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Updated: Jun 15, 2025

Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor

Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor

Published on: April 6, 2016

Microbial reaction rate estimation using proteins and proteomes.

J Scott P McCain^1,2, Gregory L Britten^2,3, Sean R Hackett⁴

¹Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.

Biorxiv : the Preprint Server for Biology

|August 26, 2024

Summary

This summary is machine-generated.

Measuring microbial reaction rates is difficult. However, global proteomic data can accurately predict individual microbial reaction rates, even without specific environmental context.

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Last Updated: Jun 15, 2025

Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor

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Published on: April 6, 2016

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Published on: August 17, 2015

An Aquatic Microbial Metaproteomics Workflow: From Cells to Tryptic Peptides Suitable for Tandem Mass Spectrometry-based Analysis

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Published on: September 15, 2015

Area of Science:

Microbiology
Biochemistry
Proteomics

Background:

Microbes drive environmental transformations via enzymatic reactions.
Quantifying microbial reaction rates in situ remains a significant challenge.
The link between enzyme abundance and reaction rate is not well understood.

Purpose of the Study:

To investigate the predictive power of enzyme abundance for microbial reaction rates.
To determine if global proteomic measurements can accurately estimate individual reaction rates.
To assess the necessity of mechanistic knowledge or environmental context for rate prediction.

Main Methods:

Collected matched proteomic and reaction rate data from microbial cultures.
Analyzed the correlation between enzyme abundance and specific reaction rates.
Evaluated the accuracy of rate predictions using global proteomic data.

Main Results:

Enzyme abundance alone is often insufficient to predict reaction rates.
Global proteomic measurements accurately predicted individual reaction rates (median R² = 0.78).
Accurate predictions required minimal proteins and no prior mechanistic or environmental data.

Conclusions:

Proteomes serve as effective encoders of cellular reaction rates.
In situ proteomic measurements can estimate microbially mediated reaction rates in natural environments.
This approach offers a novel way to study microbial activity in ecosystems.