PredRet: Prediction of Retention Time by Direct Mapping between Multiple Chromatographic Systems

Abstract

Demands in research investigating small molecules by applying untargeted approaches have been a key motivator for the development of repositories for mass spectrometry spectra and automated tools to aid compound identification. Comparatively little attention has been afforded to using retention times (RTs) to distinguish compounds and for liquid chromatography there are currently no coordinated efforts to share and exploit RT information. We therefore present PredRet; the first tool that makes community sharing of RT information possible across laboratories and chromatographic systems (CSs). At http://predret.org , a database of RTs from different CSs is available and users can upload their own experimental RTs and download predicted RTs for compounds which they have not experimentally determined in their own experiments. For each possible pair of CSs in the database, the RTs are used to construct a projection model between the RTs in the two CSs. The number of compounds for which RTs can be predicted and the accuracy of the predictions are dependent upon the compound coverage overlap between the CSs used for construction of projection models. At the moment, it is possible to predict up to 400 RTs with a median error between 0.01 and 0.28 min depending on the CS and the median width of the prediction interval ranging from 0.08 to 1.86 min. By comparing experimental and predicted RTs, the user can thus prioritize which isomers to target for further characterization and potentially exclude some structures completely. As the database grows, the number and accuracy of predictions will increase.

Comprehensive identification of sphingolipid species by in silico retention time and tandem mass spectral library

“…It also exploits MS/MS databases such as MassBank (26,296 MS/MS spectra covering 3127 authentic compound structures) and NIST14 (234,284 MS/MS spectra covering 9344 authentic compound structures) for comprehensive metabolome analysis [3]. We can also predict the RT by the quantitative structure retention relationship (QSRR) in combination with multivariate analysis [4, 5], but these reference databases are not yet comprehensive: the human metabolome database (HMDB) contains 41,993 unique structures in contrast to their number in MassBank and NIST14 [6]. …”

Annotation: A Computational Solution for Streamlining Metabolomics Analysis

“…An interesting concept was recently introduced by Stanstrup et al [85], a collaborative web-based platform for RT prediction named PredRet. Users upload their empirical RT along with the description of the chromatographic method used.…”

Mass spectrometric strategies for the investigation of biomarkers of illicit drug use in wastewater

“…These t R predictors are based on quantitative structure‐retention relationships (QSRRs) that vary from very simple, which incorporate a single descriptor (Kern et al, ; Bade et al, ), to more complex, which use a large number of descriptors (Miller et al, ; Bade et al, ; Gago‐Ferrero et al, ; Munro et al, ; Barron and McEneff, ); however, all of these predictors are based on a single column, commonly a reversed phase (C 18 ) column. A direct‐mapping technique (PredRet) able to predict retention times across different chromatographic systems has also recently been developed (Stanstrup, Neumann, & Vrhovšek, ), but is limited to compounds having to be within the PredRet database. Although this area is rapidly advancing, the principal limitation is the use of a single column for most predictions, with further optimization needed for a truly transferable prediction technique.…”