Cited by 4Computational Drug Discovery MethodsSynthesis and Characterization of Heterocyclic CompoundsProtein Kinase Regulation and GTPase Signaling

AI-Assisted chemical probe discovery for the understudied Calcium-Calmodulin Dependent Kinase, PNCK

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Essegian, Derek J., Chavez, Valery, Khurshid, Rabia, Merchan, Jaime R., Schürer, Stephan C.·2023·PLOS Computational Biology

Published version of a preprint

Abstract

PNCK, or CAMK1b, is an understudied kinase of the calcium-calmodulin dependent kinase family which recently has been identified as a marker of cancer progression and survival in several large-scale multi-omics studies. The biology of PNCK and its relation to oncogenesis has also begun to be elucidated, with data suggesting various roles in DNA damage response, cell cycle control, apoptosis and HIF-1-alpha related pathways. To further explore PNCK as a clinical target, potent small-molecule molecular probes must be developed. Currently, there are no targeted small molecule inhibitors in pre-clinical or clinical studies for the CAMK family. Additionally, there exists no experimentally derived crystal structure for PNCK. We herein report a three-pronged chemical probe discovery campaign which utilized homology modeling, machine learning, virtual screening and molecular dynamics to identify small molecules with low-micromolar potency against PNCK activity from commercially available compound libraries. We report the discovery of a hit-series for the first targeted effort towards discovering PNCK inhibitors that will serve as the starting point for future medicinal chemistry efforts for hit-to-lead optimization of potent chemical probes.

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Crossref · 10.1371/journal.pcbi.1010263

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Authors
Essegian, D. J., Chavez, V., Khurshid, R., Merchan, J. R., & Schürer, S. C.
Affiliations
University of Miami (US)
Corresponding author
Schürer, Stephan C.
Venue
PLOS Computational Biology
Published
2023-05-26
Volume
19
Issue
5
Pages
e1010263
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
OpenAlex
W4378373554
Cited by
4
References
2
Citations by year
2025: 2 · 2023: 1 · 2022: 1
FWCI
0.43
Type
journal article
Language
en
Open access
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institutes of Health (U54HL127624, U24TR002278, U01LM012630)
  • Sylvester Comprehensive Cancer Center Support Grant (P30CA240139)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Kinase Regulation and GTPase Signaling
Keywords
calcium/calmodulin-dependent protein kinase, kinase inhibitors, small molecule inhibitors, homology modeling, virtual screening, molecular dynamics, tumor progression markers, carcinogenesis
MeSH terms
Artificial Intelligence, Calcium, Calmodulin

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