Dataset Information

MALDI efficiency of metabolites quantitatively associated with their structural properties: a quantitative structure-property relationship (QSPR) approach.

ABSTRACT: Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) experiments require a suitable match of the matrix and target compounds to achieve a selective and sensitive analysis. However, it is still difficult to predict which metabolites are ionizable with a given matrix and which factors lead to an efficient ionization. In the present study, we extracted structural properties of metabolites that contribute to their ionization in MALDI-MS analyses exploiting our experimental data set. The MALDI-MS experiment was performed for 200 standard metabolites using 9-aminoacridine (9-AA) as the matrix. We then developed a prediction model for the ionization profiles (both the ionizability and ionization efficiency) of metabolites using a quantitative structure-property relationship (QSPR) approach. The classification model for the ionizability achieved a 91% accuracy, and the regression model for the ionization efficiency reached a rank correlation coefficient of 0.77. An analysis of the descriptors contributing to such model construction suggested that the proton affinity is a major determinant of the ionization, whereas some substructures hinder efficient ionization. This study will lead to the development of more rational and predictable MALDI-MS analyses.

SUBMITTER: Yukihira D

PROVIDER: S-EPMC3880474 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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MALDI efficiency of metabolites quantitatively associated with their structural properties: a quantitative structure-property relationship (QSPR) approach.

Yukihira Daichi D Miura Daisuke D Fujimura Yoshinori Y Umemura Yoshikatsu Y Yamaguchi Shinichi S Funatsu Shinji S Yamazaki Makoto M Ohta Tetsuya T Inoue Hiroaki H Shindo Mitsuru M Wariishi Hiroyuki H

Journal of the American Society for Mass Spectrometry 20131119 1

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) experiments require a suitable match of the matrix and target compounds to achieve a selective and sensitive analysis. However, it is still difficult to predict which metabolites are ionizable with a given matrix and which factors lead to an efficient ionization. In the present study, we extracted structural properties of metabolites that contribute to their ionization in MALDI-MS analyses exploiting our experimental data s ...[more]

PMID: 24249043

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Project description:Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred's modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a "plug-and-play" manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred's functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred .Scientific ContributionQSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models.

Dataset Information

MALDI efficiency of metabolites quantitatively associated with their structural properties: a quantitative structure-property relationship (QSPR) approach.

Publications

MALDI efficiency of metabolites quantitatively associated with their structural properties: a quantitative structure-property relationship (QSPR) approach.

Similar Datasets

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