Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery

CPPCGM: A Highly Efficient Sequence-Based Tool for Simultaneously Identifying and Generating Cell-Penetrating Peptides

Cell-penetrating peptides (CPPs) are usually short oligopeptides with 5-30 amino acid residues. CPPs have been proven as important drug delivery vehicles into cells through different mechanisms, demonstrating their potential as therapeutic candidates. However, experimental screening and synthesis of CPPs could be time-consuming and expensive. Recently, numerous attempts have been made to develop computational methods as a cost-effective way for screening a number of potential CPP candidates. Despite significant advancements, current methods exhibit limited feature representation capabilities, thereby constraining the potential for further performance enhancements. In this study, we developed a deep learning framework called CPPCGM, which uses protein language models (PLMs) to identify and generate novel CPPs. There are two separate blocks in this framework: CPPClassifier and CPPGenerator. The former utilizes three pretrained models for simple voting, thereby accurately categorizing CPPs and non-CPPs. The latter, similar to a generative adversarial network, including a discriminator and a generator, generates peptides that are not present in the training data set. Our proposed CPPCGM has achieved remarkably high Matthews correlation coefficient scores of 0.876, 0.923, and 0.664 on three data sets based on the classification results. Compared with the state-of-the-art methods, the performance of our method is significantly improved. The results also demonstrated the generating potential of CPPCGM through qualitative and quantitative evaluation of the generated samples. Significantly, using PLM-based methods can optimize peptides for biochemical functions, benefiting drug delivery and biomedical applications. Materials related are publicly available at https://github.com/QiufenChen/CPPCGM.

To conjugate or not to conjugate? evaluating the potential use of cell-penetrating peptides for conjugation or complexation with oligonucleotides by surface plasmon resonance

Oligonucleotides represent a class of molecules that exhibit remarkable therapeutic potential due to their unparalleled target specificity, yet they suffer from limited cellular uptake and lack of tissue selectivity. Extensive research is conducted with cell-penetrating peptides (CPPs) as delivery excipients due to their ability to translocate across cellular membranes and deliver cargo into cells. This study aims to investigate an innovative approach to rapidly, and with small amounts of compound, analyze and compare complexation of CPPs to oligonucleotides. The study applies surface plasmon resonance (SPR) to evaluate a comprehensive library of CPPs regarding their interaction with a double-stranded oligonucleotide to assess their potential as complexing molecules or whether the CPP should be chemically linked to the negatively charged oligonucleotide to ensure proximity. Specifically, a small interfering RNA (siRNA) was immobilized on a biotinylated chip, and solutions of 66 CPPs were subsequently injected to determine their binding stoichiometry with the siRNA. The most influential molecular properties of the CPPs were determined to be the positive charge-to-length ratio, the total number of positive charges, and the overall hydrophobicity of the CPP. These findings demonstrate the effectiveness and utility of SPR as a high throughput screening tool for selecting peptide/oligonucleotide pairs intended for complexation or conjugation.

GraphCPP: The new state-of-the-art method for cell-penetrating peptide prediction via graph neural networks

BACKGROUND AND PURPOSE

Cell-penetrating peptides (CPPs) are short amino acid sequences that can penetrate cell membranes and deliver molecules into cells. Several models have been developed for their discovery, yet these models often face challenges in accurately predicting membrane penetration due to the complex nature of peptide-cell interactions. Hence, there is a need for innovative approaches that can enhance predictive performance.

EXPERIMENTAL APPROACH

In this study, we present the application GraphCPP, a novel graph neural network (GNN) for the prediction of membrane penetration capability of peptides.

KEY RESULTS

A new comprehensive dataset-dubbed CPP1708-was constructed resulting in the largest reliable database of CPPs to date. Comparative analyses with previous methods, such as MLCPP2, C2Pred, CellPPD and CellPPD-Mod, demonstrated the superior predictive performance of our model. Upon testing against other published methods, GraphCPP performs exceptionally, achieving 0.5787 Matthews correlation coefficient and 0.8459 area under the curve (AUC) values on one dataset. This means a 92.8% and 23.3% improvement in Matthews correlation coefficient and AUC measures respectively compared with the next best model. The capability of the model to effectively learn peptide representations was demonstrated through t-distributed stochastic neighbour embedding plots. Additionally, the uncertainty analysis revealed that GraphCPP maintains high confidence in predictions for peptides shorter than 40 amino acids. The source code is available at https://github.com/attilaimre99/GraphCPP.

CONCLUSION AND IMPLICATIONS

These findings indicate the potential of GNN-based models to improve CPP penetration prediction and it may contribute towards the development of more efficient drug delivery systems.

Exploring the Chemical Features and Biomedical Relevance of Cell-Penetrating Peptides

Cell-penetrating peptides (CPPs) are a diverse group of peptides, typically composed of 4 to 40 amino acids, known for their unique ability to transport a wide range of substances-such as small molecules, plasmid DNA, small interfering RNA, proteins, viruses, and nanoparticles-across cellular membranes while preserving the integrity of the cargo. CPPs exhibit passive and non-selective behavior, often requiring functionalization or chemical modification to enhance their specificity and efficacy. The precise mechanisms governing the cellular uptake of CPPs remain ambiguous; however, electrostatic interactions between positively charged amino acids and negatively charged glycosaminoglycans on the membrane, particularly heparan sulfate proteoglycans, are considered the initial crucial step for CPP uptake. Clinical trials have highlighted the potential of CPPs in diagnosing and treating various diseases, including cancer, central nervous system disorders, eye disorders, and diabetes. This review provides a comprehensive overview of CPP classifications, potential applications, transduction mechanisms, and the most relevant algorithms to improve the accuracy and reliability of predictions in CPP development.

Development of bioconjugate-based delivery systems for nucleic acids

Nucleic acids are a class of drugs that can modulate gene and protein expression by various mechanisms, namely, RNAi, mRNA degradation by RNase H cleavage, splice modulation, and steric blocking of protein binding or mRNA translation, thus exhibiting immense potential to treat various genetic and rare diseases. Unlike protein-targeted therapeutics, the clinical use of nucleic acids relies on Watson-Crick sequence recognition to regulate aberrant gene expression and impede protein translation. Though promising, targeted delivery remains a bottleneck for the clinical adoption of nucleic acid-based therapeutics. To overcome the delivery challenges associated with nucleic acids, various chemical modifications and bioconjugation-based delivery strategies have been explored. Currently, liver targeting by N-acetyl galactosamine (GalNAc) conjugation has been at the forefront for the treatment of rare and various metabolic diseases, which has led to FDA approval of four nucleic acid drugs. In addition, various other bioconjugation strategies have been explored to facilitate active organ and cell-enriched targeting. This review briefly covers the different classes of nucleic acids, their mechanisms of action, and their challenges. We also elaborate on recent advances in bioconjugation strategies in developing a diverse set of ligands for targeted delivery of nucleic acid drugs.

Endosomolytic peptides enable the cellular delivery of peptide nucleic acids

Precision genetic medicine enlists antisense oligonucleotides (ASOs) to bind to nucleic acid targets important for human disease. Peptide nucleic acids (PNAs) have many desirable attributes as ASOs but lack cellular permeability. Here, we use an assay based on the corrective splicing of an mRNA to assess the ability of synthetic peptides to deliver a functional PNA into a human cell. We find that the endosomolytic peptides L17E and L17ER4 are highly efficacious delivery vehicles. Co-treatment of a PNA with low micromolar L17E or L17ER4 enables robust corrective splicing in nearly all treated cells. Peptide-PNA conjugates are even more effective. These results enhance the utility of PNAs as research tools and potential therapeutic agents.

MuCoCP: a priori chemical knowledge-based multimodal contrastive learning pre-trained neural network for the prediction of cyclic peptide membrane penetration ability

MOTIVATION

There has been a burgeoning interest in cyclic peptide therapeutics due to their various outstanding advantages and strong potential for drug formation. However, it is undoubtedly costly and inefficient to use traditional wet lab methods to clarify their biological activities. Using artificial intelligence instead is a more energy-efficient and faster approach. MuCoCP aims to build a complete pre-trained model for extracting potential features of cyclic peptides, which can be fine-tuned to accurately predict cyclic peptide bioactivity on various downstream tasks. To maximize its effectiveness, we use a novel data augmentation method based on a priori chemical knowledge and multiple unsupervised training objective functions to greatly improve the information-grabbing ability of the model.

RESULTS

To assay the efficacy of the model, we conducted validation on the membrane-permeability of cyclic peptides which achieved an accuracy of 0.87 and R-squared of 0.503 on CycPeptMPDB using semi-supervised training and obtained an accuracy of 0.84 and R-squared of 0.384 using a model with frozen parameters on an external dataset. This result has achieved state-of-the-art, which substantiates the stability and generalization capability of MuCoCP. It means that MuCoCP can fully explore the high-dimensional information of cyclic peptides and make accurate predictions on downstream bioactivity tasks, which will serve as a guide for the future de novo design of cyclic peptide drugs and promote the development of cyclic peptide drugs.

AVAILABILITY AND IMPLEMENTATION

All code used in our proposed method can be found at https://github.com/lennonyu11234/MuCoCP.

Conditional Cell-Penetrating Peptide Exposure as Selective Nanoparticle Uptake Signal

A major bottleneck diminishing the therapeutic efficacy of various drugs is that only small proportions of the administered dose reach the site of action. One promising approach to increase the drug amount in the target tissue is the delivery via nanoparticles (NPs) modified with ligands of cell surface receptors for the selective identification of target cells. However, since receptor binding can unintentionally trigger intracellular signaling cascades, our objective was to develop a receptor-independent way of NP uptake. Cell-penetrating peptides (CPPs) are an attractive tool since they allow efficient cell membrane crossing. So far, their applicability is severely limited as their uptake-promoting ability is nonspecific. Therefore, we aimed to achieve a conditional CPP-mediated NP internalization exclusively into target cells. We synthesized different CPP candidates and investigated their influence on nanoparticle stability, ζ-potential, and uptake characteristics in a core-shell nanoparticle system consisting of poly(lactid-co-glycolid) (PLGA) and poly(lactic acid)-poly(ethylene glycol) (PLA10kPEG2k) block copolymers with CPPs attached to the PEG part. We identified TAT47-57 (TAT) as the most promising candidate and subsequently combined the TAT-modified PLA10kPEG2k polymer with longer PLA10kPEG5k polymer chains, modified with the potent angiotensin-converting enzyme 2 (ACE2) inhibitor MLN-4760. While MLN-4760 enables selective target cell identification, the additional PEG length hides the CPP during a first unspecific cell contact. Only after the previous selective binding of MLN-4760 to ACE2, the established spatial proximity exposes the CPP, triggering cell uptake. We found an 18-fold uptake improvement in ACE2-positive cells compared to unmodified particles. In summary, our work paves the way for a conditional and thus highly selective receptor-independent nanoparticle uptake, which is beneficial in terms of avoiding side effects.

Endosomolytic Peptides Enable the Cellular Delivery of Peptide Nucleic Acids

Precision genetic medicine enlists antisense oligonucleotides (ASOs) to bind to nucleic acid targets important for human disease. Peptide nucleic acids (PNAs) have many desirable attributes as ASOs but lack cellular permeability. Here, we use an assay based on the corrective splicing of an mRNA to assess the ability of synthetic peptides to deliver a functional PNA into a human cell. We find that the endosomolytic peptides L17E and L17ER 4 are highly efficacious delivery vehicles. Co-treatment of a PNA with low micromolar L17E or L17ER 4 enables robust corrective splicing in nearly all treated cells. Peptide-PNA conjugates are even more effective. These results enhance the utility of PNAs as research tools and potential therapeutic agents.

Machine learning for antimicrobial peptide identification and design

Artificial intelligence (AI) and machine learning (ML) models are being deployed in many domains of society and have recently reached the field of drug discovery. Given the increasing prevalence of antimicrobial resistance, as well as the challenges intrinsic to antibiotic development, there is an urgent need to accelerate the design of new antimicrobial therapies. Antimicrobial peptides (AMPs) are therapeutic agents for treating bacterial infections, but their translation into the clinic has been slow owing to toxicity, poor stability, limited cellular penetration and high cost, among other issues. Recent advances in AI and ML have led to breakthroughs in our abilities to predict biomolecular properties and structures and to generate new molecules. The ML-based modelling of peptides may overcome some of the disadvantages associated with traditional drug discovery and aid the rapid development and translation of AMPs. Here, we provide an introduction to this emerging field and survey ML approaches that can be used to address issues currently hindering AMP development. We also outline important limitations that can be addressed for the broader adoption of AMPs in clinical practice, as well as new opportunities in data-driven peptide design.

Dynamic carriers for therapeutic RNA delivery

Carriers for RNA delivery must be dynamic, first stabilizing and protecting therapeutic RNA during delivery to the target tissue and across cellular membrane barriers and then releasing the cargo in bioactive form. The chemical space of carriers ranges from small cationic lipids applied in lipoplexes and lipid nanoparticles, over medium-sized sequence-defined xenopeptides, to macromolecular polycations applied in polyplexes and polymer micelles. This perspective highlights the discovery of distinct virus-inspired dynamic processes that capitalize on mutual nanoparticle-host interactions to achieve potent RNA delivery. From the host side, subtle alterations of pH, ion concentration, redox potential, presence of specific proteins, receptors, or enzymes are cues, which must be recognized by the RNA nanocarrier via dynamic chemical designs including cleavable bonds, alterable physicochemical properties, and supramolecular assembly-disassembly processes to respond to changing biological microenvironment during delivery.

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.

mCherry on Top: A Positive Read-Out Cellular Platform for Screening DMD Exon Skipping Xenopeptide-PMO Conjugates

Phosphorodiamidate morpholino oligomers (PMOs) are a special type of antisense oligonucleotides (ASOs) that can be used as therapeutic modulators of pre-mRNA splicing. Application of nucleic-acid-based therapeutics generally requires suitable delivery systems to enable efficient transport to intended tissues and intracellular targets. To identify potent formulations of PMOs, we established a new in vitro-in vivo screening platform based on mdx exon 23 skipping. Here, a new in vitro positive read-out system (mCherry-DMDEx23) is presented that is sensitive toward the PMO(Ex23) sequence mediating DMD exon 23 skipping and, in this model, functional mCherry expression. After establishment of the reporter system in HeLa cells, a set of amphiphilic, ionizable xenopeptides (XPs) was screened in order to identify potent carriers for PMO delivery. The identified best-performing PMO formulation with high splice-switching activity at nanomolar concentrations in vitro was then translated to in vivo trials, where exon 23 skipping in different organs of healthy BALB/c mice was confirmed. The predesigned in vitro-in vivo workflow enables evaluation of PMO(Ex23) carriers without change of the PMO sequence and formulation composition. Furthermore, the identified PMO-XP conjugate formulation was found to induce highly potent exon skipping in vitro and redistributed PMO activity in different organs in vivo.

Discovery of reactive peptide inhibitors of human papillomavirus oncoprotein E6

Human papillomavirus (HPV) infections account for nearly all cervical cancer cases, which is the fourth most common cancer in women worldwide. High-risk variants, including HPV16, drive tumorigenesis in part by promoting the degradation of the tumor suppressor p53. This degradation is mediated by the HPV early protein 6 (E6), which recruits the E3 ubiquitin ligase E6AP and redirects its activity towards ubiquitinating p53. Targeting the protein interaction interface between HPV E6 and E6AP is a promising modality to mitigate HPV-mediated degradation of p53. In this study, we designed a covalent peptide inhibitor, termed reactide, that mimics the E6AP LXXLL binding motif by selectively targeting cysteine 58 in HPV16 E6 with quantitative conversion. This reactide provides a starting point in the development of covalent peptidomimetic inhibitors for intervention against HPV-driven cancers.

Review on Uric Acid Recognition by MOFs with a Future in Machine Learning

Uric acid (UA) is produced from purine metabolism and serves as a prevalent biomarker for multiple diseases including cancer. Hyperuricemia or hypouricemia can cause multiple dysfunctions throughout the biological processes. Consequently, there is a pressing need for monitoring UA concentration in body fluid. While clinical methods are known, the availability of a point-of-care testing (PoCT) kit remains conspicuously absent. In the case of electrochemical recognition of UA, the oxidation potential of ascorbic acid closely aligns with that of UA and thus it hinders the detection process, which eventually may result in false positive signals. Several chemosensors are known in the field of supramolecular chemistry, and metal-organic frameworks (MOFs) are one of the best-performing contenders due to their robustness, stability, and versatile structures. In this review, we tried to unbox the up-to-date development of UA sensing by MOFs. We delve into the state of UA recognition by MOFs, exploring both electrochemical and fluorometric pathways and drawing comparisons with structurally similar probes like covalent organic frameworks (COFs) to understand/establish the advantages of MOFs specifically in UA sensing. In the absence of a PoCT kit, we have provided the conceptual outlook for designing a PoCT device termed a "Urimeter" via electrochemical operation. For the first time, we have proposed different methods of how UA sensing can be tied up with artificial intelligence and machine learning (AI-ML).

In-Cell Penetration Selection-Mass Spectrometry Produces Noncanonical Peptides for Antisense Delivery

Peptide-mediated delivery of macromolecules in cells has significant potential therapeutic benefits, but no therapy employing cell-penetrating peptides (CPPs) has reached the market after 30 years of investigation due to challenges in the discovery of new, more efficient sequences. Here, we demonstrate a method for in-cell penetration selection-mass spectrometry (in-cell PS-MS) to discover peptides from a synthetic library capable of delivering macromolecule cargo to the cytosol. This method was inspired by recent in vivo selection approaches for cell-surface screening, with an added spatial dimension resulting from subcellular fractionation. A representative peptide discovered in the cytosolic extract, Cyto1a, is nearly 100-fold more active toward antisense phosphorodiamidate morpholino oligomer (PMO) delivery compared to a sequence identified from a whole cell extract, which includes endosomes. Cyto1a is composed of d-residues and two non-α-amino acids, is more stable than its all-l isoform, and is less toxic than known CPPs with comparable activity. Pulse-chase and microscopy experiments revealed that while the PMO-Cyto1a conjugate is likely taken up by endosomes, it can escape to localize to the nucleus without nonspecifically releasing other endosomal components. In-cell PS-MS introduces a means to empirically discover unnatural synthetic peptides for subcellular delivery of therapeutically relevant cargo.

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.

Enhancing Methotrexate Delivery in the Brain by Mesoporous Silica Nanoparticles Functionalized with Cell-Penetrating Peptide using in Vivo and ex Vivo Monitoring

The blood-brain barrier (BBB) acts as a physical/biochemical barrier that protects brain parenchyma from potential hazards exerted by different xenobiotics found in the systemic circulation. This barrier is created by "a lipophilic gate" as well as a series of highly organized influx/efflux mechanisms. The BBB bottleneck adversely affects the efficacy of chemotherapeutic agents in treating different CNS malignancies such as glioblastoma, an aggressive type of cancer affecting the brain. In the present study, mesoporous silica nanoparticles (MSNs) were conjugated with the transactivator of transcription (TAT) peptide, a cell-penetrating peptide, to produce MSN-NH-TAT with the aim of improving methotrexate (MTX) penetration into the brain. The TAT-modified nanosystem was characterized by Fourier transform infrared spectrometry (FTIR), field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), dynamic light scattering (DLS), and N₂ adsorption-desorption analysis. In vitro hemolysis and cell viability studies confirmed the biocompatibility of the MSN-based nanocarriers. In addition, in vivo studies showed that the MTX-loaded MSN-NH-TAT improved brain-to-plasma concentration ratio, brain uptake clearance, and the drug's blood terminal half-life, compared with the use of free MTX. Taken together, the results of the present study indicate that MSN functionalization with TAT is crucial for delivery of MTX into the brain. The present nanosystem represents a promising alternative drug carrier to deliver MTX into the brain via overcoming the BBB.

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.

SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning

BACKGROUND

Cell-penetrating peptides (CPPs) have received considerable attention as a means of transporting pharmacologically active molecules into living cells without damaging the cell membrane, and thus hold great promise as future therapeutics. Recently, several machine learning-based algorithms have been proposed for predicting CPPs. However, most existing predictive methods do not consider the agreement (disagreement) between similar (dissimilar) CPPs and depend heavily on expert knowledge-based handcrafted features.

RESULTS

In this study, we present SiameseCPP, a novel deep learning framework for automated CPPs prediction. SiameseCPP learns discriminative representations of CPPs based on a well-pretrained model and a Siamese neural network consisting of a transformer and gated recurrent units. Contrastive learning is used for the first time to build a CPP predictive model. Comprehensive experiments demonstrate that our proposed SiameseCPP is superior to existing baseline models for predicting CPPs. Moreover, SiameseCPP also achieves good performance on other functional peptide datasets, exhibiting satisfactory generalization ability.

MicroRNA-375 is a therapeutic target for castration-resistant prostate cancer through the PTPN4/STAT3 axis

The functional role of microRNA-375 (miR-375) in the development of prostate cancer (PCa) remains controversial. Previously, we found that plasma exosomal miR-375 is significantly elevated in castration-resistant PCa (CRPC) patients compared with castration-sensitive PCa patients. Here, we aimed to determine how miR-375 modulates CRPC progression and thereafter to evaluate the therapeutic potential of human umbilical cord mesenchymal stem cell (hucMSC)-derived exosomes loaded with miR-375 antisense oligonucleotides (e-375i). We used miRNA in situ hybridization technique to evaluate miR-375 expression in PCa tissues, gain- and loss-of-function experiments to determine miR-375 function, and bioinformatic methods, dual-luciferase reporter assay, qPCR, IHC and western blotting to determine and validate the target as well as the effects of miR-375 at the molecular level. Then, e-375i complexes were assessed for their antagonizing effects against miR-375. We found that the expression of miR-375 was elevated in PCa tissues and cancer exosomes, correlating with the Gleason score. Forced expression of miR-375 enhanced the expression of EMT markers and AR but suppressed apoptosis markers, leading to enhanced proliferation, migration, invasion, and enzalutamide resistance and decreased apoptosis of PCa cells. These effects could be reversed by miR-375 silencing. Mechanistically, miR-375 directly interfered with the expression of phosphatase nonreceptor type 4 (PTPN4), which in turn stabilized phosphorylated STAT3. Application of e-375i could inhibit miR-375, upregulate PTPN4 and downregulate p-STAT3, eventually repressing the growth of PCa. Collectively, we identified a novel miR-375 target, PTPN4, that functions upstream of STAT3, and targeting miR-375 may be an alternative therapeutic for PCa, especially for CRPC with high AR levels.

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.

Can pulmonary RNA delivery improve our pandemic preparedness?

The coronavirus pandemic has changed our perception of RNA medicines, and RNA vaccines have revolutionized our pandemic preparedness. But are we indeed prepared for the next variant or the next emerging virus? How can we prepare? And what does the role of inhaled antiviral RNA play in this regard? When the pandemic started, I rerouted much of the ongoing inhaled RNA delivery research in my group towards the inhibition and treatment of respiratory viral infections. Two years later, I have taken the literature, past and ongoing clinical trials into consideration and have gained new insights based on our collaborative research which I will discuss in this oration.

Cell-Penetrating d-Peptides Retain Antisense Morpholino Oligomer Delivery Activity

Cell-penetrating peptides (CPPs) can cross the cell membrane to enter the cytosol and deliver otherwise nonpenetrant macromolecules such as proteins and oligonucleotides. For example, recent clinical trials have shown that a CPP attached to phosphorodiamidate morpholino oligomers (PMOs) resulted in higher muscle concentration, increased exon skipping, and dystrophin production relative to another study of the PMO alone in patients of Duchenne muscular dystrophy. Therefore, effective design and the study of CPPs could help enhance therapies for difficult-to-treat diseases. So far, the study of CPPs for PMO delivery has been restricted to predominantly canonical l-peptides. We hypothesized that mirror-image d-peptides could have similar PMO delivery activity as well as enhanced proteolytic stability, facilitating their characterization and quantification from biological milieu. We found that several enantiomeric peptide sequences could deliver a PMO-biotin cargo with similar activities while remaining stable against serum proteolysis. The biotin label allowed for affinity capture of fully intact PMO-peptide conjugates from whole-cell and cytosolic lysates. By profiling a mixture of these constructs in cells, we determined their relative intracellular concentrations. When combined with PMO activity, these concentrations provide a new metric for delivery efficiency, which may be useful for determining which peptide sequence to pursue in further preclinical studies.

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.

Transcytosis mechanisms of cell-penetrating peptides: Cation independent CC12 and cationic penetratin

Cell-penetrating peptides (CPPs) can aid in intracellular and in vivo drug delivery. However, the mechanisms of CPP-mediated penetration remain unclear, limiting the development and further application of CPPs. Flow cytometry and laser confocal fluorescence microscopy were performed to detect the effects of different endocytosis inhibitors on the internalization of CC12 and penetratin in ARPE-19 cells. The co-localization of CPPs with the lysosome and macropinosome was detected via an endocytosis tracing experiment. The flow cytometry results showed that chlorpromazine, wortmannin, cytochalasin D, and the ATP inhibitor oligomycin had dose-dependent endocytosis-inhibitory effects on CC12. The laser confocal fluorescence results showed that oligomycin had the most significant inhibitory effect on CC12 uptake; CC12 was co-located with the lysosome, but not with the macropinosome. For penetratin, cytochalasin D and oligomycin had obvious inhibitory effects. The laser confocal fluorescence results indicated that oligomycin had the most significant inhibitory effect on penetratin uptake; the co-localization of penetratin with the lysosome was higher than that with the macropinosome. Cation-independent CC12 and cationic penetratin may be internalized into cells primarily through caveolae and clathrin-mediated endocytosis, and they are typically dependent on ATP. The transport of penetratin could be partly achieved through the direct transmembrane pathway, as the positive charge of penetratin interacts with the negative charge of the cell membrane, and partly through the endocytic pathway.

Label-Free and In Situ Identification of Cells via Combinational Machine Learning Models

Cell identification and counting in living and coculture systems are crucial in cell interaction studies, but current methods primarily rely on complicated and time-consuming staining techniques. Here, a label-free method to precisely recognize, identify, and instantly count cells in situ in coculture systems via combinational machine learning models s presented. A convolutional neural network (CNN) model is first used to generate virtual images of cell nuclei based on unlabeled phase-contrast images. Coordinates of all the cells are then returned according to the virtual nucleus images using two clustering algorithms. Finally, phase-contrast images of single cells are cropped based on the coordinates and sent into another CNN model for cell-type identification. This combinational approach is highly automatic and efficient, which requires few to no manual annotations of images in the training phase. It shows practical performance in different cell culture conditions including cell ratios, densities, and substrate materials, having great potential in real-time cell tracking and analyzing.

Cell-Penetrating Peptides

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

Synthetic Molecular Evolution of Cell Penetrating Peptides

Rational design and optimization of cell penetrating peptides (CPPs) is difficult to accomplish because of the lack of quantitative sequence-structure-function rules describing the activity and because of the complex, poorly understood mechanisms of CPPs. Synthetic molecular evolution is a powerful method to identify gain-of-function cell penetrating peptide variants in this situation. Synthetic molecular evolution requires the design and synthesis of iterative, knowledge-based peptide libraries and the screening of such libraries in complex orthogonal cell-based screens for improved activity. In this chapter, we describe methods for synthesizing powerful combinatorial peptide libraries for synthetic molecular evolution.

Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers

Therapeutic macromolecules such as proteins and oligonucleotides can be highly efficacious but are often limited to extracellular targets due to the cell's impermeable membrane. Cell-penetrating peptides (CPPs) are able to deliver such macromolecules into cells, but limited structure-activity relationships and inconsistent literature reports make it difficult to design effective CPPs for a given cargo. For example, polyarginine motifs are common in CPPs, promoting cell uptake at the expense of systemic toxicity. Machine learning may be able to address this challenge by bridging gaps between experimental data in order to discern sequence-activity relationships that evade our intuition. Our earlier data set and deep learning model led to the design of miniproteins (>40 amino acids) for antisense delivery. Here, we leveraged and expanded our model with data augmentation in the short CPP sequence space of the data set to extrapolate and discover short, low-arginine-content CPPs that would be easier to synthesize and amenable to rapid conjugation to desired cargo, and with minimal in vivo toxicity. The lead predicted peptide, termed P6, is as active as a polyarginine CPP for the delivery of an antisense oligomer, while having only one arginine side chain and 18 total residues. We determined the pentalysine motif and the C-terminal cysteine of P6 to be the main drivers of activity. The antisense conjugate was able to enhance corrective splicing in an animal model to produce functional eGFP in heart tissue in vivo while remaining nontoxic up to a dose of 60 mg/kg. In addition, P6 was able to deliver an enzyme to the cytosol of cells. Our findings suggest that, given a data set of long CPPs, we can discover by extrapolation short, active sequences that deliver antisense oligomers.

Synthetic molecule libraries for nucleic acid delivery: Design parameters in cationic/ionizable lipids and polymers

Recent progress in the design of cationic lipids and polymers has successfully translated nucleic acid drugs into clinical applications, such as the treatment of liver diseases and the prevention of virus infection. Small or large libraries of delivery molecules have been used to find the key chemical structures to protect nucleic acids from nucleases in the extracellular milieu and to facilitate the endosomal escape after endocytosis. This review introduces three essential design parameters (i.e., acid dissociation constant, hydrophobicity, and biodegradability) to develop synthetic molecules for nucleic acid delivery. The significance and mechanism of each parameter are described based on the results obtained from in vitro and in vivo evaluations. Other design parameters were then discussed to create the next generation of delivery molecules for future nucleic acid therapeutics.

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.

An Insight into FDA Approved Antibody-Drug Conjugates for Cancer Therapy

The large number of emerging antibody-drug conjugates (ADCs) for cancer therapy has resulted in a significant market 'boom', garnering worldwide attention. Despite ADCs presenting huge challenges to researchers, particularly regarding the identification of a suitable combination of antibody, linker, and payload, as of September 2021, 11 ADCs have been granted FDA approval, with eight of these approved since 2017 alone. Optimism for this therapeutic approach is clear, despite the COVID-19 pandemic, 2020 was a landmark year for deals and partnerships in the ADC arena, suggesting that there remains significant interest from Big Pharma. Herein we review the enthusiasm for ADCs by focusing on the features of those approved by the FDA, and offer some thoughts as to where the field is headed.

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.

PyPEF-An Integrated Framework for Data-Driven Protein Engineering

Data-driven strategies are gaining increased attention in protein engineering due to recent advances in access to large experimental databanks of proteins, next-generation sequencing (NGS), high-throughput screening (HTS) methods, and the development of artificial intelligence algorithms. However, the reliable prediction of beneficial amino acid substitutions, their combination, and the effect on functional properties remain the most significant challenges in protein engineering, which is applied to develop proteins and enzymes for biocatalysis, biomedicine, and life sciences. Here, we present a general-purpose framework (PyPEF: pythonic protein engineering framework) for performing data-driven protein engineering using machine learning methods combined with techniques from signal processing and statistical physics. PyPEF guides the identification and selection of beneficial proteins of a defined sequence space by systematically or randomly exploring the fitness of variants and by sampling random evolution pathways. The performance of PyPEF was evaluated concerning its predictive accuracy and throughput on four public protein and enzyme data sets using common regression models. It was proved that the program could efficiently predict the fitness of protein sequences for different target properties (predictive models with coefficient of determination values ranging from 0.58 to 0.92). By combining machine learning and protein evolution, PyPEF enabled the screening of proteins with various functions, reaching a screening capacity of more than 500,000 protein sequence variants in the timeframe of only a few minutes on a personal computer. PyPEF displayed significant accuracies on four public data sets (different proteins and properties) and underlined the potential of integrating data-driven technologies for covering different philosophies by either predicting the fitness of the variants to the highest accuracy accounting for epistatic effects or capturing the general trend of introduced mutations on the fitness in directed protein evolution campaigns. In essence, PyPEF can provide a powerful solution to current sequence exploration and combinatorial problems faced in protein engineering through exhaustive in silico screening of the sequence space.

Cyclisation strategies for stabilising peptides with irregular conformations

Cyclisation is a common synthetic strategy for enhancing the therapeutic potential of peptide-based molecules. While there are extensive studies on peptide cyclisation for reinforcing regular secondary structures such as α-helices and β-sheets, there are remarkably few reports of cyclising peptides which adopt irregular conformations in their bioactive target-bound state. In this review, we highlight examples where cyclisation techniques have been successful in stabilising irregular conformations, then discuss how the design of cyclic constraints for irregularly structured peptides can be informed by existing β-strand stabilisation approaches, new computational design techniques, and structural principles extracted from cyclic peptide library screening hits. Through this analysis, we demonstrate how existing peptide cyclisation techniques can be adapted to address the synthetic design challenge of stabilising irregularly structured binding motifs.

Progress in the therapeutic inhibition of Cdc42 signalling

Cdc42 is a member of the Rho family of small GTPases and a key regulator of the actin cytoskeleton, controlling cell motility, polarity and cell cycle progression. It signals downstream of the master regulator Ras and is essential for cell transformation by this potent oncogene. Overexpression of Cdc42 is observed in several cancers, where it is linked to poor prognosis. As a regulator of both cell architecture and motility, deregulation of Cdc42 is also linked to tumour metastasis. Like Ras, Cdc42 and other components of the signalling pathways it controls represent important potential targets for cancer therapeutics. In this review, we consider the progress that has been made targeting Cdc42, its regulators and effectors, including new modalities and new approaches to inhibition. Strategies under consideration include inhibition of lipid modification, modulation of Cdc42-GEF, Cdc42-GDI and Cdc42-effector interactions, and direct inhibition of downstream effectors.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space

Cell-penetrating peptides (CPPs) are naturally able to cross the lipid bilayer membrane that protects cells. These peptides share common structural and physicochemical properties and show different pharmaceutical applications, among which drug delivery is the most important. Due to their ability to cross the membranes by pulling high-molecular-weight polar molecules, they are termed Trojan horses. In this study, we proposed a machine learning (ML)-based framework named BChemRF-CPPred (beyond chemical rules-based framework for CPP prediction) that uses an artificial neural network, a support vector machine, and a Gaussian process classifier to differentiate CPPs from non-CPPs, using structure- and sequence-based descriptors extracted from PDB and FASTA formats. The performance of our algorithm was evaluated by tenfold cross-validation and compared with those of previously reported prediction tools using an independent dataset. The BChemRF-CPPred satisfactorily identified CPP-like structures using natural and synthetic modified peptide libraries and also obtained better performance than those of previously reported ML-based algorithms, reaching the independent test accuracy of 90.66% (AUC = 0.9365) for PDB, and an accuracy of 86.5% (AUC = 0.9216) for FASTA input. Moreover, our analyses of the CPP chemical space demonstrated that these peptides break some molecular rules related to the prediction of permeability of therapeutic molecules in cell membranes. This is the first comprehensive analysis to predict synthetic and natural CPP structures and to evaluate their chemical space using an ML-based framework. Our algorithm is freely available for academic use at http://comptools.linc.ufpa.br/BChemRF-CPPred .

Peptide-based inhibitors of protein-protein interactions: biophysical, structural and cellular consequences of introducing a constraint

Protein-protein interactions (PPIs) are implicated in the majority of cellular processes by enabling and regulating the function of individual proteins. Thus, PPIs represent high-value, but challenging targets for therapeutic intervention. The development of constrained peptides represents an emerging strategy to generate peptide-based PPI inhibitors, typically mediated by α-helices. The approach can confer significant benefits including enhanced affinity, stability and cellular penetration and is ingrained in the premise that pre-organization simultaneously pays the entropic cost of binding, prevents a peptide from adopting a protease compliant β-strand conformation and shields the hydrophilic amides from the hydrophobic membrane. This conceptual blueprint for the empirical design of peptide-based PPI inhibitors is an exciting and potentially lucrative way to effect successful PPI inhibitor drug-discovery. However, a plethora of more subtle effects may arise from the introduction of a constraint that include changes to binding dynamics, the mode of recognition and molecular properties. In this review, we summarise the influence of inserting constraints on biophysical, conformational, structural and cellular behaviour across a range of constraining chemistries and targets, to highlight the tremendous success that has been achieved with constrained peptides alongside emerging design opportunities and challenges.

Synthetic Biology and Computer-Based Frameworks for Antimicrobial Peptide Discovery

Antibiotic resistance is one of the greatest challenges of our time. This global health problem originated from a paucity of truly effective antibiotic classes and an increased incidence of multi-drug-resistant bacterial isolates in hospitals worldwide. Indeed, it has been recently estimated that 10 million people will die annually from drug-resistant infections by the year 2050. Therefore, the need to develop out-of-the-box strategies to combat antibiotic resistance is urgent. The biological world has provided natural templates, called antimicrobial peptides (AMPs), which exhibit multiple intrinsic medical properties including the targeting of bacteria. AMPs can be used as scaffolds and, via engineering, can be reconfigured for optimized potency and targetability toward drug-resistant pathogens. Here, we review the recent development of tools for the discovery, design, and production of AMPs and propose that the future of peptide drug discovery will involve the convergence of computational and synthetic biology principles.

Optimizing synthetic nucleic acid and protein nanocarriers: The chemical evolution approach

Optimizing synthetic nanocarriers is like searching for a needle in a haystack. How to find the most suitable carrier for intracellular delivery of a specified macromolecular nanoagent for a given disease target location? Here, we review different synthetic 'chemical evolution' strategies that have been pursued. Libraries of nanocarriers have been generated either by unbiased combinatorial chemistry or by variation and novel combination of known functional delivery elements. As in natural evolution, definition of nanocarriers as sequences, as barcode or design principle, may fuel chemical evolution. Screening in appropriate test system may not only provide delivery candidates, but also a refined understanding of cellular delivery including novel, unpredictable mechanisms. Combined with rational design and computational algorithms, candidates can be further optimized in subsequent evolution cycles into nanocarriers with improved safety and efficacy. Optimization of nanocarriers differs for various cargos, as illustrated for plasmid DNA, siRNA, mRNA, proteins, or genome-editing nucleases.

Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis

The chemical synthesis of polypeptides involves stepwise formation of amide bonds on an immobilized solid support. The high yields required for efficient incorporation of each individual amino acid in the growing chain are often impacted by sequence-dependent events such as aggregation. Here, we apply deep learning over ultraviolet-visible (UV-vis) analytical data collected from 35 427 individual fluorenylmethyloxycarbonyl (Fmoc) deprotection reactions performed with an automated fast-flow peptide synthesizer. The integral, height, and width of these time-resolved UV-vis deprotection traces indirectly allow for analysis of the iterative amide coupling cycles on resin. The computational model maps structural representations of amino acids and peptide sequences to experimental synthesis parameters and predicts the outcome of deprotection reactions with less than 6% error. Our deep-learning approach enables experimentally aware computational design for prediction of Fmoc deprotection efficiency and minimization of aggregation events, building the foundation for real-time optimization of peptide synthesis in flow.

Nucleic acid therapeutics in neurodevelopmental disease

Nucleic acid therapeutics allow sequence-based targeting of mutation-harboring genes. They can be used to increase the expression and function of disease genes or to decrease the expression of toxic gene products. Antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), and gene-replacement therapies have received FDA approval, and in vivo gene editing applications are currently under development. Special consideration should be given to target engagement in neurons and amelioration of neurological phenotypes. Here we discuss the uses and limitations of different nucleic acid therapeutics, highlighting examples in the clinical and pre-clinical application of these modalities for the treatment of neurodevelopmental diseases.

Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks

Antimicrobial peptides are a potential solution to the threat of multidrug-resistant bacterial pathogens. Recently, deep generative models including generative adversarial networks (GANs) have been shown to be capable of designing new antimicrobial peptides. Intuitively, a GAN controls the probability distribution of generated sequences to cover active peptides as much as possible. This paper presents a peptide-specialized model called PepGAN that takes the balance between covering active peptides and dodging nonactive peptides. As a result, PepGAN has superior statistical fidelity with respect to physicochemical descriptors including charge, hydrophobicity, and weight. Top six peptides were synthesized, and one of them was confirmed to be highly antimicrobial. The minimum inhibitory concentration was 3.1 μg/mL, indicating that the peptide is twice as strong as ampicillin.

Prediction of Amphiphilic Cell-Penetrating Peptide Building Blocks from Protein-Derived Amino Acid Sequences for Engineering of Drug Delivery Nanoassemblies

Amphiphilic molecules, forming self-assembled nanoarchitectures, are typically composed of hydrophobic and hydrophilic domains. Peptide amphiphiles can be designed from two, three, or four building blocks imparting novel structural and functional properties and affinities for interaction with cellular membranes or intracellular organelles. Here we present a combined numerical approach to design amphiphilic peptide scaffolds that are derived from the human nuclear K_i-67 protein. K_i-67 acts, like a biosurfactant, as a steric and electrostatic charge barrier against the collapse of mitotic chromosomes. The proposed predictive design of new K_i-67 protein-derived amphiphilic amino acid sequences exploits the computational outcomes of a set of web-accessible predictors, which are based on machine learning methods. The ensemble of such artificial intelligence algorithms, involving support vector machine (SVM), random forest (RF) classifiers, and neural networks (NN), enables the nanoengineering of a broad range of innovative peptide materials for therapeutic delivery in various applications. Amphiphilic cell-penetrating peptides (CPP), derived from natural protein sequences, may spontaneously form self-assembled nanocarriers characterized by enhanced cellular uptake. Thanks to their inherent low immunogenicity, they may enable the safe delivery of therapeutic molecules across the biological barriers.

Advances in the use of cell penetrating peptides for respiratory drug delivery

Introduction: Respiratory diseases are leading causes of death in the world, still inhalation therapies are the largest fail in drug development. There is an evident need to develop new therapies. Biomolecules represent apotential therapeutic agent in this regard, however their translation to the clinic is hindered by the lack of tools to efficiently deliver molecules. Cell penetrating peptides (CPPs) have arisen as apotential strategy for intracellular delivery that could theoretically enable the translation of new therapies.Areas covered: In this review, the use of CPPs as astrategy to deliver different molecules (cargoes) to treat lung-relateddiseases will be the focus. Abrief description of these molecules and the innovative methods in designing new CPPs is presented. The delivery of different cargoes (proteins, peptides, poorly soluble drugs and nucleic acids) using CPPs is discussed, focusing on benefits to treat different respiratory diseases like inflammatory disorders, cystic fibrosis and lung cancer.Expert opinion: The advantages of using CPPs to deliver biomolecules and poorly soluble drugs to the lungs is evident. This field has advanced in the past few years toward targeted intracellular delivery, although further studies are needed to fully understand its potential and limitations in vitro and in vivo.

Cell-Penetrating Streptavidin: A General Tool for Bifunctional Delivery with Spatiotemporal Control, Mediated by Transport Systems Such as Adaptive Benzopolysulfane Networks

In this report, cell-penetrating streptavidin (CPS) is introduced to exploit the full power of streptavidin-biotin biotechnology in cellular uptake. For this purpose, transporters, here cyclic oligochalcogenides (COCs), are covalently attached to lysines of wild-type streptavidin. This leaves all four biotin binding sites free for at least bifunctional delivery. To maximize the standards of the quantitative evaluation of cytosolic delivery, the recent chloroalkane penetration assay (CAPA) is coupled with automated high content (HC) imaging, a technique that combines the advantages of fluorescence microscopy and flow cytometry. According to the resulting HC-CAPA, cytosolic delivery of CPS equipped with four benzopolysulfanes was the best among all tested CPSs, also better than the much smaller TAT peptide, the original cell-penetrating peptide from HIV. HaloTag-GFP fusion proteins expressed on mitochondria were successfully targeted using CPS carrying two different biotinylated ligands, HaloTag substrates or anti-GFP nanobodies, interfaced with peptide nucleic acids, flipper force probes, or fluorescent substrates. The delivered substrates could be released from CPS into the cytosol through desthiobiotin-biotin exchange. These results validate CPS as a general tool which enables unrestricted use of streptavidin-biotin biotechnology in cellular uptake.