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Novel pharmacological maps of protein lysine methyltransferases: key for target deorphanization

Overview of attention for article published in Journal of Cheminformatics, July 2018
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Title
Novel pharmacological maps of protein lysine methyltransferases: key for target deorphanization
Published in
Journal of Cheminformatics, July 2018
DOI 10.1186/s13321-018-0288-5
Pubmed ID
Authors

Obdulia Rabal, Andrea Castellar, Julen Oyarzabal

Abstract

Epigenetic therapies are being investigated for the treatment of cancer, cognitive disorders, metabolic alterations and autoinmune diseases. Among the different epigenetic target families, protein lysine methyltransferases (PKMTs), are especially interesting because it is believed that their inhibition may be highly specific at the functional level. Despite its relevance, there are currently known inhibitors against only 10 out of the 50 SET-domain containing members of the PKMT family. Accordingly, the identification of chemical probes for the validation of the therapeutic impact of epigenetic modulation is key. Moreover, little is known about the mechanisms that dictate their substrate specificity and ligand selectivity. Consequently, it is desirable to explore novel methods to characterize the pharmacological similarity of PKMTs, going beyond classical phylogenetic relationships. Such characterization would enable the prediction of ligand off-target effects caused by lack of ligand selectivity and the repurposing of known compounds against alternative targets. This is particularly relevant in the case of orphan targets with unreported inhibitors. Here, we first perform a systematic study of binding modes of cofactor and substrate bound ligands with all available SET domain-containing PKMTs. Protein ligand interaction fingerprints were applied to identify conserved hot spots and contact-specific residues across subfamilies at each binding site; a relevant analysis for guiding the design of novel, selective compounds. Then, a recently described methodology (GPCR-CoINPocket) that incorporates ligand contact information into classical alignment-based comparisons was applied to the entire family of 50 SET-containing proteins to devise pharmacological similarities between them. The main advantage of this approach is that it is not restricted to proteins for which crystallographic data with bound ligands is available. The resulting family organization from the separate analysis of both sites (cofactor and substrate) was retrospectively and prospectively validated. Of note, three hits (inhibition > 50% at 10 µM) were identified for the orphan NSD1.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 29%
Student > Master 4 29%
Researcher 3 21%
Student > Doctoral Student 1 7%
Unknown 2 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 36%
Agricultural and Biological Sciences 2 14%
Medicine and Dentistry 2 14%
Business, Management and Accounting 1 7%
Veterinary Science and Veterinary Medicine 1 7%
Other 1 7%
Unknown 2 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 July 2018.
All research outputs
#13,996,860
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#685
of 891 outputs
Outputs of similar age
#165,621
of 332,770 outputs
Outputs of similar age from Journal of Cheminformatics
#16
of 17 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 332,770 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.