Bioinformatics tools for the study of bioactive peptides from vegetal sources: evolution and future perspectives.

Bioactive peptides from vegetal sources have been shown to have functional properties as anti-inflammatory, antioxidant, antihypertensive or antidiabetic capacity. For this reason, they have been proposed as an interesting and promising alternative to improve human health. In recent years, the numerous advances in the bioinformatics field for in silico prediction have speeded up the discovery of bioactive peptides, also reducing the associated costs when using an integrated approach between the classical and bioinformatics discovery. This review aims to provide an overview of the evolution, limitations and latest advances in the field of bioinformatics and computational tools, and specifically make a critical and comprehensive insight into computational techniques used to study the mechanism of interaction that allows the explanation of plant bioactive peptide functionality. In particular, molecular docking is considered key to explain the different functionalities that have been previously identified. The assumptions to simplify such a high complex environment implies a degree of uncertainty that can only be guaranteed and validated by in vitro or in vivo studies, however, the combination of databases, software and bioinformatics applications with the classical approach has become a promising procedure for the study of bioactive peptides.

Brewers' spent hop revalorization for the production of high added-value cosmetics ingredients with elastase inhibition capacity.

This article summarizes the analysis of α and ß-acids and prenylflavonoids from brewers' spent hop (BSH) as source of bioactive molecules to improve skin integrity by inhibiting elastase activity. To maximize the efficacy of the BSH extracts, it was necessary to identify the most bioactive hop compounds and the extraction parameters to maximize elastase inhibition and total antioxidant capacity. Thus, a computational methodology was carried out to test the anti-elastase potential of these hop molecules, detecting cis-iso-α-cohumulone and 8-prenylnaringenin as main inhibitors. Then, BSH extracts were optimized to ensure the maximum extraction of bioactive compounds, using compatible solvents (water and 100% plant-based propanediol) according to the green cosmetic standards. Finally, a determination and quantification method based on HPLC-MS/MS was used to guarantee the presence of the bioactive molecules, detecting a higher concentration of cis-iso-α-cohumulone and 8-prenylnaringenin in those samples with high anti-elastase activity. By optimizing extraction conditions and agents, a BSH extract was designed, showing high antioxidant (81.9 mmol Trolox/L) and high anti-elastase capacities.

Computational Prediction of Potential Inhibitors of the Main Protease of SARS-CoV-2.

The rapidly developing pandemic, known as coronavirus disease 2019 (COVID-19) and caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has recently spread across 213 countries and territories. This pandemic is a dire public health threat-particularly for those suffering from hypertension, cardiovascular diseases, pulmonary diseases, or diabetes; without approved treatments, it is likely to persist or recur. To facilitate the rapid discovery of inhibitors with clinical potential, we have applied ligand- and structure-based computational approaches to develop a virtual screening methodology that allows us to predict potential inhibitors. In this work, virtual screening was performed against two natural products databases, Super Natural II and Traditional Chinese Medicine. Additionally, we have used an integrated drug repurposing approach to computationally identify potential inhibitors of the main protease of SARS-CoV-2 in databases of drugs (both approved and withdrawn). Roughly 360,000 compounds were screened using various molecular fingerprints and molecular docking methods; of these, 80 docked compounds were evaluated in detail, and the 12 best hits from four datasets were further inspected via molecular dynamics simulations. Finally, toxicity and cytochrome inhibition profiles were computationally analyzed for the selected candidate compounds.