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The pervasive presence of plastic in the environment has reached a concerning scale, being identified in many ecosystems. Bioremediation is the cheapest and most eco-friendly alternative to remove this polymer from affected areas. Recent work described that a novel cold-active esterase enzyme extracted from the bacteria Kaistella jeonii could promiscuously degrade PET. Compared to the well-known PETase from Ideonella sakaiensis, this novel esterase presents a low sequence identity yet has a remarkably similar folding. However, enzymatic assays demonstrated a lower catalytic efficiency. In this work, we employed a strict computational approach to investigate the binding mechanism between the esterase and PET. Understanding the underlying mechanism of binding can shed light on the evolutive mechanism of how enzymes have been evolving to degrade these artificial molecules and help develop rational engineering approaches to improve PETase-like enzymes. Our results indicate that this esterase misses a disulfide bridge, keeping the catalytic residues closer and possibly influencing its catalytic efficiency. Moreover, we describe the structural response to the interaction between enzyme and PET, indicating local and global effects. Our results aid in deepening the knowledge behind the mechanism of biological catalysis of PET degradation and as a base for the engineering of novel PETases.

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Since starts the coronavirus disease 2019 (COVID-19) pandemic identified the presence of genomic fragments of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in various environmental matrices: domestic sewage, surface waters, and contaminated freshwater. Environmental monitoring of SARS-CoV-2 is a tool for evaluating trend curves over the months, compared to several clinical cases of the disease. The objective of this study was to monitor the SARS-CoV-2 in environmental samples collected in different sites in a metropolitan area of Porto Alegre, Southern Brazil. During 10 months from 2020 to 2021, 300 samples were collected weekly and biweekly from nine points located in 3 cities: one point from a wastewater treatment plant (WWTP) in São Leopoldo (fortnightly collection), two points in Dilúvio Stream in Porto Alegre (fortnightly collection), two points in Pampa and Luiz Rau Streams (weekly collection), and two points in public fountains (fortnightly collection) in Novo Hamburgo. After collection, samples were concentrated by ultracentrifugation, and viral nucleic acids were extracted using MagMax® Core Nucleic Acid Purifications kits and submitted to RT-qPCR, using E, N1, and N2 gene targets of SARS-CoV-2. Only 7% (3/41) samples from public fountains were positive, with a mean viral load (VL) of SARS-CoV-2 RNA of 5.02 × 101 gc/l (2.41~8.59 × 101 gc/l), while the streams had average VL of 7.43 × 105 gc/l (Pampa), 7.06 × 105 gc/l (Luiz Rau), 2.01 × 105 gc/l (Dilúvio), and 4.46 × 105 cg/l (WWTP). The results showed varying levels of viral presence in different sample types, with a demonstrated correlation between environmental viral load and clinical COVID-19 cases. These findings contribute to understanding virus persistence and transmission pathways in the environment. Continuous monitoring, especially in less developed regions, is crucial for early detection of vaccine resistance, new variants, and potential COVID-19 resurgence.

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Increasing the repertoire of available complementary tools to advance the knowledge of protein structures is fundamental for structural biology. The Neighbors Influence of Amino Acids and Secondary Structures (NIAS) is a server that analyzes a protein's conformational preferences of amino acids. NIAS is based on the Angle Probability List, representing the normalized frequency of empirical conformational preferences, such as torsion angles, of different amino acid pairs and their corresponding secondary structure information, as available in the Protein Data Bank. In this work, we announce the updated NIAS server with the data comprising all structures deposited until Sep 2022, 7 years after the initial release. Unlike the original publication, which accounted for only studies conducted with X-ray crystallography, we added data from solid nuclear magnetic resonance (NMR), solution NMR, CullPDB, Electron Microscopy, and Electron Crystallography using multiple filtering parameters. We also provide examples of how NIAS can be applied as a complementary analysis tool for different structural biology works and what are its limitations.

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Wastewater-based epidemiology (WBE) may be successfully used to comprehensively monitor and determine the scale and dynamics of some infections in the community. We monitored severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in raw wastewater samples from Porto Alegre, Southern Brazil. The samples were collected and analyzed every week between May 2020 to May 2021. Meanwhile, different social restrictions were applied according to the number of hospitalized patients in the region. Weekly samples were obtained from two wastewater treatment plants (WWTP), named Navegantes and Serraria. To determine the SARS-CoV-2 RNA titers in wastewater, we performed RT-qPCR analysis targeting the N gene (N1). The highest titer of SARS-CoV-2 RNA was observed between epidemiological weeks (EWs) 33-37 (August), 42-43 (October), 45-46 (November), 49-51 (December) in 2020, and 1-3 (January), 7-13 (February to March) in 2021, with viral loads ranging from 1 × 106-3 × 106 genomic copies/Liter. An increase in positive confirmed cases followed such high viral loads. Depending on the sampling method used, positive cases increased in 6-7 days and 15 days after the rise of viral RNA titers in wastewater, with composite sampling methods showing a lower time lag and a higher resolution on the analyses. The results showed a direct relation between strict social restrictions and the loads of detected RNA reduction in wastewater, corroborating the number of confirmed cases. Differences in viral loads between different sampling points and methods were observed, as composite samples showed more stable results during the analyzed period. Besides, viral loads obtained from samples collected at Serraria WWTP were consistently higher than the ones obtained at Navegantes WWTP, indicating differences in local dynamics of SARS-CoV-2 spread in different regions of Porto Alegre. In conclusion, wastewater sampling to monitor SARS-CoV-2 is a robust tool to evaluate the viral loads contributing to hospitalized patients' data and confirmed cases. In addition, SARS-CoV-2 detection in sewage may inform and alert the government when there are asymptomatic or non-tested patients.