3D Chemical Similarity Networks for Structure-Based Target Prediction and Scaffold Hopping

Target identification remains a major challenge for modern drug discovery programs aimed at understanding the molecular mechanisms of drugs. Computational target prediction approaches like 2D chemical similarity searches have been widely used but are limited to structures sharing high chemical similarity. Here, we present a new computational approach called chemical similarity network analysis pull-down 3D (CSNAP3D) that combines 3D chemical similarity metrics and network algorithms for structure-based drug target profiling, ligand deorphanization, and automated identification of scaffold hopping compounds. In conjunction with 2D chemical similarity fingerprints, CSNAP3D achieved a >95% success rate in correctly predicting the drug targets of 206 known drugs. Significant improvement in target prediction was observed for HIV reverse transcriptase (HIVRT) compounds, which consist of diverse scaffold hopping compounds targeting the nucleotidyltransferase binding site. CSNAP3D was further applied to a set of antimitotic compounds identified in a cell-based chemical screen and identified novel small molecules that share a pharmacophore with Taxol and display a Taxol-like mechanism of action, which were validated experimentally using <i>in vitro</i> microtubule polymerization assays and cell-based assays

3D Chemical Similarity Networks for Structure-Based Target Prediction and Scaffold Hopping

Target identification remains a major challenge for modern drug discovery programs aimed at understanding the molecular mechanisms of drugs. Computational target prediction approaches like 2D chemical similarity searches have been widely used but are limited to structures sharing high chemical similarity. Here, we present a new computational approach called chemical similarity network analysis pull-down 3D (CSNAP3D) that combines 3D chemical similarity metrics and network algorithms for structure-based drug target profiling, ligand deorphanization, and automated identification of scaffold hopping compounds. In conjunction with 2D chemical similarity fingerprints, CSNAP3D achieved a >95% success rate in correctly predicting the drug targets of 206 known drugs. Significant improvement in target prediction was observed for HIV reverse transcriptase (HIVRT) compounds, which consist of diverse scaffold hopping compounds targeting the nucleotidyltransferase binding site. CSNAP3D was further applied to a set of antimitotic compounds identified in a cell-based chemical screen and identified novel small molecules that share a pharmacophore with Taxol and display a Taxol-like mechanism of action, which were validated experimentally using <i>in vitro</i> microtubule polymerization assays and cell-based assays

The five common chemical features in Hypo1 with their geometric constraints.

<p>Green, magenta and cyan represent hydrogen bond acceptor, hydrogen bond donor and hydrophobic, respectively. Edges represent distances in Angstroms.</p

Docking of hit leads within the Plk1-PBD.

<p>A) Overlay of Hypo1 and the critical residues in the active site of Plk1-PDB with hit lead compound Chemistry_6177. B) Chemistry 6177 forms hydrogen bond interactions with key residues (Trp414, Asp416, His538, and Lys540) in the Plk1-PBD active site.</p

Analyses of critical amino acids for Plk1-PBD inhibition from 23 Plk1-PBD-ligand co-crystal structures deposited in the protein data bank.

<p>X denotes hydrogen bonding with indicated amino acid.</p

<i>In vitro</i> evaluation of the 93 putative Plk1-PBD inhibitors.

<p>A) Fluorescence polarization assay to measure the effect of DMSO (green bar), 100 µM Poloxin (red bar) or 100 µM of each of the 93 test compounds (blue bars) on the percent binding of Plk1-PBD to its substrate peptide 5-carboxyfluorescein-GPMQSpTPLNG. B) Chemical structure of Chemistry_28272 and Poloxin and their respective IC_50s for Plk1-PBD inhibition in fluorescence polarization assays described in A. C) Overlay of Hypo1 and the critical residues in the active site of the Plk1-PDB with hit lead compound Chemistry_28272. D) Zoomed view of ligand protein interaction showing that Chemistry_28272 forms hydrogen bond interactions with key residues (Trp414, Asp416, His538, and Lys540) in the Plk1-PBD active site.</p

Integrative workflow for designing and virtual screening of polo-box domain inhibitors.

<p>Integrative workflow combines, pharmacophore modeling, generation of a drug-like database, virtual screening and molecular docking approaches to define the Plk1-PBD-ligand interaction and to identify Plk1-PBD inhibitors.</p

Generation of a common structure-based pharmacophore hypotheses using LigandScout.

<p>Hypo1 represents the five common chemical features present in all 9 hypotheses. Green, magenta, blue, red and cyan represents hydrogen bond acceptor, hydrogen bond donor, positive ionization, negative ionization and hydrophobic, respectively.</p

Hit compounds with a maximum fit value greater than 3.

<p>Representation of six compounds with a fit value greater than 3 identified through virtual screening. Note that compounds with diverse scaffolds are able to satisfy the geometric constraints of Hypo1 to form similar interactions. Green, magenta and cyan represents hydrogen bond acceptor, hydrogen bond donor and hydrophobic, respectively.</p

Integration of CSNAP with knowledge databases for mitotic target prediction and phenotypic target validation.

<p>(A) Mitotic compound chemical similarity network. CSNAP analysis of 212 mitotic compounds yielded 85 chemical similarity clusters representing diverse chemotypes, only 21 compounds were not clustered into annotated similarity graphs. (B) LTIF analysis of CSNAP mitotic target predictions. The target spectrum identified four major classes of targets from the top peaks including fatty acid desaturase (SCD), ABL kinase (ABL1), phosphatase (PTPN) and tubulin (TUBB). An independent LTIF analysis of each target class is presented in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004153#pcbi.1004153.s002" target="_blank">S2 Fig</a>. (C) Mitotic compound deconvolution. Target associated chemical similarity sub-networks of four predicted targets (SCD, ABL1, PTPN and TUBB) were “pulled-down” from the mitotic CSN. For each cluster, at least one mitotic compound connected to one or more reference nodes with Tc threshold> 0.7. Note that the predicted SCD and ABL1 compounds display over-lapping neighbors, indicating that the predicted targets may be modulated by both compound sets. (D) Phenotypic validation of predicted mitotic targets. Asynchronous HeLa cells were treated with indicated compounds for 24 hours, fixed and stained for DNA and Tubulin. The observed compound-induced cell division defects were compared to target gene expression knockdown defects within the MitoCheck database. All compounds matched the previously characterized phenotypes associated with knockdown of target protein expression. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004153#pcbi.1004153.s006" target="_blank">S6 Fig</a> for complete compound-induced phenotypes.</p