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SARS‐CoV‐2 virus has triggered a global pandemic with devastating consequences. The understanding of fundamental aspects of this virus is of extreme importance. In this work, we studied the viral ribonuclease nsp14, one of the most interferon antagonists from SARS‐CoV‐2. Nsp14 is a multifunctional protein with two distinct activities, an N‐terminal 3’‐to‐5’ exoribonuclease (ExoN) and a C‐terminal N7‐methyltransferase (N7‐MTase), both critical for coronaviruses life cycle, indicating nsp14 as a prominent target for the development of antiviral drugs. In coronaviruses, nsp14 ExoN activity is stimulated through the interaction with the nsp10 protein. We have performed a biochemical characterization of nsp14‐nsp10 complex from SARS‐CoV‐2. We confirm the 3’‐5’ exoribonuclease and MTase activities of nsp14 and the critical role of nsp10 in upregulating the nsp14 ExoN activity. Furthermore, we demonstrate that SARS‐CoV‐2 nsp14 N7‐MTase activity is functionally independent of the ExoN activity and nsp10. A model from SARS‐CoV‐2 nsp14‐nsp10 complex allowed mapping key nsp10 residues involved in this interaction. Our results show that a stable interaction between nsp10 and nsp14 is required for the nsp14‐mediated ExoN activity of SARS‐CoV‐2. We studied the role of conserved DEDD catalytic residues of SARS‐CoV‐2 nsp14 ExoN. Our results show that motif I of ExoN domain is essential for the nsp14 function, contrasting to the functionality of these residues in other coronaviruses, which can have important implications regarding the specific pathogenesis of SARS‐CoV‐2. This work unraveled a basis for discovering inhibitors targeting specific amino acids in order to disrupt the assembly of this complex and interfere with coronaviruses replication., In this report, we have shown that SARS‐CoV‐2 nsp14 is a multifunctional protein with two distinct activities, an N‐terminal 3’‐to‐5’ exoribonuclease (ExoN) and a C‐terminal N7‐methyltransferase (N7‐MTase). Nsp10 upregulates the nsp14 ExoN activity and does not interfere with nsp14 N7‐MTase. A stable interaction between these proteins is required for an active nsp14‐mediated ExoN activity.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, emerged in late 2019 and quickly spread worldwide, resulting in over 125 million infections and 2.7 million deaths as of March 2021 accordingly to the World Health Organization. Despite the great advances achieved by the scientific community in providing crucial information about this virus, we are still far from completely understanding it. SARS-CoV-2 is an enveloped virus, meaning that it is encapsulated by a lipid membrane, which needs to be fused to the host membrane to begin the infection process. Fusion between viral and host membrane is catalyzed by the spike (S) glycoprotein. The S-protein is composed of essential elements for the infection mechanism, namely the receptor-binding domain known to bind to angiotensin-converting enzyme 2 during the viral entry pathway. Another important region, known as the fusion peptide (FP), plays an essential part in the fusion mechanism, by inserting into and disturbing the host membrane. There is still not a consensus among scientists in terms of the fusion peptide location on the S-protein sequence, with two major candidate regions having been proposed. We recently used a machine learning-based tool developed by us to identify viral FPs with accuracies over 85%. With this tool a putative FP, previously suggested in the literature, has been identified, as well as other proposals including the requirement of more than one FP. To further address this question, we are performing a systematic analysis of the SARS-CoV-2 putative FPs, using Molecular Dynamics (MD) simulations, which provide a detailed perspective of how these peptides insert and interact with the membrane. In parallel, we are characterizing these systems experimentally. Additionally we are exploring therapeutic strategies targeting these regions. Given the major role of the FP in the virus infection process, this work provides relevant insights and contributes to the fight against COVID-19., info:eu-repo/semantics/publishedVersion

EJIBCE 2016 - IV Encontro de Jovens Investigadores de Biologia Computacional Estrutural, In this post-genomic era, gene homology annotations have become the foundation of systems biology. However, errors spread easily when functional annotation is not performed carefully due to overly unconstrained homology metrics [1,2]. As metabolic model reconstructions become a relevant tool for performing fundamental studies and bioprocess design, the impact of accurate enzymatic function assignments becomes evident [3]. The uncertainty of the usage of NADP(H) or NAD(H) as co-factors [4], even in well performed annotations, has a major impact in metabolic engineering applications, severely affecting Genome-scale metabolic model reconstruction due to the potential insertion of misleading reactions. In this work, we unveiled the molecular determinants for cofactor speci- ficity, using enzyme structural information. In order to do so, we created a representative dataset of all enzymes present in the PDB with NAD(P)(H) as cofactors and measured the occurrence of every aminoacid residue at a distance of 3.5 Angstrom[5] of all cofactor atoms. This allowed us to create a matrix with the total number of aminoacid residues at interacting distances from all cofactor atoms, for all structures. After analyzing the matrix, we identified the residues that had a signifi- cantly higher number of contacts with the cofactors, and in which atoms this occurred. With the intent of applying these findings in the unveiling of cofactor specificity for enzymes that are not structurally characterized experimentally we successfully replicated these findings using machine learning algorithms. In future work we will apply the results and machine learning models obtained in order to assign cofactor specificity to enzymes with uncertain cofactor specificity. These results may represent an important development in systems biology by allowing the reduction of annotation errors and the implementation of erroneous or redundant reactions in GEM models, improving the overall performance of metabolic simulations., info:eu-repo/semantics/publishedVersion