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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Acute lymphoblastic leukemia is the most common cancer among children worldwide, characterized by an overproduction of undifferentiated lymphoblasts in the bone marrow. The enzyme l-asparaginase isolated from Escherichia coli and Dickeya chrysanthemi is a key factor in multiple therapies against this disease. Regardless of their effectiveness, these formulations present well-known adverse effects, highlighting the immunogenicity and allergenicity they cause. Some strategies have been adopted in this regard, such as PEGylation and modification by bioengineering as well as the search for new non-bacterial microorganisms producing the enzyme. Fungi have been shown to be asparaginase producers with high antitumor activity; however, little is known about the immunological features of fungal asparaginase. In this work, we developed the first immunoinformatics study focused on revealing the antigenic determinants that contribute to the immunogenicity of nine asparaginases of filamentous fungi experimentally tested, and compared them with the enzyme from E. coli. We were able to predict that the fungal asparaginases evaluated have a high degree of immunogenicity, which is an important result to consider if the aim is to produce clinical l-asparaginase from a fungal source. Likewise, for the first time, a bioinformatics-based approach was used to predict the immunogenic and allergenic epitopes present in fungal asparaginases.

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