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Mass spectrometry-guided refinement of chemical energy buffers.

ABSTRACT: Biocatalytic reactions often require supplying chemical energy and phosphate groups in the form of adenosine triphosphate (ATP). Auxiliary enzymes can be used to convert a reaction by-product-adenosine diphosphate (ADP)-back to ATP. By employing real-time mass spectrometry (RTMS), one can gain an insight into inter-conversions of reactants in multi-enzyme reaction systems and optimize the reaction conditions. In this study, temporal traces of ions corresponding to adenosine monophosphate (AMP), ADP and ATP provided vital information that could be used to adjust activities of the 'buffering enzymes'. Using the RTMS results as a feedback, we also characterized a bienzymatic energy buffer that enables the recovery of ATP in the cases where it is directly hydrolysed to AMP in the main enzymatic reaction. The significance of careful selection of enzyme activities-guided by RTMS-is exemplified in the synthesis of glucose-6-phosphate by hexokinase in the presence of a buffering enzyme, pyruvate kinase. Relative activities of the two enzymes, present in the reaction mixture, influence biosynthetic reaction yields. This observation supports the conclusion that optimization of chemical energy recycling procedures is critical for the biosynthetic reaction economy.

SUBMITTER: Chen TR

PROVIDER: S-EPMC4950186 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Mass spectrometry-guided refinement of chemical energy buffers.

Chen T-R TR Urban P L PL

Proceedings. Mathematical, physical, and engineering sciences 20160601 2190

Biocatalytic reactions often require supplying chemical energy and phosphate groups in the form of adenosine triphosphate (ATP). Auxiliary enzymes can be used to convert a reaction by-product-adenosine diphosphate (ADP)-back to ATP. By employing real-time mass spectrometry (RTMS), one can gain an insight into inter-conversions of reactants in multi-enzyme reaction systems and optimize the reaction conditions. In this study, temporal traces of ions corresponding to adenosine monophosphate (AMP), ...[more]

PMID: 27436961

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Project description:MotivationA post-translational modification (PTM) is a chemical modification of a protein that occurs naturally. Many of these modifications, such as phosphorylation, are known to play pivotal roles in the regulation of protein function. Henceforth, PTM perturbations have been linked to diverse diseases like Parkinson's, Alzheimer's, diabetes and cancer. To discover PTMs on a genome-wide scale, there is a recent surge of interest in analyzing tandem mass spectrometry data, and several unrestrictive (so-called 'blind') PTM search methods have been reported. However, these approaches are subject to noise in mass measurements and in the predicted modification site (amino acid position) within peptides, which can result in false PTM assignments.ResultsTo address these issues, we devised a machine learning algorithm, PTMClust, that can be applied to the output of blind PTM search methods to improve prediction quality, by suppressing noise in the data and clustering peptides with the same underlying modification to form PTM groups. We show that our technique outperforms two standard clustering algorithms on a simulated dataset. Additionally, we show that our algorithm significantly improves sensitivity and specificity when applied to the output of three different blind PTM search engines, SIMS, InsPecT and MODmap. Additionally, PTMClust markedly outperforms another PTM refinement algorithm, PTMFinder. We demonstrate that our technique is able to reduce false PTM assignments, improve overall detection coverage and facilitate novel PTM discovery, including terminus modifications. We applied our technique to a large-scale yeast MS/MS proteome profiling dataset and found numerous known and novel PTMs. Accurately identifying modifications in protein sequences is a critical first step for PTM profiling, and thus our approach may benefit routine proteomic analysis.AvailabilityOur algorithm is implemented in Matlab and is freely available for academic use. The software is available online from http://genes.toronto.edu.

Dataset Information

Mass spectrometry-guided refinement of chemical energy buffers.

Publications

Mass spectrometry-guided refinement of chemical energy buffers.

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