Due to their small size, nanoparticles have distinct properties compared with the bulk form of the same materials. These properties are rapidly revolutionizing many areas of medicine and technology. Despite the remarkable speed of development of nanoscience, relatively little is known about the interaction of nanoscale objects with living systems. In a biological fluid, proteins associate with nanoparticles, and the amount and presentation of the proteins on the surface of the particles leads to an in vivo response. Proteins compete for the nanoparticle "surface," leading to a protein "corona" that largely defines the biological identity of the particle. Thus, knowledge of rates, affinities, and stoichiometries of protein association with, and dissociation from, nanoparticles is important for understanding the nature of the particle surface seen by the functional machinery of cells. Here we develop approaches to study these parameters and apply them to plasma and simple model systems, albumin and fibrinogen. A series of copolymer nanoparticles are used with variation of size and composition (hydrophobicity). We show that isothermal titration calorimetry is suitable for studying the affinity and stoichiometry of protein binding to nanoparticles. We determine the rates of protein association and dissociation using surface plasmon resonance technology with nanoparticles that are thiol-linked to gold, and through size exclusion chromatography of protein-nanoparticle mixtures. This method is less perturbing than centrifugation, and is developed into a systematic methodology to isolate nanoparticle-associated proteins. The kinetic and equilibrium binding properties depend on protein identity as well as particle surface characteristics and size.

https://www.pnas.org/content/pnas/104/7/2050.full.pdf

Understanding the nanoparticle-protein corona using methods to quantify exchange rates and affinities of proteins for nanoparticles.

http://csmres.co.uk/cs.public.upd/article-downloads/lynch.pdf

Protein-nanoparticle interactions

In this work, we explore the formation of the protein corona after exposure of metallic Au nanoparticles (NPs), with sizes ranging from 4 to 40 nm, to cell culture media containing 10% of fetal bovine serum. Under in vitro cell culture conditions, zeta potential measurements, UV-vis spectroscopy, dynamic light scattering and transmission electron microscope analysis were used to monitor the time evolution of the inorganic NP-protein corona formation and to characterize the stability of the NPs and their surface state at every stage of the experiment. As expected, the red-shift of the surface plasmon resonance peak, as well as the drop of surface charge and the increase of the hydrodynamic diameter indicated the conjugation of proteins to NPs. Remarkably, an evolution from a loosely attached toward an irreversible attached protein corona over time was observed. Mass spectrometry of the digested protein corona revealed albumin as the most abundant component which suggests an improved biocompatibility.

Time Evolution of the Nanoparticle Protein Corona

Protein interaction networks and protein compartmentalization underlie all signaling and regulatory processes in cells. Enzyme-catalyzed proximity labeling (PL) has emerged as a new approach to study the spatial and interaction characteristics of proteins in living cells. However, current PL methods require over 18 h of labeling time or utilize chemicals with limited cell permeability or high toxicity. We used yeast display-based directed evolution to engineer two promiscuous mutants of biotin ligase, TurboID and miniTurbo, which catalyze PL with much greater efficiency than BioID or BioID2, and enable 10-min PL in cells with non-toxic and easily deliverable biotin. Furthermore, TurboID extends biotin-based PL to flies and worms.

Efficient proximity labeling in living cells and organisms with TurboID.

HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate-anti-coagulated plasma; and (3) created a publicly-available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty-five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS-MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS-MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multiple matches of peptide sequences yielded 9504 IPI proteins identified with one or more peptides and 3020 proteins identified with two or more peptides (the Core Dataset). These proteins have been characterized with Gene Ontology, InterPro, Novartis Atlas, OMIM, and immunoassay-based concentration determinations. The database permits examination of many other subsets, such as 1274 proteins identified with three or more peptides. Reverse protein to DNA matching identified proteins for 118 previously unidentified ORFs. We recommend use of plasma instead of serum, with EDTA (or citrate) for anticoagulation. To improve resolution, sensitivity and reproducibility of peptide identifications and protein matches, we recommend combinations of depletion, fractionation, and MS/MS technologies, with explicit criteria for evaluation of spectra, use of search algorithms, and integration of homologous protein matches. This Special Issue of PROTEOMICS presents papers integral to the collaborative analysis plus many reports of supplementary work on various aspects of the PPP workplan. These PPP results on complexity, dynamic range, incomplete sampling, false-positive matches, and integration of diverse datasets for plasma and serum proteins lay a foundation for development and validation of circulating protein biomarkers in health and disease.

/pdf/overview-of-the-hupo-plasma-proteome-project-results-from-4wmgc4w193.pdf

Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database.

Plasma is one of the best studied compartments in the human body and serves as an ideal body fluid for the diagnosis of diseases. This report provides a detailed functional annotation of all the plasma proteins identified to date. In all, gene products encoded by 3778 distinct genes were annotated based on proteins previously published in the literature as plasma proteins and the identification of multiple peptides from proteins under HUPO's Plasma Proteome Project. Our analysis revealed that 51% of these genes encoded more than one protein isoform. All single nucleotide polymorphisms involving protein-coding regions were mapped onto the protein sequences. We found a number of examples of isoform-specific subcellular localization as well as tissue expression. This database is an attempt at comprehensive annotation of a complex subproteome and is available on the web at http://www.plasmaproteomedatabase.org.

S. Lynate John

Papers

Plasma Proteome Database as a resource for proteomics research