CPPsite 2.0: a repository of experimentally validated cell-penetrating peptides

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.

In Silico Screening and Optimization of Cell-Penetrating Peptides Using Deep Learning Methods

Cell-penetrating peptides (CPPs) have great potential to deliver bioactive agents into cells. Although there have been many recent advances in CPP-related research, it is still important to develop more efficient CPPs. The development of CPPs by in silico methods is a very useful addition to experimental methods, but in many cases it can lead to a large number of false-positive results. In this study, we developed a deep-learning-based CPP prediction method, AiCPP, to develop novel CPPs. AiCPP uses a large number of peptide sequences derived from human-reference proteins as a negative set to reduce false-positive predictions and adopts a method to learn small-length peptide sequence motifs that may have CPP tendencies. Using AiCPP, we found that short peptide sequences derived from amyloid precursor proteins are efficient new CPPs, and experimentally confirmed that these CPP sequences can be further optimized.

Topical Delivery of Cell-Penetrating Peptide-Modified Human Growth Hormone for Enhanced Wound Healing

Protein drugs have been emerging as a class of promising therapeutics. However, their topical application has been limited by their high molecular weight and poor permeability to the cell membrane. In this study, we aimed to enhance human growth hormone (hGH) permeability for topical application by conjugation of TAT peptide, a cell-penetrating peptide, to hGH via crosslinker. After TAT was conjugated to hGH, TAT-hGH was purified by affinity chromatography. TAT-hGH significantly increased cell proliferation compared with the control. Interestingly, the effect of TAT-hGH was higher than hGH at the same concentration. Furthermore, the conjugation of TAT to hGH enhanced the permeability of TAT-hGH across the cell membrane without affecting its biological activity in vitro. In vivo, the topical application of TAT-hGH into scar tissue markedly accelerated wound healing. Histological results showed that TAT-hGH dramatically promoted the re-epithelialization of wounds in the initial stage. These results demonstrate TAT-hGH as a new therapeutic potential drug for wound healing treatment. This study also provides a new method for topical protein application via enhancement of their permeability.

Peptide-drug conjugates (PDCs): a novel trend of research and development on targeted therapy, hype or hope?

Peptide-drug conjugates (PDCs) are the next generation of targeted therapeutics drug after antibody-drug conjugates (ADCs), with the core benefits of enhanced cellular permeability and improved drug selectivity. Two drugs are now approved for market by US Food and Drug Administration (FDA), and in the last two years, the pharmaceutical companies have been developing PDCs as targeted therapeutic candidates for cancer, coronavirus disease 2019 (COVID-19), metabolic diseases, and so on. The therapeutic benefits of PDCs are significant, but poor stability, low bioactivity, long research and development time, and slow clinical development process as therapeutic agents of PDC, how can we design PDCs more effectively and what is the future direction of PDCs? This review summarises the components and functions of PDCs for therapeutic, from drug target screening and PDC design improvement strategies to clinical applications to improve the permeability, targeting, and stability of the various components of PDCs. This holds great promise for the future of PDCs, such as bicyclic peptide‒toxin coupling or supramolecular nanostructures for peptide-conjugated drugs. The mode of drug delivery is determined according to the PDC design and current clinical trials are summarised. The way is shown for future PDC development.

Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models

In the last three decades, a wide range of protein features have been discovered to annotate a protein. Numerous attempts have been made to integrate these features in a software package/platform so that the user may compute a wide range of features from a single source. To complement the existing methods, we developed a method, Pfeature, for computing a wide range of protein features. Pfeature allows to compute more than 200,000 features required for predicting the overall function of a protein, residue-level annotation of a protein, and function of chemically modified peptides. It has six major modules, namely, composition, binary profiles, evolutionary information, structural features, patterns, and model building. Composition module facilitates to compute most of the existing compositional features, plus novel features. The binary profile of amino acid sequences allows to compute the fraction of each type of residue as well as its position. The evolutionary information module allows to compute evolutionary information of a protein in the form of a position-specific scoring matrix profile generated using Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST); fit for annotation of a protein and its residues. A structural module was developed for computing of structural features/descriptors from a tertiary structure of a protein. These features are suitable to predict the therapeutic potential of a protein containing non-natural or chemically modified residues. The model-building module allows to implement various machine learning techniques for developing classification and regression models as well as feature selection. Pfeature also allows the generation of overlapping patterns and features from a protein. A user-friendly Pfeature is available as a web server python library and stand-alone package.

Antiviral Peptide-Based Conjugates: State of the Art and Future Perspectives

Infectious diseases caused by microbial pathogens (bacteria, virus, fungi, parasites) claim millions of deaths per year worldwide and have become a serious challenge to global human health in our century. Viral infections are particularly notable in this regard, not only because humankind is facing some of the deadliest viral pandemics in recent history, but also because the arsenal of drugs to combat the high levels of mutation, and hence the antigenic variability of (mostly RNA) viruses, is disturbingly scarce. Therefore, the search for new antivirals able to successfully fight infection with minimal or no adverse effects on the host is a pressing task. Traditionally, antiviral therapies have relied on relatively small-sized drugs acting as proteases, polymerases, integrase inhibitors, etc. In recent decades, novel approaches involving targeted delivery such as that achieved by peptide-drug conjugates (PDCs) have gained attention as alternative (pro)drugs for tackling viral diseases. Antiviral PDC therapeutics typically involve one or more small drug molecules conjugated to a cell-penetrating peptide (CPP) carrier either directly or through a linker. Such integration of two bioactive elements into a single molecular entity is primarily aimed at achieving improved bioavailability in conditions where conventional drugs are challenged, but may also turn up novel unexpected functionalities and applications. Advances in peptide medicinal chemistry have eased the way to antiviral PDCs, but challenges remain on the way to therapeutic success. In this paper, we review current antiviral CPP-drug conjugates (antiviral PDCs), with emphasis on the types of CPP and antiviral cargo. We integrate the conjugate and the chemical approaches most often applied to combine both entities. Additionally, we comment on various obstacles faced in the design of antiviral PDCs and on the future outlooks for this class of antiviral therapeutics.

Peptide Utility (PU) search server: A new tool for peptide sequence search from multiple databases

Proteins are essential building blocks in humans that have garnered huge attention from researchers worldwide due to their numerous therapeutic applications. To date, different computational tools have been developed to extract pre-existing information on these biological molecules, but most of these tools suffer from limitations such as non-user friendly interface, redundancy of data, etc. To overcome these limitations, a user-friendly interface, the Peptide Utility (PU) webserver (https://chain-searching.herokuapp.com/) has been developed for searching and analyzing homologous and identical protein/peptide sequences that can be searched from approximately 0.4 million sequences (structural and sequence information) in both online and offline modes. The PU web server can also be used to study different types of interactions in PDBSum, identifying the most dominating interface residues, the most prevalent interactions, and the interaction preferences of different residues. The webserver would also pave way for the design of novel therapeutic peptides and folds by identifying conserved residues in the three-dimensional structure space of proteins.

Synthesis and Characterization of a Novel Peptide Targeting Human Tenon Fibroblast Cells To Modulate Fibrosis: An Integrated Empirical Approach

Fibrosis is the primary factor influencing the prognosis of glaucoma post-trabeculectomy surgery, an eye condition characterized by increased intraocular pressure (IOP). Despite advancements in surgical procedures and aftercare, it continues to be a serious impediment. During the clinical intervention of scarring, fibrosis is managed by using topical application of combined antifibrotic drugs (mitomycin C). But still, scarring remains a key problem due to minimal drug penetration and nonbioavailability. In this study, we synthesized a cell-specific peptide for modulating scarring in human tenon fibroblasts undergoing epithelial-mesenchymal transition (EMT). The peptide was also conjugated with mitomycin C in order to investigate the effect of the drug conjugation on human tenon fibroblasts from the nanofiber composite system and to evaluate the fibrosis process. Peptide VRF2019 was identified using a subtractive proteomics approach, including solubility, cell penetration, and amphipathic properties. The peptide structure was determined using circular dichroism spectroscopy. The peptide and drug was conjugated using N-ethyl-N'-(3-(dimethylamino)propyl) carbodiimide/N-hydroxysuccinimide (EDC-NHS) chemistry, and the conjugation efficiency was evaluated using high-pressure liquid chromatography. The conjugated product and polycaprolactone (PCL) were electrospun to form a composite nanofiber, which was tested for cytotoxicity and drug release on human tenon fibroblast cells. The modeled VRF2019 peptide structure formed an α-helical structure with all residues spanning the allowed regions of the Ramachandran plot. Subsequent molecular dynamics simulations also demonstrated its membrane penetration potential. The peptide uptake was also studied in human tenon fibroblast cells. High-pressure liquid chromatography (HPLC) and mass spectrometry measurements confirmed peptide-drug conjugation and stability. Furthermore, scanning electron microscopy (SEM) investigation revealed the structure and size of the PCL composite nanofiber. We infer from early research that the PCL composite nanofiber matrix can greatly increase drug delivery and bioavailability.

Cell-Penetrating Peptides (CPPs) as Therapeutic and Diagnostic Agents for Cancer

Cell-Penetrating Peptides (CPPs) are short peptides consisting of <30 amino acids. Their ability to translocate through the cell membrane while carrying large cargo biomolecules has been the topic of pre-clinical and clinical trials. The ability to deliver cargo complexes through membranes yields potential for therapeutics and diagnostics for diseases such as cancer. Upon cellular entry, some CPPs have the ability to target specific organelles. CPP-based intracellular targeting strategies hold tremendous potential as they can improve efficacy and reduce toxicities and side effects. Further, recent clinical trials show a significant potential for future CPP-based cancer treatment. In this review, we summarize recent advances in CPPs based on systematic searches in PubMed, Embase, Web of Science, and Scopus databases until 30 September 2022. We highlight targeted delivery and explore the potential uses for CPPs as diagnostics, drug delivery, and intrinsic anti-cancer agents.

Screening for effective cell-penetrating peptides with minimal impact on epithelial cells and gut commensals in vitro

One of the biggest challenges for oral drug absorption is the epithelial barrier of the gastrointestinal tract. The use of cell-penetrating peptides (CPPs) to modulate the epithelial barrier function is known to be an effective strategy to improve drug absorption and bioavailability. In this study we compare side-by-side, 9 most promising CPPs to study their cytotoxicity (Cytotox Red dye staining) and cell viability (AlamarBlue staining) on epithelial cells and their effects on paracellular permeability of the intestinal barrier in vitro in a differentiated Caco-2 epithelial monolayer model. The data revealed that 4 out of 9 well-studied CPPs significantly improved Caco-2 paracellular permeability without compromising on cellular health. To assess the impact of CPPs on the human microbiota we studied the antimicrobial effects of the 4 effective CPPs from our permeation studies against 10 representative strains of the gut microbiota in vitro using microbroth dilution. Our data revealed that these 4 CPPs affected the growth of almost all tested commensal strains. Interestingly, we found that two synthetic CPPs (Shuffle and Penetramax) outperformed all the other CPPs in their ability to increase intestinal paracellular permeability at 50 µM and had only a small to moderate effect on the tested gut commensal strains. Based on these data Shuffle and Penetramax represent relevant CPPs to be further characterized in vivo for safe delivery of poorly absorbed therapeutics while minimizing negative impacts on the gut microbiota.

Bioinformatics-based Characterization of the Sequence Variability of Zika Virus Polyprotein and Envelope Protein (E)

Background:

Zika virus, which is widely spread and infects humans through the bites of Aedes albopictus and Aedes aegypti female mosquitoes, represents a serious global health issue.

Objective:

The objective of the present study is to computationally characterize Zika virus polyproteins (UniProt Name: PRO_0000443018 [residues 1-3423], PRO_0000445659 [residues 1-3423] and PRO_0000435828 [residues 1-3419]) and their envelope proteins using their physico-chemical properties.

Methods:

To achieve this, the Polarity Index Method (PIM) profile and the Protein Intrinsic Disorder Predisposition (PIDP) profile of 3 main groups of proteins were evaluated: structural proteins extracted from specific Databases, Zika virus polyproteins, and their envelope proteins (E) extracted from UniProt Database. Once the PIM profile of the Zika virus envelope proteins (E) was obtained and since the Zika virus polyproteins were also identified with this profile, the proteins defined as “reviewed proteins” extracted from the UniProt Database were searched for the similar PIM profile. Finally, the difference between the PIM profiles of the Zika virus polyproteins and their envelope proteins (E) was tested using 2 non-parametric statistical tests.

Results:

It was found and tested that the PIM profile is an efficient discriminant that allows obtaining a “computational fingerprint” of each Zika virus polyprotein from its envelope protein (E).

Conclusion:

PIM profile represents a computational tool, which can be used to effectively discover Zika virus polyproteins from Databases, from their envelope proteins (E) sequences.

Approaches for evaluation of novel CPP-based cargo delivery systems

Cell penetrating peptides (CPPs) can be broadly defined as relatively short synthetic, protein derived or chimeric peptides. Their most remarkable property is their ability to cross cell barriers and facilitate the translocation of cargo, such as drugs, nucleic acids, peptides, small molecules, dyes, and many others across the plasma membrane. Over the years there have been several approaches used, adapted, and developed for the evaluation of CPP efficacies as delivery systems, with the fluorophore attachment as the most widely used approach. It has become progressively evident, that the evaluation method, in order to lead to successful outcome, should concede with the specialties of the delivery. For characterization and assessment of CPP-cargo a combination of research tools of chemistry, physics, molecular biology, engineering, and other fields have been applied. In this review, we summarize the diverse, in silico, in vitro and in vivo approaches used for evaluation and characterization of CPP-based cargo delivery systems.

The effect of helix-inducing constraints and downsizing upon a transcription block survival-derived functional cJun antagonist

Inhibition of cJun is established as a promising therapeutic approach, particularly in cancer. We recently developed the "transcription block survival" (TBS) screening platform to derive functional peptide antagonists of transcription factor activity by ablating their ability to bind to cognate DNA. Using TBS, we screened a >131,000-member peptide library to select a 63-mer peptide that bound cJun and prevented 12-O-tetradecanoylphorbol-13-acetate response element (TRE) DNA binding. Iterative truncation was next combined with a systematic exploration of side-chain cyclization to derive a minimal active sequence. The resulting dual lactamized sequence was >40% smaller and retained low nM target affinity (equilibrium binding constant [K _D ] = 0.2 versus 9.7 nM), with 8 residues at the acidic region required for functional antagonism. However, even modest C-terminal truncation resulted in functional loss. The peptide functionally antagonizes cJun (half-maximal inhibitory concentration [IC₅₀] = 13 versus 45 μM) and is considerably more stable in human serum relative to its non-lactamized counterpart and HingeW.

Peptides as Pharmacological Carriers to the Brain: Promises, Shortcomings and Challenges

Central nervous system (CNS) diseases are among the most difficult to treat, mainly because the vast majority of the drugs fail to cross the blood-brain barrier (BBB) or to reach the brain at concentrations adequate to exert a pharmacological activity. The obstacle posed by the BBB has led to the in-depth study of strategies allowing the brain delivery of CNS-active drugs. Among the most promising strategies is the use of peptides addressed to the BBB. Peptides are versatile molecules that can be used to decorate nanoparticles or can be conjugated to drugs, with either a stable link or as pro-drugs. They have been used to deliver to the brain both small molecules and proteins, with applications in diverse therapeutic areas such as brain cancers, neurodegenerative diseases and imaging. Peptides can be generally classified as receptor-targeted, recognizing membrane proteins expressed by the BBB microvessels (e.g., Angiopep2, CDX, and iRGD), "cell-penetrating peptides" (CPPs; e.g. TAT_47-57, SynB1/3, and Penetratin), undergoing transcytosis through unspecific mechanisms, or those exploiting a mixed approach. The advantages of peptides have been extensively pointed out, but so far few studies have focused on the potential negative aspects. Indeed, despite having a generally good safety profile, some peptide conjugates may display toxicological characteristics distinct from those of the peptide itself, causing for instance antigenicity, cardiovascular alterations or hemolysis. Other shortcomings are the often brief lifetime in vivo, caused by the presence of peptidases, the vulnerability to endosomal/lysosomal degradation, and the frequently still insufficient attainable increase of brain drug levels, which remain below the therapeutically useful concentrations. The aim of this review is to analyze not only the successful and promising aspects of the use of peptides in brain targeting but also the problems posed by this strategy for drug delivery.

Peptide-based assembled nanostructures that can direct cellular responses

Natural originated materials have been well-studied over the past several decades owing to their higher biocompatibility compared to the traditional polymers. Peptides, consisting of amino acids, are among the most popular programable building blocks, which is becoming a growing interest in nanobiotechnology. Structures assembled using those biomimetic peptides allow the exploration of chemical sequences beyond those been routinely used in biology. In this Review, we discussed the most recent experimental discoveries on the peptide-based assembled nanostructures and their potential application at the cellular level such as drug delivery. In particular, we explored the fundamental principles of peptide self-assembly and the most recent development in improving their interactions with biological systems. We believe that as the fundamental knowledge of the peptide assemblies evolves, the more sophisticated and versatile nanostructures can be built, with promising biomedical applications.

Antimicrobial peptides with cell-penetrating activity as prophylactic and treatment drugs

Health is fundamental for the development of individuals and evolution of species. In that sense, for human societies is relevant to understand how the human body has developed molecular strategies to maintain health. In the present review, we summarize diverse evidence that support the role of peptides in this endeavor. Of particular interest to the present review are antimicrobial peptides (AMP) and cell-penetrating peptides (CPP). Different experimental evidence indicates that AMP/CPP are able to regulate autophagy, which in turn regulates the immune system response. AMP also assists in the establishment of the microbiota, which in turn is critical for different behavioral and health aspects of humans. Thus, AMP and CPP are multifunctional peptides that regulate two aspects of our bodies that are fundamental to our health: autophagy and microbiota. While it is now clear the multifunctional nature of these peptides, we are still in the early stages of the development of computational strategies aimed to assist experimentalists in identifying selective multifunctional AMP/CPP to control nonhealthy conditions. For instance, both AMP and CPP are computationally characterized as amphipatic and cationic, yet none of these features are relevant to differentiate these peptides from non-AMP or non-CPP. The present review aims to highlight current knowledge that may facilitate the development of AMP’s design tools for preventing or treating illness.

Design of Peptides for Membrane Insertion: The Critical Role of Charge Separation

A physical understanding of membrane permeation and translocation by small, positively charged molecules can illuminate cell penetrating peptide mechanisms of entry and inform drug design. We have previously investigated the permeation of the doubly charged peptide WKW and proposed a defect-assisted permeation mechanism where a small molecule with +2 charge can achieve a metastable state spanning the bilayer by forming a membrane defect with charges stabilized by phospholipid phosphate groups. Here, we investigate the membrane permeation of two doubly charged peptides, WWK and WWWK, with charges separated by different lengths. Through complementary experiments and molecular dynamics simulations, we show that membrane permeation was an order of magnitude more favorable when charges were separated by an ∼2-3 Å greater distance on WWWK compared to WWK. These results agree with the previously proposed defect-assisted permeation mechanism, where a greater distance between positive charges would require a less extreme membrane defect to stabilize the membrane-spanning metastable state. We discuss the implications of these results in understanding the membrane permeation of cell-penetrating peptides and other small, positively charged membrane permeants.

De novo designed peptides for cellular delivery and subcellular localisation

Increasingly, it is possible to design peptide and protein assemblies de novo from first principles or computationally. This approach provides new routes to functional synthetic polypeptides, including designs to target and bind proteins of interest. Much of this work has been developed in vitro. Therefore, a challenge is to deliver de novo polypeptides efficiently to sites of action within cells. Here we describe the design, characterisation, intracellular delivery, and subcellular localisation of a de novo synthetic peptide system. This system comprises a dual-function basic peptide, programmed both for cell penetration and target binding, and a complementary acidic peptide that can be fused to proteins of interest and introduced into cells using synthetic DNA. The designs are characterised in vitro using biophysical methods and X-ray crystallography. The utility of the system for delivery into mammalian cells and subcellular targeting is demonstrated by marking organelles and actively engaging functional protein complexes.

Delivery of Small Molecules by Syndiotactic Peptides for Breast Cancer Therapy

The utilization of peptide-based drug delivery systems has been suboptimal due to their poor proteolytic susceptibility, poor cell permeability, and limited tumor homing capabilities. Earlier attempts in using d-enantiomers in peptide sequences increased proteolytic stability but have compromised the overall penetration capability. We designed a series of peptides (STRAPs) with a syndiotactic polypeptide backbone that can potentially form a spatial array of cationic groups, an important feature that facilitates cellular uptake. The peptides penetrate cell membranes through a combination of active and passive modes. Furthermore, the cellular uptake of the peptides was unaffected by the presence of or treatment with bovine serum and human plasma. The designed peptides successfully delivered methotrexate, an anticancer drug, to the in vitro and in vivo models of breast cancer, with the best performing peptide STRAP-4-MTX conjugate having an EC50 value of 1.34 μM. Peptide drug delivery in mouse xenograft models showed a greater reduction of primary tumor and metastasis of breast cancer, in comparison to methotrexate of the same dose. The in vivo biodistribution assay of the STRAP-4 peptide suggests that the peptide accumulates at the tumor site after 2 h of treatment, and in the absence of tumors, the peptide gets metabolized and excreted from the system.

The Potential of Cell-Penetrating Peptides for mRNA Delivery to Cancer Cells

In vitro transcribed mRNA for the synthesis of any given protein has shown great potential in cancer gene therapy, especially in cancer vaccines for immunotherapy. To overcome physiological barriers, such as rapid degradation by enzymatic attack and poor cellular uptake due to their large size and hydrophilic properties, many delivery carriers for mRNAs are being investigated for improving the bioavailability of mRNA. Recently, cell-penetrating peptides (CPPs) have received attention as promising tools for gene delivery. In terms of their biocompatibility and the ability to target specific cells with the versatility of peptide sequences, they may provide clues to address the challenges of conventional delivery systems for cancer mRNA delivery. In this study, optimal conditions for the CPP/mRNA complexes were identified in terms of complexation capacity and N/P ratio, and protection against RNase was confirmed. When cancer cells were treated at a concentration of 6.8 nM, which could deliver the highest amount of mRNA without toxicity, the amphipathic CPP/mRNA complexes with a size less than 200 nm showed high cellular uptake and protein expression. With advances in our understanding of CPPs, CPPs designed to target tumor tissues will be promising for use in developing a new class of mRNA delivery vehicles in cancer therapy.

Deep generative models for peptide design

Computers can already be programmed for superhuman pattern recognition of images and text. For machines to discover novel molecules, they must first be trained to sort through the many characteristics of molecules and determine which properties should be retained, suppressed, or enhanced to optimize functions of interest. Machines need to be able to understand, read, write, and eventually create new molecules. Today, this creative process relies on deep generative models, which have gained popularity since powerful deep neural networks were introduced to generative model frameworks. In recent years, they have demonstrated excellent ability to model complex distribution of real-word data (e.g., images, audio, text, molecules, and biological sequences). Deep generative models can generate data beyond those provided in training samples, thus yielding an efficient and rapid tool for exploring the massive search space of high-dimensional data such as DNA/protein sequences and facilitating the design of biomolecules with desired functions. Here, we review the emerging field of deep generative models applied to peptide science. In particular, we discuss several popular deep generative model frameworks as well as their applications to generate peptides with various kinds of properties (e.g., antimicrobial, anticancer, cell penetration, etc). We conclude our review with a discussion of current limitations and future perspectives in this emerging field.

Effect of biphosphate salt on dipalmitoylphosphatidylcholine bilayer deformation by Tat polypeptide

Translocation of positively charged cell penetrating peptides (CPP) through cell membrane is important in drug delivery. Here we report all-atom molecular dynamics simulations to investigate how a biphosphate salt in a solvent affects the interaction of a CPP, HIV-1 Tat peptide with model dipalmitoylphosphatidylcholine (DPPC) lipid bilayer. Tat peptide has a large number of basic arginines and a couple of polar glutamines. We observe that in absence of salt, the basic residues of the polypeptide get localized in the vicinity of the membrane without altering the bilayer properties much; polypeptide induce local thinning of the bilayer membrane at the area of localization. In presence of biphosphate salt, the basic residues, dressed by the biphosphate ions, are repelled by the phosphate head groups of the lipid molecules. However, polar glutamine prefers to stay in the vicinity of the bilayer. This leads to larger local bilayer thickness at the contact point by the polar residue and non-uniform bilayer thickness profile. The thickness deformation of bilayer structure disappears upon mutating the polar residue, suggesting importance of the polar residue in bilayer deformation. Our studies point to control bilayer deformation by appropriate peptide sequence and solvent conditions.

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.

Peptide Permeation across a Phosphocholine Membrane: An Atomically Detailed Mechanism Determined through Simulations and Supported by Experimentation

Cell-penetrating peptides (CPPs) facilitate translocation across biological membranes and are of significant biological and medical interest. Several CPPs can permeate into specific cells and organelles. We examine the incorporation and translocation of a novel anticancer CPP in a dioleoylphosphatidylcholine (DOPC) lipid bilayer membrane. The peptide, NAF-144-67, is a short fragment of a transmembrane protein, consisting of hydrophobic N-terminal and charged C-terminal segments. Experiments using fluorescently labeled NAF-144-67 in ∼100 nm DOPC vesicles and atomically detailed simulations conducted with Milestoning support a model in which a significant barrier for peptide-membrane entry is found at the interface between the aqueous solution and membrane. The initial step is the insertion of the N-terminal segment and the hydrophobic helix into the membrane, passing the hydrophilic head groups. Both experiments and simulations suggest that the free energy difference in the first step of the permeation mechanism in which the hydrophobic helix crosses the phospholipid head groups is -0.4 kcal mol-1 slightly favoring motion into the membrane. Milestoning calculations of the mean first passage time and the committor function underscore the existence of an early polar barrier followed by a diffusive barrierless motion in the lipid tail region. Permeation events are coupled to membrane fluctuations that are examined in detail. Our study opens the way to investigate in atomistic resolution the molecular mechanism, kinetics, and thermodynamics of CPP permeation to diverse membranes.

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.

Bioinformatics-Based Characterization of Proteins Related to SARS-CoV- 2 Using the Polarity Index Method® (PIM®) and Intrinsic Disorder Predisposition

Background:

The global outbreak of the 2019 novel Coronavirus Disease (COVID-19) caused by the infection with the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), which appeared in China at the end of 2019, signifies a major public health issue at the current time.

Objective:

The objective of the present study is to characterize the physicochemical properties of the SARS-CoV-2 proteins at a residues level, and to generate a “bioinformatics fingerprint” in the form of a “PIM® profile” created for each sequence utilizing the Polarity Index Method® (PIM®), suitable for the identification of these proteins.

Methods:

Two different bioinformatics approaches were used to analyze sequence characteristics of these proteins at the residues level, an in-house bioinformatics system PIM®, and a set of the commonly used algorithms for the predic-tion of protein intrinsic disorder predisposition, such as PONDR® VLXT, PONDR® VL3, PONDR® VSL2, PONDR® FIT, IUPred_short and IUPred_long. The PIM® profile was generated for four SARS-CoV-2 structural proteins and compared with the corresponding profiles of the SARS-CoV-2 non-structural proteins, SARS-CoV-2 putative proteins, SARS-CoV proteins, MERS-CoV proteins, sets of bacterial, fungal, and viral proteins, cell-penetrating peptides, and a set of intrinsically disordered proteins. We also searched for the UniProt proteins with PIM® profiles similar to those of SARS-CoV-2 structural, non-structural, and putative proteins.

Results:

We show that SARS-CoV-2 structural, non-structural, and putative proteins are characterized by a unique PIM® profile. A total of 1736 proteins were identified from the 562,253 “reviewed” proteins from the UniProt database, whose PIM® profile was similar to that of the SARS-CoV-2 structural, non-structural, and putative proteins.

Conclusion:

The PIM® profile represents an important characteristic that might be useful for the identification of proteins similar to SARS-CoV-2 proteins.

Bio-Membrane Internalization Mechanisms of Arginine-Rich Cell-Penetrating Peptides in Various Species

Recently, membrane-active peptides or proteins that include antimicrobial peptides (AMPs), cytolytic proteins, and cell-penetrating peptides (CPPs) have attracted attention due to their potential applications in the biomedical field. Among them, CPPs have been regarded as a potent drug/molecules delivery system. Various cargoes, such as DNAs, RNAs, bioactive proteins/peptides, nanoparticles and drugs, can be carried by CPPs and delivered into cells in either covalent or noncovalent manners. Here, we focused on four arginine-rich CPPs and reviewed the mechanisms that these CPPs used for intracellular uptake across cellular plasma membranes. The varying transduction efficiencies of them alone or with cargoes were discussed, and the membrane permeability was also expounded for CPP/cargoes delivery in various species. Direct membrane translocation (penetration) and endocytosis are two principal mechanisms for arginine-rich CPPs mediated cargo delivery. Furthermore, the amino acid sequence is the primary key factor that determines the cellular internalization mechanism. Importantly, the non-cytotoxic nature and the wide applicability make CPPs a trending tool for cellular delivery.

Multivariate Information Fusion for Identifying Antifungal Peptides with Hilbert-Schmidt Independence Criterion

Background:

Antifungal Peptides (AFP) have been found to be effective against many fungal infections.

Objective:

However, it is difficult to identify AFP. Therefore, it is great practical significance to identify AFP via machine learning methods (with sequence information).

Method:

In this study, a Multi-Kernel Support Vector Machine (MKSVM) with Hilbert-Schmidt Independence Criterion (HSIC) is proposed. Proteins are encoded with five types of features (188-bit, AAC, ASDC, CKSAAP, DPC), and then construct kernels using Gaussian kernel function. HSIC are used to combine kernels and multi-kernel SVM model is built.

Results:

Our model performed well on three AFPs datasets and the performance is better than or comparable to other state-of-art predictive models.

Conclusion:

Our method will be a useful tool for identifying antifungal peptides.

Cell Penetrating Peptide-Based Self-Assembly for PD-L1 Targeted Tumor Regression

Cell penetrating peptides (CPPs) are peptides that can directly adapt to cell membranes and then permeate into cells. CPPs are usually covalently linked to the surface of nanocarriers to endow their permeability to the whole system. However, hybrids with lipids or polymers make the metabolism much more sophisticated and even more difficult to determine. In this study, we present a continuous sequence of 18 amino acids (FFAARTMIWY(d-P)GAWYKRI). It forms nanospheres around 170 nm, which increase slightly after loading with siRNA and DOX. Notably, it can be internalized by cancer cells mainly through electronic interactions and PD-L1-mediated endocytosis. Compared with poly-l-lysine and polyethyleneimine, it has a much higher efficiency (about four times) of gene transduction while lowering toxicity. In the treatment of cancer, it causes apoptosis (21%) and inhibits the expression of SURVIVIN protein in vitro. In vivo, it shows good biocompatibility as there are no changes in mice’s body weight. When administering peptide-siRNA-DOX, tumor growth is inhibited the most (about three times). These results above prove the sequence to be a good candidate for gene therapy and drug delivery.

Synthesis of Cell-Penetrating Peptide Coated Silica Nanoparticles and Their Physicochemical and Biological Characterization

The surface decoration of nanoparticles with cell-penetrating peptides (CPPs) represents a common technique for intracellular delivery of nanotherapeutics. Conjugate formation can be performed via covalent or non-covalent strategies. Here, we describe on the synthesis of silica nanoparticles, a well-known inorganic drug delivery vehicle type, and their surface modification with cell-penetrating peptides using sC18 and derivatives thereof. Moreover, physicochemical as well as biological characterization methods, including cellular uptake measurements, of particle-peptide conjugates are described.

Peptide-Based Drug Delivery Systems

Peptide-based drug delivery systems have many advantages when compared to synthetic systems in that they have better biocompatibility, biochemical and biophysical properties, lack of toxicity, controlled molecular weight via solid phase synthesis and purification. Lysosomes, solid lipid nanoparticles, dendrimers, polymeric micelles can be applied by intravenous administration, however they are of artificial nature and thus may induce side effects and possess lack of ability to penetrate the blood-brain barrier. An analysis of nontoxic drug delivery systems and an establishment of prospective trends in the development of drug delivery systems was needed. This review paper summarizes data, mainly from the past 5 years, devoted to the use of peptide-based carriers for delivery of various toxic drugs, mostly anticancer or drugs with limiting bioavailability. Peptide-based drug delivery platforms are utilized as peptide–drug conjugates, injectable biodegradable particles and depots for delivering small molecule pharmaceutical substances (500 Da) and therapeutic proteins. Controlled drug delivery systems that can effectively deliver anticancer and peptide-based drugs leading to accelerated recovery without significant side effects are discussed. Moreover, cell penetrating peptides and their molecular mechanisms as targeting peptides, as well as stimuli responsive (enzyme-responsive and pH-responsive) peptides and peptide-based self-assembly scaffolds are also reviewed.

Synergistic Interaction of CPP2 Coupled with Thiazole Derivates Combined with Clotrimazole and Antineoplastic Drugs in Prostate and Colon Cancer Cell Lines

Cell-penetrating peptides (CPPs) are small peptide sequences used mainly as cellular delivery agents that are able to efficiently deliver cargo into cells. Some CPPs also demonstrate intrinsic anticancer properties. Previously, our group developed a new family of CPP2-thiazole conjugates that have been shown to effectively reduce the proliferation of different cancer cells. This work aimed to combine these CPP2-thiazole conjugates with paclitaxel (PTX) and 5-fluorouracil (5-FU) in PC-3 prostate and HT-29 colon cancer cells, respectively, to evaluate the cytotoxic effects of these combinations. We also combined these CPP2-thiazole conjugates with clotrimazole (CLZ), an antifungal agent that has been shown to decrease cancer cell proliferation. Cell viability was evaluated using MTT and SRB assays. Drug interaction was quantified using the Chou-Talalay method. We determined that CPP2 did not have significant activity in these cells and demonstrate that N-terminal modification of this peptide enhanced its anticancer activity in both cell lines. Our results also showed an uneven response between cell lines to the proposed combinations. PC-3 cells were more responsive to the combination of CPP2-thiazole conjugates with CLZ than PTX and were more sensitive to these combinations than HT-29 cells. In addition, the interaction of drugs resulted in more synergism in PC-3 cells. These results suggest that N-terminal modification of CPP2 results in the enhanced anticancer activity of the peptide and demonstrates the potential of CPPs as adjuvants in cancer therapy. These results also validate that CLZ has significant anticancer activity both alone and in combination and support the strategy of drug repurposing coupled to drug combination for prostate cancer therapy.

A Model System to Explore the Detection Limits of Antibody-Based Immuno-SPECT Imaging of Exclusively Intranuclear Epitopes

Imaging of intranuclear epitopes using antibodies tagged to cell-penetrating peptides has great potential given its versatility, specificity, and sensitivity. However, this process is technically challenging because of the location of the target. Previous research has demonstrated a variety of intranuclear epitopes that can be targeted with antibody-based radioimmunoconjugates. Here, we developed a controlled-expression model of nucleus-localized green fluorescent protein (GFP) to interrogate the technical limitations of intranuclear SPECT using radioimmunoconjugates, notably the lower target-abundance detection threshold. Methods: We stably transfected the lung adenocarcinoma cell line H1299 with an enhanced GFP (EGFP)-tagged histone 2B (H2B) and generated 4 cell lines expressing increasing levels of GFP. EGFP levels were quantified using Western blot, flow cytometry, and enzyme-linked immunosorbent assay. An anti-GFP antibody (GFP-G1) was modified using dibenzocyclooctyne-N3-based strain-promoted azide-alkyne cycloaddition with the cell-penetrating peptide TAT (GRKKRRQRRRPPQGYG), which also includes a nuclear localization sequence, and the metal ion chelator N3-Bn-diethylenetriamine pentaacetate (DTPA) to allow radiolabeling with 111In. Cell uptake of 111In-GFP-G1-TAT was evaluated across 5 cell clones expressing different levels of H2B-EGFP in vitro. Tumor uptake in xenograft-bearing mice was quantified to determine the smallest amount of target epitope that could be detected using 111In-GFP-G1-TAT. Results: We generated 4 H1299 cell clones expressing different levels of H2B-EGFP (0-1 million copies per cell, including wild-type H1299 cells). GFP-G1 monoclonal antibody was produced and purified in house, and selective binding to H2B-EGFP was confirmed. The affinity (dissociation constant) of GFP-G1 was determined as 9.1 ± 3.0 nM. GFP-G1 was conjugated to TAT and DTPA. 111In-GFP-G1-TAT uptake in H2B-EGFP-expressing cell clones correlated linearly with H2B-EGFP expression (P < 0.001). In vivo xenograft studies demonstrated that 111In-GFP-G1-TAT uptake in tumor tissue correlated linearly with expression of H2B-EGFP (P = 0.004) and suggested a lower target-abundance detection threshold of approximately 240,000 copies per cell. Conclusion: Here, we present a proof-of-concept demonstration that antibody-based imaging of intranuclear targets is capable both of detecting the presence of an epitope of interest with a copy number above 240,000 copies per cell and of determining differences in expression level above this threshold.

Molecular modelling of the FOXO4-TP53 interaction to design senolytic peptides for the elimination of senescent cancer cells

BACKGROUND

Senescent cells accumulate in tissues over time as part of the natural ageing process and the removal of senescent cells has shown promise for alleviating many different age-related diseases in mice. Cancer is an age-associated disease and there are numerous mechanisms driving cellular senescence in cancer that can be detrimental to recovery. Thus, it would be beneficial to develop a senolytic that acts not only on ageing cells but also senescent cancer cells to prevent cancer recurrence or progression.

METHODS

We used molecular modelling to develop a series of rationally designed peptides to mimic and target FOXO4 disrupting the FOXO4-TP53 interaction and releasing TP53 to induce apoptosis. We then tested these peptides as senolytic agents for the elimination of senescent cells both in cell culture and in vivo.

FINDINGS

Here we show that these peptides can act as senolytics for eliminating senescent human cancer cells both in cell culture and in orthotopic mouse models. We then further characterized one peptide, ES2, showing that it disrupts FOXO4-TP53 foci, activates TP53 mediated apoptosis and preferentially binds FOXO4 compared to TP53. Next, we show that intratumoural delivery of ES2 plus a BRAF inhibitor results in a significant increase in apoptosis and a survival advantage in mouse models of melanoma. Finally, we show that repeated systemic delivery of ES2 to older mice results in reduced senescent cell numbers in the liver with minimal toxicity.

INTERPRETATION

Taken together, our results reveal that peptides can be generated to specifically target and eliminate FOXO4+ senescent cancer cells, which has implications for eradicating residual disease and as a combination therapy for frontline treatment of cancer.

FUNDING

This work was supported by the Cancer Early Detection Advanced Research Center at Oregon Health & Science University.

Deep learning to design nuclear-targeting abiotic miniproteins

There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we show how machine learning enables the de novo design of abiotic nuclear-targeting miniproteins to traffic antisense oligomers to the nucleus of cells. We combined high-throughput experimentation with a directed evolution-inspired deep-learning approach in which the molecular structures of natural and unnatural residues are represented as topological fingerprints. The model is able to predict activities beyond the training dataset, and simultaneously deciphers and visualizes sequence-activity predictions. The predicted miniproteins, termed 'Mach', reach an average mass of 10 kDa, are more effective than any previously known variant in cells and can also deliver proteins into the cytosol. The Mach miniproteins are non-toxic and efficiently deliver antisense cargo in mice. These results demonstrate that deep learning can decipher design principles to generate highly active biomolecules that are unlikely to be discovered by empirical approaches.

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.

Cell-Penetrating Peptides Predicted From CASC3, AKIP1, and AHRR Proteins

Peptides can be used as research tools and for diagnostic or therapeutic applications. Peptides, alongside small molecules and antibodies, are used and are gaining further interest as protein-protein interaction (PPI) modulators. Peptides have high target specificity and high affinity, but, unlike small molecule modulators, they are not able to cross the cell membranes to reach their intracellular targets. To overcome this limitation, the special property of the cell-penetrating peptides (CPPs) could benefit their cause. CPPs are a class of peptides that can enter the cells and with them also deliver the attached cargoes. Today, with the advancement of in silico prediction tools and the availability of protein databases, designing new and multifunctional peptides that are able to reach intracellular targets and inhibit certain cellular processes in a very specific manner is reachable. Although there are several efficient CPP sequences already known, the discovery of new CPPs is crucial for the development of efficient delivery methods for both biotechnological and therapeutic applications. In this work, we chose 10 human nuclear proteins from which we predicted new potential CPP sequences by using three different CPP predictors: cell-penetrating peptide prediction tool, CellPPD, and SkipCPP-Pred. From each protein, one predicted CPP sequence was synthesized and its internalization into cells was assessed. Out of the tested sequences, three peptides displayed features characteristic to CPPs. These peptides and also the predicted peptide sequences could be used to design and modify new CPPs. In this work, we show that we can use protein sequences as input for generating new peptides with cell internalization properties. Three new CPPs, AHRR_8-24, CASC3_251-264, and AKIP1_27-37, can be further used for the delivery of other cargoes or designed into multifunctional peptides with capability of internalizing cells.

Development and Characterization of High Efficacy Cell-Penetrating Peptide via Modulation of the Histidine and Arginine Ratio for Gene Therapy

Cell-penetrating peptides (CPPs), as non-viral gene delivery vectors, are considered with lower immunogenic response, and safer and higher gene capacity than viral systems. In our previous study, a CPP peptide called RALA (arginine rich) presented desirable transfection efficacy and owns a potential clinic use. It is believed that histidine could enhance the endosome escaping ability of CPPs, yet RALA peptide contains only one histidine in each chain. In order to develop novel superior CPPs, by using RALA as a model, we designed a series of peptides named HALA (increased histidine ratio). Both plasmid DNA (pDNA) and siRNA transfection results on three cell lines revealed that the transfection efficacy is better when histidine replacements were on the C-terminal instead of on the N-terminal, and two histidine replacements are superior to three. By investigating the mechanism of endocytosis of the pDNA nanocomplexes, we discovered that there were multiple pathways that led to the process and caveolae played the main role. During the screening, we discovered a novel peptide-HALA2 of high cellular transfection efficacy, which may act as an exciting gene delivery vector for gene therapy. Our findings also bring new insights on the development of novel robust CPPs.

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.

Amyloid-like Self-Assembly of a Hydrophobic Cell-Penetrating Peptide and Its Use as a Carrier for Nucleic Acids

Cell-penetrating peptides (CPPs) are a topical subject potentially exploitable for creating nanotherapeutics for the delivery of bioactive loads. These compounds are often classified into three major categories according to their physicochemical characteristics: cationic, amphiphilic, and hydrophobic. Among them, the group of hydrophobic CPPs has received increasing attention in recent years due to toxicity concerns posed by highly cationic CPPs. The hexapeptide PFVYLI (P, proline; F, phenylalanine; V, valine; Y, tyrosine; L, leucine; and I, isoleucine), a fragment derived from the C-terminal portion of α1-antitrypsin, is a prototypal example of hydrophobic CPP. This sequence shows reduced cytotoxicity and a capacity of nuclear localization, and its small size readily hints at its suitability as a building block to construct nanostructured materials. In this study, we examine the self-assembling properties of PFVYLI and investigate its ability to form noncovalent complexes with nucleic acids. By using a combination of biophysical tools including synchrotron small-angle X-ray scattering and atomic force microscopy-based infrared spectroscopy, we discovered that this CPP self-assembles into discrete nanofibrils with remarkable amyloidogenic features. Over the course of days, these fibrils coalesce into rodlike crystals that easily reach the micrometer range. Despite lacking cationic residues in the composition, PFVYLI forms noncovalent complexes with nucleic acids that retain β-sheet pairing found in amyloid aggregates. In vitro vectorization experiments performed with double-stranded DNA fragments indicate that complexes promote the internalization of nucleic acids, revealing that tropism toward cell membranes is preserved upon complexation. On the other hand, transfection assays with splice-correction oligonucleotides (SCOs) for luciferase expression show limited bioactivity across a narrow concentration window, suggesting that the propensity to form amyloidogenic aggregates may trigger endosomal entrapment. We anticipate that the findings presented here open perspectives for using this archetypical hydrophobic CPP in the fabrication of nanostructured scaffolds, which potentially integrate properties of amyloids and translocation capabilities of CPPs.

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.

CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework

Cell-penetrating anticancer peptides (Cp-ACPs) are considered promising candidates in solid tumor and hematologic cancer therapies. Current approaches for the design and discovery of Cp-ACPs trust the expensive high-throughput screenings that often give rise to multiple obstacles, including instrumentation adaptation and experimental handling. The application of machine learning (ML) tools developed for peptide activity prediction is importantly of growing interest. In this study, we applied the random forest (RF)-, support vector machine (SVM)-, and eXtreme gradient boosting (XGBoost)-based algorithms to predict the active Cp-ACPs using an experimentally validated data set. The model, CpACpP, was developed on the basis of two independent cell-penetrating peptide (CPP) and anticancer peptide (ACP) subpredictors. Various compositional and physiochemical-based features were combined or selected using the multilayered recursive feature elimination (RFE) method for both data sets. Our results showed that the ACP subclassifiers obtain a mean performance accuracy (ACC) of 0.98 with an area under curve (AUC) ≈ 0.98 vis-à-vis the CPP predictors displaying relevant values of ∼0.94 and ∼0.95 via the hybrid-based features and independent data sets, respectively. Also, the predicting evaluation of Cp-ACPs gave accuracies of ∼0.79 and 0.89 on a series of independent sequences by applying our CPP and ACP classifiers, respectively, which leaves the performance of our predictors better than the earlier reported ACPred, mACPpred, MLCPP, and CPPred-RF. The described consensus-based fusion method additionally reached an AUC of 0.94 for the prediction of Cp-ACP (http://cbb1.ut.ac.ir/CpACpP/Index).

p28-functionalized PLGA nanoparticles loaded with gefitinib reduce tumor burden and metastases formation on lung cancer

Lung cancer is still the main cause of cancer-related deaths worldwide. Its treatment generally includes surgical resection, immunotherapy, radiotherapy, and chemo-targeted therapies such as the application of tyrosine kinase inhibitors. Gefitinib (GEF) is one of them, but its poor solubility in gastric fluids weakens its bioavailability and therapeutic activity. In addition, like all other chemotherapy treatments, GEF administration can cause damage to healthy tissues. Therefore, the development of novel GEF delivery systems to increase its bioavailability and distribution in tumor site is highly demanded. Herein, an innovative strategy for GEF delivery, by functionalizing PLGA nanoparticles with p28 (p28-NPs), a cell-penetrating peptide derived from the bacterial protein azurin, was developed. Our data indicated that p28 potentiates the selective interaction of these nanosystems with A549 lung cancer cells (active targeting). Further p28-NPs delivering GEF (p28-NPs-GEF) were able to selectively reduce the metabolic activity of A549 cells, while no impact was observed in non-tumor cells (16HBE14o-). In vivo studies using A549 subcutaneous xenograft showed that p28-NPs-GEF reduced A549 primary tumor burden and lung metastases formation. Overall, the design of a p28-functionalized delivery nanosystem to effectively penetrate the membranes of cancer cells while deliver GEF could provide a new strategy to improve lung cancer therapy.