Chapter 7
Bioinformatics Approach to Identify Novel AMPK Targets
Brendan Gongol, Traci Marin, David A. Johnson, and John Y.-J. Shyy
Abstract
In silico analysis of Big Data is a useful tool to identify putative kinase targets as well as nodes of signaling
cascades that are difficult to discover by traditional single molecule experimentation. System approaches
that use a multi-tiered investigational methodology have been instrumental in advancing our understand-
ing of cellular mechanisms that result in phenotypic changes. Here, we present a bioinformatics approach to
identify AMP-activated protein kinase (AMPK) target proteins on a proteome-wide scale and an in vitro
method for preliminary validation of these targets. This approach offers an initial screening for the
identification of AMPK targets that can be further validated using mutagenesis and molecular biology
techniques.
Key wordsAMPK target validation, AMPK kinase assay, Consensus sequence mapping, R program-
ming, Bioinformatics, Systems biology1 Introduction
In silico bioinformatics provides powerful tools and methods to
delineate novel insights from complex biological datasets (Big
Data) such as signaling cascades emanating from posttranslational
modifying enzymes including kinases [1, 2]. AMP-activated pro-
tein kinase (AMPK), a master regulator of cellular stress response
and energy status, is a serine/threonine kinase that is a potential
actionable clinical target [1, 2]. However, because knowledge of its
target spectrum is incomplete, consensus sequence mapping can be
utilized to reveal unrecognized targets. The most stringent phos-
phorylation consensus sequence (PCS) of AMPK isβφβXXXS/
TXXXφ, where hydrophobic, φ ¼ M, L, I, F, or V; basic,
β¼R, K, or H; X¼any amino acid; and S/T¼phosphorylation
site [ 3, 4]. However, AMPK phosphorylation sites have been iden-
tified that lack the basic residues [1]. Several databases have been
established that predict putative AMPK targets including Scansite
[5]. A major hurdle in the identification of AMPK targets is the
structural variations due to Brownian motion that occur betweenDietbert Neumann and Benoit Viollet (eds.),AMPK:MethodsandProtocols, Methods in Molecular Biology, vol. 1732,
https://doi.org/10.1007/978-1-4939-7598-3_7,©Springer Science+Business Media, LLC 2018
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