Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery

StackCPPred: a stacking and pairwise energy content-based prediction of cell-penetrating peptides and their uptake efficiency

Motivation

Cell-penetrating peptides (CPPs) are a vehicle for transporting into living cells pharmacologically active molecules, such as short interfering RNAs, nanoparticles, plasmid DNAs and small peptides, thus offering great potential as future therapeutics. Existing experimental techniques for identifying CPPs are time-consuming and expensive. Thus, the prediction of CPPs from peptide sequences by using computational methods can be useful to annotate and guide the experimental process quickly. Many machine learning-based methods have recently emerged for identifying CPPs. Although considerable progress has been made, existing methods still have low feature representation capabilities, thereby limiting further performance improvements.

Results

We propose a method called StackCPPred, which proposes three feature methods on the basis of the pairwise energy content of the residue as follows: RECM-composition, PseRECM and RECM–DWT. These features are used to train stacking-based machine learning methods to effectively predict CPPs. On the basis of the CPP924 and CPPsite3 datasets with jackknife validation, StackDPPred achieved 94.5% and 78.3% accuracy, which was 2.9% and 5.8% higher than the state-of-the-art CPP predictors, respectively. StackCPPred can be a powerful tool for predicting CPPs and their uptake efficiency, facilitating hypothesis-driven experimental design and accelerating their applications in clinical therapy.

Availability and implementation

Source code and data can be downloaded from https://github.com/Excelsior511/StackCPPred.

Supplementary information

Supplementary data are available at Bioinformatics online.

Automated Flow Synthesis of Tumor Neoantigen Peptides for Personalized Immunotherapy

High-throughput genome sequencing and computation have enabled rapid identification of targets for personalized medicine, including cancer vaccines. Synthetic peptides are an established mode of cancer vaccine delivery, but generating the peptides for each patient in a rapid and affordable fashion remains difficult. High-throughput peptide synthesis technology is therefore urgently needed for patient-specific cancer vaccines to succeed in the clinic. Previously, we developed automated flow peptide synthesis technology that greatly accelerates the production of synthetic peptides. Herein, we show that this technology permits the synthesis of high-quality peptides for personalized medicine. Automated flow synthesis produces 30-mer peptides in less than 35 minutes and 15- to 16-mer peptides in less than 20 minutes. The purity of these peptides is comparable with or higher than the purity of peptides produced by other methods. This work illustrates how automated flow synthesis technology can enable customized peptide therapies by accelerating synthesis and increasing purity. We envision that implementing this technology in clinical settings will greatly increase capacity to generate clinical-grade peptides on demand, which is a key step in reaching the full potential of personalized vaccines for the treatment of cancer and other diseases.

Trapped! A Critical Evaluation of Methods for Measuring Total Cellular Uptake versus Cytosolic Localization

Biomolecules have many properties that make them promising for intracellular therapeutic applications, but delivery remains a key challenge because large biomolecules cannot easily enter the cytosol. Furthermore, quantification of total intracellular versus cytosolic concentrations remains demanding, and the determination of delivery efficiency is thus not straightforward. In this review, we discuss strategies for delivering biomolecules into the cytosol and briefly summarize the mechanisms of uptake for these systems. We then describe commonly used methods to measure total cellular uptake and, more selectively, cytosolic localization, and discuss the major advantages and drawbacks of each method. We critically evaluate methods of measuring "cell penetration" that do not adequately distinguish total cellular uptake and cytosolic localization, which often lead to inaccurate interpretations of a molecule's cytosolic localization. Finally, we summarize the properties and components of each method, including the main caveats of each, to allow for informed decisions about method selection for specific applications. When applied correctly and interpreted carefully, methods for quantifying cytosolic localization offer valuable insight into the bioactivity of biomolecules and potentially the prospects for their eventual development into therapeutics.

Pharmacological inhibition of Bax-induced cell death: Bax-inhibiting peptides and small compounds inhibiting Bax

IMPACT STATEMENT

Bax induces mitochondria-dependent programed cell death. While cytotoxic drugs activating Bax have been developed for cancer treatment, clinically effective therapeutics suppressing Bax-induced cell death rescuing essential cells have not been developed. This mini-review will summarize previously reported Bax inhibitors including peptides, small compounds, and antibodies. We will discuss potential applications and the future direction of these Bax inhibitors.

Prediction of Antimicrobial Potential of a Chemically Modified Peptide From Its Tertiary Structure

Designing novel antimicrobial peptides is a hot area of research in the field of therapeutics especially after the emergence of resistant strains against the conventional antibiotics. In the past number of in silico methods have been developed for predicting the antimicrobial property of the peptide containing natural residues. This study describes models developed for predicting the antimicrobial property of a chemically modified peptide. Our models have been trained, tested and evaluated on a dataset that contains 948 antimicrobial and 931 non-antimicrobial peptides, containing chemically modified and natural residues. Firstly, the tertiary structure of all peptides has been predicted using software PEPstrMOD. Structure analysis indicates that certain type of modifications enhance the antimicrobial property of peptides. Secondly, a wide range of features was computed from the structure of these peptides using software PaDEL. Finally, models were developed for predicting the antimicrobial potential of chemically modified peptides using a wide range of structural features of these peptides. Our best model based on support vector machine achieve maximum MCC of 0.84 with an accuracy of 91.62% on training dataset and MCC of 0.80 with an accuracy of 89.89% on validation dataset. To assist the scientific community, we have developed a web server called "AntiMPmod" which predicts the antimicrobial property of the chemically modified peptide. The web server is present at the following link (http://webs.iiitd.edu.in/raghava/antimpmod/).

Synthetic molecular evolution of hybrid cell penetrating peptides

Peptides and analogs such as peptide nucleic acids (PNA) are promising tools and therapeutics, but the cell membrane remains a barrier to intracellular targets. Conjugation to classical cell penetrating peptides (CPPs) such as pTat_48–60 (tat) and pAntp_43–68 (penetratin) facilitates delivery; however, efficiencies are low. Lack of explicit design principles hinders rational improvement. Here, we use synthetic molecular evolution (SME) to identify gain-of-function CPPs with dramatically improved ability to deliver cargoes to cells at low concentration. A CPP library containing 8192 tat/penetratin hybrid peptides coupled to an 18-residue PNA is screened using the HeLa pTRE-LucIVS2 splice correction reporter system. The daughter CPPs identified are one to two orders of magnitude more efficient than the parent sequences at delivery of PNA, and also deliver a dye cargo and an anionic peptide cargo. The significant increase in performance following a single iteration of SME demonstrates the power of this approach to peptide sequence optimization.

Therapeutic peptide nucleic acids can be delivered into cells by conjugation to cell penetrating peptides (CPPs), but efficiency is usually low. Here the authors use synthetic molecular evolution and a luciferase-based library screen to generate new CPPs with improved efficiency and lower toxicity.