RATIONALE: High-throughput screening (HTS) is a critical step in the drug discovery process. However, most mass spectrometry (MS)-based HTS methods require sample cleanup steps prior to analysis. In this work we present the utility of infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) for monitoring an enzymatic reaction directly from a biological buffer system with no sample cleanup and at high throughput. METHODS: IR-MALDESI was used to directly analyze reaction mixtures from a well plate at different time points after reaction initiation. The percent conversion of precursors to products was used to screen the enzyme activity. The reaction was performed with two different concentrations of precursors and enzyme in order to assess the dynamic range of the assay. Eventually, a pseudo-HTS study was designed to investigate the utility of IR-MALDESI screening enzyme activity in a high-throughput manner. RESULTS: IR-MALDESI was able to readily monitor the activity of IDH1 over time at two different concentrations of precursors and enzyme. The calculated Z-factors of 0.65 and 0.41 confirmed the suitability of the developed method for screening enzyme activity in HTS manner. Finally, in a single-blind pseudo-HTS analysis IR-MALDESI was able to correctly predict the identity of all samples, where 8/10 samples were identified with high confidence and the other two samples with lower confidence. CONCLUSIONS: The enzymatic activity of IDH1 was screened by directly analyzing the reaction content from the buffer in well plates with no sample cleanup steps. This proof-of-concept study demonstrates the robustness of IR-MALDESI for direct analysis of enzymatic reactions from biological buffers with no sample cleanup and its immense potential for HTS applications.
RATIONALE: High-throughput screening (HTS) is a critical step in the drug discovery process. However, most mass spectrometry (MS)-based HTS methods require sample cleanup steps prior to analysis. In this work we present the utility of infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) for monitoring an enzymatic reaction directly from a biological buffer system with no sample cleanup and at high throughput. METHODS: IR-MALDESI was used to directly analyze reaction mixtures from a well plate at different time points after reaction initiation. The percent conversion of precursors to products was used to screen the enzyme activity. The reaction was performed with two different concentrations of precursors and enzyme in order to assess the dynamic range of the assay. Eventually, a pseudo-HTS study was designed to investigate the utility of IR-MALDESI screening enzyme activity in a high-throughput manner. RESULTS: IR-MALDESI was able to readily monitor the activity of IDH1 over time at two different concentrations of precursors and enzyme. The calculated Z-factors of 0.65 and 0.41 confirmed the suitability of the developed method for screening enzyme activity in HTS manner. Finally, in a single-blind pseudo-HTS analysis IR-MALDESI was able to correctly predict the identity of all samples, where 8/10 samples were identified with high confidence and the other two samples with lower confidence. CONCLUSIONS: The enzymatic activity of IDH1 was screened by directly analyzing the reaction content from the buffer in well plates with no sample cleanup steps. This proof-of-concept study demonstrates the robustness of IR-MALDESI for direct analysis of enzymatic reactions from biological buffers with no sample cleanup and its immense potential for HTS applications.
Authors: Can C Ozbal; William A LaMarr; John R Linton; Donald F Green; Arrin Katz; Thomas B Morrison; Colin J H Brenan Journal: Assay Drug Dev Technol Date: 2004-08 Impact factor: 1.738
Authors: Ian Sinclair; Rick Stearns; Steven Pringle; Jonathan Wingfield; Sammy Datwani; Eric Hall; Luke Ghislain; Lars Majlof; Martin Bachman Journal: J Lab Autom Date: 2015-12-02
Authors: Melanie Leveridge; Rachel Buxton; Argyrides Argyrou; Peter Francis; Bill Leavens; Andy West; Mike Rees; Philip Hardwicke; Angela Bridges; Steven Ratcliffe; Chun-wa Chung Journal: J Biomol Screen Date: 2013-07-29
Authors: Ricardo Macarron; Martyn N Banks; Dejan Bojanic; David J Burns; Dragan A Cirovic; Tina Garyantes; Darren V S Green; Robert P Hertzberg; William P Janzen; Jeff W Paslay; Ulrich Schopfer; G Sitta Sittampalam Journal: Nat Rev Drug Discov Date: 2011-03 Impact factor: 84.694
Authors: Måns Ekelöf; Erin K McMurtrie; Milad Nazari; Suzanne D Johanningsmeier; David C Muddiman Journal: J Am Soc Mass Spectrom Date: 2016-11-15 Impact factor: 3.109
Authors: Milad Nazari; Alexandra A Malico; Måns Ekelöf; Sean Lund; Gavin J Williams; David C Muddiman Journal: Anal Bioanal Chem Date: 2017-08-17 Impact factor: 4.142
Authors: Alexandra A Malico; Miles A Calzini; Anuran K Gayen; Gavin J Williams Journal: J Ind Microbiol Biotechnol Date: 2020-09-03 Impact factor: 3.346
Authors: Kevan T Knizner; Michael C Bagley; Fan Pu; Nathaniel L Elsen; Jon D Williams; David C Muddiman Journal: J Mass Spectrom Date: 2022-06 Impact factor: 2.394
Authors: Måns Ekelöf; James Dodds; Sitora Khodjaniyazova; Kenneth P Garrard; Erin S Baker; David C Muddiman Journal: J Am Soc Mass Spectrom Date: 2020-02-11 Impact factor: 3.109
Authors: Fred A M G van Geenen; Frank W Claassen; Maurice C R Franssen; Han Zuilhof; Michel W F Nielen Journal: J Am Soc Mass Spectrom Date: 2020-01-08 Impact factor: 3.109
Authors: Kenneth P Garrard; Måns Ekelöf; Sitora Khodjaniyazova; M Caleb Bagley; David C Muddiman Journal: J Am Soc Mass Spectrom Date: 2020-06-30 Impact factor: 3.109
Authors: Yogini S Jaiswal; Aaron M Yerke; M Caleb Bagley; Måns Ekelöf; Daniel Weber; Daniel Haddad; Anthony Fodor; David C Muddiman; Leonard L Williams Journal: Gigascience Date: 2020-09-22 Impact factor: 6.524