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Decoding sequence-level information to predict membrane protein expression

Shyam M. Saladi, Nauman Javed, Axel Müller, View ORCID ProfileWilliam M. Clemons Jr.
doi: https://doi.org/10.1101/098673
Shyam M. Saladi
Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125
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Nauman Javed
Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125
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Axel Müller
Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125
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William M. Clemons Jr.
Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125
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  • ORCID record for William M. Clemons Jr.
  • For correspondence: clemons@caltech.edu
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Abstract

The expression and purification of integral membrane proteins remains a major bottleneck in the characterization of these important proteins. Expression levels are currently unpredictable, which renders the pursuit of these targets challenging and highly inefficient. Evidence demonstrates that small changes in the nucleotide or amino-acid sequence can dramatically affect membrane protein biogenesis; yet these observations have not resulted in generalizable approaches to improve expression. In this study, we develop a data-driven statistical model that predicts membrane protein expression in E. coli directly from sequence. The model, trained on experimental data, combines a set of sequence-derived variables resulting in a score that predicts the likelihood of expression. We test the model against various independent datasets from the literature that contain a variety of scales and experimental outcomes demonstrating that the model significantly enriches expressed proteins. The model is then used to score expression for membrane proteomes and protein families highlighting areas where the model excels. Surprisingly, analysis of the underlying features reveals an importance in nucleotide sequence-derived parameters for expression. This computational model, as illustrated here, can immediately be used to identify favorable targets for characterization.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted January 05, 2017.
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Decoding sequence-level information to predict membrane protein expression
Shyam M. Saladi, Nauman Javed, Axel Müller, William M. Clemons Jr.
bioRxiv 098673; doi: https://doi.org/10.1101/098673
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Decoding sequence-level information to predict membrane protein expression
Shyam M. Saladi, Nauman Javed, Axel Müller, William M. Clemons Jr.
bioRxiv 098673; doi: https://doi.org/10.1101/098673

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