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KiSSim: Predicting Off-Targets from Structural Similarities in the Kinome.

Dominique Sydow1, Eva Aßmann1, Albert J Kooistra2

  • 1In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.

Journal of Chemical Information and Modeling
|May 10, 2022
PubMed
Summary
This summary is machine-generated.

A new kinase structural similarity (KiSSim) fingerprint identifies potential drug off-targets by analyzing protein kinase structures. This method improves prediction accuracy compared to sequence-based approaches, aiding drug discovery.

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Area of Science:

  • Biochemistry and structural biology
  • Pharmacology and drug discovery

Background:

  • Protein kinases are crucial drug targets, but developing selective inhibitors is difficult due to conserved binding sites.
  • Dysregulated kinases are implicated in cancer, inflammatory, and degenerative diseases.

Purpose of the Study:

  • To develop a novel computational method for predicting kinase off-targets.
  • To enhance the understanding of kinase structural similarities and their implications for drug selectivity.

Main Methods:

  • Introduction of the kinase-focused, subpocket-enhanced KiSSim fingerprint (Kinase Structural Similarity).
  • Utilizing the KLIFS pocket definition for residue-by-residue comparison across protein kinase structures.
  • Encoding physicochemical and spatial properties within structural context, including key subpockets.

Main Results:

  • KiSSim identified unexpected off-targets by analyzing structural similarities, outperforming sequence-based classifications.
  • Calculated all-against-all similarities within the kinome, revealing high structural similarity between EGFR and off-targets SLK and LOK.
  • KiSSim demonstrated comparable or superior performance to existing methods like KLIFS sequence identity, IFPs, and SiteAlign.

Conclusions:

  • The KiSSim fingerprint is a valuable tool for predicting kinase off-targets and polypharmacology.
  • Structural information provides complementary insights to sequence data for kinase inhibitor design.
  • The method is available as an open-source Python package and conda package.