A Computational Workflow for the Identification of Novel Fragments Acting as Inhibitors of the Activity of Protein Kinase CK1δ

Bolcato, Giovanni; Cescon, Eleonora; Pavan, Matteo; Bissaro, Maicol; Bassani, Davide; Federico, Stephanie; Spalluto, Giampiero; Sturlese, Mattia; Moro, Stefano

doi:10.3390/ijms22189741

Cited by 15 publications

(17 citation statements)

References 61 publications

(66 reference statements)

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“…Our model is disposed on a triangle conformation, wherein the first one of the vertexes is situated in the buried region, the second one is situated interacting with the adenine region, and the third one is situated at the phosphate binding region. This model is compatible with the one proposed by Bolcato et al for CK1δ inhibitors [40], but implies improvement since our analysis revealed additional hotspots.…”

Section: Discussionsupporting

confidence: 92%

Repositioning of Etravirine as a Potential CK1ε Inhibitor by Virtual Screening

et al. 2021

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CK1ε is a key regulator of WNT/β-catenin and other pathways that are linked to tumor progression; thus, CK1ε is considered a target for the development of antineoplastic therapies. In this study, we performed a virtual screening to search for potential CK1ε inhibitors. First, we characterized the dynamic noncovalent interactions profiles for a set of reported CK1ε inhibitors to generate a pharmacophore model, which was used to identify new potential inhibitors among FDA-approved drugs. We found that etravirine and abacavir, two drugs that are approved for HIV infections, can be repurposed as CK1ε inhibitors. The interaction of these drugs with CK1ε was further examined by molecular docking and molecular dynamics. Etravirine and abacavir formed stable complexes with the target, emulating the binding behavior of known inhibitors. However, only etravirine showed high theoretical binding affinity to CK1ε. Our findings provide a new pharmacophore for targeting CK1ε and implicate etravirine as a CK1ε inhibitor and antineoplastic agent.

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Section: Discussionsupporting

confidence: 92%

Repositioning of Etravirine as a Potential CK1ε Inhibitor by Virtual Screening

et al. 2021

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“…Encouraged by the results of our benchmark runs, we decided to perform a prospective run with the IFP CS protocol, applying the same operating conditions as before. This time, the starting library was modified to add to the compounds used for the benchmark runs seven fragment ATP-competitive CK1δ inhibitors identified during a previous virtual screening campaign from our laboratory ( Bolcato et al, 2021 ). The idea behind this run was to evaluate the ability of our IFP CS scoring protocol to generate interesting novel potential CK1δ inhibitors derived from in-house, readily available compounds.…”

Section: Resultsmentioning

confidence: 99%

“… Chemical structure of the seven fragment CK1δ inhibitors derived from the work of Bolcato et al (2021) . …”

Section: Resultsmentioning

confidence: 99%

“…A recent scientific work published by our laboratory led to the identification of seven novel fragment compounds that bind the hinge region of CK1δ with a low-micromolar IC 50 ( Bolcato et al, 2021 ). Attracted by the idea of exploiting a semi-automatized computational protocol for the optimization of our newly discovered fragment compounds, we decided to investigate if this protocol would be suitable for our needs.…”

Section: Introductionmentioning

confidence: 99%

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Implementing a Scoring Function Based on Interaction Fingerprint for Autogrow4: Protein Kinase CK1δ as a Case Study

Pavan

Menin

Bassani

et al. 2022

Front. Mol. Biosci.

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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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“…To further investigate this aspect, we expanded our computational analysis by performing a molecular dynamics (MD) experiment, in which each crystallographic ligand was left free to move for 5ns (three replicas per system). This approach (known as "MD post-docking") has already become part of our computational protocol [27,28] and is based on the fact that the conformations of the ligands, which are less prone to be displaced from their initial posi-tion during the simulation, are related to higher stability and binding strength with the target. In the case presented, this principle was applied directly to the crystallographic conformations of the ligands rather than to docking poses.…”

Section: Introductionmentioning

confidence: 99%

Re-Exploring the Ability of Common Docking Programs to Correctly Reproduce the Binding Modes of Non-Covalent Inhibitors of SARS-CoV-2 Protease Mpro

et al. 2022

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In the latest few decades, molecular docking has imposed itself as one of the most used approaches for computational drug discovery. Several docking benchmarks have been published, comparing the performance of different algorithms in respect to a molecular target of interest, usually evaluating their ability in reproducing the experimental data, which, in most cases, comes from X-ray structures. In this study, we elucidated the variation of the performance of three docking algorithms, namely GOLD, Glide, and PLANTS, in replicating the coordinates of the crystallographic ligands of SARS-CoV-2 main protease (Mpro). Through the comparison of the data coming from docking experiments and the values derived from the calculation of the solvent exposure of the crystallographic ligands, we highlighted the importance of this last variable for docking performance. Indeed, we underlined how an increase in the percentage of the ligand surface exposed to the solvent in a crystallographic complex makes it harder for the docking algorithms to reproduce its conformation. We further validated our hypothesis through molecular dynamics simulations, showing that the less stable protein–ligand complexes (in terms of root-mean-square deviation and root-mean-square fluctuation) tend to be derived from the cases in which the solvent exposure of the ligand in the starting system is higher.

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A Computational Workflow for the Identification of Novel Fragments Acting as Inhibitors of the Activity of Protein Kinase CK1δ

Cited by 15 publications

References 61 publications

Repositioning of Etravirine as a Potential CK1ε Inhibitor by Virtual Screening

Repositioning of Etravirine as a Potential CK1ε Inhibitor by Virtual Screening

Implementing a Scoring Function Based on Interaction Fingerprint for Autogrow4: Protein Kinase CK1δ as a Case Study

Re-Exploring the Ability of Common Docking Programs to Correctly Reproduce the Binding Modes of Non-Covalent Inhibitors of SARS-CoV-2 Protease Mpro

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