The Glycyrrhiza genus, generally well-known as licorice, is broadly used for food and medicinal purposes around the globe. The genus encompasses a rich pool of bioactive molecules including triterpene saponins (e.g., glycyrrhizin) and flavonoids (e.g., liquiritigenin, liquiritin). This genus is being increasingly exploited for its biological effects such as antioxidant, antibacterial, antifungal, anti-inflammatory, antiproliferative, and cytotoxic activities. The species Glycyrrhiza glabra L. and the compound glycyrrhizin (glycyrrhizic acid) have been studied immensely for their effect on humans. The efficacy of the compound has been reported to be significantly higher on viral hepatitis and immune deficiency syndrome. This review provides up-to-date data on the most widely investigated Glycyrrhiza species for food and medicinal purposes, with special emphasis on secondary metabolites’ composition and bioactive effects.

Covalent attachment of therapeutic proteins to polyethylene glycol (PEG) is widely used for the improvement of its pharmacokinetic and pharmacological properties, as well as the reduction in reactogenicity and related side effects. This technique named PEGylation has been successfully employed in several approved drugs to treat various diseases, even cancer. Some methods have been developed to obtain PEGylated proteins, both in multiple protein sites or in a selected amino acid residue. This review focuses mainly on traditional and novel examples of chemical and enzymatic methods for site-selective PEGylation, emphasizing in N-terminal PEGylation, that make it possible to obtain products with a high degree of homogeneity and preserve bioactivity. In addition, the main assay methods that can be applied for the characterization of PEGylated molecules in complex biological samples are also summarized in this paper.

A B S T R A C TViruses are worldwide pathogens with a high impact on the human population. Despite the constant efforts to fight viral infections, there is a need to discover and design new drug candidates. Antiviral peptides are molecules with confirmed activity and constitute excellent alternatives for the treatment of viral infections. In the present study, we developed AntiVPP 1.0, an accurate bioinformatic tool that uses the Random Forest algorithm for antiviral peptide predictions. The model of AntiVPP 1.0 for antiviral peptide predictions uses several features of 1088 peptides for training and validation. During the validation of the model we achieved the TPR = 0.87, SPC = 0.97, ACC = 0.93 and MCC = 0.87 performance measures, which were indicative of a robust model. AntiVPP 1.0 is a fast, accurate and intuitive software focused on the assessment of antiviral peptides candidates.

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Antimicrobial peptides (AMPs) are ubiquitous host defense peptides characterized by antibiotic activity and lower propensity for developing resistance compared to classic antibiotics. While several AMPs have shown activity against antibiotic-sensitive...