With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Mass vaccination offers a promising exit strategy for the COVID-19 pandemic. However, as vaccination progresses, demands to lift restrictions increase, despite most of the population remaining susceptible. Using our age-stratified SEIRD-ICU compartmental model and curated epidemiological and vaccination data, we quantified the rate (relative to vaccination progress) at which countries can lift non-pharmaceutical interventions without overwhelming their healthcare systems. We analyzed scenarios ranging from immediately lifting restrictions (accepting high mortality and morbidity) to reducing case numbers to a level where test-trace-and-isolate (TTI) programs efficiently compensate for local spreading events. In general, the age-dependent vaccination roll-out implies a transient decrease of more than ten years in the average age of ICU patients and deceased. The pace of vaccination determines the speed of lifting restrictions; Taking the European Union (EU) as an example case, all considered scenarios allow for steadily increasing contacts starting in May 2021 and relaxing most restrictions by autumn 2021. Throughout summer 2021, only mild contact restrictions will remain necessary. However, only high vaccine uptake can prevent further severe waves. Across EU countries, seroprevalence impacts the long-term success of vaccination campaigns more strongly than age demographics. In addition, we highlight the need for preventive measures to reduce contagion in school settings throughout the year 2021, where children might be drivers of contagion because of them remaining susceptible. Strategies that maintain low case numbers, instead of high ones, reduce infections and deaths by factors of eleven and five, respectively. In general, policies with low case numbers significantly benefit from vaccination, as the overall reduction in susceptibility will further diminish viral spread. Keeping case numbers low is the safest long-term strategy because it considerably reduces mortality and morbidity and offers better preparedness against emerging escape or more contagious virus variants while still allowing for higher contact numbers (freedom) with progressing vaccinations. In this work, we quantify the rate at which non-pharmaceutical interventions can be lifted as COVID-19 vaccination campaigns progress. With the constraint of not exceeding ICU capacity, there exists only a relatively narrow range of plausible scenarios. We selected different scenarios ranging from the immediate release of restrictions to more conservative approaches aiming at low case numbers. In all considered scenarios, the increasing overall immunity (due to vaccination or post-infection) will allow for a steady increase in contacts. However, deaths and total cases (potentially leading to long covid) are only minimized when aiming for low case numbers, and restrictions are lifted at the pace of vaccination. These qualitative results are general. Taking EU countries as quantitative examples, we observe larger differences only in the long-term perspectives, mainly due to varying seroprevalence and vaccine uptake. Thus, the recommendation is to keep case numbers as low as possible to facilitate test-trace-and-isolate programs, reduce mortality and morbidity, and offer better preparedness against emerging variants, potentially escaping immune responses. Keeping moderate preventive measures in place (such as improved hygiene, use of face masks, and moderate contact reduction) is highly recommended will further facilitate control.

Delphinidin anthocyanins have previously been associated with the inhibition of glucose absorption. Blood glucose lowering effects have been ascribed to maqui berry (Aristotelia chilensis) extracts in humans after boiled rice consumption. In this study, we aimed to explore whether a standardized delphinidin-rich extract from maqui berry (Delphinol) affects glucose metabolism in prediabetic humans based on glycemia and insulinemia curves obtained from an oral glucose tolerance test (OGTT) after a challenge with pure glucose. Volunteers underwent four consecutive OGTTs with at least one week washout period, in which different doses of Delphinol were administered one hour before glucose intake. Delphinol significantly and dose-dependently lowered basal glycemia and insulinemia. Lower doses delayed postprandial glycemic and insulinemic peaks, while higher doses reversed this tendency. Glycemia peaks were dose-dependently lowered, while insulinemia peaks were higher for the lowest dose and lower for other doses. The total glucose available in blood was unaffected by treatments, while the total insulin availability was increased by low doses and decreased by the highest dose. Taken together, these open exploratory results suggest that Delphinol could be acting through three possible mechanisms: by inhibition of intestinal glucose transporters, by an incretin-mediated effect, or by improving insulin sensitivity.

Wolbachia are alpha-proteobacteria known to infect arthropods, which are of interest for disease control since they have been associated with improved resistance to viral infection. Although several genomes for different strains have been sequenced, there is little knowledge regarding the relationship between this bacterium and their hosts, particularly on their dependency for survival. Motivated by the potential applications on disease control, we developed genome-scale models of four Wolbachia strains known to infect arthropods: wAlbB (Aedes albopictus), wVitA (Nasonia vitripennis), wMel and wMelPop (Drosophila melanogaster). The obtained metabolic reconstructions exhibit a metabolism relying mainly on amino acids for energy production and biomass synthesis. A gap analysis was performed to detect metabolic candidates which could explain the endosymbiotic nature of this bacterium, finding that amino acids, requirements for ubiquinone precursors and provisioning of metabolites such as riboflavin could play a crucial role in this relationship. This work provides a systems biology perspective for studying the relationship of Wolbachia with its host and the development of new approaches for control of the spread of arboviral diseases. This approach, where metabolic gaps are key objects of study instead of just additions to complete a model, could be applied to other endosymbiotic bacteria of interest. The expansion of the geographic distribution of arthropods has led to an increase in the number of infections of Zika and Dengue. This motivates the search for new alternative approaches for disease control. Wolbachia pipientis, an obligate intracellular bacteria known to provide pathogen-blocking capabilities to its host, has been used to this end. Wolbachia-infected mosquitoes have been released into the environment as a strategy for controlling the mosquito population and hence the spread of arboviral-derived diseases. However, there is little knowledge regarding the specific interactions that occur between Wolbachia and its host, particularly those associated with its obligate intracellular nature. In this work we studied Wolbachia endosymbiosis from a systems biology perspective. By analyzing the gaps in the metabolic networks of four Wolbachia strains we were able to identify key interaction points between this intracellular bacteria and its host, which play a crucial role in their metabolic relationship. This approach focuses on what is missing to allow unveiling new information regarding the interplay between interacting metabolic networks, and it is especially useful in the case of intracellular bacteria where experimental information is scarce. This additional insight on the metabolic interactions between host and parasite hold great potential for the development of arboviral disease control strategies.