Morphing of Amphipathic Helices to Explore the Activity and Selectivity of Membranolytic Antimicrobial Peptides.

Naturally occurring membranolytic antimicrobial peptides (AMPs) are rarely cell-type selective and highly potent at the same time. Template-based peptide design can be used to generate AMPs with improved properties de novo. Following this approach, 18 linear peptides were obtained by computationally morphing the natural AMP Aurein 2.2d2 GLFDIVKKVVGALG into the synthetic model AMP KLLKLLKKLLKLLK. Eleven of the 18 chimeric designs inhibited the growth of Staphylococcus aureus, and six peptides were tested and found to be active against one resistant pathogenic strain or more. One of the peptides was broadly active against bacterial and fungal pathogens without exhibiting toxicity to certain human cell lines. Solution nuclear magnetic resonance and molecular dynamics simulation suggested an oblique-oriented membrane insertion mechanism of this helical de novo peptide. Temperature-resolved circular dichroism spectroscopy pointed to conformational flexibility as an essential feature of cell-type selective AMPs.

Simulated Molecular Evolution for Anticancer Peptide Design.

A computational technique based on a simulated molecular evolution protocol was employed for anticancer peptide (ACP) design. Starting from known ACPs, innovative bioactive peptides were automatically generated in computer-assisted design-synthesize-test cycles. This design algorithm offers a viable strategy for the generation of novel peptide sequences, without requiring a priori structure-activity knowledge. Sequence morphing and activity improvement were achieved through iterative amino acid variation and selection. Results show that not only the interaction of ACPs with the target membrane is important for their anticancer activity, but also the degree of peptide dimerization, which was corroborated by temperature profiling and electrospray mass spectrometry.

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.

From Complex Natural Products to Simple Synthetic Mimetics by Computational De Novo Design.

We present the computational de novo design of synthetically accessible chemical entities that mimic the complex sesquiterpene natural product (-)-Englerin A. We synthesized lead-like probes from commercially available building blocks and profiled them for activity against a computationally predicted panel of macromolecular targets. Both the design template (-)-Englerin A and its low-molecular weight mimetics presented nanomolar binding affinities and antagonized the transient receptor potential calcium channel TRPM8 in a cell-based assay, without showing target promiscuity or frequent-hitter properties. This proof-of-concept study outlines an expeditious solution to obtaining natural-product-inspired chemical matter with desirable properties.

In silico design and optimization of selective membranolytic anticancer peptides.

Membranolytic anticancer peptides represent a potential strategy in the fight against cancer. However, our understanding of the underlying structure-activity relationships and the mechanisms driving their cell selectivity is still limited. We developed a computational approach as a step towards the rational design of potent and selective anticancer peptides. This machine learning model distinguishes between peptides with and without anticancer activity. This classifier was experimentally validated by synthesizing and testing a selection of 12 computationally generated peptides. In total, 83% of these predictions were correct. We then utilized an evolutionary molecular design algorithm to improve the peptide selectivity for cancer cells. This simulated molecular evolution process led to a five-fold selectivity increase with regard to human dermal microvascular endothelial cells and more than ten-fold improvement towards human erythrocytes. The results of the present study advocate for the applicability of machine learning models and evolutionary algorithms to design and optimize novel synthetic anticancer peptides with reduced hemolytic liability and increased cell-type selectivity.

De novo design of anticancer peptides by ensemble artificial neural networks.

Membranolytic anticancer peptides (ACPs) are drawing increasing attention as potential future therapeutics against cancer, due to their ability to hinder the development of cellular resistance and their potential to overcome common hurdles of chemotherapy, e.g., side effects and cytotoxicity. In this work, we present an ensemble machine learning model to design potent ACPs. Four counter-propagation artificial neural-networks were trained to identify peptides that kill breast and/or lung cancer cells. For prospective application of the ensemble model, we selected 14 peptides from a total of 1000 de novo designs, for synthesis and testing in vitro on breast cancer (MCF7) and lung cancer (A549) cell lines. Six de novo designs showed anticancer activity in vitro, five of which against both MCF7 and A549 cell lines. The novel active peptides populate uncharted regions of ACP sequence space.

Designing Anticancer Peptides by Constructive Machine Learning.

Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a deep machine learning model to design membranolytic anticancer peptides (ACPs) de novo. A recurrent neural network with long short-term memory cells was trained on α-helical cationic amphipathic peptide sequences and then fine-tuned with 26 known ACPs by transfer learning. This optimized model was used to generate unique and novel amino acid sequences. Twelve of the peptides were synthesized and tested for their activity on MCF7 human breast adenocarcinoma cells and selectivity against human erythrocytes. Ten of these peptides were active against cancer cells. Six of the active peptides killed MCF7 cancer cells without affecting human erythrocytes with at least threefold selectivity. These results advocate constructive machine learning for the automated design of peptides with desired biological activities.

Kinetics of lipid bilayer permeation of a series of ionisable drugs and their correlation with human transporter-independent intestinal permeability.

For low molecular weight drugs, lipid bilayer permeation is considered the major route for in vivo cell barrier passage. We recently introduced a fluorescence assay with liposomes to determine permeation kinetics of ionisable compounds across the lipid bilayer by monitoring drug-induced pH changes inside the liposomes. Here, we determined the permeability coefficients (PFLipP, FLipP for "Fluorescence Liposomal Permeability") across 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers of 35 ionisable drugs at pH6.0 and compared them to available in vivo human jejunal permeability (Peff) data. PFLipP values were furthermore compared with published Caco-2 cell permeability coefficients (PCaco-2), permeability coefficients determined with the parallel artificial membrane permeability assay (PAMPA) and with log D (pH6.0). The log PFLipP, corrected for predicted para-cellular diffusion, and log PCaco-2 correlated best with log Peff, with similar adjusted R2 (0.75 and 0.74, n=12). Our results suggest that transporter-independent intestinal drug absorption is predictable from liposomal permeability.

When barriers ignore the "rule-of-five".

Why are a few drugs with properties beyond the rule of 5 (bRo5) absorbed across the intestinal mucosa while most other bRo5 compounds are not? Are such exceptional bRo5 compounds exclusively taken up by carrier-mediated transport or are they able to permeate the lipid bilayer (passive lipoidal diffusion)? Our experimental data with liposomes indicate that tetracycline, which violates one rule of the Ro5, and rifampicin, violating three of the rules, significantly permeate a phospholipid bilayer with kinetics similar to labetalol and metoprolol, respectively. Published data from experimental work and molecular dynamics simulations suggest that the formation of intramolecular H-bonds and the possibility to adopt an elongated shape besides the presence of a significant fraction of net neutral species facilitate lipid bilayer permeation. As an alternative to lipid bilayer permeation, carrier proteins can be targeted to improve absorption, with the potential drawbacks of drug-drug interactions and non-linear pharmacokinetics.