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

Genome-Wide Assessment in Escherichia coli Reveals Time-Dependent Nanotoxicity Paradigms

The use of engineered nanomaterials (eNM) in consumer and industrial products is increasing exponentially. Our ability to rapidly assess their potential effects on human and environmental health is limited by our understanding of nanomediated toxicity. High-throughput screening (HTS) enables the investigation of nanomediated toxicity on a genome-wide level, thus uncovering their novel mechanisms and paradigms. Herein, we investigate the toxicity of zinc-containing nanomaterials (Zn-eNMs) using a time-resolved HTS methodology in an arrayed Escherichia coli genome-wide knockout (KO) library. The library was screened against nanoscale zerovalent zinc (nZn), nanoscale zinc oxide (nZnO), and zinc chloride (ZnCl₂) salt as reference. Through sequential screening over 24 h, our method identified 173 sensitive clones from diverse biological pathways, which fell into two general groups: early and late responders. The overlap between these groups was small. Our results suggest that bacterial toxicity mechanisms change from pathways related to general metabolic function, transport, signaling, and metal ion homeostasis to membrane synthesis pathways over time. While all zinc sources shared pathways relating to membrane damage and metal ion homeostasis, Zn-eNMs and ZnCl₂ displayed differences in their sensitivity profiles. For example, ZnCl₂ and nZnO elicited unique responses in pathways related to two-component signaling and monosaccharide biosynthesis, respectively. Single isolated measurements, such as MIC or IC₅₀, are inadequate, and time-resolved approaches utilizing genome-wide assays are therefore needed to capture this crucial dimension and illuminate the dynamic interplay at the nano-bio interface

Genome-Wide Assessment in Escherichia coli Reveals Time-Dependent Nanotoxicity Paradigms

The use of engineered nanomaterials (eNM) in consumer and industrial products is increasing exponentially. Our ability to rapidly assess their potential effects on human and environmental health is limited by our understanding of nanomediated toxicity. High-throughput screening (HTS) enables the investigation of nanomediated toxicity on a genome-wide level, thus uncovering their novel mechanisms and paradigms. Herein, we investigate the toxicity of zinc-containing nanomaterials (Zn-eNMs) using a time-resolved HTS methodology in an arrayed Escherichia coli genome-wide knockout (KO) library. The library was screened against nanoscale zerovalent zinc (nZn), nanoscale zinc oxide (nZnO), and zinc chloride (ZnCl₂) salt as reference. Through sequential screening over 24 h, our method identified 173 sensitive clones from diverse biological pathways, which fell into two general groups: early and late responders. The overlap between these groups was small. Our results suggest that bacterial toxicity mechanisms change from pathways related to general metabolic function, transport, signaling, and metal ion homeostasis to membrane synthesis pathways over time. While all zinc sources shared pathways relating to membrane damage and metal ion homeostasis, Zn-eNMs and ZnCl₂ displayed differences in their sensitivity profiles. For example, ZnCl₂ and nZnO elicited unique responses in pathways related to two-component signaling and monosaccharide biosynthesis, respectively. Single isolated measurements, such as MIC or IC₅₀, are inadequate, and time-resolved approaches utilizing genome-wide assays are therefore needed to capture this crucial dimension and illuminate the dynamic interplay at the nano-bio interface

Sub-image segments for defining Semi-global edge histograms.

<p>The segmentations are corresponding to the typical layouts of zebrafish embryos of the constructed GSEHD descriptor.</p

Additional file 1: of When more is less: Emergent suppressive interactions in three-drug combinations

 Figure S1. Comparison of relative optical density measurement to growth rate measurement. Figure S2. Comparison of relative optical density measurement to colony forming units. Figure S3. Emergent three-way interaction measures in E. coli BW25113. Figure S4. Suppressive three-drug interactions in S. epidermidis 14990 and E. coli CFT073. Figure S5. Suppressor and suppressee antibiotics for S. epidermidis 14990 and E. coli CFT07. Table S1. Full data set for 14 drugs in E. coli BW25113, S. epidermidis 14990, and E. coli CFT073. Table S2. Emergent suppressive three-drug combinations from 14 antibiotics (see Methods, Fig. 1) for E. coli CFT073, E. coli BW25113, S. epidermidis 14990. (PDF 7668 kb

Geometric description of SVM for a binary classification problem.

<p>Geometric description of SVM for a binary classification problem.</p

Large-Scale Chemical Similarity Networks for Target Profiling of Compounds Identified in Cell-Based Chemical Screens

<div><p>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 <i>in silico</i> 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 (<a href="http://services.mbi.ucla.edu/CSNAP/" target="_blank">http://services.mbi.ucla.edu/CSNAP/</a>).</p></div

Examples of the three phenotypes and their corresponding image descriptors.

<p>(a). Local Edge Histogram for each of 4×4 image blocks (<i>y</i> axis of each of the 4×4 image blocks is from 0 to 6. (b). Representative Color (i.e., the average color (grayscale) for each of the 4×4 image blocks). (c). Color Histogram (<i>x</i> axis is the graryscale that ranges from 0 to 255; <i>y</i> axis is from 0 to 16×104 identifying the number of pixels which grayscale are within the bin range).</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