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Functional annotation of chemical libraries across diverse biological processes

An Erratum to this article was published on 21 November 2017

An Erratum to this article was published on 21 November 2017

This article has been updated


Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound's mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

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Figure 1: Miniaturizing chemical-genetic profiling.
Figure 2: Two-dimensional hierarchical clustering of chemical-genetic interactions.
Figure 3: The functional landscape of diverse compound collections.
Figure 4: Functional signatures of compound collections.
Figure 5: Large-scale validation of predicted target processes.
Figure 6: Identification of compounds with dual targets.

Change history

  • 07 August 2017

    In the version of this article initially published online, there were several typographical errors introducing scientific inaccuracies. In Figure 5a, the compound name NPE1136 was incorrectly written NPD1136. In the Discussion, the number of strains tested, 5,000, was incorrectly given as 4,900. In the Online Methods, several strain descriptions in the sections "Constructing a genome-wide drug sensitive yeast deletion correction" and "Assessing compound hit rate of sensitized yeast strains" were incorrect or unclear (in particular, the MATa xxxΔ::kanMX yeast strain was indicated as MATa xxxΔ:kanMX); the unit mL was given in place of the correct μL in several places in the sections "Multi-parameter validation of cell wall targeting compounds" and "Zymolyase sensitivity assay"; references 14 and 15 were cited instead of references 8 and 17 in the section "Comparison with other chemical-genetic data sets section"; and the list of molecular descriptors calculated using PaDEL-Descriptor in the section "Computing molecular descriptors for all screened compounds" should have started with column L, not J. These errors have been corrected in all versions of the article.

  • 04 October 2017

    In the version of this article initially published, the artemisinin structure in Figure 4b was shown with incorrect stereochemistry. The error has been corrected in the HTML and PDF versions of the article.


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This work was supported by RIKEN Strategic Programs for R&D. J.S.P. and S.C.L. were funded by a RIKEN Foreign Postdoctoral Fellowship. S.W.S. is supported by an NSF Graduate Research Fellowship (00039202), an NIH Biotechnology training grant (T32GM008347), and a one-year BICB fellowship from the University of Minnesota. H.O. is a research fellow of the Japan Society for the Promotion of Science (JSPS). R.D., J.N., E.W., and C.L.M. are supported by National Institutes of Health Grants 1R01HG005084-01A1, 1R01GM104975-01, and R01HG005853 and National Science Foundation Grant DBI 0953881. C.B. and Y.O. are supported by JSPS KAKENHI Grant Numbers 15H04483. C.B. and B.A. were supported by the Canadian Institutes of Health Research, grants FDN-143264 and FDN-143265, respectively. C.L.M., C.B., M.C., J.L., and B.A. are supported by the Canadian Institute for Advanced Research Genetic Networks Program. Y.O. is supported by Ministry of Education, Culture, Sports, Science and Technology, Japan Grant for Scientific Research 24370002 and JSPS KAKENHI Grant Numbers 15H04402. M. Yoshida is supported by JSPS KAKENHI Grant Number 26221204. A.B. is supported by a Lewis Sigler fellowship at Princeton University. G.W.B. and N.P.T. are supported by Canadian Cancer Society Research Institute impact grant 702310. K.S. is supported by The Core Research for Evolutional Science and Technology (CREST) from the Japan Science and Technology Agency (JST). We thank Astellas Pharma, Inc. (Tokyo, Japan) for their kind gift of micafungin. We thank T. Saito for help with NPDepo compound access. Sequencing was provided by RIKEN Center for Life Science Technologies, Division of Genomic Technologies, Genome Network Analysis Support Facility (GeNAS) RIKEN CLST and the University of Chicago.

Author information

Authors and Affiliations



C.B., M. Yoshida, C.L.M., J.S.P. and S.C.L. conceived the project. J.S.P., S.C.L., R.D., and S.W.S. designed the chemical genomic screens. J.S.P., S.C.L., J.M.B., R.O., M. Yoshimura, Y.Y., E. D.-A. performed the chemical genomic experiments. C.L.M., R.D., S.W.S., J.N., H.S., and E.W. designed the analysis software and performed analysis. G.W.B., N.P.T., and S.C.L. performed cell cycle experiments. J.P., K.A., M.A.L., and A.B. designed the sensitized yeast. Y.O., H.O., A.A.G., K.K. performed validation of cell wall targeting compounds. R.D. and M.C. designed the diagnostic mutant collection. H.O. and H.H. provided and curated NPDepo compounds. K.S. and B.A. provided analysis. J.v.L. edited the figures of the manuscript. The manuscript was written by J.S.P., S.C.L., C.L.M., and C.B. with input and editing from all authors.

Corresponding authors

Correspondence to Hiroyuki Osada, Minoru Yoshida, Chad L Myers or Charles Boone.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Results, Supplementary Figures 1–15 and Supplementary Tables 1–3 (PDF 2375 kb)

Supplementary Note

Supplementary Note (PDF 638 kb)

Supplementary Dataset 1

Number of compounds in each collection causing >20% inhibition of growth against the yeast screening pool. (XLSX 18 kb)

Supplementary Dataset 2

Diagnostic screening pool deletion mutant strain members. (XLSX 160 kb)

Supplementary Dataset 3

768 multiplex tag primers. (XLS 149 kb)

Supplementary Dataset 4

Names, structures, doses, and chemical properties of screened compounds. (XLSX 4002 kb)

Supplementary Dataset 5

Average chemical genomic profile of 11,983 compounds. (XLSX 33280 kb)

Supplementary Dataset 6

Average chemical genomic profile of 1,522 high-confidence compounds. (XLSX 4207 kb)

Supplementary Dataset 7

Number of positive (CG score ≥+2.5), negative (CG score ≤−2.5), and all chemical genetic interactions per strain for all screened compounds. (XLSX 21 kb)

Supplementary Dataset 8

Compounds passing our false discovery rate filter (high-confidence set) with top 3 GO process predictions, bioprocess predictions, and driver genes. (XLSX 892 kb)

Supplementary Dataset 9

Interaction degree (positive and negative interactions) of the 275 strains from the RIKEN subset of the data also present in the S. cerevisiae genetic interaction array. Strains classified as high degree were removed to obtain “no-high-degree” process-level predictions, as were strains classified as low degree to obtain “no-low-degree” predictions. (XLSX 60 kb)

Supplementary Dataset 10

Comparison of the compounds, and their predictions, discovered with high confidence using all, only negative, and only positive chemical-genetic interactions from the RIKEN subset of the data. Each “Negative-all” and “Positive-all” profile contained all interactions with scores below or above zero, respectively, while the pair of “Negative-equal” and “Positive-equal” profiles for each compound contained an equal number of negative and positive interactions, respectively, above a score magnitude cutoff of 1. (XLSX 61 kb)

Supplementary Dataset 11

Growth inhibition by 24 bioactive compounds (10 μg/mL) of the screening pool versus the gtr1Δ deletion mutant (MATα pdr1Δ::natMX pdr3Δ::KI.URA3 snq2Δ::KI.LEU2 gtr1Δ::kanMX can1Δ::STE2pr-Sp_his5 lyp1Δ his3Δ1 leu2Δ0 ura3Δ0 met15Δ0). (XLSX 9 kb)

Supplementary Dataset 12

Bioprocesses and associated GO annotation for compounds of the NIH-CC. (XLSX 27 kb)

Supplementary Dataset 13

Functional enrichments and known targets of the GSK kinase inhibitor collection. (XLS 58 kb)

Supplementary Dataset 14

Cellular compartments with enrichment or depletion of targets across compound collections. (XLSX 33 kb)

Supplementary Dataset 15

Functional enrichments among substructures. “All compounds with substructure” shows the compounds containing the substructure for that given row, and “hit compounds with substructure” shows the subset of those compounds that is annotated to any of the GO terms in that row. Substructures can be visualized directly at or indirectly by copy-and-pasting the semicolon-delimited compound sets into the search bar at (XLSX 3503 kb)

Supplementary Dataset 16

Validation of predicted biological process targets of 67 compounds using cell cycle analysis. (XLSX 13 kb)

Supplementary Dataset 17

Validation of cell wall–targeting compounds. (XLSX 14 kb)

Supplementary Dataset 18

Prioritized functional diversity set from public collections screened. (XLSX 15 kb)

Supplementary Dataset 19

Prioritized compound sets for 17 bioprocesses. Each high-confidence compound was placed into the group corresponding to its top bioprocess prediction in Supplementary Table 7. (XLSX 542 kb)

Supplementary Dataset 20

Number of high confidence compounds per bioprocess, and enrichments/depletions across the tested compound collections. (XLSX 16 kb)

Supplementary Dataset 21

Proportional differences in GO annotations versus background expectation for each library/group of compounds. (XLSX 641 kb)

Supplementary Dataset 22

Mapping of Lee et al. major chemical-genetic signatures to bioprocesses in this study. Pearson correlation coefficients (PCCs) > 0.3 between Lee et al. compounds and high confidence compounds (this study) were mapped to major chemical-genetic signatures (Lee et al.) or bioprocesses (this study) to generate a mapping from each major chemical-genetic signature to the bioprocesses that contained similar compounds. Only the most confident mapping, as determined by relative confidence (the product of the fractions of compounds in each set that contribute to the similarities) is shown. (XLSX 59 kb)

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Piotrowski, J., Li, S., Deshpande, R. et al. Functional annotation of chemical libraries across diverse biological processes. Nat Chem Biol 13, 982–993 (2017).

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