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Dodaro A, Pavan M, Menin S, Salmaso V, Sturlese M, Moro S. Thermal titration molecular dynamics (TTMD): shedding light on the stability of RNA-small molecule complexes. Front Mol Biosci 2023; 10:1294543. [PMID: 38028536 PMCID: PMC10679717 DOI: 10.3389/fmolb.2023.1294543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
Ribonucleic acids are gradually becoming relevant players among putative drug targets, thanks to the increasing amount of structural data exploitable for the rational design of selective and potent binders that can modulate their activity. Mainly, this information allows employing different computational techniques for predicting how well would a ribonucleic-targeting agent fit within the active site of its target macromolecule. Due to some intrinsic peculiarities of complexes involving nucleic acids, such as structural plasticity, surface charge distribution, and solvent-mediated interactions, the application of routinely adopted methodologies like molecular docking is challenged by scoring inaccuracies, while more physically rigorous methods such as molecular dynamics require long simulation times which hamper their conformational sampling capabilities. In the present work, we present the first application of Thermal Titration Molecular Dynamics (TTMD), a recently developed method for the qualitative estimation of unbinding kinetics, to characterize RNA-ligand complexes. In this article, we explored its applicability as a post-docking refinement tool on RNA in complex with small molecules, highlighting the capability of this method to identify the native binding mode among a set of decoys across various pharmaceutically relevant test cases.
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Affiliation(s)
| | | | | | | | | | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
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Costacurta F, Dodaro A, Bante D, Schöppe H, Sprenger B, Moghadasi SA, Fleischmann J, Pavan M, Bassani D, Menin S, Rauch S, Krismer L, Sauerwein A, Heberle A, Rabensteiner T, Ho J, Harris RS, Stefan E, Schneider R, Kaserer T, Moro S, von Laer D, Heilmann E. A comprehensive study of SARS-CoV-2 main protease (M pro) inhibitor-resistant mutants selected in a VSV-based system. bioRxiv 2023:2023.09.22.558628. [PMID: 37808638 PMCID: PMC10557589 DOI: 10.1101/2023.09.22.558628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Nirmatrelvir was the first protease inhibitor (PI) specifically developed against the SARS-CoV-2 main protease (3CLpro/Mpro) and licensed for clinical use. As SARS-CoV-2 continues to spread, variants resistant to nirmatrelvir and other currently available treatments are likely to arise. This study aimed to identify and characterize mutations that confer resistance to nirmatrelvir. To safely generate Mpro resistance mutations, we passaged a previously developed, chimeric vesicular stomatitis virus (VSV-Mpro) with increasing, yet suboptimal concentrations of nirmatrelvir. Using Wuhan-1 and Omicron Mpro variants, we selected a large set of mutants. Some mutations are frequently present in GISAID, suggesting their relevance in SARS-CoV-2. The resistance phenotype of a subset of mutations was characterized against clinically available PIs (nirmatrelvir and ensitrelvir) with cell-based and biochemical assays. Moreover, we showed the putative molecular mechanism of resistance based on in silico molecular modelling. These findings have implications on the development of future generation Mpro inhibitors, will help to understand SARS-CoV-2 protease-inhibitor-resistance mechanisms and show the relevance of specific mutations in the clinic, thereby informing treatment decisions.
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Affiliation(s)
- Francesco Costacurta
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Andrea Dodaro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padua, Via F. Marzolo 5, 35131, Padova, Italy
| | - David Bante
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Helge Schöppe
- Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Bernhard Sprenger
- Department of Biochemistry, University of Innsbruck, Innsbruck, 6020, Austria
| | - Seyed Arad Moghadasi
- Department of Biochemistry, Molecular Biology and Biophysics, Institute for Molecular Virology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jakob Fleischmann
- Institute of Molecular Biology, University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
- Tyrolean Cancer Research Institute (TKFI), Innrain 66, Innsbruck, 6020, Tyrol, Austria
| | - Matteo Pavan
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padua, Via F. Marzolo 5, 35131, Padova, Italy
| | - Davide Bassani
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padua, Via F. Marzolo 5, 35131, Padova, Italy
| | - Silvia Menin
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padua, Via F. Marzolo 5, 35131, Padova, Italy
| | - Stefanie Rauch
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Laura Krismer
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Anna Sauerwein
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Anne Heberle
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Toni Rabensteiner
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Joses Ho
- Bioinformatics Institute, Agency for Science Technology and Research, Singapore
| | - Reuben S. Harris
- Department of Biochemistry and Structural Biology, University of Texas Health San Antonio, San Antonio, TX 78229, United States
- Howard Hughes Medical Institute, University of Texas Health San Antonio, San Antonio, TX 78229, United States
| | - Eduard Stefan
- Institute of Molecular Biology, University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
- Tyrolean Cancer Research Institute (TKFI), Innrain 66, Innsbruck, 6020, Tyrol, Austria
| | - Rainer Schneider
- Department of Biochemistry, University of Innsbruck, Innsbruck, 6020, Austria
| | - Teresa Kaserer
- Institute of Pharmacy/Pharmaceutical Chemistry, University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padua, Via F. Marzolo 5, 35131, Padova, Italy
| | - Dorothee von Laer
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
| | - Emmanuel Heilmann
- Institute of Virology, Medical University of Innsbruck, Innsbruck, 6020, Tyrol, Austria
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Pavan M, Menin S, Bassani D, Sturlese M, Moro S. Qualitative Estimation of Protein-Ligand Complex Stability through Thermal Titration Molecular Dynamics Simulations. J Chem Inf Model 2022; 62:5715-5728. [PMID: 36315402 PMCID: PMC9709921 DOI: 10.1021/acs.jcim.2c00995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The prediction of ligand efficacy has long been linked to thermodynamic properties such as the equilibrium dissociation constant, which considers both the association and the dissociation rates of a defined protein-ligand complex. In the last 15 years, there has been a paradigm shift, with an increased interest in the determination of kinetic properties such as the drug-target residence time since they better correlate with ligand efficacy compared to other parameters. In this article, we present thermal titration molecular dynamics (TTMD), an alternative computational method that combines a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints for the qualitative estimation of protein-ligand-binding stability. The protocol has been applied to four different pharmaceutically relevant test cases, including protein kinase CK1δ, protein kinase CK2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease, on a variety of ligands with different sizes, structures, and experimentally determined affinity values. In all four cases, TTMD was successfully able to distinguish between high-affinity compounds (low nanomolar range) and low-affinity ones (micromolar), proving to be a useful screening tool for the prioritization of compounds in a drug discovery campaign.
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Pavan M, Menin S, Bassani D, Sturlese M, Moro S. Implementing a Scoring Function Based on Interaction Fingerprint for Autogrow4: Protein Kinase CK1δ as a Case Study. Front Mol Biosci 2022; 9:909499. [PMID: 35874609 PMCID: PMC9301033 DOI: 10.3389/fmolb.2022.909499] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/25/2022] [Indexed: 01/23/2023] Open
Abstract
In 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.
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