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Lou, R.

Publications and source records attributed to Lou, R..

3 recordsLinked to original sources

OST Catalytic Subunit Redundancy Enables Therapeutic Targeting of N-Glycosylation

Protein asparagine (N)-glycosylation, which promotes folding and trafficking of cell surface receptors such as the EGFR, has not been considered a viable target in oncology due to the essential and non-redundant enzymatic activities required for glycan synthesis and transfer. In mammals an exception to this rule is the presence of the oligosaccharyltransferase (OST) catalytic subunit paralogs, STT3A and STT3B. Here we delineate the chemical biology of OST inhibitors and develop an approach for limited inhibition of N-glycosylation optimized for downstream effects on EGFR. Small molecules with enhanced pharmacokinetic properties and preferences for STT3A or STT3B were synthesized, characterized in vitro, and advanced to in vivo testing. The lead from this series, NGI-189, causes tumor regression or growth delay of patient derived and TKI resistant EGFR-mutant lung cancer xenografts without toxicity. Together these results suggest that bioavailable OST inhibitors can be developed as therapeutic agents for oncology. SUMMARYA mechanistic approach for partial inhibition of N-glycosylation delivers small molecules with anti-tumor activity in EGFR mutant NSCLC.

cancer biology↗

High-throughput optimisation of protein secretion in yeast via an engineered biosensor.

Secretion of high value proteins and enzymes is fundamental to the synthetic biology economy; allowing continuous fermentation during production and protein purification without cell lysis. Most eukaryotic protein secretion is encoded by an N-terminal signal peptide; however, the strong impact of signal peptide sequence variation on the secretion efficiency of a given protein is not well defined. Despite high natural signal peptide sequence diversity, most recombinant protein secretion systems employ only a few well characterised signal peptides. Additionally, the selection of promoters and terminators can significantly affect secretion efficiency, yet screening numerous genetic constructs for optimal sequences remains inefficient. Here, we have adapted a yeast G-protein coupled receptor biosensor, to measure the concentration of a peptide tag that is co-secreted with any protein of interest. Protein secretion efficiency can thus be quantified via the induction of a fluorescent reporter that is upregulated downstream of receptor activation. This enables high-throughput screening of over 6000 combinations of promoters, signal peptides and terminators, assembled using one-pot Combinatorial Golden Gate cloning. We demonstrate this biosensor can quickly identify best combinations for secretion and quantify secretion levels. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/594099v2_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@87d612org.highwire.dtl.DTLVardef@1f73d24org.highwire.dtl.DTLVardef@cf35a7org.highwire.dtl.DTLVardef@1cb8a3b_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

A hybrid spectral library combining DIA-MS data and a targeted virtual library substantially deepens the proteome coverage

Data-independent acquisition mass spectrometry (DIA-MS) is a rapidly evolving technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein family-targeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein coupled receptors; ion channels; and transporters) in mouse brain tissues with increases in protein identification of 37-87%, and peptide identification of 58-161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement.

bioinformatics↗