Abstract
BackgroundDetailed and systematic understanding of the biological effects of millions of available compounds on living cells is a significant challenge. As most compounds impact multiple targets and pathways, traditional methods for analyzing structure-function relationships are not comprehensive enough. Therefore more advanced integrative models are needed for predicting biological effects elicited by specific chemical features. As a step towards creating such computational links we developed a data-driven chemical systems biology approach to comprehensively study the relationship of 76 structural 3D-descriptors (VolSurf, chemical space) of 1159 drugs with the microarray gene expression responses (biological space) they elicited in three cancer cell lines. The analysis covering 11350 genes was based on data from the Connectivity Map. We decomposed the biological response profiles into components, each linked to a characteristic chemical descriptor profile.ResultsIntegrated analysis of both the chemical and biological space was more informative than either dataset alone in predicting drug similarity as measured by shared protein targets. We identified ten major components that link distinct VolSurf chemical features across multiple compounds to specific cellular responses. For example, component 2 (hydrophobic properties) strongly linked to DNA damage response, while component 3 (hydrogen bonding) was associated with metabolic stress. Individual structural and biological features were often linked to one cell line only, such as leukemia cells (HL-60) specifically responding to cardiac glycosides.ConclusionsIn summary, our approach identified several novel links between specific chemical structure properties and distinct biological responses in cells incubated with these drugs. Importantly, the analysis focused on chemical-biological properties that emerge across multiple drugs. The decoding of such systematic relationships is necessary to build better models of drug effects, including unanticipated types of molecular properties having strong biological effects.
Highlights
Detailed and systematic understanding of the biological effects of millions of available compounds on living cells is a significant challenge
This is the standard paradigm in Quantitative Structure Activity Relationship (QSAR) studies, where multivariate mathematical models are used for modeling the relationships between a set of physiochemical or structural properties and biological activity
Genome-wide massively multivariate description of the cellular responses caused by the drugs, such as in the Connectivity Map program (CMap; Lamb et al, [4]), requires new kinds of tools for analysis and interpretation
Summary
Detailed and systematic understanding of the biological effects of millions of available compounds on living cells is a significant challenge. As a step towards creating such computational links we developed a data-driven chemical systems biology approach to comprehensively study the relationship of 76 structural 3D-descriptors (VolSurf, chemical space) of 1159 drugs with the microarray gene expression responses (biological space) they elicited in three cancer cell lines. The mechanism of action of drugs at the biochemical level has typically been studied by investigating specific chemical properties of the drug and the biological properties of its specific target [1,2]. Biological responses at the cellular level are diverse and each drug typically binds to a multitude of targets in the cells and elicits a number of other offtarget effects. Genome-wide massively multivariate description of the cellular responses caused by the drugs, such as in the Connectivity Map program (CMap; Lamb et al, [4]), requires new kinds of tools for analysis and interpretation
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