Statement of need

The KLIFS resource [Kanev_2021] contains information about kinases, structures, ligands, interaction fingerprints, and bioactivities. KLIFS thereby focuses especially on the ATP binding site, defined as a set of 85 residues and aligned across all structures using a multiple sequence alignment [vanLinden_2014]. Fetching, filtering, and integrating the KLIFS content on a larger scale into Python-based pipelines is currently not straight-forward, especially for users without a background in online queries. Furthermore, switching between data queries from a local KLIFS download and the remote KLIFS database is not readily possible.

OpenCADD-KLIFS is aimed at current and future users of the KLIFS database who seek to integrate kinase resources into Python-based research projects. With OpenCADD-KLIFS, KLIFS data can be queried either locally from a KLIFS download or remotely from the KLIFS webserver. The presented module provides identical APIs for the remote and local queries and streamlines all output into standardized Pandas DataFrames Pandas to allow for easy and quick downstream data analyses (Figure 1). This Pandas-focused setup is ideal if you work with Jupyter notebooks [Kluyver_2016].


Figure 1: OpenCADD-KLIFS fetches KLIFS data offline from a KLIFS download or online from the KLIFS database and formats the output as user-friendly Pandas DataFrames.


Kanev et al., (2021), KLIFS: an overhaul after the first 5 years of supporting kinase research, Nucleic Acids Research, 49(D1), D562–D569, doi:10.1093/nar/gkaa895.


van Linden et al., (2014) KLIFS: A Knowledge-Based Structural Database To Navigate Kinase–Ligand Interaction Space, Journal of Medicinal Chemistry, 57(2), 249-277, doi:10.1021/jm400378w.


Kluyver et al., (2016), Jupyter Notebooks – a publishing format for reproducible computational workflows, In Positioning and Power in Academic Publishing: Players, Agents and Agendas. IOS Press. pp. 87-90, doi:10.3233/978-1-61499-649-1-87.