1,721,003 research outputs found
Tackling the COVID-19 pandemic through the combination of established and novel Computer-Aided Drug Discovery workflows
Full text linkSince its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world to date. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action.
In the present work, several existing computer-aided drug discovery tools are successfully exploited for deciphering the recognition process between active molecules and crucial targets for the SARS-CoV-2 reproductive cycle such as the 3CL protease and the spike protein. Incidentally, the knowledge acquired upon working on SARS-CoV-2 has been successfully applied also to investigate other pharmaceutically relevant non-viral targets, including casein kinase 1 and adenosine receptors.
Finally, two novel, in-house developed, methodologies for the characterization of binding processes between biological entities are presented, with the first one being the application of Supervised Molecular Dynamics (SuMD) to the study of RNA-protein complexes formation, while the second one being Thermal Titration Molecular Dynamics (TTMD), a brand new protocol for unbinding kinetics estimation.Sin dal suo scoppio nel dicembre 2019, la pandemia da COVID-19 ha causato fino ad oggi la morte di oltre 6,5 milioni di persone in tutto il mondo. L'elevata trasmissibilità del suo agente eziologico, il virus SARS-CoV-2, combinata con il suo esito potenzialmente letale, hanno provocato una profonda crisi economica e sociale a livello globale. L'urgenza di trovare strumenti farmacologici adeguati a contrastare la pandemia ha fatto luce sulla sempre crescente importanza dei metodi computazionali nel razionalizzare e accelerare la progettazione di nuovi farmaci, sottolineando ulteriormente la necessità di sviluppare metodi rapidi e affidabili per l’identificazione di nuove molecole attive e la caratterizzazione del loro meccanismo d'azione.
Nel presente lavoro di tesi, diverse metodologie computazionali atte alla scoperta di nuovi farmaci sono state impiegate con successo per decifrare il processo di riconoscimento fra le molecole attive e alcuni bersagli molecolari cruciali per il ciclo riproduttivo di SARS-CoV-2, quali la proteasi principale 3CLpro e la proteina spike. Incidentalmente, le conoscenze acquisite lavorando su SARS-CoV-2 sono state applicate con successo anche per studiare altri bersagli non virali farmaceuticamente rilevanti, tra cui la caseina chinasi 1 e i recettori dell'adenosina.
Infine, vengono presentate due nuove metodologie, sviluppate internamente, per la caratterizzazione dei processi di legame tra entità biologiche, con la prima che consiste nell'applicazione della Dinamica Molecolare Supervisionata (SuMD) allo studio della formazione di complessi tra RNA e proteine, mentre la seconda consiste nella Titolazione Termica mediante Dinamica Molecolare (TTMD), un nuovo protocollo per la stima della cinetica dei processi di dissociazione
Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics
Full text linkSince its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19
Implementing a Scoring Function Based on Interaction Fingerprint for Autogrow4: Protein Kinase CK1δ as a Case Study
Full text linkIn the last 20 years, fragment-based drug discovery (FBDD) has become a popular and consolidated approach within the drug discovery pipeline, due to its ability to bring several drug candidates to clinical trials, some of them even being approved and introduced to the market. A class of targets that have proven to be particularly suitable for this method is represented by kinases, as demonstrated by the approval of BRAF inhibitor vemurafenib. Within this wide and diverse set of proteins, protein kinase CK1δ is a particularly interesting target for the treatment of several widespread neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Computational methodologies, such as molecular docking, are already routinely and successfully applied in FBDD campaigns alongside experimental techniques, both in the hit-discovery and in the hit-optimization stage. Concerning this, the open-source software Autogrow, developed by the Durrant lab, is a semi-automated computational protocol that exploits a combination between a genetic algorithm and a molecular docking software for de novo drug design and lead optimization. In the current work, we present and discuss a modified version of the Autogrow code that implements a custom scoring function based on the similarity between the interaction fingerprint of investigated compounds and a crystal reference. To validate its performance, we performed both a de novo and a lead-optimization run (as described in the original publication), evaluating the ability of our fingerprint-based protocol to generate compounds similar to known CK1δ inhibitors based on both the predicted binding mode and the electrostatic and shape similarity in comparison with the standard Autogrow protocol
Targeting the I7L Protease: A Rational Design for Anti-Monkeypox Drugs?
Full text linkThe latest monkeypox virus outbreak in 2022 showcased the potential threat of this viral zoonosis to public health. The lack of specific treatments against this infection and the success of viral protease inhibitors-based treatments against HIV, Hepatitis C, and SARS-CoV-2, brought the monkeypox virus I7L protease under the spotlight as a potential target for the development of specific and compelling drugs against this emerging disease. In the present work, the structure of the monkeypox virus I7L protease was modeled and thoroughly characterized through a dedicated computational study. Furthermore, structural information gathered in the first part of the study was exploited to virtually screen the DrugBank database, consisting of drugs approved by the Food and Drug Administration (FDA) and clinical-stage drug candidates, in search for readily repurposable compounds with similar binding features as TTP-6171, the only non-covalent I7L protease inhibitor reported in the literature. The virtual screening resulted in the identification of 14 potential inhibitors of the monkeypox I7L protease. Finally, based on data collected within the present work, some considerations on developing allosteric modulators of the I7L protease are reported
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
From the Wuhan-Hu-1 strain to the XD and XE variants: is targeting the SARS-CoV-2 spike protein still a pharmaceutically relevant option against COVID-19?
Full text linkSince the outbreak of the COVID-19 pandemic in December 2019, the SARS-CoV-2 genome has undergone several mutations. The emergence of such variants has resulted in multiple pandemic waves, contributing to sustaining to date the number of infections, hospitalisations, and deaths despite the swift development of vaccines, since most of these mutations are concentrated on the Spike protein, a viral surface glycoprotein that is the main target for most vaccines. A milestone in the fight against the COVID-19 pandemic has been represented by the development of Paxlovid, the first orally available drug against COVID-19, which acts on the Main Protease (Mpro). In this article, we analyse the structural features of both the Spike protein and the Mpro of the recently reported SARS-CoV-2 variant XE, as well the closely related XD and XF ones, discussing their impact on the efficacy of existing treatments against COVID-19 and on the development of future ones
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