Dataset Information

Quantitatively integrating molecular structure and bioactivity profile evidence into drug-target relationship analysis.

ABSTRACT:

Background

Public resources of chemical compound are in a rapid growth both in quantity and the types of data-representation. To comprehensively understand the relationship between the intrinsic features of chemical compounds and protein targets is an essential task to evaluate potential protein-binding function for virtual drug screening. In previous studies, correlations were proposed between bioactivity profiles and target networks, especially when chemical structures were similar. With the lack of effective quantitative methods to uncover such correlation, it is demanding and necessary for us to integrate the information from multiple data sources to produce an comprehensive assessment of the similarity between small molecules, as well as quantitatively uncover the relationship between compounds and their targets by such integrated schema.

Results

In this study a multi-view based clustering algorithm was introduced to quantitatively integrate compound similarity from both bioactivity profiles and structural fingerprints. Firstly, a hierarchy clustering was performed with the fused similarity on 37 compounds curated from PubChem. Compared to clustering in a single view, the overall common target number within fused classes has been improved by using the integrated similarity, which indicated that the present multi-view based clustering is more efficient by successfully identifying clusters with its members sharing more number of common targets. Analysis in certain classes reveals that mutual complement of the two views for compound description helps to discover missing similar compound when only single view was applied. Then, a large-scale drug virtual screen was performed on 1267 compounds curated from Connectivity Map (CMap) dataset based on the fused similarity, which obtained a better ranking result compared to that of single-view. These comprehensive tests indicated that by combining different data representations; an improved assessment of target-specific compound similarity can be achieved.

Conclusions

Our study presented an efficient, extendable and quantitative computational model for integration of different compound representations, and expected to provide new clues to improve the virtual drug screening from various pharmacological properties. Scripts, supplementary materials and data used in this study are publicly available at http://lifecenter.sgst.cn/fusion/.

SUBMITTER: Xu T

PROVIDER: S-EPMC3528629 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Publications

Quantitatively integrating molecular structure and bioactivity profile evidence into drug-target relationship analysis.

Xu Tianlei T Zhu Ruixin R Liu Qi Q Cao Zhiwei Z

BMC bioinformatics 20120504

<h4>Background</h4>Public resources of chemical compound are in a rapid growth both in quantity and the types of data-representation. To comprehensively understand the relationship between the intrinsic features of chemical compounds and protein targets is an essential task to evaluate potential protein-binding function for virtual drug screening. In previous studies, correlations were proposed between bioactivity profiles and target networks, especially when chemical structures were similar. Wi ...[more]

PMID: 22559876

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Project description:BackgroundSchistosoma flatworm parasites cause schistosomiasis, a chronic and debilitating disease of poverty in developing countries. Praziquantel is employed for treatment and disease control. However, its efficacy spectrum is incomplete (less active or inactive against immature stages of the parasite) and there is a concern of drug resistance. Thus, there is a need to identify new drugs and drug targets.Methodology/principal findingsWe show that RNA interference (RNAi) of the Schistosoma mansoni ortholog of human polo-like kinase (huPLK)1 elicits a deleterious phenotypic alteration in post-infective larvae (schistosomula or somules). Phenotypic screening and analysis of schistosomula and adult S. mansoni with small molecule inhibitors of huPLK1 identified a number of potent anti-schistosomals. Among these was a GlaxoSmithKline (GSK) benzimidazole thiophene inhibitor that has completed Phase I clinical trials for treatment of solid tumor malignancies. We then obtained GSKs Published Kinase Inhibitor Sets (PKIS) 1 and 2, and phenotypically screened an expanded series of 38 benzimidazole thiophene PLK1 inhibitors. Computational analysis of controls and PLK1 inhibitor-treated populations of somules demonstrated a distinctive phenotype distribution. Using principal component analysis (PCA), the phenotypes exhibited by these populations were mapped, visualized and analyzed through projection to a low-dimensional space. The phenotype distribution was found to have a distinct shape and topology, which could be elicited using cluster analysis. A structure-activity relationship (SAR) was identified for the benzimidazole thiophenes that held for both somules and adult parasites. The most potent inhibitors produced marked phenotypic alterations at 1-2 μM within 1 h. Among these were compounds previously characterized as potent inhibitors of huPLK1 in cell assays.Conclusions/significanceThe reverse genetic and chemical SAR data support a continued investigation of SmPLK1 as a possible drug target and/or the prosecution of the benzimidazole thiophene chemotype as a source of novel anti-schistosomals.

Project description:In a previous study, the detailed low-molecular weight polyphenolic profile of the different plant parts (leaves, stem, bark and wood) of Uncaria tomentosa was reported, the leaves being the plant part with the highest phenolic content and presenting the most heterogenous proanthocyanidin composition. Further, cytotoxicity of leaves extracts in two cancer cell lines was also found to be higher than in the remaining parts of the plant. In the present study, fractioning of U. tomentosa leaves polyphenolic extracts was performed using Diaion® HP-20 resin and a detailed characterization and quantification of fractions (n = 5) was achieved using advanced analytical techniques such as Ultra-Performance Liquid Chromatography coupled with Electrospray Ionization and Triple Quadrupole (TQD) Tandem Mass Spectrometry (UPLC/TQ-ESI-MS) and 13C-NMR. Oxygen Radical Absorbance Capacity (ORAC) and cytotoxicity on gastric adenocarcinoma AGS and colon adenocarcinoma SW20 cell lines were also determined in the different fractions. Results showed selective distribution of 32 non-flavonoid and flavonoid phenolics among the different fractions. ORAC varied between 3.2 and 11.8 ?mol TE/mg in the different fractions, whereas IC50 of cytotoxicity on gastric adenocarcinoma AGS and colon adenocarcinoma SW20 cell lines best values were between 71.4 and 75.6 µg/mL. Fractions rich in proanthocyanidins also showed the highest bioactivity. In fact, significant positive correlation was found between total proanthocyanidins (TP) quantified by UPLC-DAD and ORAC (R² = 0.970), whereas significant negative correlation was found between TP and cytotoxicity towards AGS (R² = 0.820) and SW620 (R² = 0.843) adenocarcinoma cell lines. Among proanthocyanidins, propelargonidin dimers were of particular interest, showing significant correlation with cytotoxic selectivity on both gastric AGS (R² = 0.848) and colon SW620 (R² = 0.883) adenocarcinoma cell lines. These results show further evidence of the bioactivity of U. tomentosa proanthocyanidin extracts and their potential health effects.

Dataset Information

Quantitatively integrating molecular structure and bioactivity profile evidence into drug-target relationship analysis.

Background

Results

Conclusions

Publications

Quantitatively integrating molecular structure and bioactivity profile evidence into drug-target relationship analysis.

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