Exploring biology with small organic molecules

Journal name:
Nature
Volume:
432,
Pages:
846–854
Date published:
DOI:
doi:10.1038/nature03196
Published online

Abstract

Small organic molecules have proven to be invaluable tools for investigating biological systems, but there is still much to learn from their use. To discover and to use more effectively new chemical tools to understand biology, strategies are needed that allow us to systematically explore ‘biological-activity space’. Such strategies involve analysing both protein binding of, and phenotypic responses to, small organic molecules. The mapping of biological-activity space using small molecules is akin to mapping the stars — uncharted territory is explored using a system of coordinates that describes where each new feature lies.

At a glance

Figures

  1. Comparison of diversity-oriented synthesis (DOS) and focused library synthesis (FLS).
    Figure 1: Comparison of diversity-oriented synthesis (DOS) and focused library synthesis (FLS).

    a, The goal of DOS is to create collections of compounds that are maximally diverse, thereby increasing the probability that different proteins will be targeted by different compounds in the library. In the example shown, Burke et al.65 created a library of compounds with different core structures (skeletons) starting from a common set of precursors (left). The six compounds on the right have different connectivity and are likely to interact with different proteins. b, The goal of FLS is to create analogues of the same core structure to optimize binding to a target or class of targets. If the compounds created are too diverse, they may lose their propensity to interact with the designated target protein. In this example, Sodeoka et al.15 created a collection of acyltetronic acids that act as phosphate mimetics and so are likely to inhibit phosphatases. Their synthesis resulted in a library of compounds that are identical except for the portion highlighted in orange.

  2. High-throughput-assay formats for detecting small molecule-protein interactions.
    Figure 2: High-throughput-assay formats for detecting small molecule–protein interactions.

    a, Small molecules can be covalently linked to a surface. Meanwhile, a test protein in solution is brought into contact with the surface. The protein binds to small molecules on the surface with high affinity. If the protein is tagged with a label, these interactions can be detected. b, Proteins can similarly be immobilized on a surface and brought into contact with a labelled small molecule in solution. High-affinity interactions between the small molecule and specific proteins can then be detected by imaging the locations to which the small molecule binds. c, DNA expression plasmids can be arrayed on a surface and cells subsequently plated on top of these expression plasmids. The cells take up the DNA and produce the proteins encoded by each plasmid. Thus, this method allows for the creation of a microarray of cells that overexpress defined proteins. When a labelled compound is brought into close proximity of the array, it localizes to where cells are overexpressing these high-affinity compound-binding proteins. d, Yeast three-hybrid system. Transcription factors that regulate gene expression can be divided into DNA-binding domains and transcription-activation domains. It is possible to fuse the complementary DNA sequence of a DNA-binding domain to the cDNA of an anchor protein that interacts with a known small molecule (anchor compound). The anchor compound is then chemically fused to a new test compound. If the cDNA of an activation domain is fused to the cDNA of a test protein, it is possible to determine whether the test protein interacts with the test compound with high affinity by determining whether transcription of a reporter gene has been activated.

  3. Examples of high-throughput phenotypic screens.
    Figure 3: Examples of high-throughput phenotypic screens.

    These are measurements of properties of cells that can be performed in a parallel fashion and so allow for the testing of many different chemicals at once. a, Fluorescence-based viability can be used to measure the number of living cells in a miniaturized test tube. The non-fluorescent dye calcein acetoxymethyl ester, shown schematically in blue, can be cleaved by intracellular esterases to create a fluorescent compound (shown in green). b, Such a dye can be used to measure the number of live cells in 384-well plates, which hold 384 individual miniature chambers for growing cells. For example, if a toxic gene is introduced, cells will die unless they are treated with a chemical that is able to prevent this cell death. In this example, the wells holding cells treated with such a chemical are bright green because the viability dye becomes fluorescent on being cleaved by esterases from live cells. c, A pattern of gene expression can be used as a signature of the state of a cell. In this example by Stegmeier et al.47, gene-expression signatures were obtained for: (1) human neutrophil precursors (HL-60 tumour cells, left) that have failed to differentiate and have become tumour cells; (2) primary acute myelogenous leukaemia (AML) cells from patients (right); and (3) differentiated human neutrophils (Neut, far right). A screen was performed to identify compounds that convert the signature of the HL-60 tumour cell line into the signature of differentiated neutrophils, with the goal of rendering the HL-60 tumour cells non-tumorigenic. Six compounds (of approximately 2,000 tested) were found to induce this switch in gene signatures (labelled ‘Chemical-treated HL-60, A to F’). Each row in this table shows the expression level of a different gene under these different conditions (the columns). The colour indicates whether expression in the sample is high (red) or low (blue). The six compounds shown revert the gene-expression pattern of HL-60 tumour cells to that of differentiated neutrophils.

  4. Using biological-activity matrices to determine the proteins that regulate phenotypes.
    Figure 4: Using biological-activity matrices to determine the proteins that regulate phenotypes.

    A hypothetical activity matrix for a library of nine kinase inhibitors. Each row lists the affinity (that is, the equilibrium dissociation constant, written in scientific notation, where 10e−6 represents 0.000001 M) of one compound for each of nine different kinase proteins. Smaller numbers indicate higher affinity. The affinities less than or equal to 10e−6 are highlighted in red because these correspond to high-affinity compounds for these targets. The kinase proteins are labelled K1 to K9. The same affinity matrix can be used to determine which kinases are involved in specific biological processes. In this hypothetical example, if the four compounds highlighted in blue are all capable of inhibiting the growth of a tumour cell line, the K1 kinase is probably responsible for the ability of these compounds to inhibit the growth of this cell line: this is the only kinase to be targeted by all four compounds.

Author information

Affiliations

  1. Department of Biological Sciences and Department of Chemistry, Columbia University, 614 Fairchild Center, MC 2406, New York, New York 10027, USA stockwell@biology.columbia.edu

    • Brent R. Stockwell

Competing financial interests

The author declares no competing financial interests.

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