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Dynamic maps of protein-protein-metabolite complexes in S. cerevisiae

ABSTRACT: The project aims to create dynamic maps of protein-protein-metabolite complexes in S. cerevisiae across growth phases using PROMIS (PROtein–Metabolite Interactions using Size separation). It is a biochemical, untargeted, proteome- and metabolome-wide method to study protein-protein and protein–metabolite complexes close to in vivo conditions. Approach involves using size exclusion chromatography (SEC) to separate complexes, followed by LC-MS-based proteomics and metabolomics analysis. This dataset was used for mashie learning approach: SLIMP, supervised learning of metabolite-protein interactions from multiple co-fractionation mass spectrometry datasets, to compute a global map of metabolite-protein interactions.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Saccharomyces Cerevisiae (baker's Yeast)

TISSUE(S): Cell Culture, Culture Condition:yeast Extract-grown Cell

SUBMITTER: Ewelina Sokolowska

LAB HEAD: Aleksandra Skirycz

PROVIDER: PXD026639 | Pride | 2022-10-25

REPOSITORIES: pride

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	D_A10.raw	Raw
	D_A11.raw	Raw
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	D_A14.raw	Raw

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Project description:The project aimed to create dynamic maps of protein-protein-metabolite complexes of Arabidopsis thaliana seedlings using PROMIS (PROtein–Metabolite Interactions using Size separation). The approach involves using size exclusion chromatography (SEC) to separate complexes, followed by LC-MS-based proteomics and metabolomics analysis of the obtained fractions. Co-elution is used to reconstruct the protein-metabolite interactions (PMIs) networks. PROMIS strongly progresses understanding protein-small molecule interactions due to its non-targeted manner, cell-wide scale, and generic nature, making it suitable across biological systems. Combining PROMIS with mashing learning approach SLIMP “supervised learning of metabolite-protein interactions from multiple co-fractionation mass spectrometry datasets” allows computing a global map of metabolite-protein interactions in vivo.

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