Microbial reaction rate estimation using proteins and proteomes.

Area of Science:

• Microbiology
• Biochemistry
• Proteomics

Background:

• Microbes drive environmental transformations through enzymatic reactions.
• Quantifying microbial reaction rates in situ remains a significant challenge.
• The relationship between enzyme abundance and reaction rate is not fully 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 explore the potential of proteomes as encoders of cellular reaction rates.

Main Methods:

• Matched proteomic and reaction rate data were collected from microbial cultures.
• Statistical analysis was used to correlate proteomic data with measured reaction rates.
• Machine learning approaches were employed to build predictive models.

Main Results:

• Enzyme abundance alone is often insufficient to predict specific reaction rates.
• Global proteomic measurements enabled accurate prediction of individual reaction rates (median R2 = 0.78).
• Accurate predictions required a limited number of proteins and no prior mechanistic or environmental context.

Conclusions:

• Proteomes serve as effective encoders of cellular reaction rates.
• Proteomic measurements hold potential for estimating microbially mediated reaction rates in natural systems.
• This approach offers a novel way to study microbial functions in situ.