Dataset Information

Single-Molecule Topochemical Analyses for Large-Scale Multiplexing Tasks.

ABSTRACT: Multitasking is the pivotal feature in next-generation chemo- or bioanalyses. However, simultaneous analyses rarely exceed over three different tasks, which is ascribed to the limited space to accommodate analyzing units and the compromised signal-to-noise (S/N) level as the number of tasks increases. Here, by leveraging superior S/N of single-molecule techniques, we analyzed five microRNA biomarkers by spatially encoding miRNA recognition units with nanometers resolution in a DNA template, while decoding the analyte binding temporally in seconds. The hairpin stem is interspersed by internal loops to encode recognition units for miRNA. By mechanical unfolding of the hairpin, individual internal loops are sequentially interrogated for the binding of each miRNA. Using this so-called topochemical spatiotemporal analysis, we were able to achieve subpicomolar detection limits of miRNAs. We anticipate that this new single-molecule topochemical analysis can massively analyze single-molecule targets.

SUBMITTER: Mandal S

PROVIDER: S-EPMC7011503 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

Single-Molecule Topochemical Analyses for Large-Scale Multiplexing Tasks.

Mandal Shankar S Zhang Xiaoqing X Pandey Shankar S Mao Hanbin H

Analytical chemistry 20191009 21

Multitasking is the pivotal feature in next-generation chemo- or bioanalyses. However, simultaneous analyses rarely exceed over three different tasks, which is ascribed to the limited space to accommodate analyzing units and the compromised signal-to-noise (S/N) level as the number of tasks increases. Here, by leveraging superior S/N of single-molecule techniques, we analyzed five microRNA biomarkers by spatially encoding miRNA recognition units with nanometers resolution in a DNA template, whil ...[more]

PMID: 31553880

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Project description:BACKGROUND: In ligand-based screening, as well as in other chemoinformatics applications, one seeks to effectively search large repositories of molecules in order to retrieve molecules that are similar typically to a single molecule lead. However, in some case, multiple molecules from the same family are available to seed the query and search for other members of the same family.Multiple-molecule query methods have been less studied than single-molecule query methods. Furthermore, the previous studies have relied on proprietary data and sometimes have not used proper cross-validation methods to assess the results. In contrast, here we develop and compare multiple-molecule query methods using several large publicly available data sets and background. We also create a framework based on a strict cross-validation protocol to allow unbiased benchmarking for direct comparison in future studies across several performance metrics. RESULTS: Fourteen different multiple-molecule query methods were defined and benchmarked using: (1) 41 publicly available data sets of related molecules with similar biological activity; and (2) publicly available background data sets consisting of up to 175,000 molecules randomly extracted from the ChemDB database and other sources. Eight of the fourteen methods were parameter free, and six of them fit one or two free parameters to the data using a careful cross-validation protocol. All the methods were assessed and compared for their ability to retrieve members of the same family against the background data set by using several performance metrics including the Area Under the Accumulation Curve (AUAC), Area Under the Curve (AUC), F1-measure, and BEDROC metrics.Consistent with the previous literature, the best parameter-free methods are the MAX-SIM and MIN-RANK methods, which score a molecule to a family by the maximum similarity, or minimum ranking, obtained across the family. One new parameterized method introduced in this study and two previously defined methods, the Exponential Tanimoto Discriminant (ETD), the Tanimoto Power Discriminant (TPD), and the Binary Kernel Discriminant (BKD), outperform most other methods but are more complex, requiring one or two parameters to be fit to the data. CONCLUSION: Fourteen methods for multiple-molecule querying of chemical databases, including novel methods, (ETD) and (TPD), are validated using publicly available data sets, standard cross-validation protocols, and established metrics. The best results are obtained with ETD, TPD, BKD, MAX-SIM, and MIN-RANK. These results can be replicated and compared with the results of future studies using data freely downloadable from http://cdb.ics.uci.edu/.

Dataset Information

Single-Molecule Topochemical Analyses for Large-Scale Multiplexing Tasks.

Publications

Single-Molecule Topochemical Analyses for Large-Scale Multiplexing Tasks.

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