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Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens.

Lo YC, Senese S, Li CM, Hu Q, Huang Y, Damoiseaux R, Torres JZ - PLoS Comput. Biol. (2015)

Bottom Line: Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling.Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60-70%).Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America; Program in Bioengineering, University of California, Los Angeles, Los Angeles, California, United States of America.

ABSTRACT
Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60-70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/).

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Network-based elucidation of a novel tubulin-targeting chemotype.(A) In-vitro tubulin polymerization assays were used to test the effect of the 212 mitotic compounds on microtubule assembly at 50μM concentration. The end-point absorbance, based on change in OD (dOD), was used to quantify the degree of microtubule polymerization and was converted to percentage fold change relative to DMSO (0%). Among the tested compounds, 134 compounds (63%) had an effect (>20% fold change) on tubulin polymerization. (B) Chemical similarity sub-network consisting of 7 novel anti-tubulin ligands based on a phenyl-sulfanyl-thiazol-acetamide privilege scaffold. The connected analogues within the network showed a consensus tubulin destabilization effect where each step in the path (red) of the sub-network corresponded to a minimum structural change correlating with the observed structure-activity-relationship (SAR). (C) Docking of compound 6 into the β-tubulin colchicine-binding site based on the crystal structure (PDB: 1AS0) exhibited a similar predicted binding mode to colchicine. (D) Ligand alignment between compound 6 and colchicine identified a conserved pharmacophore critical for ligand binding, including the 2 and 10-methoxy groups and a 9-keto group that interacts with Cys-241 of beta tubulin and Val-181 (not shown) of alpha tubulin respectively. (E) Hydrophobicity map of docked compound 6 within the colchicine-binding site revealed a hydrophobic sub-pocket enclosed by Leu-248 and Lys-352. The model showed that compounds 7 and 8 enhance binding affinity by fitting the N-propyl and N-phenyl group in the hydrophobic cavity, consistent with the SAR analysis. See S11 Fig for molecular modeling of compounds 6–12. (F) The most potent compound 8 was tested for direct colchicine site binding using mass spectrometry competitive binding assays. Compound 8 competed strongly with colchicine for the colchicine-binding site, similar to the colchicine-site binder podophyllotoxin. Note that the negative control vincristine did not compete. (G) Immunofluorescence microscopy images of HeLa cells treated with DMSO, taxol, colchicine or compounds 6–8 for 20 hours. Cells were fixed and stained for DNA (Hoechst 33342) and tubulin (primary rat anti-tubulin antibodies and secondary anti-rat Cy3 antibodies). Scale = 5 μm. Note that colchicine, and compounds 6–8 depolymerize microtubules. See S10 Fig for compound 6–12 induced phenotypes.
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pcbi.1004153.g005: Network-based elucidation of a novel tubulin-targeting chemotype.(A) In-vitro tubulin polymerization assays were used to test the effect of the 212 mitotic compounds on microtubule assembly at 50μM concentration. The end-point absorbance, based on change in OD (dOD), was used to quantify the degree of microtubule polymerization and was converted to percentage fold change relative to DMSO (0%). Among the tested compounds, 134 compounds (63%) had an effect (>20% fold change) on tubulin polymerization. (B) Chemical similarity sub-network consisting of 7 novel anti-tubulin ligands based on a phenyl-sulfanyl-thiazol-acetamide privilege scaffold. The connected analogues within the network showed a consensus tubulin destabilization effect where each step in the path (red) of the sub-network corresponded to a minimum structural change correlating with the observed structure-activity-relationship (SAR). (C) Docking of compound 6 into the β-tubulin colchicine-binding site based on the crystal structure (PDB: 1AS0) exhibited a similar predicted binding mode to colchicine. (D) Ligand alignment between compound 6 and colchicine identified a conserved pharmacophore critical for ligand binding, including the 2 and 10-methoxy groups and a 9-keto group that interacts with Cys-241 of beta tubulin and Val-181 (not shown) of alpha tubulin respectively. (E) Hydrophobicity map of docked compound 6 within the colchicine-binding site revealed a hydrophobic sub-pocket enclosed by Leu-248 and Lys-352. The model showed that compounds 7 and 8 enhance binding affinity by fitting the N-propyl and N-phenyl group in the hydrophobic cavity, consistent with the SAR analysis. See S11 Fig for molecular modeling of compounds 6–12. (F) The most potent compound 8 was tested for direct colchicine site binding using mass spectrometry competitive binding assays. Compound 8 competed strongly with colchicine for the colchicine-binding site, similar to the colchicine-site binder podophyllotoxin. Note that the negative control vincristine did not compete. (G) Immunofluorescence microscopy images of HeLa cells treated with DMSO, taxol, colchicine or compounds 6–8 for 20 hours. Cells were fixed and stained for DNA (Hoechst 33342) and tubulin (primary rat anti-tubulin antibodies and secondary anti-rat Cy3 antibodies). Scale = 5 μm. Note that colchicine, and compounds 6–8 depolymerize microtubules. See S10 Fig for compound 6–12 induced phenotypes.

Mentions: Based on target prediction, we selected microtubules (α and β-tubulin) as our target for in-vitro validation. To test CSNAP’s prediction that 51 of the 212 mitotic compounds were targeting microtubules, we re-acquired all 212 compounds and tested their ability to perturb microtubule polymerization (stabilize or destabilize microtubules) in an in-vitro microtubule polymerization assay at 50μM concentration (Fig. 5A). The end-point absorbance (dOD) was used to quantify the degree of microtubule polymerization and was converted to percent fold change (F) relative to DMSO drug vehicle (0%), as previously described (Fig. 5A and S1 Text) [63]. Of the 51 compounds predicted to be targeting microtubules, 36 had more than 20% fold change in microtubule polymerization and 14 had no measurable effect (S5B Fig). Thus CSNAP was able to predict the targets of this set with > 70% accuracy. In addition, in-vitro testing led to the discovery of 96 additional compounds for a total of 132 anti-tubulin agents, including structurally diverse compounds covering ~54 novel chemotypes not discovered in previous chemical screens (S3 Table).


Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens.

Lo YC, Senese S, Li CM, Hu Q, Huang Y, Damoiseaux R, Torres JZ - PLoS Comput. Biol. (2015)

Network-based elucidation of a novel tubulin-targeting chemotype.(A) In-vitro tubulin polymerization assays were used to test the effect of the 212 mitotic compounds on microtubule assembly at 50μM concentration. The end-point absorbance, based on change in OD (dOD), was used to quantify the degree of microtubule polymerization and was converted to percentage fold change relative to DMSO (0%). Among the tested compounds, 134 compounds (63%) had an effect (>20% fold change) on tubulin polymerization. (B) Chemical similarity sub-network consisting of 7 novel anti-tubulin ligands based on a phenyl-sulfanyl-thiazol-acetamide privilege scaffold. The connected analogues within the network showed a consensus tubulin destabilization effect where each step in the path (red) of the sub-network corresponded to a minimum structural change correlating with the observed structure-activity-relationship (SAR). (C) Docking of compound 6 into the β-tubulin colchicine-binding site based on the crystal structure (PDB: 1AS0) exhibited a similar predicted binding mode to colchicine. (D) Ligand alignment between compound 6 and colchicine identified a conserved pharmacophore critical for ligand binding, including the 2 and 10-methoxy groups and a 9-keto group that interacts with Cys-241 of beta tubulin and Val-181 (not shown) of alpha tubulin respectively. (E) Hydrophobicity map of docked compound 6 within the colchicine-binding site revealed a hydrophobic sub-pocket enclosed by Leu-248 and Lys-352. The model showed that compounds 7 and 8 enhance binding affinity by fitting the N-propyl and N-phenyl group in the hydrophobic cavity, consistent with the SAR analysis. See S11 Fig for molecular modeling of compounds 6–12. (F) The most potent compound 8 was tested for direct colchicine site binding using mass spectrometry competitive binding assays. Compound 8 competed strongly with colchicine for the colchicine-binding site, similar to the colchicine-site binder podophyllotoxin. Note that the negative control vincristine did not compete. (G) Immunofluorescence microscopy images of HeLa cells treated with DMSO, taxol, colchicine or compounds 6–8 for 20 hours. Cells were fixed and stained for DNA (Hoechst 33342) and tubulin (primary rat anti-tubulin antibodies and secondary anti-rat Cy3 antibodies). Scale = 5 μm. Note that colchicine, and compounds 6–8 depolymerize microtubules. See S10 Fig for compound 6–12 induced phenotypes.
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pcbi.1004153.g005: Network-based elucidation of a novel tubulin-targeting chemotype.(A) In-vitro tubulin polymerization assays were used to test the effect of the 212 mitotic compounds on microtubule assembly at 50μM concentration. The end-point absorbance, based on change in OD (dOD), was used to quantify the degree of microtubule polymerization and was converted to percentage fold change relative to DMSO (0%). Among the tested compounds, 134 compounds (63%) had an effect (>20% fold change) on tubulin polymerization. (B) Chemical similarity sub-network consisting of 7 novel anti-tubulin ligands based on a phenyl-sulfanyl-thiazol-acetamide privilege scaffold. The connected analogues within the network showed a consensus tubulin destabilization effect where each step in the path (red) of the sub-network corresponded to a minimum structural change correlating with the observed structure-activity-relationship (SAR). (C) Docking of compound 6 into the β-tubulin colchicine-binding site based on the crystal structure (PDB: 1AS0) exhibited a similar predicted binding mode to colchicine. (D) Ligand alignment between compound 6 and colchicine identified a conserved pharmacophore critical for ligand binding, including the 2 and 10-methoxy groups and a 9-keto group that interacts with Cys-241 of beta tubulin and Val-181 (not shown) of alpha tubulin respectively. (E) Hydrophobicity map of docked compound 6 within the colchicine-binding site revealed a hydrophobic sub-pocket enclosed by Leu-248 and Lys-352. The model showed that compounds 7 and 8 enhance binding affinity by fitting the N-propyl and N-phenyl group in the hydrophobic cavity, consistent with the SAR analysis. See S11 Fig for molecular modeling of compounds 6–12. (F) The most potent compound 8 was tested for direct colchicine site binding using mass spectrometry competitive binding assays. Compound 8 competed strongly with colchicine for the colchicine-binding site, similar to the colchicine-site binder podophyllotoxin. Note that the negative control vincristine did not compete. (G) Immunofluorescence microscopy images of HeLa cells treated with DMSO, taxol, colchicine or compounds 6–8 for 20 hours. Cells were fixed and stained for DNA (Hoechst 33342) and tubulin (primary rat anti-tubulin antibodies and secondary anti-rat Cy3 antibodies). Scale = 5 μm. Note that colchicine, and compounds 6–8 depolymerize microtubules. See S10 Fig for compound 6–12 induced phenotypes.
Mentions: Based on target prediction, we selected microtubules (α and β-tubulin) as our target for in-vitro validation. To test CSNAP’s prediction that 51 of the 212 mitotic compounds were targeting microtubules, we re-acquired all 212 compounds and tested their ability to perturb microtubule polymerization (stabilize or destabilize microtubules) in an in-vitro microtubule polymerization assay at 50μM concentration (Fig. 5A). The end-point absorbance (dOD) was used to quantify the degree of microtubule polymerization and was converted to percent fold change (F) relative to DMSO drug vehicle (0%), as previously described (Fig. 5A and S1 Text) [63]. Of the 51 compounds predicted to be targeting microtubules, 36 had more than 20% fold change in microtubule polymerization and 14 had no measurable effect (S5B Fig). Thus CSNAP was able to predict the targets of this set with > 70% accuracy. In addition, in-vitro testing led to the discovery of 96 additional compounds for a total of 132 anti-tubulin agents, including structurally diverse compounds covering ~54 novel chemotypes not discovered in previous chemical screens (S3 Table).

Bottom Line: Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling.Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60-70%).Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America; Program in Bioengineering, University of California, Los Angeles, Los Angeles, California, United States of America.

ABSTRACT
Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60-70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/).

Show MeSH
Related in: MedlinePlus