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Systematic analysis of genome-wide fitness data in yeast reveals novel gene function and drug action.

Hillenmeyer ME, Ericson E, Davis RW, Nislow C, Koller D, Giaever G - Genome Biol. (2010)

Bottom Line: Co-inhibitory compounds were weakly correlated by structure and therapeutic class.We developed an algorithm predicting protein targets of chemical compounds and verified its accuracy with experimental testing.Fitness data provide a novel, systems-level perspective on the cell.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biomedical Informatics, 251 Campus Drive, MSOB, Stanford University, Stanford, CA 94305, USA. maureenh@stanford.edu

ABSTRACT
We systematically analyzed the relationships between gene fitness profiles (co-fitness) and drug inhibition profiles (co-inhibition) from several hundred chemogenomic screens in yeast. Co-fitness predicted gene functions distinct from those derived from other assays and identified conditionally dependent protein complexes. Co-inhibitory compounds were weakly correlated by structure and therapeutic class. We developed an algorithm predicting protein targets of chemical compounds and verified its accuracy with experimental testing. Fitness data provide a novel, systems-level perspective on the cell.

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The limited correlation between Tanimoto structural similarity and co-fitness in the heterozygous and homozygous datasets suggests that chemical structure influences inhibition patterns but does not exclusively define them. Each point represents a pair of compounds; to allow for comparison between (a) heterozygous and (b) homozygous datasets, for this figure we used only pairs of compounds that were tested in both datasets.
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Figure 3: The limited correlation between Tanimoto structural similarity and co-fitness in the heterozygous and homozygous datasets suggests that chemical structure influences inhibition patterns but does not exclusively define them. Each point represents a pair of compounds; to allow for comparison between (a) heterozygous and (b) homozygous datasets, for this figure we used only pairs of compounds that were tested in both datasets.

Mentions: The clusters highlighted in Figure 2 suggest that co-inhibition can reveal both shared structure and common therapeutic use. We observed a weak correlation between structural similarity and co-inhibition (Figure 3), suggesting that chemical structure may influence patterns of inhibition, but further data on this topic are needed. We note that the compounds used to collect the genome-wide fitness data were chosen to be as diverse as possible; a set of compounds that were more similar would be expected to show a greater correlation between co-inhibition and structural similarity. We also found significant relationships between shared Anatomical Therapeutic Chemical (ATC) therapeutic class [39] and co-fitness profiles, especially for the homozygous dataset (P < 3e-9; Figure 4). This finding suggests that a drug's behavior in the yeast chemogenomic assays can be predictive of its therapeutic potential in humans. We noted a correlation between chemical structure and therapeutic class, but a compound's structure alone did not explain the therapeutic relation to co-inhibition. For pairs of compounds that both were positively co-inhibiting (correlation >0) and shared a therapeutic class, more than 70% did not share significant structural similarity (that is, Tanimoto similiarity <0.2). This observation indicates that compounds with very different structures can still produce similar genome-wide effects. This finding can be attributed to structurally diverse compounds that inhibit different proteins within the same pathway, or to different compound structures that inhibit the same target [45,46]. Co-inhibition interactions are available for visualization [32] and download [33].


Systematic analysis of genome-wide fitness data in yeast reveals novel gene function and drug action.

Hillenmeyer ME, Ericson E, Davis RW, Nislow C, Koller D, Giaever G - Genome Biol. (2010)

The limited correlation between Tanimoto structural similarity and co-fitness in the heterozygous and homozygous datasets suggests that chemical structure influences inhibition patterns but does not exclusively define them. Each point represents a pair of compounds; to allow for comparison between (a) heterozygous and (b) homozygous datasets, for this figure we used only pairs of compounds that were tested in both datasets.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2864570&req=5

Figure 3: The limited correlation between Tanimoto structural similarity and co-fitness in the heterozygous and homozygous datasets suggests that chemical structure influences inhibition patterns but does not exclusively define them. Each point represents a pair of compounds; to allow for comparison between (a) heterozygous and (b) homozygous datasets, for this figure we used only pairs of compounds that were tested in both datasets.
Mentions: The clusters highlighted in Figure 2 suggest that co-inhibition can reveal both shared structure and common therapeutic use. We observed a weak correlation between structural similarity and co-inhibition (Figure 3), suggesting that chemical structure may influence patterns of inhibition, but further data on this topic are needed. We note that the compounds used to collect the genome-wide fitness data were chosen to be as diverse as possible; a set of compounds that were more similar would be expected to show a greater correlation between co-inhibition and structural similarity. We also found significant relationships between shared Anatomical Therapeutic Chemical (ATC) therapeutic class [39] and co-fitness profiles, especially for the homozygous dataset (P < 3e-9; Figure 4). This finding suggests that a drug's behavior in the yeast chemogenomic assays can be predictive of its therapeutic potential in humans. We noted a correlation between chemical structure and therapeutic class, but a compound's structure alone did not explain the therapeutic relation to co-inhibition. For pairs of compounds that both were positively co-inhibiting (correlation >0) and shared a therapeutic class, more than 70% did not share significant structural similarity (that is, Tanimoto similiarity <0.2). This observation indicates that compounds with very different structures can still produce similar genome-wide effects. This finding can be attributed to structurally diverse compounds that inhibit different proteins within the same pathway, or to different compound structures that inhibit the same target [45,46]. Co-inhibition interactions are available for visualization [32] and download [33].

Bottom Line: Co-inhibitory compounds were weakly correlated by structure and therapeutic class.We developed an algorithm predicting protein targets of chemical compounds and verified its accuracy with experimental testing.Fitness data provide a novel, systems-level perspective on the cell.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biomedical Informatics, 251 Campus Drive, MSOB, Stanford University, Stanford, CA 94305, USA. maureenh@stanford.edu

ABSTRACT
We systematically analyzed the relationships between gene fitness profiles (co-fitness) and drug inhibition profiles (co-inhibition) from several hundred chemogenomic screens in yeast. Co-fitness predicted gene functions distinct from those derived from other assays and identified conditionally dependent protein complexes. Co-inhibitory compounds were weakly correlated by structure and therapeutic class. We developed an algorithm predicting protein targets of chemical compounds and verified its accuracy with experimental testing. Fitness data provide a novel, systems-level perspective on the cell.

Show MeSH