English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In silico methods are being widely used for identifying substrates for various kinases and deciphering cell signaling networks. However, most of the available phosphorylation site prediction methods use motifs or profiles derived from a known data set of kinase substrates and hence, their applicability is limited to only those kinase families for which experimental substrate data is available. This prompted us to develop a novel multi-scale structure-based approach which does not require training using experimental substrate data.

Identification of substrates for Ser/Thr kinases using residue-based statistical pair potentials