title: Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens.
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/).
authors.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.
authors.forename: Yu-Chen
authors.initials: YC
authors.lastname: Lo
authors.affiliation: Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Silvia
authors.initials: S
authors.lastname: Senese
authors.affiliation: Drug Studies Unit, Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, California, United States of America.
authors.forename: Chien-Ming
authors.initials: CM
authors.lastname: Li
authors.affiliation: Institute for Digital Research and Education, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Qiyang
authors.initials: Q
authors.lastname: Hu
authors.affiliation: Drug Studies Unit, Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, California, United States of America.
authors.forename: Yong
authors.initials: Y
authors.lastname: Huang
authors.affiliation: California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Robert
authors.initials: R
authors.lastname: Damoiseaux
authors.affiliation: Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, California, United States of America; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, United States of America.
authors.forename: Jorge Z
authors.initials: JZ
authors.lastname: Torres
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D015195:Drug Design; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D008024:Ligands
chemical_list: D008024:Ligands
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