Ribo-seq guided design of enhanced protein secretion in Komagataella phaffii

preprint OA: closed
📄 Open PDF Full text JSON View at publisher
Full text 1,536 characters · extracted from oa-doi-fallback · click to expand
Abstract The production of recombinant proteins requires the precise coordination of various biological processes, including protein synthesis, folding, trafficking, and secretion. The overproduction of a heterologous protein can impose various bottlenecks on these networks. Identifying and alleviating these bottlenecks can guide strain engineering efforts to enhance protein production. The methylotrophic yeast Komagataella phaffii is used for its high capacity to produce recombinant proteins. Here, we use ribosome profiling to identify bottlenecks in protein secretion during heterologous expression of human serum albumin (HSA). Validation of this analysis showed that the knockout of non-essential genes whose gene products target the ER, through co- and post-translational mechanisms, and have high ribosome utilization can increase production of a heterologous protein, HSA. A triple knockout in co-translationally translocated carbohydrate and acetate transporter Gal2p, cell wall maintenance protein Ydr134cp, and the post-translationally translocated cell wall protein Aoa65896.1 increased HSA production by 35%. This data-driven strain engineering approach uses cell-level information to identify gene targets for phenotype improvement. This specific case identifies hits and creates strains with improved HSA production, with Ribo-seq and bioinformatic analysis to identify non-essential ER targeted proteins that are high ribosome utilizers. Competing Interest Statement The authors have declared no competing interest.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00