2021
DOI: 10.1101/2021.06.16.448636
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SLIMP: Supervised learning of metabolite-protein interactions from co-fractionation mass spectrometry data

Abstract: Metabolite-protein interactions affect and shape diverse cellular processes. Yet, despite advances, approaches for identifying metabolite-protein interactions at a genome-wide scale are lacking. Here we present an approach termed SLIMP that predicts metabolite-protein interactions using supervised machine learning on features engineered from metabolic and proteomic profiles from a co-fractionation mass spectrometry-based technique. By applying SLIMP with gold standards, assembled from public databases, along w… Show more

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Cited by 4 publications
(12 citation statements)
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“…Moreover, the combined increased Glu and Glu-containing dipeptides at the same time points in the dark might indicate altered N metabolism (Hildebrandt et al, 2015) along the day under limited C supplied for growth (SD), although dipeptides regulatory roles FIGURE 4 | Pro-Gln/protein interaction network. (A) Pro-Gln interactors were predicted by SLIMP (Zühlke et al, 2021) with a score above 0.5. Protein-protein interactions were imported from STRING (Szklarczyk et al, 2016) based on the experimental and database evidence.…”
Section: Discussionmentioning
confidence: 99%
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“…Moreover, the combined increased Glu and Glu-containing dipeptides at the same time points in the dark might indicate altered N metabolism (Hildebrandt et al, 2015) along the day under limited C supplied for growth (SD), although dipeptides regulatory roles FIGURE 4 | Pro-Gln/protein interaction network. (A) Pro-Gln interactors were predicted by SLIMP (Zühlke et al, 2021) with a score above 0.5. Protein-protein interactions were imported from STRING (Szklarczyk et al, 2016) based on the experimental and database evidence.…”
Section: Discussionmentioning
confidence: 99%
“…Pro-Gln interactors were retrieved from SLIMP ( Zühlke et al, 2021 ). Protein-protein interactions were imported from STRING ( Szklarczyk et al, 2016 ; Supplementary Table 6 ), and used to build the interaction network of the Pro-Gln interactors into Cytoscape ( Shannon et al, 2003 ; Supplementary Table 7 ).…”
Section: Methodsmentioning
confidence: 99%
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“…It has been shown that 2′,3′-cAMP can bind to RNA-binding motives in the protein sequence of Rbp47b, an SG marker to facilitate SG formation (Kosmacz et al, 2018). Interestingly, it was further confirmed by small molecule-protein complex studies that seven out of the 31 high-confidence 2′, 3′-cAMP targets predicted by SLIMP (Zuhlke et al, 2021) contain an RRM domain. The identified list also contains a plastidial protein CP29, a component of the plastidial SG (Chodasiewicz et al, 2020).…”
Section: Discussionmentioning
confidence: 93%