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Inception: the 3D-BioInfo-PT project is born, partially as a re-branding under Biodata.pt of the community involved in the EJIBCE meetings – Encontros de Jovens Investigadores em Biologia Computacional Estrutural. Materialization: Planned roadmap presented and debated at the Biodata.pt Technical Meeting. Teaching: the first 3D-BioInfo-PT workshop takes place at ITQB NOVA. It is an event with 35 participants (out of 62 applicants) hailing from 7 different institutions. Main support from the Oeiras Valley initiative and Wallfuture Lda. Continuity: maintain the 3D Biotalks seminar cycle and expand its reach. Networking: maintain the EJIBCE tradition of the annual meeting. Organize a satellite workshop. Collaboration: bring together the 3D-BioInfo-PT coordinating groups to write a perspective article on computational structural research in Portugal., https://3d-bioinfo-pt.github.io/

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

Bioelectrochemical systems (BES) are emerging as a suite of versatile sustainable technologies to produce electricity and added‐value compounds from renewable and carbon‐neutral sources using electroactive organisms. The incomplete knowledge on the molecular processes that allow electroactive organisms to exchange electrons with electrodes has prevented their real‐world implementation. In this manuscript we investigate the extracellular electron transfer processes performed by the thermophilic Gram‐positive bacteria belonging to the Thermincola genus, which were found to produce higher levels of current and tolerate higher temperatures in BES than mesophilic Gram‐negative bacteria. In our study, three multiheme c‐type cytochromes, Tfer_0070, Tfer_0075, and Tfer_1887, proposed to be involved in the extracellular electron transfer pathway of T. ferri-acetica, were cloned and over‐expressed in E. coli. Tfer_0070 (ImdcA) and Tfer_1887 (PdcA) were purified and biochemically characterized. The electrochemical characterization of these proteins supports a pathway of extracellular electron transfer via these two proteins. By contrast, Tfer_0075 (CwcA) could not be stabilized in solution, in agreement with its proposed insertion in the pepti-doglycan wall. However, based on the homology with the outer‐membrane cytochrome OmcS, a structural model for CwcA was developed, providing a molecular perspective into the mechanisms of electron transfer across the peptidoglycan layer in Thermincola. publishersversion published

One of the most challenging problems in bioinformatics is to computationally characterize sequences, structures and functions of proteins. Sequence-derived structural and physicochemical properties of proteins have been used in the development of machine learning models in protein related problems. However, tools and platforms to calculate features and perform Machine learning (ML) with proteins are scarce and have their limitations in terms of effectiveness, user-friendliness and applicability. Here, a generic modular automated ML-based platform for the classification of proteins based on their physicochemical properties is proposed. ProPythia, developed as a Python package, facilitates the major tasks of ML and includes modules to read and alter sequences, calculate protein features, pre-process datasets, execute feature reduction and selection, perform clustering, train and optimize ML models and make predictions. This platform was validated by testing its ability to classify anticancer and antimicrobial peptides and further used to explore viral fusion peptides. Membrane-interacting peptides play a crucial role in several biological processes. Fusion peptides are a subclass found in enveloped viruses, that are particularly relevant for membrane fusion. Determining what are the properties that characterize fusion peptides and distinguishing them from other proteins is a very relevant scientific question with important technological implications. Using three different datasets composed by well annotated sequences, different feature extraction techniques and feature selection methods, ML models were trained, tested and used to predict the location of a known fusion peptide in a protein sequence from the Dengue virus. Feature importance was also analysed. The models obtained will be useful in future research, also providing a biological insight into the distinctive physicochemical characteristics of fusion peptides. This work presents a freely available tool to perform ML-based protein classification and the first global analysis and prediction of viral fusion peptides using ML, reinforcing the usability and importance of ML in protein classification problems., info:eu-repo/semantics/publishedVersion

The phenomenon known as "ligand imprinting" or "ligand-induced enzyme memory" was first reported in 1988, when Russell and Klibanov observed that lyophilizing subtilisin in the presence of competitive inhibitors (that were subsequently removed) could significantly enhance its activity in an apolar solvent. (Russell and Klibanov, J Biol Chem 1988;263:11624-11626). They further observed that this enhancement did not occur when similar assays were carried out in water. Herein, we shed light on the molecular determinants of ligand imprinting using a molecular dynamics (MD) approach. To simulate the effect of placing an enzyme in the presence of a ligand before its lyophilization, an inhibitor was docked in the active site of subtilisin and 20 ns MD simulations in water were performed. The ligand was then removed and the resulting structure was used for subsequent MD runs using hexane and water as solvents. As a control, the same simulation setup was applied using the structure of subtilisin in the absence of the inhibitor. We observed that the ligand maintains the active site in an open conformation and that this configuration is retained after the removal of the inhibitor, when the simulations are carried out in hexane. In agreement with experimental findings, the structural configuration induced by the ligand is lost when the simulations take place in water. Our analysis of fluctuations indicates that this behavior is a result of the decreased flexibility displayed by enzymes in an apolar solvent, relatively to the aqueous situation.