Approaches towards the automated interpretation and prediction of electrospray tandem mass spectra of non-peptidic combinatorial compounds

Klagkou, Katerina, Pullen, Frank, Harrison, Mark, Organ, Andy, Firth, Alistair and Langley, G. John (2003) Approaches towards the automated interpretation and prediction of electrospray tandem mass spectra of non-peptidic combinatorial compounds. Rapid Communications in Mass Spectrometry, 17 (11), 1163-1168. (doi:10.1002/rcm.987).

Abstract

Combinatorial chemistry is widely used within the pharmaceutical industry as a means of rapid identification of potential drugs. With the growth of combinatorial libraries, mass spectrometry (MS) became the key analytical technique because of its speed of analysis, sensitivity, accuracy and ability to be coupled with other analytical techniques. In the majority of cases, electrospray mass spectrometry (ES-MS) has become the default ionisation technique. However, due to the absence of fragment ions in the resulting spectra, tandem mass spectrometry (MS/MS) is required to provide structural information for the identification of an unknown analyte. This work discusses the first steps of an investigation into the fragmentation pathways taking place in electrospray tandem mass spectrometry. The ultimate goal for this project is to set general fragmentation rules for non-peptidic, pharmaceutical, combinatorial compounds. As an aid, an artificial intelligence (AI) software package is used to facilitate interpretation of the spectra. This initial study has focused on determining the fragmentation rules for some classes of compound types that fit the remit as outlined above. Based on studies carried out on several combinatorial libraries of these compounds, it was established that different classes of drug molecules follow unique fragmentation pathways. In addition to these general observations, the specific ionisation processes and the fragmentation pathways involved in the electrospray mass spectra of these systems were explored. The ultimate goal will be to incorporate our findings into the computer program and allow identification of an unknown, non-peptidic compound following insertion of its ES-MS/MS spectrum into the AI package. The work herein demonstrates the potential benefit of such an approach in addressing the issue of high-throughput, automated MS/MS data interpretation.

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Contributors

Author: G. John Langley

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