Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit

Morgane Boone (), Pathmanaban Ramasamy, Jasper Zuallaert, Robbin Bouwmeester, Berre Moer, Davy Maddelein, Demet Turan, Niels Hulstaert, Hannah Eeckhaut, Elien Vandermarliere, Lennart Martens, Sven Degroeve, Wesley Neve, Wim Vranken and Nico Callewaert ()
Additional contact information
Morgane Boone: Center for Medical Biotechnology, VIB
Pathmanaban Ramasamy: Center for Medical Biotechnology, VIB
Jasper Zuallaert: Center for Medical Biotechnology, VIB
Robbin Bouwmeester: Center for Medical Biotechnology, VIB
Berre Moer: Center for Medical Biotechnology, VIB
Davy Maddelein: Center for Medical Biotechnology, VIB
Demet Turan: Center for Medical Biotechnology, VIB
Niels Hulstaert: Center for Medical Biotechnology, VIB
Hannah Eeckhaut: Center for Medical Biotechnology, VIB
Elien Vandermarliere: Center for Medical Biotechnology, VIB
Lennart Martens: Center for Medical Biotechnology, VIB
Sven Degroeve: Center for Medical Biotechnology, VIB
Wesley Neve: Ghent University Global Campus
Wim Vranken: Structural Biology Brussels, VUB
Nico Callewaert: Center for Medical Biotechnology, VIB

Nature Communications, 2021, vol. 12, issue 1, 1-16

Abstract: Abstract While transcriptome- and proteome-wide technologies to assess processes in protein biogenesis are now widely available, we still lack global approaches to assay post-ribosomal biogenesis events, in particular those occurring in the eukaryotic secretory system. We here develop a method, SECRiFY, to simultaneously assess the secretability of >105 protein fragments by two yeast species, S. cerevisiae and P. pastoris, using custom fragment libraries, surface display and a sequencing-based readout. Screening human proteome fragments with a median size of 50–100 amino acids, we generate datasets that enable datamining into protein features underlying secretability, revealing a striking role for intrinsic disorder and chain flexibility. The SECRiFY methodology generates sufficient amounts of annotated data for advanced machine learning methods to deduce secretability patterns. The finding that secretability is indeed a learnable feature of protein sequences provides a solid base for application-focused studies.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26720-y

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DOI: 10.1038/s41467-021-26720-y

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