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Abstract The COVID-19 caused by the SARS-CoV-2 virus was declared a pandemic disease in March 2020 by the World Health Organization (WHO). Structure-Based Drug Design strategies based on docking methodologies have been widely used for both new drug development and drug repurposing to find effective treatments against this disease. In this work, we present the developments implemented in the DockThor-VS web server to provide a virtual screening (VS) platform with curated structures of potential therapeutic targets from SARS-CoV-2 incorporating genetic information regarding relevant non-synonymous variations. The web server facilitates repurposing VS experiments providing curated libraries of currently available drugs on the market. At present, DockThor-VS provides ready-for-docking 3D structures for wild type and selected mutations for Nsp3 (papain-like, PLpro domain), Nsp5 (Mpro, 3CLpro), Nsp12 (RdRp), Nsp15 (NendoU), N protein, and Spike. We performed VS experiments of FDA-approved drugs considering the therapeutic targets available at the web server to assess the impact of considering different structures and mutations to identify possible new treatments of SARS-CoV-2 infections. The DockThor-VS is freely available at www.dockthor.lncc.br .

Abstract Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein–ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein–protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein–protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br .

Abstract Extensive rodent literature suggests that the endocannabinoid (eCB) system present in the nucleus accumbens (NAc) modulates dopamine (DA) release in this area. However, expression patterns of the cannabinoid receptor type 1 (CB1R), the synthesizing enzyme N-acyl phosphatidylethanolamine phospholipase D (NAPE-PLD), and the degradation enzyme fatty acid amide hydrolase (FAAH) in the NAc have not yet been described in non-human primates. The goal of this study is therefore to characterize the expression and localization of the eCB system within the NAc of vervet monkeys (Chlorocebus sabaeus) using Western blots and immunohistochemistry. Results show that CB1R, NAPE-PLD, and FAAH are expressed across the NAc rostrocaudal axis, both in the core and shell. CB1R, NAPE-PLD, and FAAH are localized in medium spiny neurons (MSNs) and fast-spiking GABAergic interneurons (FSIs). Dopaminergic projections and astrocytes did not express CB1R, NAPE-PLD, or FAAH. These data show that the eCB system is present in the vervet monkey NAc and supports its role in the primate brain reward circuit.

The majority of approved CAR T cell products are based on the FMC63-scFv directed against CD19. Surprisingly, although antigen binding affinity is a major determinant for CAR function, the affinity of the benchmark FMC63-scFv has not been unambiguously determined. That is, a wide range of affinities have been reported in literature, differing by more than 100-fold. Using a range of techniques, we demonstrate that suboptimal experimental designs can cause artefacts that lead to over- or underestimation of the affinity. To minimize these artefacts, we performed SPR with strictly monomeric and correctly folded soluble CD19, yielding an FMC63-scFv affinity of 2-6 nM. Together, apart from analyzing the FMC63-scFv affinity under optimized conditions, we also provide potential explanations for the wide range of published affinities. We expect that this study will be highly valuable for interpretations of CAR affinity-function relationships, as well as for the design of future CAR T cell generations., (© 2023. The Author(s).)

Concomitant infection or co-infection with distinct SARS-CoV-2 genotypes has been reported as part of the epidemiological surveillance of the COVID-19 pandemic. In the context of the spread of more transmissible variants during 2021, co-infections are not only important due to the possible changes in the clinical outcome, but also the chance to generate new genotypes by recombination. However, a few approaches have developed bioinformatic pipelines to identify co-infections. Here we present a metagenomic pipeline based on the inference of multiple fragments similar to amplicon sequence variant (ASV-like) from sequencing data and a custom SARS-CoV-2 database to identify the concomitant presence of divergent SARS-CoV-2 genomes, i.e., variants of concern (VOCs). This approach was compared to another strategy based on whole-genome (metagenome) assembly. Using single or pairs of sequencing data of COVID-19 cases with distinct SARS-CoV-2 VOCs, each approach was used to predict the VOC classes (Alpha, Beta, Gamma, Delta, Omicron or non-VOC and their combinations). The performance of each pipeline was assessed using the ground-truth or expected VOC classes. Subsequently, the ASV-like pipeline was used to analyze 1021 cases of COVID-19 from Costa Rica to investigate the possible occurrence of co-infections. After the implementation of the two approaches, an accuracy of 96.2% was revealed for the ASV-like inference approach, which contrasts with the misclassification found (accuracy 46.2%) for the whole-genome assembly strategy. The custom SARS-CoV-2 database used for the ASV-like analysis can be updated according to the appearance of new VOCs to track co-infections with eventual new genotypes. In addition, the application of the ASV-like approach to all the 1021 sequenced samples from Costa Rica in the period October 12th-December 21th 2021 found that none corresponded to co-infections with VOCs. In conclusion, we developed a metagenomic pipeline based on ASV-like inference for the identification of co-infection with distinct SARS-CoV-2 VOCs, in which an outstanding accuracy was achieved. Due to the epidemiological, clinical, and molecular relevance of the concomitant infection with distinct genotypes, this work represents another piece in the process of the surveillance of the COVID-19 pandemic in Costa Rica and worldwide., (© 2022. The Author(s).)