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Improving membrane protein expression and function using genomic edits.

ABSTRACT: Expression of membrane proteins often leads to growth inhibition and perturbs central metabolism and this burden varies with the protein being overexpressed. There are also known strain backgrounds that allow greater expression of membrane proteins but that differ in efficacy across proteins. We hypothesized that for any membrane protein, it may be possible to identify a modified strain background where its expression can be accommodated with less burden. To directly test this hypothesis, we used a bar-coded transposon insertion library in tandem with cell sorting to assess genome-wide impact of gene deletions on membrane protein expression. The expression of five membrane proteins (CyoB, CydB, MdlB, YidC, and LepI) and one soluble protein (GST), each fused to GFP, was examined. We identified Escherichia coli mutants that demonstrated increased membrane protein expression relative to that in wild type. For two of the proteins (CyoB and CydB), we conducted functional assays to confirm that the increase in protein expression also led to phenotypic improvement in function. This study represents a systematic approach to broadly identify genetic loci that can be used to improve membrane protein expression, and our method can be used to improve expression of any protein that poses a cellular burden.

SUBMITTER: Jensen HM

PROVIDER: S-EPMC5638813 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Improving membrane protein expression and function using genomic edits.

Jensen Heather M HM Eng Thomas T Chubukov Victor V Herbert Robin A RA Mukhopadhyay Aindrila A

Scientific reports 20171012 1

Expression of membrane proteins often leads to growth inhibition and perturbs central metabolism and this burden varies with the protein being overexpressed. There are also known strain backgrounds that allow greater expression of membrane proteins but that differ in efficacy across proteins. We hypothesized that for any membrane protein, it may be possible to identify a modified strain background where its expression can be accommodated with less burden. To directly test this hypothesis, we use ...[more]

PMID: 29026162

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Project description:Most sequence alignment tools can successfully align protein sequences with higher levels of sequence identity. The accuracy of corresponding structure alignment, however, decreases rapidly when considering distantly related sequences (<20% identity). In this range of identity, alignments optimized so as to maximize sequence similarity are often inaccurate from a structural point of view. Over the last two decades, most multiple protein aligners have been optimized for their capacity to reproduce structure-based alignments while using sequence information. Methods currently available differ essentially in the similarity measurement between aligned residues using substitution matrices, Fourier transform, sophisticated profile-profile functions, or consistency-based approaches, more recently.In this paper, we present a flexible similarity measure for residue pairs to improve the quality of protein sequence alignment. Our approach, called SymAlign, relies on the identification of conserved words found across a sizeable fraction of the considered dataset, and supported by evolutionary analysis. These words are then used to define a position specific substitution matrix that better reflects the biological significance of local similarity. The experiment results show that the SymAlign scoring scheme can be incorporated within T-Coffee to improve sequence alignment accuracy. We also demonstrate that SymAlign is less sensitive to the presence of structurally non-similar proteins. In the analysis of the relationship between sequence identity and structure similarity, SymAlign can better differentiate structurally similar proteins from non- similar proteins. We show that protein sequence alignments can be significantly improved using a similarity estimation based on weighted n-grams. In our analysis of the alignments thus produced, sequence conservation becomes a better indicator of structural similarity. SymAlign also provides alignment visualization that can display sub-optimal alignments on dot-matrices. The visualization makes it easy to identify well-supported alternative alignments that may not have been identified by dynamic programming. SymAlign is available at http://bio-cluster.iis.sinica.edu.tw/SymAlign/.

Dataset Information

Improving membrane protein expression and function using genomic edits.

Publications

Improving membrane protein expression and function using genomic edits.

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