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KiSSim: Predicting Off-Targets from Structural Similarities in the Kinome
Open accessPublished version of a preprint
Abstract
Protein kinases are among the most important drug targets because their dysregulation can cause cancer, inflammatory and degenerative diseases, and many more. Developing selective inhibitors is challenging due to the highly conserved binding sites across the roughly 500 human kinases. Thus, detecting subtle similarities on a structural level can help explain and predict off-targets among the kinase family. Here, we present the kinase-focused, subpocket-enhanced KiSSim fingerprint (Kinase Structural Similarity). The fingerprint builds on the KLIFS pocket definition, composed of 85 residues aligned across all available protein kinase structures, which enables residue-by-residue comparison without a computationally expensive alignment. The residues' physicochemical and spatial properties are encoded within their structural context including key subpockets at the hinge region, the DFG motif, and the front pocket. Since structure was found to contain information complementary to sequence, we used the fingerprint to calculate all-against-all similarities within the structurally covered kinome. We could identify off-targets that are unexpected if solely considering the sequence-based kinome tree grouping; for example, Erlobinib's known kinase off-targets SLK and LOK show high similarities to the key target EGFR (TK group), although belonging to the STE group. KiSSim reflects profiling data better or at least as well as other approaches such as KLIFS pocket sequence identity, KLIFS interaction fingerprints (IFPs), or SiteAlign. To rationalize observed (dis)similarities, the fingerprint values can be visualized in 3D by coloring structures with residue and feature resolution. We believe that the KiSSim fingerprint is a valuable addition to the kinase research toolbox to guide off-target and polypharmacology prediction. The method is distributed as an open-source Python package on GitHub and as a conda package: https://github.com/volkamerlab/kissim.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025
Research works printed but not linked: 36
- 10.1021/cb300610s · DOI
- 10.1038/nrd.2016.230 · DOI · Paper page
- 10.1038/s41573-021-00195-4 · DOI
- 10.1021/jm901132v · DOI
- 10.1021/jm400378w · DOI
- 10.1126/science.1075762 · DOI · Paper page
- 10.1038/s41598-019-56499-4 · DOI
- 10.1038/nbt1358 · DOI · Paper page
- 10.1016/bs.armc.2017.08.001 · DOI
- 10.3390/molecules26030629 · DOI
- 10.1002/cmdc.200700075 · DOI
- 10.1093/nar/gkaa895 · DOI
- 10.1016/j.str.2012.01.010 · DOI
- 10.3390/molecules23040908 · DOI
- 10.1093/nar/gkw1074 · DOI
- 10.1002/prot.21858 · DOI
- 10.1021/acs.jcim.0c00839 · DOI
- 10.1002/jcc.20681 · DOI
- 10.1093/bioinformatics/btp163 · DOI · Paper page
- 10.1002/prot.20379 · DOI
- 10.1021/ci200314j · DOI
- Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning ; MIT Press, 2016; Chapter 2, pp 37–38, http://www.deeplearningbook.org.
- 10.1093/nar/gkv402 · DOI · Paper page
- Nguyen H., 2017, Bioinformatics, 34, 1241, 10.1093/bioinformatics/btx789 · DOI
- 10.1038/s41592-019-0686-2 · DOI · Paper page
- 10.21105/joss.00279 · DOI
- 10.1038/s41586-020-2649-2 · DOI · Paper page
- Pedregosa F., 2011, J Mach. Learn. Res., 12, 2825
- Van Rossum G., 2009, Python 3 Reference Manual
- 10.1038/nbt.1990 · DOI · Paper page
- 10.1186/s12859-016-1433-7 · DOI
- 10.1021/ci600342e · DOI
- 10.1002/prot.22458 · DOI
- 10.1016/j.chembiol.2019.02.018 · DOI
- Modi V., 2021, Nucleic Acids Res., 50, D654, 10.1093/nar/gkab920 · DOI · Paper page
- Bennett L, 2021, Curta: A General-Purpose High-Performance Computer at ZEDAT
Metadata
- Authors
- Sydow, D., Aßmann, E., Kooistra, A. J., Rippmann, F., & Volkamer, A.
- Affiliations
- Humboldt-Universität zu Berlin (DE)
- University of Copenhagen (DK)
- Merck KGaA, Darmstadt (Germany) (DE)
- Corresponding author
- Volkamer, Andrea
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-05-10
- Volume
- 62
- Issue
- 10
- Pages
- 2600-2616
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W4280513351
- Cited by
- 6
- References
- 3
- Citations by year
- 2026: 2 · 2024: 2 · 2023: 2
- FWCI
- 0.54
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
- Funders
- Bundesministerium f?r Bildung und Forschung (031A262C)
- Deutsche Forschungsgemeinschaft (VO 2353/1-1)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein kinases, off-target prediction, structural similarity, DFG motif, hinge region, kinase inhibitors, polypharmacology, EGFR
- MeSH terms
- Binding Sites, Humans, Ligands, Protein Kinases, Protein Kinase Inhibitors, Polypharmacology
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