emtansine (T-DM1), a HER2-targeted antibody-drug conjugate, could improve its anti-tumor activity by enhancing targeted delivery efficiency and increasing lysosomal drug delivery. This study designed a new NCR vector to non-covalently bind T-DM1 by fusing domain Z, which can specifically bind to the Fc region of immunoglobulin G and effectively deliver T-DM1 to lysosomes. MTT results showed that the domain Z-NCR vector significantly enhanced the cytotoxicity of T-DM1 against HER2-positive tumor cells while maintaining drug specificity. Our results make a useful attempt to explore the potential application of CPP as a lysosome-targeted delivery tool.

Peptides as Targeting Agents and Therapeutics: A Brief Overview

The controllability and specificity of peptides make them ideal for targeting therapeutic delivery systems and as therapeutic agents that interfere with the essential functions of pathogens and tumors. Peptides can also mimic natural protein structures or parts thereof, agonize receptors, and be conjugated to other molecules that will self-assemble. In this short Review, we discuss research from the last ten years into peptide use in three arenas: the treatment of cancer, the treatment of pathogens, and the targeting of specific organs and organelles. These studies demonstrate the successful application of targeting and therapeutic peptides in vitro and in vivo and show the promising range of applications peptides can have going forward.

Unlocking Genome Editing: Advances and Obstacles in CRISPR/Cas Delivery Technologies

CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats associated with protein 9) was first identified as a component of the bacterial adaptive immune system and subsequently engineered into a genome-editing tool. The key breakthrough in this field came with the realization that CRISPR/Cas9 could be used in mammalian cells to enable transformative genetic editing. This technology has since become a vital tool for various genetic manipulations, including gene knockouts, knock-in point mutations, and gene regulation at both transcriptional and post-transcriptional levels. CRISPR/Cas9 holds great potential in human medicine, particularly for curing genetic disorders. However, despite significant innovation and advancement in genome editing, the technology still possesses critical limitations, such as off-target effects, immunogenicity issues, ethical considerations, regulatory hurdles, and the need for efficient delivery methods. To overcome these obstacles, efforts have focused on creating more accurate and reliable Cas9 nucleases and exploring innovative delivery methods. Recently, functional biomaterials and synthetic carriers have shown great potential as effective delivery vehicles for CRISPR/Cas9 components. In this review, we attempt to provide a comprehensive survey of the existing CRISPR-Cas9 delivery strategies, including viral delivery, biomaterials-based delivery, synthetic carriers, and physical delivery techniques. We underscore the urgent need for effective delivery systems to fully unlock the power of CRISPR/Cas9 technology and realize a seamless transition from benchtop research to clinical applications.

Virtual high throughput screening of natural peptides against ErbB1 and ErbB2 to identify potential inhibitors for cancer chemotherapy

Human epidermal growth factor receptors (EGFR), namely ErbB1/HER1, ErbB2/HER2/neu, ErbB3/HER3, and ErbB4/HER4, the trans-membrane family of tyrosine kinase receptors, are overexpressed in many types of cancers. These receptors play an important role in cell proliferation, differentiation, invasion, metastasis and angiogenesis including unregulated activation of cancer cells. Overexpression of ErbB1 and ErbB2 that occurs in several types of cancers is associated with poor prognosis leading to resistance to ErbB1-directed therapies. In this connection, promising strategy to overcome the disadvantages of the existing chemotherapeutic drugs is the use of short peptides as anticancer agents. In the present study, we have performed virtual high throughput screening of natural peptides against ErbB1 and ErbB2 to identify potential dual inhibitors and identified five inhibitors based on their binding affinities, ADMET analysis, MD simulation studies and calculation of free energy of binding. These natural peptides could be further exploited for developing drugs for treating cancer.Communicated by Ramaswamy H. Sarma.

A new trivalent recombinant protein for type 2 diabetes mellitus with oral delivery potential: design, expression, and experimental validation

Glucagon-like peptide-1 (GLP-1) receptor agonists are increasingly used in clinical practice for the management of type 2 diabetes mellitus. However, the extremely short half-life of GLP-1 and the need for subcutaneous administration limit its clinical application. Thus, half-life extension and alternative delivery methods are highly desired. DARPin domains with high affinity for human serum albumin (HSA) have been selected for the half-life extension of therapeutic peptides and proteins. In the present study, novel trivalent fusion proteins as long-acting GLP-1 receptor agonists with potential for oral delivery were computationally engineered by incorporating a protease-resistant modified GLP-1, an anti-human serum albumin DARPin, and an approved cell-penetrating peptide (Penetratin, Tat, and Polyarginine) linked either by rigid or flexible linkers. Theoretical studies and molecular dynamics simulation results suggested that mGLP1-DARPin-Pen has acceptable quality and stability. Moreover, the potential affinity of the selected fusion proteins for GLP-1 receptor and human serum albumin was explored by molecular docking. The recombinant construct was cloned into the pET28a vector and expressed in Escherichia coli. SDS-PAGE analysis of the purified fusion protein matched its molecular size and was confirmed by western blot analysis. The results demonstrated that the engineered fusion protein could bind HSA with high affinity. Importantly, insulin secretion assays using a mouse pancreatic β-cell line (β-TC6) revealed that the engineered trivalent fusion protein retained the ability to stimulate cellular insulin secretion. Immunofluorescence microscopy analysis indicated the CPP-dependent cellular uptake of mGLP1-DARPin-Pen. These findings demonstrated that mGLP1-DARPin-Pen is a highly potent oral drug candidate that could be particularly useful in the treatment of type 2 diabetes mellitus.

Dynamics and recognition of homeodomain containing protein-DNA complex of IRX4

Iroquois Homeobox 4 (IRX4) belongs to a family of homeobox TFs having roles in embryogenesis, cell specification, and organ development. Recently, large scale genome-wide association studies and epigenetic studies have highlighted the role of IRX4 and its associated variants in prostate cancer. No studies have investigated and characterized the structural aspect of the IRX4 homeodomain and its potential to bind to DNA. The current study uses sequence analysis, homology modeling, and molecular dynamics simulations to explore IRX4 homeodomain-DNA recognition mechanisms and the role of somatic mutations affecting these interactions. Using publicly available databases, gene expression of IRX4 was found in different tissues, including prostate, heart, skin, vagina, and the protein expression was found in cancer cell lines (HCT166, HEK293), B cells, ascitic fluid, and brain. Sequence conservation of the homeodomain shed light on the importance of N- and C-terminal residues involved in DNA binding. The specificity of IRX4 homodimer bound to consensus human DNA sequence was confirmed by molecular dynamics simulations, representing the role of conserved amino acids including R145, A194, N195, S190, R198, and R199 in binding to DNA. Additional N-terminal residues like T144 and G143 were also found to have specific interactions highlighting the importance of N-terminus of the homeodomain in DNA recognition. Additionally, the effects of somatic mutations, including the conserved Arginine (R145, R198, and R199) residues on DNA binding elucidated the importance of these residues in stabilizing the protein-DNA complex. Secondary structure and hydrogen bonding analysis showed the roles of specific residues (R145, T191, A194, N195, R198, and R199) in maintaining the homogeneity of the structure and its interaction with DNA. The differences in relative binding free energies of all the mutants shed light on the structural modularity of this protein and the dynamics behind protein-DNA interaction. We also have predicted that the C-terminal sequence of the IRX4 homeodomain could act as a potential cell-penetrating peptide, emphasizing the role these small peptides could play in targeting homeobox TFs.

PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction

MOTIVATION

Effective drug delivery systems are paramount in enhancing pharmaceutical outcomes, particularly through the use of cell-penetrating peptides (CPPs). These peptides are gaining prominence due to their ability to penetrate eukaryotic cells efficiently without inflicting significant damage to the cellular membrane, thereby ensuring optimal drug delivery. However, the identification and characterization of CPPs remain a challenge due to the laborious and time-consuming nature of conventional methods, despite advances in proteomics. Current computational models, however, are predominantly tailored for balanced datasets, an approach that falls short in real-world applications characterized by a scarcity of known positive CPP instances.

RESULTS

To navigate this shortfall, we introduce PractiCPP, a novel deep-learning framework tailored for CPP prediction in highly imbalanced data scenarios. Uniquely designed with the integration of hard negative sampling and a sophisticated feature extraction and prediction module, PractiCPP facilitates an intricate understanding and learning from imbalanced data. Our extensive computational validations highlight PractiCPP's exceptional ability to outperform existing state-of-the-art methods, demonstrating remarkable accuracy, even in datasets with an extreme positive-to-negative ratio of 1:1000. Furthermore, through methodical embedding visualizations, we have established that models trained on balanced datasets are not conducive to practical, large-scale CPP identification, as they do not accurately reflect real-world complexities. In summary, PractiCPP potentially offers new perspectives in CPP prediction methodologies. Its design and validation, informed by real-world dataset constraints, suggest its utility as a valuable tool in supporting the acceleration of drug delivery advancements.

AVAILABILITY AND IMPLEMENTATION

The source code of PractiCPP is available on Figshare at https://doi.org/10.6084/m9.figshare.25053878.v1.

Cell-penetrating peptides predictors: A comparative analysis of methods and datasets

Cell-Penetrating Peptides (CPP) are emerging as an alternative to small-molecule drugs to expand the range of biomolecules that can be targeted for therapeutic purposes. Due to the importance of identifying and designing new CPP, a great variety of predictors have been developed to achieve these goals. To establish a ranking for these predictors, a couple of recent studies compared their performances on specific datasets, yet their conclusions cannot determine if the ranking obtained is due to the model, the set of descriptors or the datasets used to test the predictors. We present a systematic study of the influence of the peptide sequence's similarity of the datasets on the predictors' performance. The analysis reveals that the datasets used for training have a stronger influence on the predictors performance than the model or descriptors employed. We show that datasets with low sequence similarity between the positive and negative examples can be easily separated, and the tested classifiers showed good performance on them. On the other hand, a dataset with high sequence similarity between CPP and non-CPP will be a hard dataset, and it should be the one to be used for assessing the performance of new predictors.

Structural dynamics influences the antibacterial activity of a cell-penetrating peptide (KFF)3K

Given the widespread demand for novel antibacterial agents, we modified a cell-penetrating peptide (KFF)3K to transform it into an antibacterial peptide. Namely, we inserted a hydrocarbon staple into the (KFF)3K sequence to induce and stabilize its membrane-active secondary structure. The staples were introduced at two positions, (KFF)3K[5-9] and (KFF)3K[2-6], to retain the initial amphipathic character of the unstapled peptide. The stapled analogues are protease resistant contrary to (KFF)3K; 90% of the stapled (KFF)3K[5-9] peptide remained undigested after incubation in chymotrypsin solution. The stapled peptides showed antibacterial activity (with minimal inhibitory concentrations in the range of 2-16 µM) against various Gram-positive and Gram-negative strains, contrary to unmodified (KFF)3K, which had no antibacterial effect against any strain at concentrations up to 32 µM. Also, both stapled peptides adopted an α-helical structure in the buffer and micellar environment, contrary to a mostly undefined structure of the unstapled (KFF)3K in the buffer. We found that the antibacterial activity of (KFF)3K analogues is related to their disruptive effect on cell membranes and we showed that by stapling this cell-penetrating peptide, we can induce its antibacterial character.

Molecular Dynamics Simulations of Drug-Conjugated Cell-Penetrating Peptides

Cell-penetrating peptides (CPPs) are small peptides capable of translocating through biological membranes carrying various attached cargo into cells and even into the nucleus. They may also participate in transcellular transport. Our in silico study intends to model several peptides and their conjugates. We have selected three CPPs with a linear backbone, including penetratin, a naturally occurring oligopeptide; two of its modified sequence analogues (6,14-Phe-penetratin and dodeca-penetratin); and three natural CPPs with a cyclic backbone: Kalata B1, the Sunflower trypsin inhibitor 1 (SFT1), and Momordica cochinchinensis trypsin inhibitor II (MCoTI-II). We have also built conjugates with the small-molecule drug compounds doxorubicin, zidovudine, and rasagiline for each peptide. Molecular dynamics (MD) simulations were carried out with explicit membrane models. The analysis of the trajectories showed that the interaction of penetratin with the membrane led to spectacular rearrangements in the secondary structure of the peptide, while cyclic peptides remained unchanged due to their high conformational stability. Membrane-peptide and membrane-conjugate interactions have been identified and compared. Taking into account well-known examples from the literature, our simulations demonstrated the utility of computational methods for CPP complexes, and they may contribute to a better understanding of the mechanism of penetration, which could serve as the basis for delivering conjugated drug molecules to their intracellular targets.

Membrane-Active Peptides and Their Potential Biomedical Application

Membrane-active peptides (MAPs) possess unique properties that make them valuable tools for studying membrane structure and function and promising candidates for therapeutic applications. This review paper provides an overview of the fundamental aspects of MAPs, focusing on their membrane interaction mechanisms and potential applications. MAPs exhibit various structural features, including amphipathic structures and specific amino acid residues, enabling selective interaction with multiple membranes. Their mechanisms of action involve disrupting lipid bilayers through different pathways, depending on peptide properties and membrane composition. The therapeutic potential of MAPs is significant. They have demonstrated antimicrobial activity against bacteria and fungi, making them promising alternatives to conventional antibiotics. MAPs can selectively target cancer cells and induce apoptosis, opening new avenues in cancer therapeutics. Additionally, MAPs serve as drug delivery vectors, facilitating the transport of therapeutic cargoes across cell membranes. They represent a fascinating class of biomolecules with significant potential in basic research and clinical applications. Understanding their mechanisms of action and designing peptides with enhanced selectivity and efficacy will further expand their utility in diverse fields. Exploring MAPs holds promise for developing novel therapeutic strategies against infections, cancer, and drug delivery challenges.

Effect of the Lipid Landscape on the Efficacy of Cell-Penetrating Peptides

Every cell biological textbook teaches us that the main role of the plasma membrane is to separate cells from their neighborhood to allow for a controlled composition of the intracellular space. The mostly hydrophobic nature of the cell membrane presents an impenetrable barrier for most hydrophilic molecules larger than 1 kDa. On the other hand, cell-penetrating peptides (CPPs) are capable of traversing this barrier without compromising membrane integrity, and they can do so on their own or coupled to cargos. Coupling biologically and medically relevant cargos to CPPs holds great promise of delivering membrane-impermeable drugs into cells. If the cargo is able to interact with certain cell types, uptake of the CPP-drug complex can be tailored to be cell-type-specific. Besides outlining the major membrane penetration pathways of CPPs, this review is aimed at deciphering how properties of the membrane influence the uptake mechanisms of CPPs. By summarizing an extensive body of experimental evidence, we argue that a more ordered, less flexible membrane structure, often present in the very diseases planned to be treated with CPPs, decreases their cellular uptake. These correlations are not only relevant for understanding the cellular biology of CPPs, but also for rationally improving their value in translational or clinical applications.

Passing of Nanocarriers across the Histohematic Barriers: Current Approaches for Tumor Theranostics

Over the past several decades, nanocarriers have demonstrated diagnostic and therapeutic (i.e., theranostic) potencies in translational oncology, and some agents have been further translated into clinical trials. However, the practical application of nanoparticle-based medicine in living organisms is limited by physiological barriers (blood-tissue barriers), which significantly hampers the transport of nanoparticles from the blood into the tumor tissue. This review focuses on several approaches that facilitate the translocation of nanoparticles across blood-tissue barriers (BTBs) to efficiently accumulate in the tumor. To overcome the challenge of BTBs, several methods have been proposed, including the functionalization of particle surfaces with cell-penetrating peptides (e.g., TAT, SynB1, penetratin, R8, RGD, angiopep-2), which increases the passing of particles across tissue barriers. Another promising strategy could be based either on the application of various chemical agents (e.g., efflux pump inhibitors, disruptors of tight junctions, etc.) or physical methods (e.g., magnetic field, electroporation, photoacoustic cavitation, etc.), which have been shown to further increase the permeability of barriers.

Targeted nanomedicine: Lessons learned and future directions

Designing a therapeutic modality that will reach a certain organ, tissue, or cell type is crucial for both the therapeutic efficiency and to limit off-target adverse effects. Nanoparticles carrying various drugs, such as nucleic acids, small molecules and proteins, are promoting modalities to this end. Beyond the need to identify a target for a specific indication, an adequate design has to address the multiple biological barriers, such as systemic barriers, dilution and unspecific distribution, tissue penetration and intracellular trafficking. The field of targeted delivery has developed rapidly in recent years, with tremendous progress made in understating the biological barriers, and new technologies to functionalize nanoparticles with targeting moieties for an accurate, specific and highly selective delivery. Implementing new approaches like multi-functionalized nanocarriers and machine learning models will advance the field for designing safe, cell -specific nanoparticle delivery systems. Here, we will critically review the current progress in the field and suggest novel strategies to improve cell specific delivery of therapeutic payloads.

Interaction of Amphipathic Peptide from Influenza Virus M1 Protein with Mitochondrial Cytochrome Oxidase

The Bile Acid Binding Site (BABS) of cytochrome oxidase (CcO) binds numerous amphipathic ligands. To determine which of the BABS-lining residues are critical for interaction, we used the peptide P4 and its derivatives A1-A4. P4 is composed of two flexibly bound modified α-helices from the M1 protein of the influenza virus, each containing a cholesterol-recognizing CRAC motif. The effect of the peptides on the activity of CcO was studied in solution and in membranes. The secondary structure of the peptides was examined by molecular dynamics, circular dichroism spectroscopy, and testing the ability to form membrane pores. P4 was found to suppress the oxidase but not the peroxidase activity of solubilized CcO. The K_i(app) is linearly dependent on the dodecyl-maltoside (DM) concentration, indicating that DM and P4 compete in a 1:1 ratio. The true K_i is 3 μM. The deoxycholate-induced increase in K_i(app) points to a competition between P4 and deoxycholate. A1 and A4 inhibit solubilized CcO with K_i(app)~20 μM at 1 mM DM. A2 and A3 hardly inhibit CcO either in solution or in membranes. The mitochondrial membrane-bound CcO retains sensitivity to P4 and A4 but acquires resistance to A1. We associate the inhibitory effect of P4 with its binding to BABS and dysfunction of the proton channel K. Trp residue is critical for inhibition. The resistance of the membrane-bound enzyme to inhibition may be due to the disordered secondary structure of the inhibitory peptide.

Exploring Cell-Penetrating Peptides as Penetration Enhancers in Eye Drop Formulations Using a Reconstructed Human Corneal Epithelial Model

Ocular tissues function as biological barriers that hinder drug delivery, depending on the target tissue and route of administration, and must be overcome to achieve the desired therapeutic effect. Penetration enhancers have long been investigated to improve corneal drug penetration via eye drop instillation; however, further development is warranted owing to potential safety concerns. In the present study, we focused on cell-penetrating peptides (CPPs) as a penetration enhancer to address the requirements and explored CPP candidates suitable for corneal drug delivery. Using a reconstructed human corneal epithelial tissue model, LabCyte CORNEA-MODEL24 as an alternative to animal testing that is expected to have higher reproducibility than extracted eyeballs and octa-arginine (R8) as a representative model CPP with simple structure, we investigated the enhancement of 6-carboxyfluorescein (6-FAM) uptake by fluorescence imaging and the potential of eye irritation by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Also, surface plasmon resonance (SPR) evaluated the interaction between R8 and model compounds, suggesting that the stronger interaction could facilitate the corneal uptake of compounds. A comparative screening study of corneal uptake using various CPPs showed that the CPPs other than R8 also have the potential to enhance the corneal uptake of 6-FAM. In particular, penetratin (PNT) showed stronger fluorescence intensity. Through these findings, this manuscript provides beneficial information for the development of a novel corneal penetration enhancer with CPPs. In the future, it is expected that the basic findings with R8 will be verified to be applicable to other CPPs for development as penetration enhancers for eye drop formulation.

Potential of Nuclear Imaging Techniques to Study the Oral Delivery of Peptides

Peptides are small biomolecules known to stimulate or inhibit important functions in the human body. The clinical use of peptides by oral delivery, however, is very limited due to their sensitive structure and physiological barriers present in the gastrointestinal tract. These barriers can be overcome with chemical and mechanical approaches protease inhibitors, permeation enhancers, and polymeric encapsulation. Studying the success of these approaches pre-clinically with imaging techniques such as fluorescence imaging (IVIS) and optical microscopy is difficult due to the lack of in-depth penetration. In comparison, nuclear imaging provides a better platform to observe the gastrointestinal transit and quantitative distribution of radiolabeled peptides. This review provides a brief background on the oral delivery of peptides and states examples from the literature on how nuclear imaging can help to observe and analyze the gastrointestinal transit of oral peptides. The review connects the fields of peptide delivery and nuclear medicine in an interdisciplinary way to potentially overcome the challenges faced during the study of oral peptide formulations.

In Silico Prediction of Anti-Infective and Cell-Penetrating Peptides from Thalassophryne nattereri Natterin Toxins

The therapeutic potential of venom-derived peptides, such as bioactive peptides (BAPs), is determined by specificity, stability, and pharmacokinetics properties. BAPs, including anti-infective or antimicrobial peptides (AMPs) and cell-penetrating peptides (CPPs), share several physicochemical characteristics and are potential alternatives to antibiotic-based therapies and drug delivery systems, respectively. This study used in silico methods to predict AMPs and CPPs derived from natterins from the venomous fish Thalassophryne nattereri. Fifty-seven BAPs (19 AMPs, 8 CPPs, and 30 AMPs/CPPs) were identified using the web servers CAMP, AMPA, AmpGram, C2Pred, and CellPPD. The physicochemical properties were analyzed using ProtParam, PepCalc, and DispHred tools. The membrane-binding potential and cellular location of each peptide were analyzed using the Boman index by APD3, and TMHMM web servers. All CPPs and two AMPs showed high membrane-binding potential. Fifty-four peptides were located in the plasma membrane. Peptide immunogenicity, toxicity, allergenicity, and ADMET parameters were evaluated using several web servers. Sixteen antiviral peptides and 37 anticancer peptides were predicted using the web servers Meta-iAVP and ACPred. Secondary structures and helical wheel projections were predicted using the PEP-FOLD3 and Heliquest web servers. Fifteen peptides are potential lead compounds and were selected to be further synthesized and tested experimentally in vitro to validate the in silico screening. The use of computer-aided design for predicting peptide structure and activity is fast and cost-effective and facilitates the design of potent therapeutic peptides. The results demonstrate that toxins form a natural biotechnological platform in drug discovery, and the presence of CPP and AMP sequences in toxin families opens new possibilities in toxin biochemistry research.

DeepCPPred: A Deep Learning Framework for the Discrimination of Cell-Penetrating Peptides and Their Uptake Efficiencies

Cell-penetrating peptides (CPPs) are special peptides capable of carrying a variety of bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles, into cells. Recently, research on CPP has gained substantial interest from researchers, and the biological mechanisms of CPPS have been assessed in the context of safe drug delivery agents and therapeutic applications. Correct identification and synthesis of CPPs using traditional biochemical methods is an extremely slow, expensive and laborious task particularly due to the large volume of unannotated peptide sequences accumulating in the World Bank repository. Hence, a powerful bioinformatics predictor that rapidly identifies CPPs with a high recognition rate is urgently needed. To date, numerous computational methods have been developed for CPP prediction. However, the available machine-learning (ML) tools are unable to distinguish both the CPPs and their uptake efficiencies. This study aimed to develop a two-layer deep learning framework named DeepCPPred to identify both CPPs in the first phase and peptide uptake efficiency in the second phase. The DeepCPPred predictor first uses four types of descriptors that cover evolutionary, energy estimation, reduced sequence and amino-acid contact information. Then, the extracted features are optimized through the elastic net algorithm and fed into a cascade deep forest algorithm to build the final CPP model. The proposed method achieved 99.45 percent overall accuracy with the CPP924 benchmark dataset in the first layer and 95.43 percent accuracy in the second layer with the CPPSite3 dataset using a 5-fold cross-validation test. Thus, our proposed bioinformatics tool surpassed all the existing state-of-the-art sequence-based CPP approaches.

AntiDMPpred: a web service for identifying anti-diabetic peptides

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

Design and use of chimeric peptides in a new non-destructive ecological process applied to the extraction of all trans/9-cis β-carotene isomers from Dunaliella salina

Recently, β-carotene has gained tremendous importance as a bioactive molecule due to the growing awareness of the harmful effects of synthetic products. β-carotene is a high-value natural pigment that has the highest demand in the global carotenoid market owing to its proven antioxidant properties relevant for several diseases. To date, Dunaliella salina is the most important producer of natural β-carotene and is the subject of important industrial efforts. However, the extraction of β-carotene remains challenging since all the proposed techniques present a risk of product contamination or loss of quality due to solvent residuals and low yields. The purpose of this study was to set up a green, ecological, and innovative process of extraction of the two major β-carotene isomers from the halophilic microalgae Dunaliella salina. Based on molecular modeling, docking, and drug design, we conceived and synthesized two chimeric peptides (PP2, PP3) targeting specifically the two major isomers: all-trans or 9-cis β-carotene. The experimental protocol used in this study demonstrated the ability and the efficacy of those two peptides to cross the cell membrane and bind with high affinity to β-carotene isomers and exclude them toward the extracellular medium while preserving the integrity of living cells. Interestingly, the tested peptides (PP2, PP3) exhibit significant β-carotene extraction yields 58% and 34%, respectively, from the total of the β-carotene in microalgae cells. In addition to its simplicity, this process is fast, independent of the source of the β-carotene, and selective. These results would allow us to set up a green, ecological, and very profitable process of extraction from microalgae containing high amounts of β-carotene. Our innovative approach is highly promising for the extraction of Dunaliella salina biomass on an industrial scale.

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy.

An in silico scientific basis for LL-37 as a therapeutic for Covid-19

A multi-pronged approach with help in all forms possible is essential to completely overcome the Covid-19 pandemic. There is a requirement to research as many new and different types of approaches as possible to cater to the entire world population, complementing the vaccines with promising results. The need is also because SARS-CoV-2 has several unknown or variable facets which get revealed from time to time. In this work, in silico scientific findings are presented, which are indicative of the potential for the use of the LL-37 human anti-microbial peptide as a therapeutic against SARS-CoV-2. This indication is based on the high structural similarity of LL-37 to the N-terminal helix, with which the virus interacts, of the receptor for SARS-CoV-2, Angiotensin Converting Enzyme 2. Moreover, there is positive prediction of binding of LL-37 to the receptor-binding domain of SARS-CoV-2; this is the first study to have described this interaction. In silico data on the safety of LL-37 are also reported. As Vitamin D is known to upregulate the expression of LL-37, the vitamin is a candidate preventive molecule. This work provides the possible basis for an inverse correlation between Vitamin D levels in the body and the severity of or susceptibility to Covid-19, as widely reported in literature. With the scientific link put forth herein, Vitamin D could be used at an effective, medically prescribed, safe dose as a preventive. The information in this report would be valuable in bolstering the worldwide efforts to eliminate the pandemic as early as possible.

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.

Characterization of Proteins from Putative Human DNA and RNA Viruses

Background:

In the vast variety of viruses known, there is a particular interest in those transmitted to humans and whose ability to disseminate represents a significant public health issue.

Objective:

The present study’s objective is to bioinformatically characterize the proteins of the two main divisions of viruses, RNA-viruses and DNA-viruses.

Methods:

In this work, a set of in-house computational programs was used to calculate the polarity/charge profiles and intrinsic disorder predisposition profiles of the proteins of several groups of viruses representing both types extracted from UniProt database. The efficiency of these computational programs was statistically verified.

Results:

It was found that the polarity/charge profile of the proteins is, in most cases, an efficient discriminant that allows the re-creation of the taxonomy known for both viral groups. Additionally, the entire set of "reviewed" proteins in UniProt database was analyzed to find proteins with the polarity/charge profiles similar to those obtained for each viral group. This search revealed a substantial number of proteins with such polarity-charge profiles.

Conclusion:

Polarity/charge profile represents a physicochemical metric, which is easy to calculate, and which can be used to effectively identify viral groups from their protein sequences.

Cell-Penetrating Peptides

In this introductory chapter, we first define cell-penetrating peptides (CPPs), give short overview of CPP history and discuss several aspects of CPP classification. Next section is devoted to the mechanism of CPP penetration into the cells, where direct and endocytic internalization of CPP is explained. Kinetics of internalization is discussed more extensively, since this topic is not discussed in other chapters of this book. At the end of this section some features of the thermodynamics of CPP interaction with the membrane is also presented. Finally, we present different cargoes that can be transferred into the cells by CPPs and briefly discuss the effect of cargo on the rate and efficiency of penetration into the cells.

Better Performance with Transformer: CPPFormer in precise prediction of cell-Penetrating Peptides

With its superior performance, the Transformer model, which is based on the 'Encoder-Decoder' paradigm, has become the mainstream in natural language processing. On the other hand, bioinformatics has embraced machine learning and made great progress in drug design and protein property prediction. Cell-penetrating peptides (CPPs) are one kind of permeable protein that is convenient as a kind of 'postman' in drug penetration tasks. However, a small number of CPPs have been discovered by research, let alone practical applications in drug permeability. Therefore, correctly identifying the CPPs has opened up a new way to take macromolecules into cells without other potentially harmful materials in the drug. Most of the previous work only uses trivial machine learning techniques and hand-crafted features to construct a simple classifier. In CPPFormer, we learn from the idea of implementing the attention structure of Transformer, rebuilding the network based on the characteristics of CPPs according to its short length, and using an automatic feature extractor with a few manual engineered features to co-direct the predicted results. Compared to all previous methods and other classic text classification models, the empirical result has shown that our proposed deep model-based method has achieved the best performance of 92.16% accuracy in the CPP924 dataset and has passed various index tests.

Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20-25 residues long than peptides in other length ranges.

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

The blood–brain barrier is a major obstacle in treating brain-related disorders, as it does not allow the delivery of drugs into the brain. We developed a method for predicting blood–brain barrier penetrating peptides to facilitate drug delivery into the brain. These blood–brain barrier penetrating peptides (B3PPs) can act as therapeutics, as well as drug delivery agents. We trained, tested, and evaluated our models on blood–brain barrier peptides obtained from the B3Pdb database. First, we computed a wide range of peptide features. Then, we selected relevant peptide features. Finally, we developed numerous machine-learning-based models for predicting blood–brain barrier peptides using the selected features. The random-forest-based model performed the best with respect to the top 80 selected features and achieved a maximal 85.08% accuracy with an AUROC of 0.93. We also developed a webserver, B3pred, that implements our best models. It has three major modules that allow users to predict/design B3PPs and scan B3PPs in a protein sequence.

Antibiotic-cell-penetrating peptide conjugates targeting challenging drug-resistant and intracellular pathogenic bacteria

The failure to treat everyday bacterial infections is a current threat as pathogens are finding new ways to thwart antibiotics through mechanisms of resistance and intracellular refuge, thus rendering current antibiotic strategies ineffective. Cell-penetrating peptides (CPPs) are providing a means to improve antibiotics that are already approved for use. Through coadministration and conjugation of antibiotics with CPPs, improved accumulation and selectivity with alternative and/or additional modes of action against infections have been observed. Herein, we review the recent progress of this antibiotic-cell-penetrating peptide strategy in combatting sensitive and drug-resistant pathogens. We take a closer look into the specific antibiotics that have been enhanced, and in some cases repurposed as broad-spectrum drugs. Through the addition and conjugation of cell-penetrating peptides to antibiotics, increased permeation across mammalian and/or bacterial membranes and a broader range in bacterial selectivity have been achieved.

Cell-penetrating peptides in the intracellular delivery of viral nanoparticles

Cell-penetrating peptides (CPPs) are a promising tool for the intracellular delivery of cargo. Due to their ability to cross membranes while also cotransporting various cargoes, they offer great potential for biomedical applications. Several CPPs have been derived from viral proteins with natural roles in the viral replication cycle that require them to breach or fuse to cellular membranes. Additionally, the ability of viruses to cross membranes makes viruses and virus-based particles a convenient model for research on nanoparticle delivery and nanoparticle-mediated gene therapy. In this chapter, we aim to characterize CPPs derived from both structural and nonstructural viral proteins. Their function as enhancers of viral infection and transduction by viral nanoparticles as well as the main features of viral CPPs employed in intracellular cargo delivery are summarized to emphasize their potential use in nanomedicine.

Source and exploration of the peptides used to construct peptide-drug conjugates

Peptide-drug conjugates (PDCs) are a class of novel molecules widely designed and synthesized for delivering payload drugs. The peptide part plays a vital role in the whole molecule, because they determine the ability of the molecules to penetrate the membrane and target to the specific targets. Here, we introduce the source of different kinds of cell-penetrating peptides (CPPs) and cell-targeting peptides (CTPs) that have been used or could be used in constructing PDCs as well as their latest application in delivering drugs. What's more, the approaches of developing CPPs and CTPs and the techniques to discover novel peptides are focused on and summarized in the review. This review aims to help relevant researchers fast understand the research status of peptides in PDCs and carry forward the process of novel peptides discovery.

Peptide-Enabled Targeted Delivery Systems for Therapeutic Applications

Receptor-targeting peptides have been extensively pursued for improving binding specificity and effective accumulation of drugs at the site of interest, and have remained challenging for extensive research efforts relating to chemotherapy in cancer treatments. By chemically linking a ligand of interest to drug-loaded nanocarriers, active targeting systems could be constructed. Peptide-functionalized nanostructures have been extensively pursued for biomedical applications, including drug delivery, biological imaging, liquid biopsy, and targeted therapies, and widely recognized as candidates of novel therapeutics due to their high specificity, well biocompatibility, and easy availability. We will endeavor to review a variety of strategies that have been demonstrated for improving receptor-specificity of the drug-loaded nanoscale structures using peptide ligands targeting tumor-related receptors. The effort could illustrate that the synergism of nano-sized structures with receptor-targeting peptides could lead to enrichment of biofunctions of nanostructures.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

In Silico Selection and Evaluation of Pugnins with Antibacterial and Anticancer Activity Using Skin Transcriptome of Treefrog (Boana pugnax)

In order to combat bacterial and cancer resistance, we identified peptides (pugnins) with dual antibacterial l-anticancer activity from the Boana pugnax (B. pugnax) skin transcriptome through in silico analysis. Pugnins A and B were selected owing to their high similarity to the DS4.3 peptide, which served as a template for their alignment to the B. pugnax transcriptome, as well as their function as part of a voltage-dependent potassium channel protein. The secondary peptide structure stability in aqueous medium was evaluated as well, and after interaction with the Escherichia coli (E. coli) membrane model using molecular dynamics. These pugnins were synthesized via solid-phase synthesis strategy and verified by Reverse phase high-performance liquid chromatography (RP-HPLC) and mass spectrometry. Subsequently, their alpha-helix structure was determined by circular dichroism, after which antibacterial tests were then performed to evaluate their antimicrobial activity. Cytotoxicity tests against cancer cells also showed selectivity of pugnin A toward breast cancer (MFC7) cells, and pugnin B toward prostate cancer (PC3) cells. Alternatively, flow cytometry revealed necrotic cell damage with a major cytotoxic effect on human keratinocytes (HaCaT) control cells. Therefore, the pugnins found in the transcriptome of B. pugnax present dual antibacterial-anticancer activity with reduced selectivity to normal eukaryotic cells.

FermFooDb: A database of bioactive peptides derived from fermented foods

Globally fermented foods are in demands due to their functional and nutritional benefits. These foods are sources of probiotic organisms and bioactive peptides, various amino acids, enzymes etc. that provides numerous health benefits. FermFooDb (https://webs.iiitd.edu.in/raghava/fermfoodb/) is a manually curated database of bioactive peptides derived from wide range of foods that maintain comprehensive information about peptides and process of fermentation. This database comprises of 2205 entries with following major fields, peptide sequence, Mass and IC50, food source, functional activity, fermentation conditions, starter culture, testing conditions of sequences in vitro or in vivo, type of model and method of analysis. The bioactive peptides in our database have wide range of therapeutic potentials that includes antihypertensive, ACE-inhibitory, antioxidant, antimicrobial, immunomodulatory and cholesterol lowering peptides. These bioactive peptides were derived from different types of fermented foods that include milk, cheese, yogurt, wheat and rice. Numerous, web-based tools have been integrated to retrieve data, peptide mapping of proteins, similarity search and multiple-sequence alignment. This database will be useful for the food industry and researchers to explore full therapeutic potential of fermented foods from specific cultures.

How arginine derivatives alter the stability of lipid membranes: dissecting the roles of side chains, backbone and termini

Arginine (R)-rich peptides constitute the most relevant class of cell-penetrating peptides and other membrane-active peptides that can translocate across the cell membrane or generate defects in lipid bilayers such as water-filled pores. The mode of action of R-rich peptides remains a topic of controversy, mainly because a quantitative and energetic understanding of arginine effects on membrane stability is lacking. Here, we explore the ability of several oligo-arginines R[Formula: see text] and of an arginine side chain mimic R[Formula: see text] to induce pore formation in lipid bilayers employing MD simulations, free-energy calculations, breakthrough force spectroscopy and leakage assays. Our experiments reveal that R[Formula: see text] but not R[Formula: see text] reduces the line tension of a membrane with anionic lipids. While R[Formula: see text] peptides form a layer on top of a partly negatively charged lipid bilayer, R[Formula: see text] leads to its disintegration. Complementary, our simulations show R[Formula: see text] causes membrane thinning and area per lipid increase beside lowering the pore nucleation free energy. Model polyarginine R[Formula: see text] similarly promoted pore formation in simulations, but without overall bilayer destabilization. We conclude that while the guanidine moiety is intrinsically membrane-disruptive, poly-arginines favor pore formation in negatively charged membranes via a different mechanism. Pore formation by R-rich peptides seems to be counteracted by lipids with PC headgroups. We found that long R[Formula: see text] and R[Formula: see text] but not short R[Formula: see text] reduce the free energy of nucleating a pore. In short R[Formula: see text], the substantial effect of the charged termini prevent their membrane activity, rationalizing why only longer [Formula: see text] are membrane-active.

Cell-Penetrating Peptides Derived from Animal Venoms and Toxins

Cell-penetrating peptides (CPPs) comprise a class of short polypeptides that possess the ability to selectively interact with the cytoplasmic membrane of certain cell types, translocate across plasma membranes and accumulate in the cell cytoplasm, organelles (e.g., the nucleus and mitochondria) and other subcellular compartments. CPPs are either of natural origin or de novo designed and synthesized from segments and patches of larger proteins or designed by algorithms. With such intrinsic properties, along with membrane permeation, translocation and cellular uptake properties, CPPs can intracellularly convey diverse substances and nanomaterials, such as hydrophilic organic compounds and drugs, macromolecules (nucleic acids and proteins), nanoparticles (nanocrystals and polyplexes), metals and radionuclides, which can be covalently attached via CPP N- and C-terminals or through preparation of CPP complexes. A cumulative number of studies on animal toxins, primarily isolated from the venom of arthropods and snakes, have revealed the cell-penetrating activities of venom peptides and toxins, which can be harnessed for application in biomedicine and pharmaceutical biotechnology. In this review, I aimed to collate examples of peptides from animal venoms and toxic secretions that possess the ability to penetrate diverse types of cells. These venom CPPs have been chemically or structurally modified to enhance cell selectivity, bioavailability and a range of target applications. Herein, examples are listed and discussed, including cysteine-stabilized and linear, α-helical peptides, with cationic and amphipathic character, from the venom of insects (e.g., melittin, anoplin, mastoparans), arachnids (latarcin, lycosin, chlorotoxin, maurocalcine/imperatoxin homologs and wasabi receptor toxin), fish (pardaxins), amphibian (bombesin) and snakes (crotamine and cathelicidins).

Propedia: a database for protein-peptide identification based on a hybrid clustering algorithm

BACKGROUND

Protein-peptide interactions play a fundamental role in a wide variety of biological processes, such as cell signaling, regulatory networks, immune responses, and enzyme inhibition. Peptides are characterized by low toxicity and small interface areas; therefore, they are good targets for therapeutic strategies, rational drug planning and protein inhibition. Approximately 10% of the ethical pharmaceutical market is protein/peptide-based. Furthermore, it is estimated that 40% of protein interactions are mediated by peptides. Despite the fast increase in the volume of biological data, particularly on sequences and structures, there remains a lack of broad and comprehensive protein-peptide databases and tools that allow the retrieval, characterization and understanding of protein-peptide recognition and consequently support peptide design.

RESULTS

We introduce Propedia, a comprehensive and up-to-date database with a web interface that permits clustering, searching and visualizing of protein-peptide complexes according to varied criteria. Propedia comprises over 19,000 high-resolution structures from the Protein Data Bank including structural and sequence information from protein-peptide complexes. The main advantage of Propedia over other peptide databases is that it allows a more comprehensive analysis of similarity and redundancy. It was constructed based on a hybrid clustering algorithm that compares and groups peptides by sequences, interface structures and binding sites. Propedia is available through a graphical, user-friendly and functional interface where users can retrieve, and analyze complexes and download each search data set. We performed case studies and verified that the utility of Propedia scores to rank promissing interacting peptides. In a study involving predicting peptides to inhibit SARS-CoV-2 main protease, we showed that Propedia scores related to similarity between different peptide complexes with SARS-CoV-2 main protease are in agreement with molecular dynamics free energy calculation.

CONCLUSIONS

Propedia is a database and tool to support structure-based rational design of peptides for special purposes. Protein-peptide interactions can be useful to predict, classifying and scoring complexes or for designing new molecules as well. Propedia is up-to-date as a ready-to-use webserver with a friendly and resourceful interface and is available at: https://bioinfo.dcc.ufmg.br/propedia.

Cppsite 2.0: An Available Database of Experimentally Validated Cell-Penetrating Peptides Predicting their Secondary and Tertiary Structures

One of the biggest barriers in drug and vaccine development is to find an effective delivery system. Cell-penetrating peptides (CPPs) play a crucial role for delivery of biological cargoes and pass them through the membranes. Several databases have been developed for therapeutic peptides as potential drug candidates and delivery vehicles. A rapid growth has occurred in many patents and research articles on CPPs as therapeutic peptides. To save time and cost in laboratories, prediction and design of CPPs before in vitro/in vivo experiments using computational methods and online web servers are rational. Various online web servers which provide prediction of CPPs including CellPPD, CPPpred, CPPred-RF and MLCPP, and also different curated databases that present validated information of CPPs such as CPPsite 2.0 have been developed up to now. Two methods including CellPPD and CPPpred were applied to predict and design potent CPPs. CPPsite 2.0 is a user-friendly updated database that provides various information about CPPs and contains 1855 entries. This database provides comprehensive information on experimentally tested CPPs and prediction of their secondary and tertiary structures to realize their structure-function relationship. Furthermore, each entry presents information of a CPP including chirality, origin, nature of peptide, sub-cellular localization, uptake mechanism and efficiency, amino acid composition, hydrophobicity, and physicochemical properties. One of main goals of CPPsite 2.0 database is to provide the latest datasets of CPPs for analysis and development of CPP prediction methods. CPPsite 2.0 is freely available at https://webs.iiitd.edu.in/raghava/cppsite.

Peptides with Dual Antimicrobial-Anticancer Activity: Strategies to Overcome Peptide Limitations and Rational Design of Anticancer Peptides

Peptides are naturally produced by all organisms and exhibit a wide range of physiological, immunomodulatory, and wound healing functions. Furthermore, they can provide with protection against microorganisms and tumor cells. Their multifaceted performance, high selectivity, and reduced toxicity have positioned them as effective therapeutic agents, representing a positive economic impact for pharmaceutical companies. Currently, efforts have been made to invest in the development of new peptides with antimicrobial and anticancer properties, but the poor stability of these molecules in physiological environments has triggered a bottleneck. Therefore, some tools, such as nanotechnology and in silico approaches can be applied as alternatives to try to overcome these obstacles. In silico studies provide a priori knowledge that can lead to the development of new anticancer peptides with enhanced biological activity and improved stability. This review focuses on the current status of research in peptides with dual antimicrobial-anticancer activity, including advances in computational biology using in silico analyses as a powerful tool for the study and rational design of these types of peptides.

Peptide Fibrillar Assemblies Exhibit Membranolytic Effects and Antimetastatic Activity on Lung Cancer Cells

Cancer metastasis is a central oncology concern that worsens patient conditions and increases mortality in a short period of time. During metastatic events, mitochondria undergo specific physiological alterations that have emerged as notable therapeutic targets to counter cancer progression. In this study, we use drug-free, cationic peptide fibrillar assemblies (PFAs) formed by poly(L-Lysine)-block-poly(L-Threonine) (Lys-b-Thr) to target mitochondria. These PFAs interact with cellular and mitochondrial membranes via electrostatic interactions, resulting in membranolysis. Charge repulsion and hydrogen-bonding interactions exerted by Lys and Thr segments dictate the packing of the peptides and enable the PFAs to display enhanced membranolytic activity toward cancer cells. Cytochrome c (cyt c), endonuclease G, and apoptosis-inducing factor were released from mitochondria after treatment of lung cancer cells, subsequently inducing caspase-dependent and caspase-independent apoptotic pathways. A metastatic xenograft mouse model was used to show how the PFAs significantly suppressed lung metastasis and inhibited tumor growth, while avoiding significant body weight loss and mortality. Antimetastatic activities of PFAs are also demonstrated by in vitro inhibition of lung cancer cell migration and clonogenesis. Our results imply that the cationic PFAs achieved the intended and targeted mitochondrial damage, providing an efficient antimetastatic therapy.

TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree

Cell-penetrating peptides (CPPs) are short length permeable proteins have emerged as drugs delivery tool of therapeutic agents including genetic materials and macromolecules into cells. Recently, CPP has become a hotspot avenue for life science research and paved a new way of disease treatment without harmful impact on cell viability due to nontoxic characteristic. Therefore, the correct identification of CPPs will provide hints for medical applications. Considering the shortcomings of traditional experimental CPPs identification, it is urgently needed to design intelligent predictor for accurate identification of CPPs for the large scale uncharacterized sequences. We develop a novel computational method, called TargetCPP, to discriminate CPPs from Non-CPPs with improved accuracy. In TargetCPP, first the peptide sequences are formulated with four distinct encoding methods i.e., composite protein sequence representation, composition transition and distribution, split amino acid composition, and information theory features. These dominant feature vectors were fused and applied intelligent minimum redundancy and maximum relevancy feature selection method to choose an optimal subset of features. Finally, the predictive model is learned through different classification algorithms on the optimized features. Among these classifiers, gradient boost decision tree algorithm achieved excellent performance throughout the experiments. Notably, the TargetCPP tool attained high prediction Accuracy of 93.54% and 88.28% using jackknife and independent test, respectively. Empirical outcomes prove the superiority and potency of proposed bioinformatics method over state-of-the-art methods. It is highly anticipated that the outcomes of this study will provide a strong background for large scale prediction of CPPs and instructive guidance in clinical therapy and medical applications.