Identifying Unexpected Therapeutic Targets via Chemical-Protein Interactome
Citation: Yang L, Chen J, Shi L, Hudock MP, Wang K, et al. (
Identifying Unexpected Therapeutic Targets via Chemical-Protein Interactome
Lun Yang 0
Jian Chen 0
Leming Shi 0
Michael P. Hudock 0
Kejian Wang 0
Lin He 0
Bostjan Kobe, University of Queensland, Australia
0 1 Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University , Shanghai , China , 2 Institutes of Biomedical Sciences, Fudan University , Shanghai , China , 3 National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, United States of America, 4 Institute for Nutritional Sciences, Shanghai Institute of Biological Sciences, Chinese Academy of Sciences , Shanghai , China
Drug medications inevitably affect not only their intended protein targets but also other proteins as well. In this study we examined the hypothesis that drugs that share the same therapeutic effect also share a common therapeutic mechanism by targeting not only known drug targets, but also by interacting unexpectedly on the same cryptic targets. By constructing and mining an Alzheimer's disease (AD) drug-oriented chemical-protein interactome (CPI) using a matrix of 10 drug molecules known to treat AD towards 401 human protein pockets, we found that such cryptic targets exist. We recovered from CPI the only validated therapeutic target of AD, acetylcholinesterase (ACHE), and highlighted several other putative targets. For example, we discovered that estrogen receptor (ER) and histone deacetylase (HDAC), which have recently been identified as two new therapeutic targets of AD, might already have been targeted by the marketed AD drugs. We further established that the CPI profile of a drug can reflect its interacting character towards multi-protein sets, and that drugs with the same therapeutic attribute will share a similar interacting profile. These findings indicate that the CPI could represent the landscape of chemical-protein interactions and uncover ''behind-the-scenes'' aspects of the therapeutic mechanisms of existing drugs, providing testable hypotheses of the key nodes for network pharmacology or brand new drug targets for one-target pharmacology paradigm.
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Competing Interests: The authors have declared that no competing interests exist.
Drug molecules inevitably affect not only their intended protein
targets but also other off-target proteins as well [1]. These
unexpected targets could, in some cases, mediate the physiological
effect of a drug, even if the drug is designed specifically to target
one particular protein [2]. Several antipsychotics, for example,
could trigger similar downstream molecular events when added to
the cell culture even without their target, the dopamine receptor,
expressed in it [3]. It is generally accepted that chemical-protein
interaction is the primary step in triggering molecular events in the
biological system when a drug is administered. The identification
of unexpected drug-protein interactions could therefore lead to the
discovery of new therapeutic targets and therapeutic pathways.
There are several strategies in mining such unexpected off-targets,
e.g., building new chemical-protein linkages in the known
therapeutic target space [2,4], investigating the pocket shape
[5,6] or sequence identity [7] between the off-target and the
known drug target. All these strategies operate on the narrow
space of the known drug targets, which represent only a small
portion of all human protein space.
Several fishing techniques such as BIACORE [8], drug
affinity pull-down [9], drug affinity responsive target stability [10]
and quantitative proteomics based affinity enrichment [11] can
also assess the unexpected drug-protein interactions from a wider
protein space. Although not offering a systematic and convincing
evaluation of specificity and sensitivity in identifying true or false
bindings [12,13], docking one drug to a multi-protein set has
been a logical approach to fishing unexpected targets. However,
none of the fishing techniques described above offer the
dramatic progress recently achieved by transcriptomics [3],
metabolomics [14] and proteomics [15] in systematically
uncovering the molecular events following the administration of
a drug into the biological system. One reason might be the
inaccuracy of the scoring functions in the fishing methodologies.
There is no guarantee, for instance, that if the docking score of
drug A to protein P1 is lower than A to P2, that P1 has a greater
affinity to A than P2 [16]. We therefore hypothesized that
investigating the relative strengths of chemical-protein
interactions from the -omics viewpoint would be much more
meaningful than merely comparing the absolute values of a
drugs effect on two proteins based on some certain scoring
function. Our second hypothesis was that drugs sharing the same
therapeutic effect also share the same therapeutic mechanisms by
targeting no (...truncated)