1
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Galvani F, Pala D, Cuzzolin A, Scalvini L, Lodola A, Mor M, Rizzi A. Unbinding Kinetics of Muscarinic M3 Receptor Antagonists Explained by Metadynamics Simulations. J Chem Inf Model 2023; 63:2842-2856. [PMID: 37053454 PMCID: PMC10170513 DOI: 10.1021/acs.jcim.3c00042] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
The residence time (RT), the time for which a drug remains bound to its biological target, is a critical parameter for drug design. The prediction of this key kinetic property has been proven to be challenging and computationally demanding in the framework of atomistic simulations. In the present work, we setup and applied two distinct metadynamics protocols to estimate the RTs of muscarinic M3 receptor antagonists. In the first method, derived from the conformational flooding approach, the kinetics of unbinding is retrieved from a physics-based parameter known as the acceleration factor α (i.e., the running average over time of the potential deposited in the bound state). Such an approach is expected to recover the absolute RT value for a compound of interest. In the second method, known as the tMETA-D approach, a qualitative estimation of the RT is given by the time of simulation required to drive the ligand from the binding site to the solvent bulk. This approach has been developed to reproduce the change of experimental RTs for compounds targeting the same target. Our analysis shows that both computational protocols are able to rank compounds in agreement with their experimental RTs. Quantitative structure-kinetics relationship (SKR) models can be identified and employed to predict the impact of a chemical modification on the experimental RT once a calibration study has been performed.
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Affiliation(s)
- Francesca Galvani
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy
| | - Daniele Pala
- Chemistry Research and Drug Design Department, Chiesi Farmaceutici S.p.A., Largo F. Belloli 11/A, 43122 Parma, Italy
| | - Alberto Cuzzolin
- Chemistry Research and Drug Design Department, Chiesi Farmaceutici S.p.A., Largo F. Belloli 11/A, 43122 Parma, Italy
| | - Laura Scalvini
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy
| | - Alessio Lodola
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy
| | - Marco Mor
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy
- Microbiome Research Hub, University of Parma, Parco Area delle Scienze 11/A, I-43124 Parma, Italy
| | - Andrea Rizzi
- Chemistry Research and Drug Design Department, Chiesi Farmaceutici S.p.A., Largo F. Belloli 11/A, 43122 Parma, Italy
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2
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El Khoury L, Jing Z, Cuzzolin A, Deplano A, Loco D, Sattarov B, Hédin F, Wendeborn S, Ho C, El Ahdab D, Jaffrelot Inizan T, Sturlese M, Sosic A, Volpiana M, Lugato A, Barone M, Gatto B, Macchia ML, Bellanda M, Battistutta R, Salata C, Kondratov I, Iminov R, Khairulin A, Mykhalonok Y, Pochepko A, Chashka-Ratushnyi V, Kos I, Moro S, Montes M, Ren P, Ponder JW, Lagardère L, Piquemal JP, Sabbadin D. Computationally driven discovery of SARS-CoV-2 M pro inhibitors: from design to experimental validation. Chem Sci 2022; 13:3674-3687. [PMID: 35432906 PMCID: PMC8966641 DOI: 10.1039/d1sc05892d] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/03/2022] [Indexed: 11/21/2022] Open
Abstract
We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (Mpro) inhibitors whose potency ranges from mM for the initial non-covalent ligands to sub-μM for the final covalent compound (IC50 = 830 ± 50 nM). The project extensively relied on high-resolution all-atom molecular dynamics simulations and absolute binding free energy calculations performed using the polarizable AMOEBA force field. The study is complemented by extensive adaptive sampling simulations that are used to rationalize the different ligand binding poses through the explicit reconstruction of the ligand–protein conformation space. Machine learning predictions are also performed to predict selected compound properties. While simulations extensively use high performance computing to strongly reduce the time-to-solution, they were systematically coupled to nuclear magnetic resonance experiments to drive synthesis and for in vitro characterization of compounds. Such a study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows the protein conformational multiplicity problem to be addressed. The proposed fluorinated tetrahydroquinolines open routes for further optimization of Mpro inhibitors towards low nM affinities. The dominant binding mode of the QUB-00006-Int-07 main protease inhibitor during absolute binding free energy simulations.![]()
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Affiliation(s)
- Léa El Khoury
- Qubit Pharmaceuticals, Incubateur Paris Biotech Santé 24 Rue du Faubourg Saint Jacques 75014 Paris France
| | - Zhifeng Jing
- Qubit Pharmaceuticals, Incubateur Paris Biotech Santé 24 Rue du Faubourg Saint Jacques 75014 Paris France
| | - Alberto Cuzzolin
- Chiesi Farmaceutici S.p.A, Nuovo Centro Ricerche Largo Belloli 11a 43122 Parma Italy
| | - Alessandro Deplano
- Pharmacelera, Torre R, 4a planta Despatx A05, Parc Cientific de Barcelona, Baldiri Reixac 8 08028 Barcelona Spain
| | - Daniele Loco
- Qubit Pharmaceuticals, Incubateur Paris Biotech Santé 24 Rue du Faubourg Saint Jacques 75014 Paris France
| | - Boris Sattarov
- Qubit Pharmaceuticals, Incubateur Paris Biotech Santé 24 Rue du Faubourg Saint Jacques 75014 Paris France
| | - Florent Hédin
- Qubit Pharmaceuticals, Incubateur Paris Biotech Santé 24 Rue du Faubourg Saint Jacques 75014 Paris France
| | - Sebastian Wendeborn
- University of Applied Sciences and Arts Northwestern Switzerland, School of LifeSciences Hofackerstrasse 30 CH-4132 Muttenz Switzerland
| | - Chris Ho
- Qubit Pharmaceuticals, Incubateur Paris Biotech Santé 24 Rue du Faubourg Saint Jacques 75014 Paris France
| | - Dina El Ahdab
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS 75005 Paris France
| | - Theo Jaffrelot Inizan
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS 75005 Paris France
| | - Mattia Sturlese
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padua via F. Marzolo 5 35131 Padova Italy
| | - Alice Sosic
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5 35131 Padova Italy
| | - Martina Volpiana
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5 35131 Padova Italy
| | - Angela Lugato
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5 35131 Padova Italy
| | - Marco Barone
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5 35131 Padova Italy
| | - Barbara Gatto
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5 35131 Padova Italy
| | - Maria Ludovica Macchia
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5 35131 Padova Italy
| | - Massimo Bellanda
- Department of Chemistry, University of Padova via Marzolo 1 35131 Padova Italy
| | - Roberto Battistutta
- Department of Chemistry, University of Padova via Marzolo 1 35131 Padova Italy
| | - Cristiano Salata
- Department of Molecular Medicine, University of Padua via Gabelli 63 35121 Padova Italy
| | | | - Rustam Iminov
- Enamine Ltd 78 Chervonotkats'ka Str. Kyiv 02094 Ukraine
| | | | | | | | | | - Iaroslava Kos
- Enamine Ltd 78 Chervonotkats'ka Str. Kyiv 02094 Ukraine
| | - Stefano Moro
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padua via F. Marzolo 5 35131 Padova Italy
| | - Matthieu Montes
- Laboratoire GBCM, EA7528, Conservatoire National des Arts et Métiers, Hesam Université 2 Rue Conte 75003 Paris France
| | - Pengyu Ren
- University of Texas at Austin, Department of Biomedical Engineering TX 78712 USA
| | - Jay W Ponder
- Department of Chemistry, Washington University in Saint Louis MO 63130 USA.,Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine MO 63110 USA
| | - Louis Lagardère
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS 75005 Paris France
| | - Jean-Philip Piquemal
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS 75005 Paris France .,Institut Universitaire de France 75005 Paris France
| | - Davide Sabbadin
- Qubit Pharmaceuticals, Incubateur Paris Biotech Santé 24 Rue du Faubourg Saint Jacques 75014 Paris France
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3
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Varela-Rial A, Majewski M, Cuzzolin A, Martínez-Rosell G, De Fabritiis G. SkeleDock: A Web Application for Scaffold Docking in PlayMolecule. J Chem Inf Model 2020; 60:2673-2677. [PMID: 32407111 DOI: 10.1021/acs.jcim.0c00143] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
SkeleDock is a scaffold docking algorithm which uses the structure of a protein-ligand complex as a template to model the binding mode of a chemically similar system. This algorithm was evaluated in the D3R Grand Challenge 4 pose prediction challenge, where it achieved competitive performance. Furthermore, we show that if crystallized fragments of the target ligand are available then SkeleDock can outperform rDock docking software at predicting the binding mode. This Application Note also addresses the capacity of this algorithm to model macrocycles and deal with scaffold hopping. SkeleDock can be accessed at https://playmolecule.org/SkeleDock/.
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Affiliation(s)
- Alejandro Varela-Rial
- Acellera Labs, Doctor Trueta 183, Barcelona, Spain.,Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain
| | - Maciej Majewski
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain
| | | | | | - Gianni De Fabritiis
- Acellera Labs, Doctor Trueta 183, Barcelona, Spain.,Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, Barcelona, Spain
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4
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Bolcato G, Cuzzolin A, Bissaro M, Moro S, Sturlese M. Can We Still Trust Docking Results? An Extension of the Applicability of DockBench on PDBbind Database. Int J Mol Sci 2019; 20:ijms20143558. [PMID: 31330841 PMCID: PMC6679043 DOI: 10.3390/ijms20143558] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 07/12/2019] [Accepted: 07/18/2019] [Indexed: 11/28/2022] Open
Abstract
The number of entries in the Protein Data Bank (PDB) has doubled in the last decade, and it has increased tenfold in the last twenty years. The availability of an ever-growing number of structures is having a huge impact on the Structure-Based Drug Discovery (SBDD), allowing investigation of new targets and giving the possibility to have multiple structures of the same macromolecule in a complex with different ligands. Such a large resource often implies the choice of the most suitable complex for molecular docking calculation, and this task is complicated by the plethora of possible posing and scoring function algorithms available, which may influence the quality of the outcomes. Here, we report a large benchmark performed on the PDBbind database containing more than four thousand entries and seventeen popular docking protocols. We found that, even in protein families wherein docking protocols generally showed acceptable results, certain ligand-protein complexes are poorly reproduced in the self-docking procedure. Such a trend in certain protein families is more pronounced, and this underlines the importance in identification of a suitable protein–ligand conformation coupled to a well-performing docking protocol.
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Affiliation(s)
- Giovanni Bolcato
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy
| | - Alberto Cuzzolin
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy
| | - Maicol Bissaro
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy.
| | - Mattia Sturlese
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy.
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5
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Jiménez J, Sabbadin D, Cuzzolin A, Martínez-Rosell G, Gora J, Manchester J, Duca J, De Fabritiis G. PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks. J Chem Inf Model 2019; 59:1172-1181. [PMID: 30586501 DOI: 10.1021/acs.jcim.8b00711] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Drug discovery suffers from high attrition because compounds initially deemed as promising can later show ineffectiveness or toxicity resulting from a poor understanding of their activity profile. In this work, we describe a deep self-normalizing neural network model for the prediction of molecular pathway association and evaluate its performance, showing an AUC ranging from 0.69 to 0.91 on a set of compounds extracted from ChEMBL and from 0.81 to 0.83 on an external data set provided by Novartis. We finally discuss the applicability of the proposed model in the domain of lead discovery. A usable application is available via PlayMolecule.org .
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Affiliation(s)
- José Jiménez
- Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain
| | - Davide Sabbadin
- Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain
| | - Alberto Cuzzolin
- Acellera , Barcelona Biomedical Research Park (PRBB) , Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain
| | - Gerard Martínez-Rosell
- Acellera , Barcelona Biomedical Research Park (PRBB) , Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain
| | - Jacob Gora
- Global Discovery Chemistry , Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States.,Department of Mathematics and Computer Science , Freie Universität Berlin , Takustr. 9 , 14195 Berlin , Germany
| | - John Manchester
- Global Discovery Chemistry , Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States
| | - José Duca
- Global Discovery Chemistry , Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States
| | - Gianni De Fabritiis
- Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain.,Acellera , Barcelona Biomedical Research Park (PRBB) , Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA) , Passeig Lluis Companys 23 , 08010 Barcelona , Spain
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6
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Cuzzolin A, Deganutti G, Salmaso V, Sturlese M, Moro S. Cover Feature: AquaMMapS: An Alternative Tool to Monitor the Role of Water Molecules During Protein-Ligand Association (ChemMedChem 6/2018). ChemMedChem 2018. [DOI: 10.1002/cmdc.201800125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Alberto Cuzzolin
- Molecular Modeling Section, MMS, Department of Pharmaceutical and Pharmacological Sciences; University of Padova; via Marzolo 5 35131 Padova Italy
| | - Giuseppe Deganutti
- Molecular Modeling Section, MMS, Department of Pharmaceutical and Pharmacological Sciences; University of Padova; via Marzolo 5 35131 Padova Italy
| | - Veronica Salmaso
- 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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7
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Cuzzolin A, Deganutti G, Salmaso V, Sturlese M, Moro S. AquaMMapS: An Alternative Tool to Monitor the Role of Water Molecules During Protein-Ligand Association. ChemMedChem 2018; 13:522-531. [DOI: 10.1002/cmdc.201700564] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 11/21/2017] [Indexed: 12/11/2022]
Affiliation(s)
- Alberto Cuzzolin
- Molecular Modeling Section, MMS, Department of Pharmaceutical and Pharmacological Sciences; University of Padova; via Marzolo 5 35131 Padova Italy
| | - Giuseppe Deganutti
- Molecular Modeling Section, MMS, Department of Pharmaceutical and Pharmacological Sciences; University of Padova; via Marzolo 5 35131 Padova Italy
| | - Veronica Salmaso
- 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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8
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Malvacio I, Cuzzolin A, Sturlese M, Vera DMA, Moyano EL, Moro S. Synthesis and preliminary structure-activity relationship study of 2-aryl-2H-pyrazolo[4,3-c]quinolin-3-ones as potential checkpoint kinase 1 (Chk1) inhibitors. J Enzyme Inhib Med Chem 2017; 33:171-183. [PMID: 29210298 PMCID: PMC6010083 DOI: 10.1080/14756366.2017.1404592] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
The serine-threonine checkpoint kinase 1 (Chk1) plays a critical role in the cell cycle arrest in response to DNA damage. In the last decade, Chk1 inhibitors have emerged as a novel therapeutic strategy to potentiate the anti-tumour efficacy of cytotoxic chemotherapeutic agents. In the search for new Chk1 inhibitors, a congeneric series of 2-aryl-2 H-pyrazolo[4,3-c]quinolin-3-one (PQ) was evaluated by in-vitro and in-silico approaches for the first time. A total of 30 PQ structures were synthesised in good to excellent yields using conventional or microwave heating, highlighting that 14 of them are new chemical entities. Noteworthy, in this preliminary study two compounds 4e2 and 4h2 have shown a modest but significant reduction in the basal activity of the Chk1 kinase. Starting from these preliminary results, we have designed the second generation of analogous in this class and further studies are in progress in our laboratories.
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Affiliation(s)
- Ivana Malvacio
- a Department of Organic Chemistry, INFIQC, School of Chemical Sciences , National University of Cordoba , Cordoba , Argentina.,b Molecular Modeling Section (MMS), Dipartimento di Scienze Farmaceutiche , Università degli Studi di Padova , via Marzolo, Padova , Italy
| | - Alberto Cuzzolin
- b Molecular Modeling Section (MMS), Dipartimento di Scienze Farmaceutiche , Università degli Studi di Padova , via Marzolo, Padova , Italy
| | - Mattia Sturlese
- b Molecular Modeling Section (MMS), Dipartimento di Scienze Farmaceutiche , Università degli Studi di Padova , via Marzolo, Padova , Italy
| | - D Mariano A Vera
- c Department of Chemistry, QUIAMM-INBIOTEC, School of Exact and Natural Sciences , National University of Mar del Plata , Mar del Plata , Buenos Aires , Argentina
| | - E Laura Moyano
- a Department of Organic Chemistry, INFIQC, School of Chemical Sciences , National University of Cordoba , Cordoba , Argentina
| | - Stefano Moro
- b Molecular Modeling Section (MMS), Dipartimento di Scienze Farmaceutiche , Università degli Studi di Padova , via Marzolo, Padova , Italy
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9
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Salmaso V, Sturlese M, Cuzzolin A, Moro S. Exploring Protein-Peptide Recognition Pathways Using a Supervised Molecular Dynamics Approach. Structure 2017; 25:655-662.e2. [DOI: 10.1016/j.str.2017.02.009] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Revised: 01/26/2017] [Accepted: 02/22/2017] [Indexed: 12/14/2022]
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10
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Salmaso V, Sturlese M, Cuzzolin A, Moro S. DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015. J Comput Aided Mol Des 2016; 30:773-789. [PMID: 27638810 DOI: 10.1007/s10822-016-9966-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 09/07/2016] [Indexed: 11/24/2022]
Abstract
Structure-based drug design (SBDD) has matured within the last two decades as a valuable tool for the optimization of low molecular weight lead compounds to highly potent drugs. The key step in SBDD requires knowledge of the three-dimensional structure of the target-ligand complex, which is usually determined by X-ray crystallography. In the absence of structural information for the complex, SBDD relies on the generation of plausible molecular docking models. However, molecular docking protocols suffer from inaccuracies in the description of the interaction energies between the ligand and the target molecule, and often fail in the prediction of the correct binding mode. In this context, the appropriate selection of the most accurate docking protocol is absolutely relevant for the final molecular docking result, even if addressing this point is absolutely not a trivial task. D3R Grand Challenge 2015 has represented a precious opportunity to test the performance of DockBench, an integrate informatics platform to automatically compare RMDS-based molecular docking performances of different docking/scoring methods. The overall performance resulted in the blind prediction are encouraging in particular for the pose prediction task, in which several complex were predicted with a sufficient accuracy for medicinal chemistry purposes.
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Affiliation(s)
- Veronica Salmaso
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, Padua, Italy
| | - Mattia Sturlese
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, Padua, Italy
| | - Alberto Cuzzolin
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, Padua, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, Padua, Italy.
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11
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Ciancetta A, Cuzzolin A, Deganutti G, Sturlese M, Salmaso V, Cristiani A, Sabbadin D, Moro S. New Trends in Inspecting GPCR-ligand Recognition Process: the Contribution of the Molecular Modeling Section (MMS) at the University of Padova. Mol Inform 2016; 35:440-8. [PMID: 27546048 DOI: 10.1002/minf.201501011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 05/10/2016] [Indexed: 11/07/2022]
Abstract
In this review, we present a survey of the recent advances carried out by our research groups in the field of ligand-GPCRs recognition process simulations recently implemented at the Molecular Modeling Section (MMS) of the University of Padova. We briefly describe a platform of tools we have tuned to aid the identification of novel GPCRs binders and the better understanding of their binding mechanisms, based on two extensively used computational techniques such as molecular docking and MD simulations. The developed methodologies encompass: (i) the selection of suitable protocols for docking studies, (ii) the exploration of the dynamical evolution of ligand-protein interaction networks, (iii) the detailed investigation of the role of water molecules upon ligand binding, and (iv) a glance at the way the ligand might go through prior reaching the binding site.
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Affiliation(s)
- Antonella Ciancetta
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5, Padova, Italy
| | - Alberto Cuzzolin
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5, Padova, Italy
| | - Giuseppe Deganutti
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5, Padova, Italy
| | - Mattia Sturlese
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5, Padova, Italy
| | - Veronica Salmaso
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5, Padova, Italy
| | - Andrea Cristiani
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5, Padova, Italy
| | - Davide Sabbadin
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5, Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova via Marzolo 5, Padova, Italy.
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12
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Cuzzolin A, Sturlese M, Deganutti G, Salmaso V, Sabbadin D, Ciancetta A, Moro S. Deciphering the Complexity of Ligand–Protein Recognition Pathways Using Supervised Molecular Dynamics (SuMD) Simulations. J Chem Inf Model 2016; 56:687-705. [DOI: 10.1021/acs.jcim.5b00702] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Alberto Cuzzolin
- Molecular
Modeling Section
(MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova, Italy
| | - Mattia Sturlese
- Molecular
Modeling Section
(MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova, Italy
| | - Giuseppe Deganutti
- Molecular
Modeling Section
(MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova, Italy
| | - Veronica Salmaso
- Molecular
Modeling Section
(MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova, Italy
| | - Davide Sabbadin
- Molecular
Modeling Section
(MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova, Italy
| | - Antonella Ciancetta
- Molecular
Modeling Section
(MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova, Italy
| | - Stefano Moro
- Molecular
Modeling Section
(MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova, Italy
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Deganutti G, Cuzzolin A, Ciancetta A, Moro S. Understanding allosteric interactions in G protein-coupled receptors using Supervised Molecular Dynamics: A prototype study analysing the human A3 adenosine receptor positive allosteric modulator LUF6000. Bioorg Med Chem 2015; 23:4065-71. [DOI: 10.1016/j.bmc.2015.03.039] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Revised: 03/12/2015] [Accepted: 03/13/2015] [Indexed: 10/23/2022]
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14
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Cuzzolin A, Sturlese M, Malvacio I, Ciancetta A, Moro S. DockBench: An Integrated Informatic Platform Bridging the Gap between the Robust Validation of Docking Protocols and Virtual Screening Simulations. Molecules 2015; 20:9977-93. [PMID: 26035098 PMCID: PMC6272630 DOI: 10.3390/molecules20069977] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 05/05/2015] [Accepted: 05/21/2015] [Indexed: 11/17/2022] Open
Abstract
Virtual screening (VS) is a computational methodology that streamlines the drug discovery process by reducing costs and required resources through the in silico identification of potential drug candidates. Structure-based VS (SBVS) exploits knowledge about the three-dimensional (3D) structure of protein targets and uses the docking methodology as search engine for novel hits. The success of a SBVS campaign strongly depends upon the accuracy of the docking protocol used to select the candidates from large chemical libraries. The identification of suitable protocols is therefore a crucial step in the setup of SBVS experiments. Carrying out extensive benchmark studies, however, is usually a tangled task that requires users' proficiency in handling different file formats and philosophies at the basis of the plethora of existing software packages. We present here DockBench 1.0, a platform available free of charge that eases the pipeline by automating the entire procedure, from docking benchmark to VS setups. In its current implementation, DockBench 1.0 handles seven docking software packages and offers the possibility to test up to seventeen different protocols. The main features of our platform are presented here and the results of the benchmark study of human Checkpoint kinase 1 (hChk1) are discussed as validation test.
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Affiliation(s)
- Alberto Cuzzolin
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
| | - Mattia Sturlese
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
| | - Ivana Malvacio
- INFIQC-Organic Chemistry Department, School of Chemical Sciences, National University of Cordoba, Cordoba, CP 5000, Argentine.
| | - Antonella Ciancetta
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
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15
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Lovisa F, Cozza G, Cristiani A, Cuzzolin A, Albiero A, Mussolin L, Pillon M, Moro S, Basso G, Rosolen A, Bonvini P. ALK kinase domain mutations in primary anaplastic large cell lymphoma: consequences on NPM-ALK activity and sensitivity to tyrosine kinase inhibitors. PLoS One 2015; 10:e0121378. [PMID: 25874976 PMCID: PMC4395299 DOI: 10.1371/journal.pone.0121378] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Accepted: 01/31/2015] [Indexed: 12/18/2022] Open
Abstract
ALK inhibitor crizotinib has shown potent antitumor activity in children with refractory Anaplastic Large Cell Lymphoma (ALCL) and the opportunity to include ALK inhibitors in first-line therapies is oncoming. However, recent studies suggest that crizotinib-resistance mutations may emerge in ALCL patients. In the present study, we analyzed ALK kinase domain mutational status of 36 paediatric ALCL patients at diagnosis to identify point mutations and gene aberrations that could impact on NPM-ALK gene expression, activity and sensitivity to small-molecule inhibitors. Amplicon ultra-deep sequencing of ALK kinase domain detected 2 single point mutations, R335Q and R291Q, in 2 cases, 2 common deletions of exon 23 and 25 in all the patients, and 7 splicing-related INDELs in a variable number of them. The functional impact of missense mutations and INDELs was evaluated. Point mutations were shown to affect protein kinase activity, signalling output and drug sensitivity. INDELs, instead, generated kinase-dead variants with dominant negative effect on NPM-ALK kinase, in virtue of their capacity of forming non-functional heterocomplexes. Consistently, when co-expressed, INDELs increased crizotinib inhibitory activity on NPM-ALK signal processing, as demonstrated by the significant reduction of STAT3 phosphorylation. Functional changes in ALK kinase activity induced by both point mutations and structural rearrangements were resolved by molecular modelling and dynamic simulation analysis, providing novel insights into ALK kinase domain folding and regulation. Therefore, these data suggest that NPM-ALK pre-therapeutic mutations may be found at low frequency in ALCL patients. These mutations occur randomly within the ALK kinase domain and affect protein activity, while preserving responsiveness to crizotinib.
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Affiliation(s)
- Federica Lovisa
- Clinica di Oncoematologia Pediatrica di Padova, Azienda Ospedaliera-Università di Padova, Padua, Italy
| | - Giorgio Cozza
- Dipartimento di Scienze Biomediche, Università di Padova, Padua, Italy
| | - Andrea Cristiani
- Dipartimento di Scienze del Farmaco, Università di Padova, Padua, Italy
| | - Alberto Cuzzolin
- Dipartimento di Scienze del Farmaco, Università di Padova, Padua, Italy
| | | | - Lara Mussolin
- Clinica di Oncoematologia Pediatrica di Padova, Azienda Ospedaliera-Università di Padova, Padua, Italy; Istituto di Ricerca Pediatrica Città della Speranza, Padua, Italy
| | - Marta Pillon
- Clinica di Oncoematologia Pediatrica di Padova, Azienda Ospedaliera-Università di Padova, Padua, Italy
| | - Stefano Moro
- Dipartimento di Scienze del Farmaco, Università di Padova, Padua, Italy
| | - Giuseppe Basso
- Clinica di Oncoematologia Pediatrica di Padova, Azienda Ospedaliera-Università di Padova, Padua, Italy
| | - Angelo Rosolen
- Clinica di Oncoematologia Pediatrica di Padova, Azienda Ospedaliera-Università di Padova, Padua, Italy
| | - Paolo Bonvini
- Clinica di Oncoematologia Pediatrica di Padova, Azienda Ospedaliera-Università di Padova, Padua, Italy; Istituto di Ricerca Pediatrica Città della Speranza, Padua, Italy
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Sabbadin D, Ciancetta A, Deganutti G, Cuzzolin A, Moro S. Exploring the recognition pathway at the human A2A adenosine receptor of the endogenous agonist adenosine using supervised molecular dynamics simulations. Med Chem Commun 2015. [DOI: 10.1039/c5md00016e] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The putative adenosine-hA2A AR recognition pathway is suggested by a series of Supervised Molecular Dynamics (SuMD) simulations.
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Affiliation(s)
- Davide Sabbadin
- Molecular Modeling Section (MMS)
- Dipartimento di Scienze del Farmaco
- Università di Padova
- via Marzolo 5
- I-35131 Padova
| | - Antonella Ciancetta
- Molecular Modeling Section (MMS)
- Dipartimento di Scienze del Farmaco
- Università di Padova
- via Marzolo 5
- I-35131 Padova
| | - Giuseppe Deganutti
- Molecular Modeling Section (MMS)
- Dipartimento di Scienze del Farmaco
- Università di Padova
- via Marzolo 5
- I-35131 Padova
| | - Alberto Cuzzolin
- Molecular Modeling Section (MMS)
- Dipartimento di Scienze del Farmaco
- Università di Padova
- via Marzolo 5
- I-35131 Padova
| | - Stefano Moro
- Molecular Modeling Section (MMS)
- Dipartimento di Scienze del Farmaco
- Università di Padova
- via Marzolo 5
- I-35131 Padova
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17
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Ciancetta A, Cuzzolin A, Moro S. Alternative quality assessment strategy to compare performances of GPCR-ligand docking protocols: the human adenosine A(2A) receptor as a case study. J Chem Inf Model 2014; 54:2243-54. [PMID: 25046649 DOI: 10.1021/ci5002857] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The progress made in the field of G protein-coupled receptors (GPCRs) structural determination has increased the adoption of docking-driven approaches for the identification or optimization of novel potent and selective ligands. In this work, we compared the performances of the 16 different docking/scoring combinations using the recently released crystal structures of the human A2A AR (hA2A AR) in complex with both agonists and antagonists. The proposed evaluation strategy encompasses the use of three complementary "quality descriptors": (a) the number of conformations generated by a docking algorithm having a RMSD value lower than the crystal structure resolution (R); (b) a novel consensus-based function defined as "protocol score"; and (c) the interaction energy maps (IEMs) analysis, based on the identification of key ligand-receptor interactions observed in the crystal structures.
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Affiliation(s)
- Antonella Ciancetta
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova , via Marzolo 5, 35131 Padova, Italy
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18
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Floris M, Sabbadin D, Ciancetta A, Medda R, Cuzzolin A, Moro S. Implementing the "Best Template Searching" tool into Adenosiland platform. In Silico Pharmacol 2013; 1:25. [PMID: 25505667 PMCID: PMC4230649 DOI: 10.1186/2193-9616-1-25] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Accepted: 11/25/2013] [Indexed: 11/17/2022] Open
Abstract
Background Adenosine receptors (ARs) belong to the G protein-coupled receptors (GCPRs) family. The recent release of X-ray structures of the human A2A AR (h A2A AR ) in complex with agonists and antagonists has increased the application of structure-based drug design approaches to this class of receptors. Among them, homology modeling represents the method of choice to gather structural information on the other receptor subtypes, namely A1, A2B, and A3 ARs. With the aim of helping users in the selection of either a template to build its own models or ARs homology models publicly available on our platform, we implemented our web-resource dedicated to ARs, Adenosiland, with the “Best Template Searching” facility. This tool is freely accessible at the following web address: http://mms.dsfarm.unipd.it/Adenosiland/ligand.php. Findings The template suggestions and homology models provided by the “Best Template Searching” tool are guided by the similarity of a query structure (putative or known ARs ligand) with all ligands co-crystallized with hA2A AR subtype. The tool computes several similarity indexes and sort the outcoming results according to the index selected by the user. Conclusions We have implemented our web-resource dedicated to ARs Adenosiland with the “Best Template Searching” facility, a tool to guide template and models selection for hARs modelling. The underlying idea of our new facility, that is the selection of a template (or models built upon a template) whose co-crystallized ligand shares the highest similarity with the query structure, can be easily extended to other GPCRs.
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Affiliation(s)
| | - Davide Sabbadin
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, I-35131 Padova, Italy
| | - Antonella Ciancetta
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, I-35131 Padova, Italy
| | | | - Alberto Cuzzolin
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, I-35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, I-35131 Padova, Italy
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