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mRNA Stability and Gene Expression

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The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...

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Antimicrobial Peptides Produced by Selective Pressure Incorporation of Non-canonical Amino Acids

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Improving Protein Expression, Stability, and Function with ProteinMPNN.

Kiera H Sumida^1,2, Reyes Núñez-Franco³, Indrek Kalvet^2,4,5

¹Department of Chemistry, University of Washington, Seattle, Washington 98195, United States.

Journal of the American Chemical Society

|January 9, 2024

Summary

This summary is machine-generated.

Researchers used the ProteinMPNN deep neural network to engineer proteins with enhanced expression, stability, and function for biotechnology. This method improves protein properties for wider technological applications.

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Area of Science:

Biochemistry
Protein Engineering
Computational Biology

Background:

Native proteins are functionally optimized but challenging to produce biotechnologically due to poor expression, solubility, and stability.
Improving protein physical properties while preserving function is crucial for advancing protein-based technologies.

Purpose of the Study:

To develop a general method for enhancing protein expression, stability, and function using computational design.
To demonstrate the utility of the ProteinMPNN deep neural network in protein engineering.

Main Methods:

Utilized the ProteinMPNN deep neural network combined with evolutionary and structural data for protein design.
Applied the method to engineer myoglobin and tobacco etch virus (TEV) protease.

Main Results:

Designed protein variants exhibited improved expression levels and increased melting temperatures.
Engineered TEV protease variants showed enhanced catalytic activity compared to the parent sequence and existing variants.
Successfully improved key physical and functional properties of target proteins.

Conclusions:

The ProteinMPNN-based approach offers a viable strategy for enhancing protein expression, stability, and function.
This method has broad applicability for improving biotechnologically relevant proteins.
Computational protein design holds significant promise for protein-based technologies.