Screening of cell-penetrating peptides using mRNA display

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.

Development of a Cyclic, Cell Penetrating Peptide Compatible with In Vitro Selection Strategies

A key limitation for the development of peptides as therapeutics is their lack of cell permeability. Recent work has shown that short, arginine-rich macrocyclic peptides containing hydrophobic amino acids are able to penetrate cells and reach the cytosol. Here, we have developed a new strategy for developing cyclic cell penetrating peptides (CPPs) that shifts some of the hydrophobic character to the peptide cyclization linker, allowing us to do a linker screen to find cyclic CPPs with improved cellular uptake. We demonstrate that both hydrophobicity and position of the alkylation points on the linker affect uptake of macrocyclic cell penetrating peptides (CPPs). Our best peptide, 4i, is on par with or better than prototypical CPPs Arg9 (R9) and CPP12 under assays measuring total cellular uptake and cytosolic delivery. 4i was also able to carry a peptide previously discovered from an in vitro selection, 8.6, and a cytotoxic peptide into the cytosol. A bicyclic variant of 4i showed even better cytosolic entry than 4i, highlighting the plasticity of this class of peptides toward modifications. Since our CPPs are cyclized via their side chains (as opposed to head-to-tail cyclization), they are compatible with powerful technologies for peptide ligand discovery including phage display and mRNA display. Access to diverse libraries with inherent cell permeability will afford the ability to find cell permeable hits to many challenging intracellular targets.

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

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

Interleukin-17A Peptide Aptamers with an Unexpected Binding Moiety Selected by cDNA Display under Heterogenous Conditions

Peptide-based drugs are an attractive new modality of therapeutics, and in vitro selection from a large-scale library is a powerful way to identify new lead sequences. In conventional screenings, peptide specificity and stability in physiological heterogenous environments are not evaluated, which sometimes makes subsequent optimization difficult. Here we show that selection using a cDNA display system can be performed in a high percentage of serum and that this might be an option to select molecules with high potency and stability in a biological context. Specifically, we chose interleukin-17A as a target protein and performed in vitro selection of cyclic peptide aptamers from a library of approximately 10¹² members in the presence of serum. The selected molecules had nanomolar affinity to the target and were stable in serum. Interestingly, we found that a component of the DNA linker that connected the peptide and cDNA may play a pivotal role in target binding.

Directing evolution of novel ligands by mRNA display

mRNA display is a powerful biological display platform for the directed evolution of proteins and peptides. mRNA display libraries covalently link the displayed peptide or protein (phenotype) with the encoding genetic information (genotype) through the biochemical activity of the small molecule puromycin. Selection for peptide/protein function is followed by amplification of the linked genetic material and generation of a library enriched in functional sequences. Iterative selection cycles are then performed until the desired level of function is achieved, at which time the identity of candidate peptides can be obtained by sequencing the genetic material. The purpose of this review is to discuss the development of mRNA display technology since its inception in 1997 and to comprehensively review its use in the selection of novel peptides and proteins. We begin with an overview of the biochemical mechanism of mRNA display and its variants with a particular focus on its advantages and disadvantages relative to other biological display technologies. We then discuss the importance of scaffold choice in mRNA display selections and review the results of selection experiments with biological (e.g., fibronectin) and linear peptide library architectures. We then explore recent progress in the development of "drug-like" peptides by mRNA display through the post-translational covalent macrocyclization and incorporation of non-proteogenic functionalities. We conclude with an examination of enabling technologies that increase the speed of selection experiments, enhance the information obtained in post-selection sequence analysis, and facilitate high-throughput characterization of lead compounds. We hope to provide the reader with a comprehensive view of current state and future trajectory of mRNA display and its broad utility as a peptide and protein design tool.

Approaches for the discovery of new cell-penetrating peptides

Introduction: The capability of cell-penetrating peptides (CPP), also known as protein transduction domains (PTD), to enter into cells possibly with an attached cargo, makes their application as delivery vectors or as direct therapeutics compelling. They are generally biocompatible, nontoxic, and easy to synthesize and modify. Three decades after the discovery of the first CPPs, ~2,000 CPP sequences have been identified, and many more predicted. Nevertheless, the field has a strong commitment to authenticate new, more efficient, and specific CPPs.Areas covered: Although a scattering of CPPs have been found by chance, various systematic approaches have been developed and refined over the years to directly aid the identification and depiction of new peptide-based delivery vectors or therapeutics. Here, the authors give an overview of CPPs, and review various approaches of discovering new ones. An emphasis is placed on in silico methods, as these have advanced rapidly in recent years.Expert opinion: Although there are many known CPPs, there is a need to find more efficient and specific CPPs. Several approaches are used to identify such sequences. The success of these approaches depends on the advancement of others and the successful prediction of CPP sequences relies on experimental data.

Discovery of Membrane-Permeating Cyclic Peptides via mRNA Display

Small synthetic peptides capable of crossing biological membranes represent valuable tools in cell biology and drug delivery. While several cell-penetrating peptides (CPPs) of natural or synthetic origin have been reported, no peptide is currently known to cross both cytoplasmic and outer embryonic membranes. Here, we describe a method to engineer membrane-permeating cyclic peptides (MPPs) with broad permeation activity by screening mRNA display libraries of cyclic peptides against embryos at different developmental stages. The proposed method was demonstrated by identifying peptides capable of permeating Drosophila melanogaster (fruit fly) embryos and mammalian cells. The selected peptide cyclo[Glut-MRKRHASRRE-K*] showed a strong permeation activity of embryos exposed to minimal permeabilization pretreatment, as well as human embryonic stem cells and a murine fibroblast cell line. Notably, in both embryos and mammalian cells, the cyclic peptide outperformed its linear counterpart and the control MPPs. Confocal microscopy and single cell flow cytometry analysis were utilized to assess the degree of permeation both qualitatively and quantitatively. These MPPs have potential application in studying and nondisruptively controlling intracellular or intraembryonic processes.

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

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

In Vitro Selection of Peptides and Proteins-Advantages of mRNA Display

mRNA display is a robust in vitro selection technique that allows the selection of peptides and proteins with desired functions from libraries of trillions of variants. mRNA display relies upon a covalent linkage between a protein and its encoding mRNA molecule; the power of the technique stems from the stability of this link, and the large degree of control over experimental conditions afforded to the researcher. This article describes the major advantages that make mRNA display the method of choice among comparable in vivo and in vitro methods, including cell-surface display, phage display, and ribosomal display. We also describe innovative techniques that harness mRNA display for directed evolution, protein engineering, and drug discovery.

Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models

Cell-penetrating peptides, a group of short peptides, can traverse cell membranes to enter cells and thus facilitate the uptake of various molecular cargoes. Thus, they have the potential to become powerful drug delivery systems. The correct identification of peptides as cell-penetrating or non-cell-penetrating would accelerate this application. In this study, we determined which features were important for a peptide to be cell-penetrating or non-cell-penetrating and built a predictive model based on the key features extracted from this analysis. The investigated peptides were retrieved from a previous study, and each was encoded as a numeric vector according to six properties of amino acids-amino acid frequency, codon diversity, electrostatic charge, molecular volume, polarity, and secondary structure-by the pseudo-amino acid composition method. Methods of minimum redundancy maximum relevance and incremental feature selection were then employed to analyze these features, and some were found to be key determinants of cell penetration. In parallel, an optimal random forest prediction model was built. We hope that our findings will provide new resources for the study of cell-penetrating peptides.