Peptide-Protein Interactions: From Drug Design to Supramolecular Biomaterials

Challenges in Peptide Solubilization - Amyloids Case Study

Peptide science has been a rapidly growing research field because of the enormous potential application of these biocompatible and bioactive molecules. However, many factors limit the widespread use of peptides in medicine, and low solubility is among the most common problems that hamper drug development in the early stages of research. Solubility is a crucial, albeit poorly understood, feature that determines peptide behavior. Several different solubility predictors have been proposed, and many strategies and protocols have been reported to dissolve peptides, but none of them is a one-size-fits-all method for solubilization of even the same peptide. In this review, we look for the reasons behind the difficulties in dissolving peptides, analyze the factors influencing peptide aggregation, conduct a critical analysis of solubilization strategies and protocols available in the literature, and give some tips on how to deal with the so-called difficult sequences. We focus on amyloids, which are particularly difficult to dissolve and handle such as amyloid beta (Aβ), insulin, and phenol-soluble modulins (PSMs).

Peptriever: a Bi-Encoder approach for large-scale protein-peptide binding search

MOTIVATION

Peptide therapeutics hinge on the precise interaction between a tailored peptide and its designated receptor while mitigating interactions with alternate receptors is equally indispensable. Existing methods primarily estimate the binding score between protein and peptide pairs. However, for a specific peptide without a corresponding protein, it is challenging to identify the proteins it could bind due to the sheer number of potential candidates.

RESULTS

We propose a transformers-based protein embedding scheme in this study that can quickly identify and rank millions of interacting proteins. Furthermore, the proposed approach outperforms existing sequence- and structure-based methods, with a mean AUC-ROC and AUC-PR of 0.73.

AVAILABILITY AND IMPLEMENTATION

Training data, scripts, and fine-tuned parameters are available at https://github.com/RoniGurvich/Peptriever. The proposed method is linked with a web application available for customized prediction at https://peptriever.app/.

Distinction and Quantification of Noncovalent Dispersive and Hydrophobic Effects

The possibilities of comparing computational results of noncovalent interactions with experimental data are discussed, first with respect to intramolecular interactions. For these a variety of experimental data such as heats of formation, crystal sublimation heats, comparison with energy minimized structures, and spectroscopic data are available, but until now largely have not found widespread application. Early force field and QM/MP2 calculations have already shown that the sublimation heats of hydrocarbons can be predicted with an accuracy of ±1%. Intermolecular interactions in solution or the gas phase are always accompanied by difficult to compute entropic contributions, like all associations between molecules. Experimentally observed T∆S values contribute 10% to 80% of the total ∆G, depending on interaction mechanisms within the complexes, such as, e.g., hydrogen bonding and ion pairing. Free energies ∆G derived from equilibrium measurements in solution allow us to define binding increments ∆∆G, which are additive and transferable to a variety of supramolecular complexes. Data from more than 90 equilibrium measurements of porphyrin receptors in water indicate that small alkanes do not bind to the hydrophobic flat surfaces within a measuring limit of ∆G = ±0.5 kJ/mol, and that 20 functions bearing heteroatoms show associations by dispersive interactions with up to ∆G = 8 kJ/mol, roughly as a function of their polarizability. Aromatic systems display size-dependent affinities ∆G as a linear function of the number of π-electrons.

Peptides with Charged Amino Acids Mitigate nZnO-Induced Growth Inhibition of Lactobacillus rhamnosus LRa05

Growing concern is about the potential side effects of nanomaterials from food packaging, notably zinc oxide nanoparticles (nZnO). Previous research revealed that walnut-derived peptides could mitigate this inhibitory effect, but the mechanism involved is unclear. Here, we found that not all peptides have such an effect. Based on the growth inhibition model of Lactobacillus rhamnosus LRa05 induced by nZnO, we assessed the protective effects of various peptides. Notably, four peptides containing charged amino acids (PPKNW, WPPKN, ADIYTE, and WEREEQE) were found to effectively alleviate the growth inhibition phenomenon. We hypothesize that the peptide-nZnO interaction modifies this effect, as confirmed through infrared, Raman, and fluorescence spectroscopy. Our results highlight amide bonds, amino groups, carboxyl groups, and benzene rings as key peptide binding sites on nZnO, with static quenching primarily due to hydrogen bonds and van der Waals forces. This study elucidates peptide characteristics in nZnO interactions, facilitating a deeper exploration of food matrix-nanocomposite interactions.

Origins, Technological Advancement, and Applications of Peptidomics

Peptidomics is the comprehensive characterization of peptides from biological sources instead of heading for a few single peptides in former peptide research. Mass spectrometry allows to detect a multitude of peptides in complex mixtures and thus enables new strategies leading to peptidomics. The term was established in the year 2001, and up to now, this new field has grown to over 3000 publications. Analytical techniques originally developed for fast and comprehensive analysis of peptides in proteomics were specifically adjusted for peptidomics. Although it is thus closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. Fundamental technological advancements of peptidomics since have occurred in mass spectrometry and data processing, including quantification, and more slightly in separation technology. Different strategies and diverse sources of peptidomes are mentioned by numerous applications, such as discovery of neuropeptides and other bioactive peptides, including the use of biochemical assays. Furthermore, food and plant peptidomics are introduced similarly. Additionally, applications with a clinical focus are included, comprising biomarker discovery as well as immunopeptidomics. This overview extensively reviews recent methods, strategies, and applications including links to all other chapters of this book.

Modulating Secondary Structure Motifs Through Photo-Labile Peptide Staples

Peptide-protein interactions (PPIs) are facilitated by the well-defined three-dimensional structure of bioactive peptides, interesting compounds for the development of new therapeutic agents. Their secondary structure and thus their propensity to engage in PPIs can be influenced by the introduction of peptide staples on the side chains. In particular, light-controlled staples based on azobenzene photoswitches and their structural influence on helical peptides have been studied extensively. In contrast, photolabile staples bearing photocages as a structural key motif, have mainly been used to block supramolecular interactions. Their influence on the secondary structure of the target peptide is under-investigated. Thus, in this study we use a combination of spectroscopic techniques and in silico simulations to systematically study a series of helical peptides with varying length of the photo-labile staple to obtain a detailed insight into the structure-property relationship in such photoresponsive biomolecules.

A Random Forest Model for Peptide Classification Based on Virtual Docking Data

The affinity of peptides is a crucial factor in studying peptide-protein interactions. Despite the development of various techniques to evaluate peptide-receptor affinity, the results may not always reflect the actual affinity of the peptides accurately. The current study provides a free tool to assess the actual peptide affinity based on virtual docking data. This study employed a dataset that combined actual peptide affinity information (active and inactive) and virtual peptide-receptor docking data, and different machine learning algorithms were utilized. Compared with the other algorithms, the random forest (RF) algorithm showed the best performance and was used in building three RF models using different numbers of significant features (four, three, and two). Further analysis revealed that the four-feature RF model achieved the highest Accuracy of 0.714 in classifying an independent unknown peptide dataset designed with the PEDV spike protein, and it also revealed overfitting problems in the other models. This four-feature RF model was used to evaluate peptide affinity by constructing the relationship between the actual affinity and the virtual docking scores of peptides to their receptors.

Development of a Chemically Modified Sensor Based on a Pentapeptide and Its Application for Sensitive Detection of Verbascoside in Extra Virgin Olive Oil

In addition to their antioxidant and antimicrobial action in functional foods, beverages, and in some dermato-cosmetic products, olive phenolic compounds are also recognized for their role in the prevention of diabetes and inflammation, treatment of heart disease and, consequently, of the numerous chronic diseases mediated by the free radicals. In recent years, attention has increased, in particular, regarding one of the most important compound in extra virgin olive oil (EVOO) having glycosidic structure, namely verbocoside, due to the existence in the literature of numerous studies demonstrating its remarkable contribution to the prophylaxis and treatment of various disorders of the human body. The purpose of this study was the qualitative and quantitative determination of verbascoside in commercial EVOOs from different regions by means of a newly developed sensor based on a screen-printed carbon electrode (SPCE) modified with graphene oxide (GPHOX), on the surface of which a pentapeptide was immobilized by means of glutaraldehyde as cross-linking agent. The modified electrode surface was investigated using both Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) methods. This newly developed sensor has shown a high sensibility compared to the unmodified electrode, a low detection limit (LOD) of up to 9.38 × 10^-8 M, and a wide linearity range between 0.1 µM and 10.55 µM. The applicability of the modified sensor was confirmed by detecting verbascoside in ten different EVOOs samples using the cyclic voltammetry (CV) method, with very good results. The validation of the electroanalytical method was performed by using the standard addition method with very good recoveries in the range of 97.48-103.77%.

Self-Assembled Peptide Nanostructures for ECM Biomimicry

Proteins are functional building blocks of living organisms that exert a wide variety of functions, but their synthesis and industrial production can be cumbersome and expensive. By contrast, short peptides are very convenient to prepare at a low cost on a large scale, and their self-assembly into nanostructures and gels is a popular avenue for protein biomimicry. In this Review, we will analyze the last 5-year progress on the incorporation of bioactive motifs into self-assembling peptides to mimic functional proteins of the extracellular matrix (ECM) and guide cell fate inside hydrogel scaffolds.

New Advances in Short Peptides: Looking Forward

It is beyond doubt that short peptides hold significant promise in bio-medicine, as the most versatile molecules, both structurally and functionally [...]

Repair of Peripheral Nerve Injury Using Hydrogels Based on Self-Assembled Peptides

Peripheral nerve injury often occurs in young adults and is characterized by complex regeneration mechanisms, poor prognosis, and slow recovery, which not only creates psychological obstacles for the patients but also causes a significant burden on society, making it a fundamental problem in clinical medicine. Various steps are needed to promote regeneration of the peripheral nerve. As a bioremediation material, self-assembled peptide (SAP) hydrogels have attracted international attention. They can not only be designed with different characteristics but also be applied in the repair of peripheral nerve injury by promoting cell proliferation or drug-loaded sustained release. SAP hydrogels are widely used in tissue engineering and have become the focus of research. They have extensive application prospects and are of great potential biological value. In this paper, the application of SAP hydrogel in peripheral nerve injury repair is reviewed, and the latest progress in peptide composites and fabrication techniques are discussed.