Dataset Information

Development of a Reverse Phase HPLC Retention Index Model for Nontargeted Metabolomics Using Synthetic Compounds.

ABSTRACT: The MolFind application has been developed as a nontargeted metabolomics chemometric tool to facilitate structure identification when HPLC biofluids analysis reveals a feature of interest. Here synthetic compounds are selected and measured to form the basis of a new, more accurate, HPLC retention index model for use with MolFind. We show that relatively inexpensive synthetic screening compounds with simple structures can be used to develop an artificial neural network model that is successful in making quality predictions for human metabolites. A total of 1955 compounds were obtained and measured for the model. A separate set of 202 human metabolites was used for independent validation. The new ANN model showed improved accuracy over previous models. The model, based on relatively simple compounds, was able to make quality predictions for complex compounds not similar to training data. Independent validation metabolites with feature combinations found in three or more training compounds were predicted with 97% sensitivity while metabolites with feature combinations found in less than three training compounds were predicted with >90% sensitivity. The study describes the method used to select synthetic compounds and new descriptors developed to encode the relationship between lipophilic molecular subgraphs and HPLC retention. Finally, we introduce the QRI (qualitative range of interest) modification of neural network backpropagation learning to generate models simultaneously based on quantitative and qualitative data.

SUBMITTER: Hall LM

PROVIDER: S-EPMC8404481 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The goal of many metabolomic studies is to identify the molecular structure of endogenous molecules that are differentially expressed among sampled or treatment groups. The identified compounds can then be used to gain an understanding of disease mechanisms. Unfortunately, despite recent advances in a variety of analytical techniques, small molecule (<1000 Da) identification remains difficult. Rarely can a chemical structure be determined from experimental "features" such as retention time, exact mass, and collision induced dissociation spectra. Thus, without knowing structure, biological significance remains obscure. In this study, we explore an identification method in which the measured exact mass of an unknown is used to query available chemical databases to compile a list of candidate compounds. Predictions are made for the candidates using models of experimental features that have been measured for the unknown. The predicted values are used to filter the candidate list by eliminating compounds with predicted values substantially different from the unknown. The intent is to reduce the list of candidates to a reasonable number that can be obtained and measured for confirmation. To facilitate this exploration, we measured data and created models for two experimental features; MS Ecom?? (the energy in electronvolts required to fragment 50% of a selected precursor ion) and HPLC retention index. Using a data set of 52 compounds, Ecom?? models were developed based on both Molconn and CODESSA structural descriptors. These models gave r² values of 0.89 to 0.94 depending on the number of inputs, the modeling algorithm chosen, and whether neutral or protonated structures were used. The retention index model was developed with 400 compounds using a back-propagation artificial neural network and 33 Molconn structure descriptors. External validation gave a v² = 0.87 and standard error of 38 retention index units. As a test of the validity of the filtering approach, the Ecom?? and retention index models, along with exact mass and collision induced dissociation spectra matching, were used to identify 1,3-dicyclohexylurea in human plasma. This compound was not previously known to exist in human biofluids and its elemental formula was identical to 315 other candidate compounds downloaded from PubChem. These results suggest that the use of Ecom?? and retention index predictive models can improve nontargeted metabolite structure identification using HPLC/MS derived structural features.

Project description:Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) is a major analytical technique used for nontargeted identification of metabolites in biological fluids. Typically, in LC-ESI-MS/MS based database assisted structure elucidation pipelines, the exact mass of an unknown compound is used to mine a chemical structure database to acquire an initial set of possible candidates. Subsequent matching of the collision induced dissociation (CID) spectrum of the unknown to the CID spectra of candidate structures facilitates identification. However, this approach often fails because of the large numbers of potential candidates (i.e., false positives) for which CID spectra are not available. To overcome this problem, CID fragmentation predication programs have been developed, but these also have limited success if large numbers of isomers with similar CID spectra are present in the candidate set. In this study, we investigated the use of a retention index (RI) predictive model as an orthogonal method to help improve identification rates. The model was used to eliminate candidate structures whose predicted RI values differed significantly from the experimentally determined RI value of the unknown compound. We tested this approach using a set of ninety-one endogenous metabolites and four in silico CID fragmentation algorithms: CFM-ID, CSI:FingerID, Mass Frontier, and MetFrag. Candidate sets obtained from PubChem and the Human Metabolite Database (HMDB) were ranked with and without RI filtering followed by in silico spectral matching. Upon RI filtering, 12 of the ninety-one metabolites were eliminated from their respective candidate sets, i.e., were scored incorrectly as negatives. For the remaining seventy-nine compounds, we show that RI filtering eliminated an average of 58% from PubChem candidate sets. This resulted in an approximately 2-fold improvement in average rankings when using CFM-ID, Mass Frontier, and MetFrag. In addition, RI filtering slightly increased the occurrence of number one rankings for all 4 fragmentation algorithms. However, RI filtering did not significantly improve average rankings when HMDB was used as the candidate database, nor did it significantly improve average rankings when using CSI:FingerID. Overall, we show that the current RI model incorrectly eliminated more true positives (12) than were expected (4-5) on the basis of the filtering method. However, it slightly improved the number of correct first place rankings and improved overall average rankings when using CFM-ID, Mass Frontier, and MetFrag.

Project description:Optical spectroscopy and reverse-phase HPLC were used to investigate the binding of Hg(II) to plant metal-binding peptides (phytochelatins) with the structure (gammaGlu-Cys)2Gly, (gammaGlu-Cys)3Gly and (gammaGlu-Cys)4Gly. Glutathione-mediated transfer of Hg(II) into phytochelatins and the transfer of the metal ion from one phytochelatin to another was also studied using reverse-phase HPLC. The saturation of Hg(II)-induced bands in the UV/visible and CD spectra of (gammaGlu-Cys)2Gly suggested the formation of a single Hg(II)-binding species of this peptide with a stoichiometry of one metal ion per peptide molecule. The separation of apo-(gammaGlu-Cys)2Gly from its Hg(II) derivative on a C18 reverse-phase column also indicated the same metal-binding stoichiometry. The UV/visible spectra of both (gammaGlu-Cys)3Gly and (gammaGlu-Cys)4Gly at pH 7.4 showed distinct shoulders in the ligand-to-metal charge-transfer region at 280-290 mm. Two distinct Hg(II)-binding species, occurring at metal-binding stoichiometries of around 1.25 and 2.0 Hg(II) ions per peptide molecule, were observed for (gammaGlu-Cys)3Gly. These species exhibited specific spectral features in the charge-transfer region and were separable by HPLC. Similarly, two main Hg(II)-binding species of (gammaGlu-Cys)4Gly were observed by UV/visible and CD spectroscopy at metal-binding stoichiometries of around 1.25 and 2.5 respectively. Only a single peak of Hg(II)-(gammaGlu-Cys)4Gly complexes was resolved under the conditions used for HPLC. The overall Hg(II)-binding stoichiometries of phytochelatins were similar at pH 2.0 and at pH 7.4, indicating that pH did not influence the final Hg(II)-binding capacity of these peptides. The reverse-phase HPLC assays indicated a rapid transfer of Hg(II) from glutathione to phytochelatins. These assays also demonstrated a facile transfer of the metal ion from shorter- to longer-chain phytochelatins. The strength of Hg(II) binding to glutathione and phytochelatins followed the order: gammaGlu-Cys-Gly<(gammaGlu-Cys)2Gly<(gammaGlu-Cy s)3Gly<(gamma Glu-Cys)4Gly.

Dataset Information

Development of a Reverse Phase HPLC Retention Index Model for Nontargeted Metabolomics Using Synthetic Compounds.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets