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

Plasma membrane damage limits cytoplasmic delivery by conventional cell penetrating peptides

Intracellular delivery of large molecule cargo via cell penetrating peptides (CPPs) is an inefficient process and despite intense efforts in past decades, improvements in efficiency have been marginal. Utilizing a standardized and comparative analysis of the delivery efficiency of previously described cationic, anionic, and amphiphilic CPPs, we demonstrate that the delivery ceiling is accompanied by irreparable plasma membrane damage that is part of the uptake mechanism. As a consequence, intracellular delivery correlates with cell toxicity and is more efficient for smaller peptides than for large molecule cargo. The delivery of pharmaceutically relevant cargo quantities with acceptable toxicity thus seems hard to achieve with the CPPs tested in our study. Our results suggest that any engineered intracellular delivery system based on conventional cationic or amphiphilic CPPs, or the design principles underlying them, needs to accept low delivery yields due to toxicity limiting efficient cytoplasmic uptake. Novel peptide designs based on detailed study of uptake mechanisms are required to overcome these limitations.

ToxinPred 3.0: An improved method for predicting the toxicity of peptides

Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we utilized a similarity/alignment-based approach employing BLAST to predict toxic peptides, which yielded satisfactory accuracy; however, the method suffered from inadequate coverage. Subsequently, we employed a motif-based approach using MERCI software to uncover specific patterns or motifs that are exclusively observed in toxic peptides. The search for these motifs in peptides allowed us to predict toxic peptides with a high level of specificity with poor sensitivity. To overcome the coverage limitations, we developed alignment-free methods using machine/deep learning techniques to balance sensitivity and specificity of prediction. Deep learning model (ANN - LSTM with fixed sequence length) developed using one-hot encoding achieved a maximum AUROC of 0.93 with MCC of 0.71 on an independent dataset. Machine learning model (extra tree) developed using compositional features of peptides achieved a maximum AUROC of 0.95 with MCC of 0.78. We also developed large language models and achieved maximum AUC of 0.93 using ESM2-t33. Finally, we developed hybrid or ensemble methods combining two or more methods to enhance performance. Our specific hybrid method, which combines a motif-based approach with a machine learning-based model, achieved a maximum AUROC of 0.98 with MCC 0.81 on an independent dataset. In this study, all models were trained and tested on 80 % of data using five-fold cross-validation and evaluated on the remaining 20 % of data called independent dataset. The evaluation of all methods on an independent dataset revealed that the method proposed in this study exhibited better performance than existing methods. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/).

Intracellular delivery strategies using membrane-interacting peptides and proteins

While the cellular cytosol and organelles contain attractive targets for disease treatments, it remains a challenge to deliver therapeutic biomacromolecules to these sites. This is due to the selective permeability of the plasma and endosomal membranes, especially for large and hydrophilic therapeutic cargos such as proteins and nucleic acids. In response, many different delivery systems and molecules have been devised to help therapeutics cross these barriers to reach cytosolic targets. Among them are peptide and protein-based systems, which have several advantages over other natural and synthetic materials including their ability to interact with cell membranes. In this review, we will describe recent advances and current challenges of peptide and protein strategies that leverage cell membrane association and modulation to enable cytosolic delivery of biomacromolecule cargo. The approaches covered here include peptides and proteins derived from or inspired by natural sequences as well as those designed de novo for delivery function.

Cell Penetrating Peptide Enhances the Aphidicidal Activity of Spider Venom-Derived Neurotoxin

HxTx-Hv1h, a neurotoxic peptide derived from spider venom, has been developed for use in commercial biopesticide formulations. Cell Penetrating Peptides (CPPs) are short peptides that facilitate the translocation of various biomolecules across cellular membranes. Here, we evaluated the aphidicidal efficacy of a conjugated peptide, HxTx-Hv1h/CPP-1838, created by fusing HxTx-Hv1h with CPP-1838. Additionally, we aimed to establish a robust recombinant expression system for HxTx-Hv1h/CPP-1838. We successfully achieved the secretory production of HxTx-Hv1h, its fusion with Galanthus nivalis agglutinin (GNA) forming HxTx-Hv1h/GNA and HxTx-Hv1h/CPP-1838 in yeast. Purified HxTx-Hv1h exhibited contact toxicity against Megoura crassicauda, with a 48 h median lethal concentration (LC50) of 860.5 μg/mL. Fusion with GNA or CPP-1838 significantly enhanced its aphidicidal potency, reducing the LC50 to 683.5 μg/mL and 465.2 μg/mL, respectively. The aphidicidal efficacy was further improved with the addition of surfactant, decreasing the LC50 of HxTx-Hv1h/CPP-1838 to 66.7 μg/mL-over four times lower compared to HxTx-Hv1h alone. Furthermore, we engineered HxTx-Hv1h/CPP-1838 multi-copy expression vectors utilizing the BglBrick assembly method and achieved high-level recombinant production in laboratory-scale fermentation. This study is the first to document a CPP fusion strategy that enhances the transdermal aphidicidal activity of a natural toxin like HxTx-Hv1h and opens up the possibility of exploring the recombinant production of HxTx-Hv1h/CPP-1838 for potential applications.

Overview and limitations of database in global traditional medicines: A narrative review

The study of traditional medicine has garnered significant interest, resulting in various research areas including chemical composition analysis, pharmacological research, clinical application, and quality control. The abundance of available data has made databases increasingly essential for researchers to manage the vast amount of information and explore new drugs. In this article we provide a comprehensive overview and summary of 182 databases that are relevant to traditional medicine research, including 73 databases for chemical component analysis, 70 for pharmacology research, and 39 for clinical application and quality control from published literature (2000-2023). The review categorizes the databases by functionality, offering detailed information on websites and capacities to facilitate easier access. Moreover, this article outlines the primary function of each database, supplemented by case studies to aid in database selection. A practical test was conducted on 68 frequently used databases using keywords and functionalities, resulting in the identification of highlighted databases. This review serves as a reference for traditional medicine researchers to choose appropriate databases and also provides insights and considerations for the function and content design of future databases.

Structural and Biochemical Characterization of Three Antimicrobial Peptides from Capsicum annuum L. var. annuum Leaves for Anti-Candida Use

The emergence of resistant microorganisms has reduced the effectiveness of currently available antimicrobials, necessitating the development of new strategies. Plant antimicrobial peptides (AMPs) are promising candidates for novel drug development. In this study, we aimed to isolate, characterize, and evaluate the antimicrobial activities of AMPs isolated from Capsicum annuum. The antifungal potential was tested against Candida species. Three AMPs from C. annuum leaves were isolated and characterized: a protease inhibitor, a defensin-like protein, and a lipid transporter protein, respectively named CaCPin-II, CaCDef-like, and CaCLTP2. All three peptides had a molecular mass between 3.5 and 6.5 kDa and caused morphological and physiological changes in four different species of the genus Candida, such as pseudohyphae formation, cell swelling and agglutination, growth inhibition, reduced cell viability, oxidative stress, membrane permeabilization, and metacaspase activation. Except for CaCPin-II, the peptides showed low or no hemolytic activity at the concentrations used in the yeast assays. CaCPin-II inhibited α-amylase activity. Together, these results suggest that these peptides have the potential as antimicrobial agents against species of the genus Candida and can serve as scaffolds for the development of synthetic peptides for this purpose.

PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation

Anticancer peptides (ACPs) perform a promising role in discovering anti-cancer drugs. The growing research on ACPs as therapeutic agent is increasing due to its minimal side effects. However, identifying novel ACPs using wet-lab experiments are generally time-consuming, labor-intensive, and expensive. Leveraging computational methods for fast and accurate prediction of ACPs would harness the drug discovery process. Herein, a machine learning-based predictor, called PLMACPred, is developed for identifying ACPs from peptide sequence only. PLMACPred adopted a set of encoding schemes representing evolutionary-property, composition-property, and protein language model (PLM), i.e., evolutionary scale modeling (ESM-2)- and ProtT5-based embedding to encode peptides. Then, two-dimensional (2D) wavelet denoising (WD) was employed to remove the noise from extracted features. Finally, ensemble-based cascade deep forest (CDF) model was developed to identify ACP. PLMACPred model attained superior performance on all three benchmark datasets, namely, ACPmain, ACPAlter, and ACP740 over tenfold cross validation and independent dataset. PLMACPred outperformed the existing models and improved the prediction accuracy by 18.53%, 2.4%, 7.59% on ACPmain, ACPalter, ACP740 dataset, respectively. We showed that embedding from ProtT5 and ESM-2 was capable of capturing better contextual information from the entire sequence than the other encoding schemes for ACP prediction. For the explainability of proposed model, SHAP (SHapley Additive exPlanations) method was used to analyze the feature effect on the ACP prediction. A list of novel sequence motifs was proposed from the ACP sequence using MEME suites. We believe, PLMACPred will support in accelerating the discovery of novel ACPs as well as other activities of microbial peptides.

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.

THPdb2: compilation of FDA approved therapeutic peptides and proteins

During the past 20 years, there has been a significant increase in the number of protein-based drugs approved by the US Food and Drug Administration (FDA). This paper presents THPdb2, an updated version of the THPdb database, which holds information about all types of protein-based drugs, including peptides, antibodies, and biosimilar proteins. THPdb2 contains a total of 6,385 entries, providing comprehensive information about 894 FDA-approved therapeutic proteins, including 354 monoclonal antibodies and 85 peptides or polypeptides. Each entry includes the name of therapeutic molecule, the amino acid sequence, physical and chemical properties, and route of drug administration. The therapeutic molecules that are included in the database target a wide range of biological molecules, such as receptors, factors, and proteins, and have been approved for the treatment of various diseases, including cancers, infectious diseases, and immune disorders.

HydrogelFinder: A Foundation Model for Efficient Self-Assembling Peptide Discovery Guided by Non-Peptidal Small Molecules

Self-assembling peptides have numerous applications in medicine, food chemistry, and nanotechnology. However, their discovery has traditionally been serendipitous rather than driven by rational design. Here, HydrogelFinder, a foundation model is developed for the rational design of self-assembling peptides from scratch. This model explores the self-assembly properties by molecular structure, leveraging 1,377 self-assembling non-peptidal small molecules to navigate chemical space and improve structural diversity. Utilizing HydrogelFinder, 111 peptide candidates are generated and synthesized 17 peptides, subsequently experimentally validating the self-assembly and biophysical characteristics of nine peptides ranging from 1-10 amino acids-all achieved within a 19-day workflow. Notably, the two de novo-designed self-assembling peptides demonstrated low cytotoxicity and biocompatibility, as confirmed by live/dead assays. This work highlights the capacity of HydrogelFinder to diversify the design of self-assembling peptides through non-peptidal small molecules, offering a powerful toolkit and paradigm for future peptide discovery endeavors.

Investigating molecular descriptors in cell-penetrating peptides prediction with deep learning: Employing N, O, and hydrophobicity according to the Eisenberg scale

Cell-penetrating peptides comprise a group of molecules that can naturally cross the lipid bilayer membrane that protects cells, sharing physicochemical and structural properties, and having several pharmaceutical applications, particularly in drug delivery. Investigations of molecular descriptors have provided not only an improvement in the performance of classifiers but also less computational complexity and an enhanced understanding of membrane permeability. Furthermore, the employment of new technologies, such as the construction of deep learning models using overfitting treatment, promotes advantages in tackling this problem. In this study, the descriptors nitrogen, oxygen, and hydrophobicity on the Eisenberg scale were investigated, using the proposed ConvBoost-CPP composed of an improved convolutional neural network with overfitting treatment and an XGBoost model with adjusted hyperparameters. The results revealed favorable to the use of ConvBoost-CPP, having as input nitrogen, oxygen, and hydrophobicity together with ten other descriptors previously investigated in this research line, showing an increase in accuracy from 88% to 91.2% in cross-validation and 82.6% to 91.3% in independent test.

Discovery of a new class of cell-penetrating peptides by novel phage display platform

The primary hurdles for small interference RNA (siRNA) in clinical use are targeted and cytosolic delivery. To overcome both challenges, we have established a novel platform based on phage display, called NNJA. In this approach, a lysosomal cathepsin substrate is engineered within the flexible loops of PIII, that is displaying a unique random sequence at its N-terminus. NNJA library selection targeting cell-expressed targets should yield specific peptides localized in the cytoplasm. That is because phage internalization and subsequent localization to lysosome, upon peptide binding to the cell expressed target, will result in cleavage of PIII, rendering phage non-infective. Such phage will be eliminated from the selected pool and only peptide-phage that escapes lysosomes will advance to the next round. Proof of concept studies with the NNJA library demonstrated cytosolic localization of selected peptide-phage and peptide-siRNA, confirmed through confocal microscopy. More importantly, conjugation of siHPRT to monomeric or multimeric NNJA peptides resulted in significant reduction in HPRT mRNA in various cell types without significant cytotoxicity. Sequence similarity and clustering analysis from NGS dataset provide insights into sequence composition facilitating cell penetration. NNJA platform offers a highly efficient peptide discovery engine for targeted delivery of oligonucleotides to cytosol.

Evidence That a Peptide-Drug/p53 Gene Complex Promotes Cognate Gene Expression and Inhibits the Viability of Glioblastoma Cells

Glioblastoma multiform (GBM) is considered the deadliest brain cancer. Conventional therapies are followed by poor patient survival outcomes, so novel and more efficacious therapeutic strategies are imperative to tackle this scourge. Gene therapy has emerged as an exciting and innovative tool in cancer therapy. Its combination with chemotherapy has significantly improved therapeutic outcomes. In line with this, our team has developed temozolomide-transferrin (Tf) peptide (WRAP5)/p53 gene nanometric complexes that were revealed to be biocompatible with non-cancerous cells and in a zebrafish model and were able to efficiently target and internalize into SNB19 and U373 glioma cell lines. The transfection of these cells, mediated by the formulated peptide-drug/gene complexes, resulted in p53 expression. The combined action of the anticancer drug with p53 supplementation in cancer cells enhances cytotoxicity, which was correlated to apoptosis activation through quantification of caspase-3 activity. In addition, increased caspase-9 levels revealed that the intrinsic or mitochondrial pathway of apoptosis was implicated. This assumption was further evidenced by the presence, in glioma cells, of Bax protein overexpression-a core regulator of this apoptotic pathway. Our findings demonstrated the great potential of peptide TMZ/p53 co-delivery complexes for cellular transfection, p53 expression, and apoptosis induction, holding promising therapeutic value toward glioblastoma.

Proteomic Barcoding Platform for Macromolecular Screening and Delivery

Engineered macromolecules offer compelling means for the therapy of conventionally undruggable interactions in human disease. However, their efficacy is limited by barriers to tissue and intracellular delivery. Inspired by recent advances in molecular barcoding and evolution, we developed BarcodeBabel, a generalized method for the design of libraries of peptide barcodes suitable for high-throughput mass spectrometry proteomics. Combined with PeptideBabel, a Monte Carlo sampling algorithm for the design of peptides with evolvable physicochemical properties and sequence complexity, we developed a barcoded library of cell penetrating peptides (CPPs) with distinct physicochemical features. Using quantitative targeted mass spectrometry, we identified CPPS with improved nuclear and cytoplasmic delivery exceeding hundreds of millions of molecules per human cell while maintaining minimal membrane disruption and negligible toxicity in vitro. These studies provide a proof of concept for peptide barcoding as a homogeneous high-throughput method for macromolecular screening and delivery. BarcodeBabel and PeptideBabel are available open-source from https://github.com/kentsisresearchgroup/.

Potential of Cell-Penetrating Peptide-Conjugated Antisense Oligonucleotides for the Treatment of SMA

Spinal muscular atrophy (SMA) is a severe neuromuscular disorder that is caused by mutations in the survival motor neuron 1 (SMN1) gene, hindering the production of functional survival motor neuron (SMN) proteins. Antisense oligonucleotides (ASOs), a versatile DNA-like drug, are adept at binding to target RNA to prevent translation or promote alternative splicing. Nusinersen is an FDA-approved ASO for the treatment of SMA. It effectively promotes alternative splicing in pre-mRNA transcribed from the SMN2 gene, an analog of the SMN1 gene, to produce a greater amount of full-length SMN protein, to compensate for the loss of functional protein translated from SMN1. Despite its efficacy in ameliorating SMA symptoms, the cellular uptake of these ASOs is suboptimal, and their inability to penetrate the CNS necessitates invasive lumbar punctures. Cell-penetrating peptides (CPPs), which can be conjugated to ASOs, represent a promising approach to improve the efficiency of these treatments for SMA and have the potential to transverse the blood-brain barrier to circumvent the need for intrusive intrathecal injections and their associated adverse effects. This review provides a comprehensive analysis of ASO therapies, their application for the treatment of SMA, and the encouraging potential of CPPs as delivery systems to improve ASO uptake and overall efficiency.

Peptide-Drug Conjugate with Statistically Designed Transcellular Peptide for Psoriasis-Like Inflammation

Peptide-drug conjugates (PDCs) are a promising class of drug delivery systems that utilize covalently conjugated carrier peptides with therapeutic agents. PDCs offer several advantages over traditional drug delivery systems including enhanced target engagement, improved bioavailability, and increased cell permeability. However, the development of efficient transcellular peptides capable of effectively transporting drugs across biological barriers remains an unmet need. In this study, physicochemical criteria based on cell-penetrating peptides are employed to design transcellular peptides derived from an antimicrobial peptides library. Among the statistically designed transcellular peptides (SDTs), SDT7 exhibits higher skin permeability, faster kinetics, and improved cell permeability in human keratinocyte cells compared to the control peptide. Subsequently, it is found that 6-Paradol (PAR) exhibits inhibitory activity against phosphodiesterase 4, which can be utilized for an anti-inflammatory PDC. The transcellular PDC (SDT7-conjugated with PAR, named TM5) is evaluated in mouse models of psoriasis, exhibiting superior therapeutic efficacy compared to PAR alone. These findings highlight the potential of transcellular PDCs (TDCs) as a promising approach for the treatment of inflammatory skin disorders.

DCTPep, the data of cancer therapy peptides

With the discovery of the therapeutic activity of peptides, they have emerged as a promising class of anti-cancer agents due to their specific targeting, low toxicity, and potential for high selectivity. In particular, as peptide-drug conjugates enter clinical, the coupling of targeted peptides with traditional chemotherapy drugs or cytotoxic agents will become a new direction in cancer treatment. To facilitate the drug development of cancer therapy peptides, we have constructed DCTPep, a novel, open, and comprehensive database for cancer therapy peptides. In addition to traditional anticancer peptides (ACPs), the peptide library also includes peptides related to cancer therapy. These data were collected manually from published research articles, patents, and other protein or peptide databases. Data on drug library include clinically investigated and/or approved peptide drugs related to cancer therapy, which mainly come from the portal websites of drug regulatory authorities and organisations in different countries and regions. DCTPep has a total of 6214 entries, we believe that DCTPep will contribute to the design and screening of future cancer therapy peptides.

Structure-aware machine learning strategies for antimicrobial peptide discovery

Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86-88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.

Acid-Activated TAT Peptide-Modified Biomimetic Boron Nitride Nanoparticles for Enhanced Targeted Codelivery of Doxorubicin and Indocyanine Green: A Synergistic Cancer Photothermal and Chemotherapeutic Approach

The evolution of nano-drug delivery systems addresses the limitations of conventional cancer treatments with stimulus-responsive nanomaterial-based delivery systems presenting temporal and spatial advantages. Among various nanomaterials, boron nitride nanoparticles (BNNs) demonstrate significant potential in drug delivery and cancer treatment, providing a high drug loading capacity, multifunctionality, and low toxicity. However, the challenge lies in augmenting nanomaterial accumulation exclusively within tumors while preserving healthy tissues. To address this, we introduce a novel approach involving cancer cell membrane-functionalized BNNs (CM-BIDdT) for the codelivery of doxorubicin (Dox) and indocyanine green to treat homologous tumor. The cancer cell membrane biomimetic CM-BIDdT nanoparticles possess highly efficient homologous targeting capabilities toward tumor cells. The surface modification with acylated TAT peptides (dTAT) further enhances the nanoparticle intracellular accumulation. Consequently, CM-BIDdT nanoparticles, responsive to the acidic tumor microenvironment, hydrolyze amide bonds, activate the transmembrane penetrating function, and achieve precise targeting with substantial accumulation at the tumor site. Additionally, the photothermal effect of CM-BIDdT under laser irradiation not only kills cells through thermal ablation but also destroys the membrane on the surface of the nanoparticles, facilitating Dox release. Therefore, the fabricated CM-BIDdT nanoparticles orchestrate chemo-photothermal combination therapy and effectively inhibit tumor growth with minimal adverse effects, holding promise as a new modality for synergistic cancer treatment.

Generation of tau dephosphorylation-targeting chimeras for the treatment of Alzheimer's disease and related tauopathies

Abnormal hyperphosphorylation and accumulation of tau protein play a pivotal role in neurodegeneration in Alzheimer's disease (AD) and many other tauopathies. Selective elimination of hyperphosphorylated tau is promising for the therapy of these diseases. We have conceptualized a strategy, named dephosphorylation-targeting chimeras (DEPTACs), for specifically hijacking phosphatases to tau to debilitate its hyperphosphorylation. Here, we conducted the step-by-step optimization of each constituent motif to generate DEPTACs with reasonable effectiveness in facilitating the dephosphorylation and subsequent clearance of pathological tau. Specifically, for one of the selected chimeras, D16, we demonstrated its significant efficiency in rescuing the neurodegeneration caused by neurotoxic K18-tau seeds in vitro. Moreover, intravenous administration of D16 also alleviated tau pathologies in the brain and improved memory deficits in AD mice. These results suggested DEPTACs as targeted modulators of tau phosphorylation, which hold therapeutic potential for AD and other tauopathies.

Unlocking the Mitochondria for Nanomedicine-based Treatments: Overcoming Biological Barriers, Improving Designs, and Selecting Verification Techniques

Enhanced targeting approaches will support the treatment of diseases associated with dysfunctional mitochondria, which play critical roles in energy generation and cell survival. Obstacles to mitochondria-specific targeting include the presence of distinct biological barriers and the need to pass through (or avoid) various cell internalization mechanisms. A range of studies have reported the design of mitochondrially-targeted nanomedicines that navigate the complex routes required to influence mitochondrial function; nonetheless, a significant journey lies ahead before mitochondrially-targeted nanomedicines become suitable for clinical use. Moving swiftly forward will require safety studies, in vivo assays confirming effectiveness, and methodologies to validate mitochondria-targeted nanomedicines' subcellular location/activity. From a nanomedicine standpoint, we describe the biological routes involved (from administration to arrival within the mitochondria), the features influencing rational design, and the techniques used to identify/validate successful targeting. Overall, rationally-designed mitochondria-targeted-based nanomedicines hold great promise for precise subcellular therapeutic delivery.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Recent advances in nanoparticulate RNA delivery systems

Nanoparticle-based RNA delivery has shown great progress in recent years with the approval of two mRNA vaccines for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and a liver-targeted siRNA therapy. Here, we discuss the preclinical and clinical advancement of new generations of RNA delivery therapies along multiple axes. Improvements in cargo design such as RNA circularization and data-driven untranslated region optimization can drive better mRNA expression. New materials discovery research has driven improved delivery to extrahepatic targets such as the lung and splenic immune cells, which could lead to pulmonary gene therapy and better cancer vaccines, respectively. Other organs and even specific cell types can be targeted for delivery via conjugation of small molecule ligands, antibodies, or peptides to RNA delivery nanoparticles. Moreover, the immune response to any RNA delivery nanoparticle plays a crucial role in determining efficacy. Targeting increased immunogenicity without induction of reactogenic side effects is crucial for vaccines, while minimization of immune response is important for gene therapies. New developments have addressed each of these priorities. Last, we discuss the range of RNA delivery clinical trials targeting diverse organs, cell types, and diseases and suggest some key advances that may play a role in the next wave of therapies.

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.Communicated by Ramaswamy H. Sarma.

Protein-based nanoparticles for therapeutic nucleic acid delivery

To realize the full potential of emerging nucleic acid therapies, there is a need for effective delivery agents to transport cargo to cells of interest. Protein materials exhibit several unique properties, including biodegradability, biocompatibility, ease of functionalization via recombinant and chemical modifications, among other features, which establish a promising basis for therapeutic nucleic acid delivery systems. In this review, we highlight progress made in the use of non-viral protein-based nanoparticles for nucleic acid delivery in vitro and in vivo, while elaborating on key physicochemical properties that have enabled the use of these materials for nanoparticle formulation and drug delivery. To conclude, we comment on the prospects and unresolved challenges associated with the translation of protein-based nucleic acid delivery systems for therapeutic applications.

Current developments and prospects of the antibiotic delivery systems

Antibiotics have remained the cornerstone for the treatment of bacterial infections ever since their discovery in the twentieth century. The uproar over antibiotic resistance among bacteria arising from genome plasticity and biofilm development has rendered current antibiotic therapies ineffective, urging the development of innovative therapeutic approaches. The development of antibiotic resistance among bacteria has further heightened the clinical failure of antibiotic therapy, which is often linked to its low bioavailability, side effects, and poor penetration and accumulation at the site of infection. In this review, we highlight the potential use of siderophores, antibodies, cell-penetrating peptides, antimicrobial peptides, bacteriophages, and nanoparticles to smuggle antibiotics across impermeable biological membranes to achieve therapeutically relevant concentrations of antibiotics and combat antimicrobial resistance (AMR). We will discuss the general mechanisms via which each delivery system functions and how it can be tailored to deliver antibiotics against the paradigm of mechanisms underlying antibiotic resistance.

Nature-inspired peptide of MtDef4 C-terminus tail enables protein delivery in mammalian cells

Cell-penetrating peptides show promise as versatile tools for intracellular delivery of therapeutic agents. Various peptides have originated from natural proteins with antimicrobial activity. We investigated the mammalian cell-penetrating properties of a 16-residue peptide with the sequence GRCRGFRRRCFCTTHC from the C-terminus tail of the Medicago truncatula defensin MtDef4. We evaluated the peptide's ability to penetrate multiple cell types. Our results demonstrate that the peptide efficiently penetrates mammalian cells within minutes and at a micromolar concentration. Moreover, upon N-terminal fusion to the fluorescent protein GFP, the peptide efficiently delivers GFP into the cells. Despite its remarkable cellular permeability, the peptide has only a minor effect on cellular viability, making it a promising candidate for developing a cell-penetrating peptide with potential therapeutic applications.

POSEIDON: Peptidic Objects SEquence-based Interaction with cellular DOmaiNs: a new database and predictor

Cell-penetrating peptides (CPPs) are short chains of amino acids that have shown remarkable potential to cross the cell membrane and deliver coupled therapeutic cargoes into cells. Designing and testing different CPPs to target specific cells or tissues is crucial to ensure high delivery efficiency and reduced toxicity. However, in vivo/in vitro testing of various CPPs can be both time-consuming and costly, which has led to interest in computational methodologies, such as Machine Learning (ML) approaches, as faster and cheaper methods for CPP design and uptake prediction. However, most ML models developed to date focus on classification rather than regression techniques, because of the lack of informative quantitative uptake values. To address these challenges, we developed POSEIDON, an open-access and up-to-date curated database that provides experimental quantitative uptake values for over 2,300 entries and physicochemical properties of 1,315 peptides. POSEIDON also offers physicochemical properties, such as cell line, cargo, and sequence, among others. By leveraging this database along with cell line genomic features, we processed a dataset of over 1,200 entries to develop an ML regression CPP uptake predictor. Our results demonstrated that POSEIDON accurately predicted peptide cell line uptake, achieving a Pearson correlation of 0.87, Spearman correlation of 0.88, and r2 score of 0.76, on an independent test set. With its comprehensive and novel dataset, along with its potent predictive capabilities, the POSEIDON database and its associated ML predictor signify a significant leap forward in CPP research and development. The POSEIDON database and ML Predictor are available for free and with a user-friendly interface at https://moreiralab.com/resources/poseidon/ , making them valuable resources for advancing research on CPP-related topics. Scientific Contribution Statement: Our research addresses the critical need for more efficient and cost-effective methodologies in Cell-Penetrating Peptide (CPP) research. We introduced POSEIDON, a comprehensive and freely accessible database that delivers quantitative uptake values for over 2,300 entries, along with detailed physicochemical profiles for 1,315 peptides. Recognizing the limitations of current Machine Learning (ML) models for CPP design, our work leveraged the rich dataset provided by POSEIDON to develop a highly accurate ML regression model for predicting CPP uptake.

Multi_CycGT: A Deep Learning-Based Multimodal Model for Predicting the Membrane Permeability of Cyclic Peptides

Cyclic peptides are gaining attention for their strong binding affinity, low toxicity, and ability to target "undruggable" proteins; however, their therapeutic potential against intracellular targets is constrained by their limited membrane permeability, and researchers need much time and money to test this property in the laboratory. Herein, we propose an innovative multimodal model called Multi_CycGT, which combines a graph convolutional network (GCN) and a transformer to extract one- and two-dimensional features for predicting cyclic peptide permeability. The extensive benchmarking experiments show that our Multi_CycGT model can attain state-of-the-art performance, with an average accuracy of 0.8206 and an area under the curve of 0.8650, and demonstrates satisfactory generalization ability on several external data sets. To the best of our knowledge, it is the first deep learning-based attempt to predict the membrane permeability of cyclic peptides, which is beneficial in accelerating the design of cyclic peptide active drugs in medicinal chemistry and chemical biology applications.

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.

Comparative transport analysis of cell penetrating peptides and Lysosomal sequences for selective tropism towards RPE cells

Cell penetrating peptides are typically nonspecific, targeting multiple cell types without discrimination. However, subsets of Cell penetrating peptides (CPP) have been found, which show a 'homing' capacity or increased likelihood of internalizing into specific cell types and subcellular locations. Therapeutics intended to be delivered to tissues with a high degree of cellular diversity, such as the intraocular space, would benefit from delivery using CPP that can discriminate across multiple cell types. Lysosomal storage diseases in the retinal pigment epithelium (RPE) can impair cargo clearance, leading to RPE atrophy and blindness. Characterizing CPP for their capacity to effectively deliver cargo to the lysosomes of different cell types may expand treatment options for lysosomal storage disorders. We developed a combinatorial library of CPP and lysosomal sorting signals, applied to ARPE19 and B3 corneal lens cells, for the purpose of determining cell line specificity and internal targeting. Several candidate classes of CPP were found to have as much as 4 times the internalization efficiency in ARPE19 compared to B3. Follow-up cargo transport studies were also performed, which demonstrate effective internalization and lysosomal targeting in ARPE19 cells.

Designing Cell Delivery Peptides and SARS-CoV-2-Targeting Small Interfering RNAs: A Comprehensive Bioinformatics Study with Generative Adversarial Network-Based Peptide Design and In Vitro Assays

Nucleic acid technologies with designed intracellular delivery systems are some of the most promising therapies of the future. Small interfering (si)RNAs inhibit gene expression and protein synthesis and may complement current vaccines with faster design and production. Although successful delivery remains an issue, delivery peptides may help to fill this gap. Here, we address this issue by applying bioinformatic approaches to design new putative cell delivery peptides and siRNAs for COVID-19 variants and other related viral diseases. Of the 29,880 RNA sequences analyzed, 62 were identified in silico as able to target the virus mRNA sequence, and from the 9,984 peptide sequences analyzed, 10 were selected as delivery peptides. From the latter, we further performed in vitro studies of the two best-ranked peptides and compared them with the broadly used TAT delivery peptide. One of them, seq5, displayed better internalization results with about double intensity signal compared to TAT after a 1 h incubation time in GFP-HeLa cells. This peptide has, thus, the features of a delivery peptide and could be used for cargo intracellular delivery.

Nanoplatforms for the Delivery of Nucleic Acids into Plant Cells

Nanocarriers are widely used for efficient delivery of different cargo into mammalian cells; however, delivery into plant cells remains a challenging issue due to physical and mechanical barriers such as the cuticle and cell wall. Here, we discuss recent progress on biodegradable and biosafe nanomaterials that were demonstrated to be applicable to the delivery of nucleic acids into plant cells. This review covers studies the object of which is the plant cell and the cargo for the nanocarrier is either DNA or RNA. The following nanoplatforms that could be potentially used for nucleic acid foliar delivery via spraying are discussed: mesoporous silica nanoparticles, layered double hydroxides (nanoclay), carbon-based materials (carbon dots and single-walled nanotubes), chitosan and, finally, cell-penetrating peptides (CPPs). Hybrid nanomaterials, for example, chitosan- or CPP-functionalized carbon nanotubes, are taken into account. The selected nanocarriers are analyzed according to the following aspects: biosafety, adjustability for the particular cargo and task (e.g., organelle targeting), penetration efficiency and ability to protect nucleic acid from environmental and cellular factors (pH, UV, nucleases, etc.) and to mediate the gradual and timely release of cargo. In addition, we discuss the method of application, experimental system and approaches that are used to assess the efficiency of the tested formulation in the overviewed studies. This review presents recent progress in developing the most promising nanoparticle-based materials that are applicable to both laboratory experiments and field applications.

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.

MFPPDB: a comprehensive multi-functional plant peptide database

Plants produce a wide range of bioactive peptides as part of their innate defense mechanisms. With the explosive growth of plant-derived peptides, verifying the therapeutic function using traditional experimental methods are resources and time consuming. Therefore, it is necessary to predict the therapeutic function of plant-derived peptides more effectively and accurately with reduced waste of resources and thus expedite the development of plant peptides. We herein developed a repository of plant peptides predicted to have multiple therapeutic functions, named as MFPPDB (multi-functional plant peptide database). MFPPDB including 1,482,409 single or multiple functional plant origin therapeutic peptides derived from 121 fundamental plant species. The functional categories of these therapeutic peptides include 41 different features such as anti-bacterial, anti-fungal, anti-HIV, anti-viral, and anti-cancer. The detailed physicochemical information of these peptides was presented in functional search and physicochemical property search module, which can help users easily access the peptide information by the plant peptide species, ID, and functions, or by their peptide ID, isoelectric point, peptide sequence, and molecular weight through web-friendly interface. We further matched the predicted peptides to nine state-of-the-art curated functional peptide databases and found that at least 293,408 of the peptides possess functional potentials. Overall, MFPPDB integrated a massive number of plant peptides have single or multiple therapeutic functions, which will facilitate the comprehensive research in plant peptidomics. MFPPDB can be freely accessed through http://124.223.195.214:9188/mfppdb/index.

Functional small peptides for enhanced protein delivery, solubility, and secretion in microbial biotechnology

In microbial biotechnology, there is a constant demand for functional peptides to give new functionality to engineered proteins to address problems such as direct delivery of functional proteins into bacterial cells, enhanced protein solubility during the expression of recombinant proteins, and efficient protein secretion from bacteria. To tackle these critical issues, we selected three types of functional small peptides: cell-penetrating peptides (CPPs) enable the delivery of diverse cargoes into bacterial cytoplasm for a variety of purposes, protein-solubilizing peptide tags demonstrate remarkable efficiency in solubilizing recombinant proteins without folding interference, and signal peptides play a key role in enabling the secretion of recombinant proteins from bacterial cells. In this review, we introduced these three functional small peptides that offer effective solutions to address emerging problems in microbial biotechnology. Additionally, we summarized various engineering efforts aimed at enhancing the activity and performance of these peptides, thereby providing valuable insights into their potential for further applications.

Cell Penetrating Peptides: Classification, Mechanisms, Methods of Study, and Applications

Cell-penetrating peptides (CPPs) encompass a class of peptides that possess the remarkable ability to cross cell membranes and deliver various types of cargoes, including drugs, nucleic acids, and proteins, into cells. For this reason, CPPs are largely investigated in drug delivery applications in the context of many diseases, such as cancer, diabetes, and genetic disorders. While sharing this functionality and some common structural features, such as a high content of positively charged amino acids, CPPs represent an extremely diverse group of elements, which can differentiate under many aspects. In this review, we summarize the most common characteristics of CPPs, introduce their main distinctive features, mechanistic aspects that drive their function, and outline the most widely used techniques for their structural and functional studies. We highlight current gaps and future perspectives in this field, which have the potential to significantly impact the future field of drug delivery and therapeutics.

Application of Cell Penetrating Peptides as a Promising Drug Carrier to Combat Viral Infections

Novel effective drugs or therapeutic vaccines have been already developed to eradicate viral infections. Some non-viral carriers have been used for effective drug delivery to a target cell or tissue. Among them, cell penetrating peptides (CPPs) attracted a special interest to enhance drug delivery into the cells with low toxicity. They were also applied to transfer peptide/protein-based and nucleic acids-based therapeutic vaccines against viral infections. CPPs-conjugated drugs or vaccines were investigated in several viral infections including poliovirus, Ebola, coronavirus, herpes simplex virus, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, Japanese encephalitis virus, and influenza A virus. Some studies showed that the uptake of CPPs or CPPs-conjugated drugs can be performed through both non-endocytic and endocytic pathways. Despite high potential of CPPs for cargo delivery, there are some serious drawbacks such as non-tissue-specificity, instability, and suboptimal pharmacokinetics features that limit their clinical applications. At present, some solutions are utilized to improve the CPPs properties such as conjugation of CPPs with targeting moieties, the use of fusogenic lipids, generation of the proton sponge effect, etc. Up to now, no CPP or composition containing CPPs has been approved by the Food and Drug Administration (FDA) due to the lack of sufficient in vivo studies on stability, immunological assays, toxicity, and endosomal escape of CPPs. In this review, we briefly describe the properties, uptake mechanisms, advantages and disadvantages, and improvement of intracellular delivery, and bioavailability of cell penetrating peptides. Moreover, we focus on their application as an effective drug carrier to combat viral infections.

Recent Advances of Cell-Penetrating Peptides and Their Application as Vectors for Delivery of Peptide and Protein-Based Cargo Molecules

Peptides and proteins, two important classes of biomacromolecules, play important roles in the biopharmaceuticals field. As compared with traditional drugs based on small molecules, peptide- and protein-based drugs offer several advantages, although most cannot traverse the cell membrane, a natural barrier that prevents biomacromolecules from directly entering cells. However, drug delivery via cell-penetrating peptides (CPPs) is increasingly replacing traditional approaches that mediate biomacromolecular cellular uptake, due to CPPs' superior safety and efficiency as drug delivery vehicles. In this review, we describe the discovery of CPPs, recent developments in CPP design, and recent advances in CPP applications for enhanced cellular delivery of peptide- and protein-based drugs. First, we discuss the discovery of natural CPPs in snake, bee, and spider venom. Second, we describe several synthetic types of CPPs, such as cyclic CPPs, glycosylated CPPs, and D-form CPPs. Finally, we summarize and discuss cell membrane permeability characteristics and therapeutic applications of different CPPs when used as vehicles to deliver peptides and proteins to cells, as assessed using various preclinical disease models. Ultimately, this review provides an overview of recent advances in CPP development with relevance to applications related to the therapeutic delivery of biomacromolecular drugs to alleviate diverse diseases.

Quantification of peptide components in cinobufacini injection and toad skin by ultra-high-performance liquid chromatography/triple quadrupole mass spectrometry

Cinobufacini injection is commonly used in the clinical treatment of tumors and hepatitis B, but the quality is uneven. Currently, the main focus of its quality assessment is on steroids and alkaloids. Based on a previous study, we screened four peptides with high reproducibility, responsiveness, and specificity. This research was the first to develop an ultra-high-performance liquid chromatography/triple quadrupole mass spectrometry approach for evaluating the quality of cinobufacini preparations from the peptide perspective. In this study, we have identified 230 peptides in cinobufacini injection by Q-Exactive mass spectrometry, which contains species-specific peptides. Then, we used ultra-high-performance liquid chromatography/triple quadrupole mass spectrometry to establish a quantitative method for species-specific peptides and carried out method validation. The result revealed that four peptides were linear in a specific range, and had great reproducibility, accuracy, and stability. Eventually, we evaluated the quality of eight batches of cinobufacini injections and 26 batches of toad skins using the total content of target peptides as the criterion. The outcomes demonstrated that the quality of cinobufacini injection is generally stable and the toad skin from Shandong is of the best quality. In conclusion, the quantitative approach that focuses on peptides will offer innovative perspectives on assessing the quality of cinobufacini preparations.

Cell-Penetrating Peptide-Based Delivery of Macromolecular Drugs: Development, Strategies, and Progress

Cell-penetrating peptides (CPPs), developed for more than 30 years, are still being extensively studied due to their excellent delivery performance. Compared with other delivery vehicles, CPPs hold promise for delivering different types of drugs. Here, we review the development process of CPPs and summarize the composition and classification of the CPP-based delivery systems, cellular uptake mechanisms, influencing factors, and biological barriers. We also summarize the optimization routes of CPP-based macromolecular drug delivery from stability and targeting perspectives. Strategies for enhanced endosomal escape, which prolong its half-life in blood, improved targeting efficiency and stimuli-responsive design are comprehensively summarized for CPP-based macromolecule delivery. Finally, after concluding the clinical trials of CPP-based drug delivery systems, we extracted the necessary conditions for a successful CPP-based delivery system. This review provides the latest framework for the CPP-based delivery of macromolecular drugs and summarizes the optimized strategies to improve delivery efficiency.

Effect of helicity and hydrophobicity on cell-penetrating ability of arginine-rich peptides

Arginine (Arg)-rich peptides are one of the typical cell-penetrating peptides (CPPs), which can deliver membrane-impermeable compounds into intracellular compartments. Guanidino groups in Arg-rich peptides are critical for their high cell-penetrating ability, although it remains unclear whether peptide secondary structures contribute to this ability. In the current study, we designed four Arg-rich peptides containing α,α-disubstituted α-amino acids (dAAs), which prefer to adopt a helical structure. The four dAA-containing peptides adopted slightly different peptide secondary structures, from a random structure to a helical structure, with different hydrophobicities. In these peptides, dipropylglycine-containing peptide exhibited the highest helicity and hydrophobicity, and showed the best cell-penetrating ability. These findings suggested that the helicity and hydrophobicity of Arg-rich peptides contributes to their high cell-penetrating ability.

Deep learning tools to accelerate antibiotic discovery

INTRODUCTION

As machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep learning models offer an efficient way to explore high-dimensional data and design compounds with desired properties, including those with antibacterial activity.

AREAS COVERED

This review covers key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. The deep learning approaches here described include discriminative models such as convolutional neural networks, recurrent neural networks, graph neural networks, and generative models like neural language models, variational autoencoders, generative adversarial networks, normalizing flow, and diffusion models. As the integration of these approaches in drug discovery continues to evolve, this review aims to provide insights into promising prospects and challenges that lie ahead in harnessing such technologies for the development of antibiotics.

EXPERT OPINION

Accurate antimicrobial prediction using deep learning faces challenges such as imbalanced data, limited datasets, experimental validation, target strains, and structure. The integration of deep generative models with bioinformatics, molecular dynamics, and data augmentation holds the potential to overcome these challenges, enhance model performance, and utlimately accelerate antimicrobial discovery.

The Study of Cell-Penetrating Peptides to Deliver dsRNA and siRNA by Feeding in the Desert Locust, Schistocerca gregaria

RNA(i) interference is a gene silencing mechanism triggered by double-stranded (ds)RNA, which promises to contribute to species-specific insect pest control strategies. The first step toward the application of RNAi as an insecticide is to enable efficient gene silencing upon dsRNA oral delivery. The desert locust, Schistocerca gregaria is a devastating agricultural pest. While this species is responsive to dsRNA delivered by intra-hemocoelic injection, it is refractory to orally delivered dsRNA. In this study, we evaluated the capacity of five cell-penetrating peptides (CPPs) to bind long dsRNA and protect it from the locust midgut environment. We then selected the CPP EB1 for further in vivo studies. EB1:dsRNA complexes failed to induce RNAi by feeding. Interestingly, we observed that intra-hemocoelic injection of small-interfering (si)RNAs does not result in a silencing response, but that this response can be obtained by injecting EB1:siRNA complexes. EB1 also protected siRNAs from midgut degradation activity. However, EB1:siRNA complexes failed as well in triggering RNAi when fed. Our findings highlight the complexity of the dsRNA/siRNA-triggered RNAi in this species and emphasize the multifactorial nature of the RNAi response in insects. Our study also stresses the importance of in vivo studies when it comes to dsRNA/siRNA delivery systems.

Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery

Sustained drug delivery strategies have many potential benefits for treating a range of diseases, particularly chronic diseases that require treatment for years. For many chronic ocular diseases, patient adherence to eye drop dosing regimens and the need for frequent intraocular injections are significant barriers to effective disease management. Here, we utilize peptide engineering to impart melanin binding properties to peptide-drug conjugates to act as a sustained-release depot in the eye. We develop a super learning-based methodology to engineer multifunctional peptides that efficiently enter cells, bind to melanin, and have low cytotoxicity. When the lead multifunctional peptide (HR97) is conjugated to brimonidine, an intraocular pressure lowering drug that is prescribed for three times per day topical dosing, intraocular pressure reduction is observed for up to 18 days after a single intracameral injection in rabbits. Further, the cumulative intraocular pressure lowering effect increases ~17-fold compared to free brimonidine injection. Engineered multifunctional peptide-drug conjugates are a promising approach for providing sustained therapeutic delivery in the eye and beyond.

Engineered Phage-Based Cancer Vaccines: Current Advances and Future Directions

Bacteriophages have emerged as versatile tools in the field of bioengineering, with enormous potential in tissue engineering, vaccine development, and immunotherapy. The genetic makeup of phages can be harnessed for the development of novel DNA vaccines and antigen display systems, as they can provide a highly organized and repetitive presentation of antigens to immune cells. Bacteriophages have opened new possibilities for the targeting of specific molecular determinants of cancer cells. Phages can be used as anticancer agents and carriers of imaging molecules and therapeutics. In this review, we explored the role of bacteriophages and bacteriophage engineering in targeted cancer therapy. The question of how the engineered bacteriophages can interact with the biological and immunological systems is emphasized to comprehend the underlying mechanism of phage use in cancer immunotherapy. The effectiveness of phage display technology in identifying high-affinity ligands for substrates, such as cancer cells and tumor-associated molecules, and the emerging field of phage engineering and its potential in the development of effective cancer treatments are discussed. We also highlight phage usage in clinical trials as well as the related patents. This review provides a new insight into engineered phage-based cancer vaccines.

A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation

The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.

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.