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Palazzotti D, Felicetti T, Sabatini S, Moro S, Barreca ML, Sturlese M, Astolfi A. Fighting Antimicrobial Resistance: Insights on How the Staphylococcus aureus NorA Efflux Pump Recognizes 2-Phenylquinoline Inhibitors by Supervised Molecular Dynamics (SuMD) and Molecular Docking Simulations. J Chem Inf Model 2023; 63:4875-4887. [PMID: 37515548 PMCID: PMC10428217 DOI: 10.1021/acs.jcim.3c00516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Indexed: 07/31/2023]
Abstract
The superbug Staphylococcus aureus (S. aureus) exhibits several resistance mechanisms, including efflux pumps, that strongly contribute to antimicrobial resistance. In particular, the NorA efflux pump activity is associated with S. aureus resistance to fluoroquinolone antibiotics (e.g., ciprofloxacin) by promoting their active extrusion from cells. Thus, since efflux pump inhibitors (EPIs) are able to increase antibiotic concentrations in bacteria as well as restore their susceptibility to these agents, they represent a promising strategy to counteract bacterial resistance. Additionally, the very recent release of two NorA efflux pump cryo-electron microscopy (cryo-EM) structures in complex with synthetic antigen-binding fragments (Fabs) represents a real breakthrough in the study of S. aureus antibiotic resistance. In this scenario, supervised molecular dynamics (SuMD) and molecular docking experiments were combined to investigate for the first time the molecular mechanisms driving the interaction between NorA and efflux pump inhibitors (EPIs), with the ultimate goal of elucidating how the NorA efflux pump recognizes its inhibitors. The findings provide insights into the dynamic NorA-EPI intermolecular interactions and lay the groundwork for future drug discovery efforts aimed at the identification of novel molecules to fight antimicrobial resistance.
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Affiliation(s)
- Deborah Palazzotti
- Department
of Pharmaceutical Sciences, Department of Excellence 2018−2022, University of Perugia, Via del Liceo, 1, 06123 Perugia, Italy
| | - Tommaso Felicetti
- Department
of Pharmaceutical Sciences, Department of Excellence 2018−2022, University of Perugia, Via del Liceo, 1, 06123 Perugia, Italy
| | - Stefano Sabatini
- Department
of Pharmaceutical Sciences, Department of Excellence 2018−2022, University of Perugia, Via del Liceo, 1, 06123 Perugia, Italy
| | - Stefano Moro
- Molecular
Modeling Section (MMS), Department of Pharmaceutical and Pharmacological
Sciences, University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Maria Letizia Barreca
- Department
of Pharmaceutical Sciences, Department of Excellence 2018−2022, University of Perugia, Via del Liceo, 1, 06123 Perugia, Italy
| | - Mattia Sturlese
- Molecular
Modeling Section (MMS), Department of Pharmaceutical and Pharmacological
Sciences, University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Andrea Astolfi
- Department
of Pharmaceutical Sciences, Department of Excellence 2018−2022, University of Perugia, Via del Liceo, 1, 06123 Perugia, Italy
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Tira R, Viola G, Barracchia CG, Parolini F, Munari F, Capaldi S, Assfalg M, D'Onofrio M. Espresso Coffee Mitigates the Aggregation and Condensation of Alzheimer's Associated Tau Protein. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023. [PMID: 37466260 PMCID: PMC10401709 DOI: 10.1021/acs.jafc.3c01072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Espresso coffee is among the most consumed beverages in the world. Recent studies report a protective activity of the coffee beverage against neurodegenerative disorders such as Alzheimer's disease. Alzheimer's disease belongs to a group of disorders, called tauopathies, which are characterized by the intraneuronal accumulation of the microtubule-associated protein tau in fibrillar aggregates. In this work, we characterized by NMR the molecular composition of the espresso coffee extract and identified its main components. We then demonstrated with in vitro and in cell experiments that the whole coffee extract, caffeine, and genistein have biological properties in preventing aggregation, condensation, and seeding activity of the repeat region of tau. We also identified a set of coffee compounds capable of binding to preformed tau fibrils. These results add insights into the neuroprotective potential of espresso coffee and suggest candidate molecular scaffolds for designing therapies targeting monomeric or fibrillized forms of tau.
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Affiliation(s)
- Roberto Tira
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 34134 Verona, Italy
| | - Giovanna Viola
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 34134 Verona, Italy
| | | | - Francesca Parolini
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 34134 Verona, Italy
| | - Francesca Munari
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 34134 Verona, Italy
| | - Stefano Capaldi
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 34134 Verona, Italy
| | - Michael Assfalg
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 34134 Verona, Italy
| | - Mariapina D'Onofrio
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 34134 Verona, Italy
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Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics. Int J Mol Sci 2023; 24:ijms24054401. [PMID: 36901832 PMCID: PMC10003049 DOI: 10.3390/ijms24054401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
Since 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.
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Pavan M, Bassani D, Sturlese M, Moro S. Investigating RNA-protein recognition mechanisms through supervised molecular dynamics (SuMD) simulations. NAR Genom Bioinform 2022; 4:lqac088. [PMID: 36458023 PMCID: PMC9706429 DOI: 10.1093/nargab/lqac088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 10/20/2022] [Accepted: 11/09/2022] [Indexed: 12/03/2022] Open
Abstract
Ribonucleic acid (RNA) plays a key regulatory role within the cell, cooperating with proteins to control the genome expression and several biological processes. Due to its characteristic structural features, this polymer can mold itself into different three-dimensional structures able to recognize target biomolecules with high affinity and specificity, thereby attracting the interest of drug developers and medicinal chemists. One successful example of the exploitation of RNA's structural and functional peculiarities is represented by aptamers, a class of therapeutic and diagnostic tools that can recognize and tightly bind several pharmaceutically relevant targets, ranging from small molecules to proteins, making use of the available structural and conformational freedom to maximize the complementarity with their interacting counterparts. In this scientific work, we present the first application of Supervised Molecular Dynamics (SuMD), an enhanced sampling Molecular Dynamics-based method for the study of receptor-ligand association processes in the nanoseconds timescale, to the study of recognition pathways between RNA aptamers and proteins, elucidating the main advantages and limitations of the technique while discussing its possible role in the rational design of RNA-based therapeutics.
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Affiliation(s)
- Matteo Pavan
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Davide Bassani
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Mattia Sturlese
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- To whom correspondence should be addressed. Tel: +39 0498275704; Fax: +39 0498275366;
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Hassankalhori M, Bolcato G, Bissaro M, Sturlese M, Moro S. Shedding Light on the Molecular Recognition of Sub-Kilodalton Macrocyclic Peptides on Thrombin by Supervised Molecular Dynamics. Front Mol Biosci 2021; 8:707661. [PMID: 34532343 PMCID: PMC8438215 DOI: 10.3389/fmolb.2021.707661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 07/15/2021] [Indexed: 11/25/2022] Open
Abstract
Macrocycles are attractive structures for drug development due to their favorable structural features, potential in binding to targets with flat featureless surfaces, and their ability to disrupt protein–protein interactions. Moreover, large novel highly diverse libraries of low-molecular-weight macrocycles with therapeutically favorable characteristics have been recently established. Considering the mentioned facts, having a validated, fast, and accurate computational protocol for studying the molecular recognition and binding mode of this interesting new class of macrocyclic peptides deemed to be helpful as well as insightful in the quest of accelerating drug discovery. To that end, the ability of the in-house supervised molecular dynamics protocol called SuMD in the reproduction of the X-ray crystallography final binding state of a macrocyclic non-canonical tetrapeptide—from a novel library of 8,988 sub-kilodalton macrocyclic peptides—in the thrombin active site was successfully validated. A comparable binding mode with the minimum root-mean-square deviation (RMSD) of 1.4 Å at simulation time point 71.6 ns was achieved. This method validation study extended the application domain of the SuMD sampling method for computationally cheap, fast but accurate, and insightful macrocycle–protein molecular recognition studies.
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Affiliation(s)
- Mahdi Hassankalhori
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Giovanni Bolcato
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Maicol Bissaro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Mattia Sturlese
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
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Bolcato G, Cescon E, Pavan M, Bissaro M, Bassani D, Federico S, Spalluto G, Sturlese M, Moro S. A Computational Workflow for the Identification of Novel Fragments Acting as Inhibitors of the Activity of Protein Kinase CK1δ. Int J Mol Sci 2021; 22:ijms22189741. [PMID: 34575906 PMCID: PMC8471300 DOI: 10.3390/ijms22189741] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/01/2021] [Accepted: 09/03/2021] [Indexed: 11/24/2022] Open
Abstract
Fragment-Based Drug Discovery (FBDD) has become, in recent years, a consolidated approach in the drug discovery process, leading to several drug candidates under investigation in clinical trials and some approved drugs. Among these successful applications of the FBDD approach, kinases represent a class of targets where this strategy has demonstrated its real potential with the approved kinase inhibitor Vemurafenib. In the Kinase family, protein kinase CK1 isoform δ (CK1δ) has become a promising target in the treatment of different neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. In the present work, we set up and applied a computational workflow for the identification of putative fragment binders in large virtual databases. To validate the method, the selected compounds were tested in vitro to assess the CK1δ inhibition.
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Affiliation(s)
- Giovanni Bolcato
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy; (G.B.); (M.P.); (M.B.); (D.B.); (M.S.)
| | - Eleonora Cescon
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via Licio Giorgeri 1, 34127 Trieste, Italy; (E.C.); (S.F.); (G.S.)
| | - Matteo Pavan
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy; (G.B.); (M.P.); (M.B.); (D.B.); (M.S.)
| | - Maicol Bissaro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy; (G.B.); (M.P.); (M.B.); (D.B.); (M.S.)
| | - Davide Bassani
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy; (G.B.); (M.P.); (M.B.); (D.B.); (M.S.)
| | - Stephanie Federico
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via Licio Giorgeri 1, 34127 Trieste, Italy; (E.C.); (S.F.); (G.S.)
| | - Giampiero Spalluto
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via Licio Giorgeri 1, 34127 Trieste, Italy; (E.C.); (S.F.); (G.S.)
| | - Mattia Sturlese
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy; (G.B.); (M.P.); (M.B.); (D.B.); (M.S.)
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy; (G.B.); (M.P.); (M.B.); (D.B.); (M.S.)
- Correspondence:
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7
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Haider K, Rehman S, Pathak A, Najmi AK, Yar MS. Advances in 2-substituted benzothiazole scaffold-based chemotherapeutic agents. Arch Pharm (Weinheim) 2021; 354:e2100246. [PMID: 34467567 DOI: 10.1002/ardp.202100246] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 08/10/2021] [Accepted: 08/13/2021] [Indexed: 01/25/2023]
Abstract
Targeted therapy plays a pivotal role in cancer therapeutics by countering the drawbacks of conventional treatment like adverse events and drug resistance. Over the last decade, heterocyclic derivatives have received considerable attention as cytotoxic agents by modulating various signaling pathways. Benzothiazole is an important heterocyclic scaffold that has been explored for its therapeutic potential. Benzothiazole-based derivatives have emerged as potent inhibitors of enzymes such as EGFR, VEGFR, PI3K, topoisomerases, and thymidylate kinases. Several researchers have designed, synthesized, and evaluated benzothiazole scaffold-based enzyme inhibitors. Of these, several inhibitors have entered various phases of clinical trials. This review describes the recent advances and developments of benzothiazole architecture-based derivatives as potent anticancer agents.
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Affiliation(s)
- Kashif Haider
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Education and Research (SPER), Jamia Hamdard, New Delhi, India
| | - Sara Rehman
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Education and Research (SPER), Jamia Hamdard, New Delhi, India
| | - Ankita Pathak
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Education and Research (SPER), Jamia Hamdard, New Delhi, India
| | - Abul K Najmi
- Department of Pharmacology, School of Pharmaceutical Education and Research (SPER), Jamia Hamdard, New Delhi, India
| | - Mohammad S Yar
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Education and Research (SPER), Jamia Hamdard, New Delhi, India
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Bissaro M, Bolcato G, Pavan M, Bassani D, Sturlese M, Moro S. Inspecting the Mechanism of Fragment Hits Binding on SARS-CoV-2 M pro by Using Supervised Molecular Dynamics (SuMD) Simulations. ChemMedChem 2021; 16:2075-2081. [PMID: 33797868 PMCID: PMC8250706 DOI: 10.1002/cmdc.202100156] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Indexed: 12/19/2022]
Abstract
Computational approaches supporting the early characterization of fragment molecular recognition mechanism represent a valuable complement to more expansive and low‐throughput experimental techniques. In this retrospective study, we have investigated the geometric accuracy with which high‐throughput supervised molecular dynamics simulations (HT‐SuMD) can anticipate the experimental bound state for a set of 23 fragments targeting the SARS‐CoV‐2 main protease. Despite the encouraging results herein reported, in line with those previously described for other MD‐based posing approaches, a high number of incorrect binding modes still complicate HT‐SuMD routine application. To overcome this limitation, fragment pose stability has been investigated and integrated as part of our in‐silico pipeline, allowing us to prioritize only the more reliable predictions.
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Affiliation(s)
- Maicol Bissaro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131, Padova, Italy
| | - Giovanni Bolcato
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131, Padova, Italy
| | - Matteo Pavan
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131, Padova, Italy
| | - Davide Bassani
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131, Padova, Italy
| | - Mattia Sturlese
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131, Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131, Padova, Italy
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