Determining the esterase activity of peptides and peptide assemblies.

Catalytic peptides are gaining attention as alternatives to enzymes, especially in industrial applications. Recent advances in peptide design have improved their catalytic efficiency with approaches such as self-assembly and metal ion complexation. However, the fundamental principles governing peptide catalysis at the sequence level are still being explored. Ester hydrolysis, a well-studied reaction, serves as a widely employed method to evaluate the catalytic potential of peptides. The standard colorimetric reaction involving para-nitrophenyl acetate hydrolysis acts as a benchmark assay, providing a straightforward and efficient screening method for rapidly identifying potential catalysts. However, maintaining standardized conditions is crucial for reproducible results, given that factors such as pH, temperature, and substrate concentration can introduce unwanted variability. This necessity becomes particularly pronounced when working with peptides, which often exhibit slower reaction rates compared to enzymes, making even minor variations significantly influential on the final outcome. In this context, we present a refined protocol for assessing the catalytic activity of peptides and peptide assemblies, addressing critical considerations for reproducibility and accuracy.

Amyloids, amorphous aggregates and assemblies of peptides - Assessing aggregation.

Amyloid and amorphous aggregates represent the two major categories of aggregates associated with diseases, and although exhibiting distinct features, researchers often treat them as equivalent, which demonstrates the need for more thorough characterization. Here, we compare amyloid and amorphous aggregates based on their biochemical properties, kinetics, and morphological features. To further decipher this issue, we propose the use of peptide self-assemblies as minimalistic models for understanding the aggregation process. Peptide building blocks are significantly smaller than proteins that participate in aggregation, however, they make a plausible means to bridge the gap in discerning the aggregation process at the more complex, protein level. Additionally, we explore the potential use of peptide-inspired models to research the liquid-liquid phase separation as a feasible mechanism preceding amyloid formation. Connecting these concepts can help clarify our understanding of aggregation-related disorders and potentially provide novel drug targets to impede and reverse these serious illnesses.

Manually curated dataset of catalytic peptides for ester hydrolysis.

Catalytic peptides are low cost biomolecules able to catalyse chemical reactions such as ester hydrolysis. This dataset provides a list of catalytic peptides currently reported in literature. Several parameters were evaluated, including sequence length, composition, net charge, isoelectric point, hydrophobicity, self-assembly propensity and mechanism of catalysis. Along with the analysis of physico-chemical properties, the SMILES representation for each sequence was generated to provide an easy-to-use means of training machine learning models. This offers a unique opportunity for the development and validation of proof-of-concept predictive models. Being a reliable manually curated dataset, it also enables the benchmark for comparison of new models or models trained on automatically gathered peptide-oriented datasets. Moreover, the dataset provides an insight in the currently developed catalytic mechanisms and can be used as the foundation for the development of next-generation peptide-based catalysts.

Antiviral Peptide-Based Conjugates: State of the Art and Future Perspectives.

Infectious diseases caused by microbial pathogens (bacteria, virus, fungi, parasites) claim millions of deaths per year worldwide and have become a serious challenge to global human health in our century. Viral infections are particularly notable in this regard, not only because humankind is facing some of the deadliest viral pandemics in recent history, but also because the arsenal of drugs to combat the high levels of mutation, and hence the antigenic variability of (mostly RNA) viruses, is disturbingly scarce. Therefore, the search for new antivirals able to successfully fight infection with minimal or no adverse effects on the host is a pressing task. Traditionally, antiviral therapies have relied on relatively small-sized drugs acting as proteases, polymerases, integrase inhibitors, etc. In recent decades, novel approaches involving targeted delivery such as that achieved by peptide-drug conjugates (PDCs) have gained attention as alternative (pro)drugs for tackling viral diseases. Antiviral PDC therapeutics typically involve one or more small drug molecules conjugated to a cell-penetrating peptide (CPP) carrier either directly or through a linker. Such integration of two bioactive elements into a single molecular entity is primarily aimed at achieving improved bioavailability in conditions where conventional drugs are challenged, but may also turn up novel unexpected functionalities and applications. Advances in peptide medicinal chemistry have eased the way to antiviral PDCs, but challenges remain on the way to therapeutic success. In this paper, we review current antiviral CPP-drug conjugates (antiviral PDCs), with emphasis on the types of CPP and antiviral cargo. We integrate the conjugate and the chemical approaches most often applied to combine both entities. Additionally, we comment on various obstacles faced in the design of antiviral PDCs and on the future outlooks for this class of antiviral therapeutics.

Esterase Sequence Composition Patterns for the Identification of Catalytic Triad Microenvironment Motifs.

Ester hydrolysis is of wide biomedical interest, spanning from the green synthesis of pharmaceuticals to biomaterials' development. Existing peptide-based catalysts exhibit low catalytic efficiency compared to natural enzymes, due to the conformational heterogeneity of peptides. Moreover, there is lack of understanding of the correlation between the primary sequence and catalytic function. For this purpose, we statistically analyzed 22 EC 3.1 hydrolases with known catalytic triads, characterized by unique and well-defined mechanisms. The aim was to identify patterns at the sequence level that will better inform the creation of short peptides containing important information for catalysis, based on the catalytic triad, oxyanion holes and the triad residues microenvironments. Moreover, fragmentation schemes of the primary sequence of selected enzymes alongside the study of their amino acid frequencies, composition, and physicochemical properties are proposed. The results showed highly conserved catalytic sites with distinct positional patterns and chemical microenvironments that favor catalysis and revealed variations in catalytic site composition that could be useful for the design of minimalistic catalysts.

Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides.

The discovery of therapeutic peptides is often accelerated by means of virtual screening supported by machine learning-based predictive models. The predictive performance of such models is sensitive to the choice of data and its representation scheme. While the peptide physicochemical and compositional representations fail to distinguish sequence permutations, the amino acid arrangement within the sequence lacks the important information contained in physicochemical, conformational, topological, and geometrical properties. In this paper, we propose a solution to the identified information gap by implementing a hybrid scheme that complements the best traits from both approaches with the aim of predicting antimicrobial and antiviral activities based on experimental data from DRAMP 2.0, AVPdb, and Uniprot data repositories. Using the Friedman test of statistical significance, we compared our hybrid, sequential properties approach to peptide properties, one-hot vector encoding, and word embedding schemes in the 10-fold cross-validation setting, with respect to the F1 score, Matthews correlation coefficient, geometric mean, recall, and precision evaluation metrics. Moreover, the sequence modeling neural network was employed to gain insight into the synergic effect of both properties- and amino acid order-based predictions. The results suggest that sequential properties significantly (P < 0.01) surpasses the aforementioned state-of-the-art representation schemes. This makes it a strong candidate for increasing the predictive power of screening methods based on machine learning, applicable to any category of peptides.

Mint (Mentha spp.) Honey: Analysis of the Phenolic Profile and Antioxidant Activity.

Research background: The composition of honey is influenced by the botanical source and geographical area of the nectar from which it is derived. Unifloral honeys reach higher market value than multifloral honeys due to their specific aromas, which result from volatile and phenolic compounds. Experimental approach: The aim of our study is to characterize the phenolic composition of a rare unifloral variety of honey - mint (Mentha spp.) honey. For this purpose, we performed standard physicochemical analyses, pollen analysis, determined total phenolic and flavonoid content, analysed antioxidant activity and performed qualitative and quantitative analyses of phenolic compounds for five mint honeys. Results and conclusions: Our results indicate that mint honey samples have high phenolic content, expressed in gallic acid equivalents, from (76.7±0.6) to (90.1±1.1) mg/100 g, and flavonoid content, expressed as quercetin equivalents, from (6.7±0.6) to (12.5±0.8) mg/100 g. These honey samples also exhibit strong antioxidant activity, expressed as Trolox equivalents, from (33.6±2.8) to (51.3±1.2) mg/100 g and from (14.4±0.8) to (55.1±2.4) mg/100 g when analysed with DPPH and ABTS assays, respectively. Quantitative LC-MS/MS analysis revealed that the most abundant phenols in all samples were chrysin, apigenin and p-coumaric acid. Qualitative LC-MS/MS analysis identified the presence of kaempferide, diosmetin, acacetin and several caffeic acid derivatives. Novelty and scientific contribution: Our study indicates that mint honey contains unique phenolic profiles, which likely contribute to its distinctive aroma and strong antioxidant activity. A detailed description of the rare honey varieties gives beekeepers greater visibility and easier access to the demanding natural product market.

Exploiting Peptide Self-Assembly for the Development of Minimalistic Viral Mimetics.

Viruses are natural supramolecular nanostructures that form spontaneously by molecular self-assembly of complex biomolecules. Peptide self-assembly is a versatile tool that allows mimicking viruses by creating their simplified versions through the design of functional, supramolecular materials with modularity, tunability, and responsiveness to chemical and physical stimuli. The main challenge in the design and fabrication of peptide materials is related to the precise control between the peptide sequence and its resulting supramolecular morphology. We provide an overview of existing sequence patterns employed for the development of spherical and fibrillar peptide assemblies that can act as viral mimetics, offering the opportunity to tackle the challenges of viral infections.

Discovery of phosphotyrosine-binding oligopeptides with supramolecular target selectivity.

We demonstrate phage-display screening on self-assembled ligands that enables the identification of oligopeptides that selectively bind dynamic supramolecular targets over their unassembled counterparts. The concept is demonstrated through panning of a phage-display oligopeptide library against supramolecular tyrosine-phosphate ligands using 9-fluorenylmethoxycarbonyl-phenylalanine-tyrosine-phosphate (Fmoc-FpY) micellar aggregates as targets. The 14 selected peptides showed no sequence consensus but were enriched in cationic and proline residues. The lead peptide, KVYFSIPWRVPM-NH2 (P7) was found to bind to the Fmoc-FpY ligand exclusively in its self-assembled state with K D = 74 ± 3 µM. Circular dichroism, NMR and molecular dynamics simulations revealed that the peptide interacts with Fmoc-FpY through the KVYF terminus and this binding event disrupts the assembled structure. In absence of the target micellar aggregate, P7 was further found to dynamically alternate between multiple conformations, with a preferred hairpin-like conformation that was shown to contribute to supramolecular ligand binding. Three identified phages presented appreciable binding, and two showed to catalyze the hydrolysis of a model para-nitro phenol phosphate substrate, with P7 demonstrating conformation-dependent activity with a modest k cat/K M = 4 ± 0.3 × 10-4 M-1 s-1.

Bottom-Up Design Approach for OBOC Peptide Libraries.

One-bead-one-compound peptide libraries, developed following the top-down experimental approach, have attracted great interest in the identification of potential ligands or active peptides. By exploiting a reverse experimental design approach based on the bottom-up strategy, we aimed to develop simplified, maximally diverse peptide libraries that resulted in the successful characterization of mixture components. We show that libraries of 32 and 48 components can be successfully detected in a single run using chromatography coupled to mass spectrometry (UPLC-MS). The proposed libraries were further theoretically evaluated in terms of their composition and physico-chemical properties. By combining the knowledge obtained on single libraries we can cover larger sequence spaces and provide a controlled exploration of the peptide chemical space both theoretically and experimentally. Designing libraries by using the bottom-up approach opens up the possibility of rationally fine-tuning the library complexity based on the available analytical methods.

Algorithm-supported, mass and sequence diversity-oriented random peptide library design.

Random peptide libraries that cover large search spaces are often used for the discovery of new binders, even when the target is unknown. To ensure an accurate population representation, there is a tendency to use large libraries. However, parameters such as the synthesis scale, the number of library members, the sequence deconvolution and peptide structure elucidation, are challenging when increasing the library size. To tackle these challenges, we propose an algorithm-supported approach to peptide library design based on molecular mass and amino acid diversity. The aim is to simplify the tedious permutation identification in complex mixtures, when mass spectrometry is used, by avoiding mass redundancy. For this purpose, we applied multi (two- and three-)-objective genetic algorithms to discriminate between library members based on defined parameters. The optimizations led to diverse random libraries by maximizing the number of amino acid permutations and minimizing the mass and/or sequence overlapping. The algorithm-suggested designs offer to the user a choice of appropriate compromise solutions depending on the experimental needs. This implies that diversity rather than library size is the key element when designing peptide libraries for the discovery of potential novel biologically active peptides.

Customizing Morphology, Size, and Response Kinetics of Matrix Metalloproteinase-Responsive Nanostructures by Systematic Peptide Design.

Overexpression and activation of matrix metalloproteinase-9 (MMP-9) is associated with multiple diseases and can serve as a stimulus to activate nanomaterials for sensing and controlled release. In order to achieve autonomous therapeutics with improved space-time targeting capabilities, several features need to be considered beyond the introduction of an enzyme-cleavable linker into a nanostructure. We introduce guiding principles for a customizable platform using supramolecular peptide nanostructures with three modular components to achieve tunable kinetics and morphology changes upon MMP-9 exposure. This approach enables (1) fine-tuning of kinetics through introduction of ordered/disordered structures, (2) a 12-fold variation in hydrolysis rates achieved by electrostatic (mis)matching of particle and enzyme charge, and (3) selection of enzymatic reaction products that are either cell-killing nanofibers or disintegrate. These guiding principles, which can be rationalized and involve exchange of just a few amino acids, enable systematic customization of enzyme-responsive peptide nanostructures for general use in performance optimization of enzyme-responsive materials.

Publisher Correction: MSK1 regulates luminal cell differentiation and metastatic dormancy in ER+ breast cancer.

In the version of this Article originally published, the boxes framing the two plots in Fig. 1g were misaligned from the axes due to a technical error. This has now been corrected in all versions of the Article.

MSK1 regulates luminal cell differentiation and metastatic dormancy in ER+ breast cancer.

For many patients with breast cancer, symptomatic bone metastases appear after years of latency. How micrometastatic lesions remain dormant and undetectable before initiating colonization is unclear. Here, we describe a mechanism involved in bone metastatic latency of oestrogen receptor-positive (ER+) breast cancer. Using an in vivo genome-wide short hairpin RNA screening, we identified the kinase MSK1 as an important regulator of metastatic dormancy in breast cancer. In patients with ER+ breast cancer, low MSK1 expression associates with early metastasis. We show that MSK1 downregulation impairs the differentiation of breast cancer cells, increasing their bone homing and growth capacities. MSK1 controls the expression of genes required for luminal cell differentiation, including the GATA3 and FOXA1 transcription factors, by modulating their promoter chromatin status. Our results indicate that MSK1 prevents metastatic progression of ER+ breast cancer, suggesting that stratifying patients with breast cancer as high or low risk for early relapse based on MSK1 expression could improve prognosis.

Cell-Penetrating Peptides: Design Strategies beyond Primary Structure and Amphipathicity.

Efficient intracellular drug delivery and target specificity are often hampered by the presence of biological barriers. Thus, compounds that efficiently cross cell membranes are the key to improving the therapeutic value and on-target specificity of non-permeable drugs. The discovery of cell-penetrating peptides (CPPs) and the early design approaches through mimicking the natural penetration domains used by viruses have led to greater efficiency of intracellular delivery. Following these nature-inspired examples, a number of rationally designed CPPs has been developed. In this review, a variety of CPP designs will be described, including linear and flexible, positively charged and often amphipathic CPPs, and more rigid versions comprising cyclic, stapled, or dimeric and/or multivalent, self-assembled peptides or peptido-mimetics. The application of distinct design strategies to known physico-chemical properties of CPPs offers the opportunity to improve their penetration efficiency and/or internalization kinetics. This led to increased design complexity of new CPPs that does not always result in greater CPP activity. Therefore, the transition of CPPs to a clinical setting remains a challenge also due to the concomitant involvement of various internalization routes and heterogeneity of cells used in the in vitro studies.

MMP-9 triggered self-assembly of doxorubicin nanofiber depots halts tumor growth.

A central challenge in cancer care is to ensure that therapeutic compounds reach their targets. One approach is to use enzyme-responsive biomaterials, which reconfigure in response to endogenous enzymes that are overexpressed in diseased tissues, as potential site-specific anti-tumoral therapies. Here we report peptide micelles that upon MMP-9 catalyzed hydrolysis reconfigure to form fibrillar nanostructures. These structures slowly release a doxorubicin payload at the site of action. Using both in vitro and in vivo models, we demonstrate that the fibrillar depots are formed at the sites of MMP-9 overexpression giving rise to enhanced efficacy of doxorubicin, resulting in inhibition of tumor growth in an animal model.

MMP-9 triggered micelle-to-fibre transitions for slow release of doxorubicin.

Phenylacetyl-peptide amphiphiles were designed, which upon cleavage by a disease-associated enzyme reconfigure from micellar aggregates to fibres. Upon this morphological change, a doxorubicin payload could be retained in the fibres formed, which makes them valuable carriers for localised formation of nanofibre depots for slow release of hydrophobic anticancer drugs.

Alignment of nanostructured tripeptide gels by directional ultrasonication.

We demonstrate an in situ ultrasonic approach to influence self-assembly across the supramolecular to micron length scales, showing enhancement of supramolecular interactions, chirality and orientation, which depends on the peptide sequence and solvent environment. This is the first successful demonstration of using oscillating pressure waves to generate anisotropic organo- and hydrogels consisting of oriented tripeptides structures.

Exploring the sequence space for (tri-)peptide self-assembly to design and discover new hydrogels.

Peptides that self-assemble into nanostructures are of tremendous interest for biological, medical, photonic and nanotechnological applications. The enormous sequence space that is available from 20 amino acids probably harbours many interesting candidates, but it is currently not possible to predict supramolecular behaviour from sequence alone. Here, we demonstrate computational tools to screen for the aqueous self-assembly propensity in all of the 8,000 possible tripeptides and evaluate these by comparison with known examples. We applied filters to select for candidates that simultaneously optimize the apparently contradicting requirements of aggregation propensity and hydrophilicity, which resulted in a set of design rules for self-assembling sequences. A number of peptides were subsequently synthesized and characterized, including the first reported tripeptides that are able to form a hydrogel at neutral pH. These tools, which enable the peptide sequence space to be searched for supramolecular properties, enable minimalistic peptide nanotechnology to deliver on its promise.