KLIFS: a structural kinase-ligand interaction database

By Albert J. Kooistra, Georgi K. Kanev, O. Linden, R. Leurs, I. D. Esch, C. Graaf. 2015

157 citations recorded by the source corpus.

Protein kinases play a crucial role in cell signaling and are important drug targets in several therapeutic areas. The KLIFS database contains detailed structural kinase-ligand interaction information derived from all (>2900) structures of catalytic domains of human and mouse protein kinases deposited in the Protein Data Bank in order to provide insights into the structural determinants of kinase-ligand binding and selectivity. The kinase structures have been processed in a consistent manner by systematically analyzing the structural features and molecular interaction fingerprints (IFPs) of a predefined set of 85 binding site residues with bound ligands. KLIFS has been completely rebuilt and extended (>65% more structures) since its first release as a data set, including: novel automated annotation methods for (i) the assessment of ligand-targeted subpockets and the analysis of (ii) DFG and (iii) αC-helix conformations; improved and automated protocols for (iv) the generation of sequence/structure alignments, (v) the curation of ligand atom and bond typing for accurate IFP analysis and (vi) weekly database updates. KLIFS is now accessible via a website (http://klifs.vu-compmedchem.nl) that provides a comprehensive visual presentation of different types of chemical, biological and structural chemogenomics data, and allows the user to easily access, compare, search and download the data.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Sabrina Jaeger
2. Samo Turk
3. Simone Fulle
4. Robert Preissner

In Molecular Biology

1. Sabrina Jaeger
2. Samo Turk
3. Mathias Wilhelm
4. B. Kuster
5. Hannes Hahne
6. Simone Fulle
7. S. Schneider

In Pharmacology

1. Sabrina Jaeger
2. Samo Turk
3. Simone Fulle

Citations in the corpus

• Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition (2018, positive): “23 The length of the target descriptor was adjusted to 2048 using a WTA-hash function to match the dimensionality of the Morgan FP. 24 Benchmarking data sets .”
• The target landscape of clinical kinase drugs (2017, background): “S4C) (5, 23, 24) confirmed that type 2 inhibitors (that is, KI binding to the inactive kinase conformation) are not necessarily more selective than type 1 inhibitors (that is, KI binding to the active kinase conformation;median CATDS of 0.”