Molecular evolution of peptide ligands with custom-tailored characteristics for targeting of glycostructures

Design of Membrane Active Peptides Considering Multi-Objective Optimization for Biomedical Application

A multitude of membrane active peptides exists that divides into subclasses, such as cell penetrating peptides (CPPs) capable to enter eukaryotic cells or antimicrobial peptides (AMPs) able to interact with prokaryotic cell envelops. Peptide membrane interactions arise from unique sequence motifs of the peptides that account for particular physicochemical properties. Membrane active peptides are mainly cationic, often primary or secondary amphipathic, and they interact with membranes depending on the composition of the bilayer lipids. Sequences of these peptides consist of short 5–30 amino acid sections derived from natural proteins or synthetic sources. Membrane active peptides can be designed using computational methods or can be identified in screenings of combinatorial libraries. This review focuses on strategies that were successfully applied to the design and optimization of membrane active peptides with respect to the fact that diverse features of successful peptide candidates are prerequisites for biomedical application. Not only membrane activity but also degradation stability in biological environments, propensity to induce resistances, and advantageous toxicological properties are crucial parameters that have to be considered in attempts to design useful membrane active peptides. Reliable assay systems to access the different biological characteristics of numerous membrane active peptides are essential tools for multi-objective peptide optimization.

finDr: A web server for in silico D-peptide ligand identification

In the rapidly expanding field of peptide therapeutics, the short in vivo half-life of peptides represents a considerable limitation for drug action. D-peptides, consisting entirely of the dextrorotatory enantiomers of naturally occurring levorotatory amino acids (AAs), do not suffer from these shortcomings as they are intrinsically resistant to proteolytic degradation, resulting in a favourable pharmacokinetic profile. To experimentally identify D-peptide binders to interesting therapeutic targets, so-called mirror-image phage display is typically performed, whereby the target is synthesized in D-form and L-peptide binders are screened as in conventional phage display. This technique is extremely powerful, but it requires the synthesis of the target in D-form, which is challenging for large proteins. Here we present finDr, a novel web server for the computational identification and optimization of D-peptide ligands to any protein structure (https://findr.biologie.uni-freiburg.de/). finDr performs molecular docking to virtually screen a library of helical 12-mer peptides extracted from the RCSB Protein Data Bank (PDB) for their ability to bind to the target. In a separate, heuristic approach to search the chemical space of 12-mer peptides, finDr executes a customizable evolutionary algorithm (EA) for the de novo identification or optimization of D-peptide ligands. As a proof of principle, we demonstrate the validity of our approach to predict optimal binders to the pharmacologically relevant target phenol soluble modulin alpha 3 (PSMα3), a toxin of methicillin-resistant Staphylococcus aureus (MRSA). We validate the predictions using in vitro binding assays, supporting the success of this approach. Compared to conventional methods, finDr provides a low cost and easy-to-use alternative for the identification of D-peptide ligands against protein targets of choice without size limitation. We believe finDr will facilitate D-peptide discovery with implications in biotechnology and biomedicine.

Artificial Evolutionary Optimization Process to Improve the Functionality of Cell Penetrating Peptides

Crossing cellular membranes is a versatile molecular property that allows for a wide variety of peptides with cell penetrating capabilities. This broadness complicates identification of candidates suited best for a specific application. To facilitate the screening of this enormous molecular space in a supervised manner we here present a method to "breed" the desired molecules by applying the rules of Darwinian evolution. With this mate-and-check protocol, which combines an in silico evolution step with an in vitro performance test, cell penetrating peptides that are optimized for a specific task can be achieved in a few rounds of breeding. The procedure is simple and straightforward on the synthetic site but requires robust, highly reproducible and close-to-reality biological assays to yield realistic functional output. With this technology even top-performing peptides can be further improved and functionally adjusted.

Increasing the activity of cell adherent cyclic NGR peptides by optimizing the peptide length and amino acid character

Cyclic peptides are an attractive modality for the development of therapeutics and the identification of functional cyclic peptides that contribute to novel drug development. The peptide array is one of the optimization methods for peptide sequences and also useful to understand sequence-function relationship of peptides. Cell adherent cyclic NGR peptide which selectively binds to the aminopeptidase N (APN or CD13) is known as an attractive tumor marker. In this study, we designed and screened a library of different length and an amino acid substitution library to identify stronger cell adhesion peptides and to reveal that the factor of higher binding between CD13 and optimized cyclic peptides. Additionally, we designed and evaluated 192 peptide libraries using eight representative amino acids to reduce the size of the library. Through these optimization steps of cyclic peptides, we identified 23 peptides that showed significantly higher cell adhesion activity than cKCNGRC, which was previously reported as a cell adhesion cyclic peptide. Among them, cCRHNGRARC showed the highest activity, that is, 1.65 times higher activity than cKCNGRC. An analysis of sequence and functional data showed that the rules which show higher cell adhesion activity for the three basic cyclic peptides (cCX₁ HNGRHX₂ C, cCX₁ HNGRAX₂ C, and cCX₁ ANGRHX₂ C) are related with the position of His residues and cationic amino acids.

Dynamics and binding interactions of peptide inhibitors of dengue virus entry

In this study, we investigate the binding interactions of two synthetic antiviral peptides (DET2 and DET4) on type II dengue virus (DENV2) envelope protein domain III. These two antiviral peptides are designed based on the domain III of the DENV2 envelope protein, which has shown significant inhibition activity in previous studies and can be potentially modified further to be active against all dengue strains. Molecular docking was performed using AutoDock Vina and the best-ranked peptide-domain III complex was further explored using molecular dynamics simulations. Molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) was used to calculate the relative binding free energies and to locate the key residues of peptide-protein interactions. The predicted binding affinity correlated well with the previous experimental studies. DET4 outperformed DET2 and is oriented within the binding site through favorable vdW and electrostatic interactions. Pairwise residue decomposition analysis has revealed several key residues that contribute to the binding of these peptides. Residues in DET2 interact relatively lesser with the domain III compared to DET4. Dynamic cross-correlation analysis showed that both the DET2 and DET4 trigger different dynamic patterns on the domain III. Correlated motions were seen between the residue pairs of DET4 and the binding site while binding of DET2 results in anti-correlated motion on the binding site. This work showcases the use of computational study in elucidating and explaining the experiment observation on an atomic level.

Breeding Cell Penetrating Peptides: Optimization of Cellular Uptake by a Function-Driven Evolutionary Process

In nature, building block-based biopolymers can adapt to functional and environmental demands by recombination and mutation of the monomer sequence. We present here an analogous, artificial evolutionary optimization process which we have applied to improve the functionality of cell-penetrating peptide molecules. The "evolution" consisted of repeated rounds of in silico peptide sequence alterations using a genetic algorithm followed by in vitro peptide synthesis, experimental analysis, and ranking according to their "fitness" (i.e., their ability to carry the cargo carboxyfluorescein into cultured cells). The genetic algorithm-based optimization method was customized and adapted from former successful applications in the lab to realize an early convergence and a minimum number of in vitro and in silico processing steps by configured settings derived from empirical in silico simulation. We started out with 20 "lead peptides" which we had previously identified as top performers regarding their ability to enter cultured cells. Ten breeding rounds comprising 240 peptides each yielded a peptide population of which the top 10 candidates displayed a 6-fold (median values) increase in its cell-penetration capability compared with the top 10 lead peptides, and two consensus sequences emerged which represent local fitness optima. In addition, the cell-penetrating potential could be proven independently of the carboxyfluorescein cargo in an alternative setting. Our results demonstrate that we have established a powerful optimization technology that can be used to further improve peptides with known functionality and adapt them to specific applications.