Piloting the membranolytic activities of peptides with a self-organizing map

Engineering a wolf spider A-family toxin towards increased antimicrobial activity but low toxicity

Spider-derived peptides with insecticidal, antimicrobial and/or cytolytic activities, also known as spider venom antimicrobial peptides (AMPs), can be found in the venoms of RTA-clade spiders. They show translational potential as therapeutic leads. A set of 52 AMPs has been described in the Chinese wolf spider (Lycosa shansia), and many have been shown to exhibit antibacterial effects. Here we explored the potential to enhance their antimicrobial activity using bioengineering. We generated a panel of artificial derivatives of an A-family peptide and screened their activity against selected microbial pathogens, vertebrate cells and insects. In several cases, we increased the antimicrobial activity of the derivatives while retaining the low cytotoxicity of the parental molecule. Furthermore, we injected the peptides into adult Drosophila suzukii and found no evidence of insecticidal effects, confirming the low levels of toxicity. Our data therefore suggest that spider venom linear peptides naturally defend the venom gland against microbial colonization and can be modified into more potent antimicrobial agents that could help to battle infectious diseases in the future.

Rational Design of Membrane-Pore-Forming Peptides

Specific interactions of peptides with lipid membranes are essential for cellular communication and constitute a central aspect of the innate host defense against pathogens. A computational method for generating innovative membrane-pore-forming peptides inspired by natural templates is presented. Peptide representation in terms of sequence- and topology-dependent hydrophobic moments is introduced. This design concept proves to be appropriate for the de novo generation of first-in-class membrane-active peptides with the anticipated mode of action. The designed peptides outperform the natural template in terms of their antibacterial activity. They form a kinked helical structure and self-assemble in the membrane by an entropy-driven mechanism to form dynamically growing pores that are dependent on the lipid composition. The results of this study demonstrate the unique potential of natural template-based peptide design for chemical biology and medicinal chemistry.

Peptide-Membrane Interaction between Targeting and Lysis

Certain cationic peptides interact with biological membranes. These often-complex interactions can result in peptide targeting to the membrane, or in membrane permeation, rupture, and cell lysis. We investigated the relationship between the structural features of membrane-active peptides and these effects, to better understand these processes. To this end, we employed a computational method for morphing a membranolytic antimicrobial peptide into a nonmembranolytic mitochondrial targeting peptide by "directed simulated evolution." The results obtained demonstrate that superficially subtle sequence modifications can strongly affect the peptides' membranolytic and membrane-targeting abilities. Spectroscopic and computational analyses suggest that N- and C-terminal structural flexibility plays a crucial role in determining the mode of peptide-membrane interaction.

Hybrid Network Model for "Deep Learning" of Chemical Data: Application to Antimicrobial Peptides

We present a "deep" network architecture for chemical data analysis and classification together with a prospective proof-of-concept application. The model features a self-organizing map (SOM) as the input layer of a feedforward neural network. The SOM converts molecular descriptors to a two-dimensional image for further processing. We implemented lateral neuron inhibition for contrast enhancement. The model achieved improved classification accuracy and predictive robustness compared to feedforward network classifiers lacking the SOM layer. By nonlinear dimensionality reduction the networks extracted meaningful chemical features from the data and outperformed linear principal component analysis (PCA). The learning machine was trained on the sequence-length independent recognition of antibacterial peptides and correctly predicted the killing activity of a synthetic test peptide against Staphylococcus aureus in an in vitro experiment.

Multidimensional Design of Anticancer Peptides

The computer-assisted design and optimization of peptides with selective cancer cell killing activity was achieved through merging the features of anticancer peptides, cell-penetrating peptides, and tumor-homing peptides. Machine-learning classifiers identified candidate peptides that possess the predicted properties. Starting from a template amino acid sequence, peptide cytotoxicity against a range of cancer cell lines was systematically optimized while minimizing the effects on primary human endothelial cells. The computer-generated sequences featured improved cancer-cell penetration, induced cancer-cell apoptosis, and were enabled a decrease in the cytotoxic concentration of co-administered chemotherapeutic agents in vitro. This study demonstrates the potential of multidimensional machine-learning methods for rapidly obtaining peptides with the desired cellular activities.

Modifying peptides to enhance permeability

Recently, peptides have been validated to address intracellular targets and/or to be orally bioavailable. This review describes some of these scaffolds, offers insight in new cyclization methodologies thought to be beneficial to enhance permeability, and highlights modification on peptides thought to improve oral bioavailability. In this context, side chains and back-bone derivatization beneficial to encourage cellular uptake are presented. In addition, new methodologies supporting the assessment of permeability are discussed.