{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["119(11)"],"submitter":["Ferdosi S"],"pubmed_abstract":["SignificanceDeep profiling of the plasma proteome at scale has been a challenge for traditional approaches. We achieve superior performance across the dimensions of precision, depth, and throughput using a panel of surface-functionalized superparamagnetic nanoparticles in comparison to conventional workflows for deep proteomics interrogation. Our automated workflow leverages competitive nanoparticle-protein binding equilibria that quantitatively compress the large dynamic range of proteomes to an accessible scale. Using machine learning, we dissect the contribution of individual physicochemical properties of nanoparticles to the composition of protein coronas. Our results suggest that nanoparticle functionalization can be tailored to protein sets. This work demonstrates the feasibility of "],"journal":["Proceedings of the National Academy of Sciences of the United States of America"],"pagination":["e2106053119"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8931255"],"repository":["biostudies-literature"],"pubmed_title":["Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactions."],"pmcid":["PMC8931255"],"pubmed_authors":["Ma P","Figa M","Benz R","Garcia VJ","Chang MEK","Langer R","Cuevas JC","Ferdosi S","Riedesel K","Zhao X","Xia H","Elgierari EM","Tangeysh B","Stolarczyk C","Wang T","McLean M","Farokhzad OC","Chu J","Blume JE","Goldberg M","Mahoney M","Hornburg D","Everley PA","Flory MR","Platt TL","Batzoglou S","Siddiqui A","Tao W","Brown TR","Harris D","O'Brien ES"],"additional_accession":[]},"is_claimable":false,"name":"Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactions.","description":"SignificanceDeep profiling of the plasma proteome at scale has been a challenge for traditional approaches. We achieve superior performance across the dimensions of precision, depth, and throughput using a panel of surface-functionalized superparamagnetic nanoparticles in comparison to conventional workflows for deep proteomics interrogation. Our automated workflow leverages competitive nanoparticle-protein binding equilibria that quantitatively compress the large dynamic range of proteomes to an accessible scale. Using machine learning, we dissect the contribution of individual physicochemical properties of nanoparticles to the composition of protein coronas. Our results suggest that nanoparticle functionalization can be tailored to protein sets. This work demonstrates the feasibility of ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2025-04-19T02:16:35.41Z","creation":"2025-04-07T12:48:30.216Z"},"accession":"S-EPMC8931255","cross_references":{"pubmed":["35275789"],"doi":["10.1073/pnas.2106053119"]}}