Identifying specific protein interaction partners using quantitative mass spectrometry and bead proteomes

The identification of interaction partners in protein complexes is a major goal in cell biology. Here we present a reliable affinity purification strategy to identify specific interactors that combines quantitative SILAC-based mass spectrometry with characterization of common contaminants binding to affinity matrices (bead proteomes). This strategy can be applied to affinity purification of either tagged fusion protein complexes or endogenous protein complexes, illustrated here using the well-characterized SMN complex as a model. GFP is used as the tag of choice because it shows minimal nonspecific binding to mammalian cell proteins, can be quantitatively depleted from cell extracts, and allows the integration of biochemical protein interaction data with in vivo measurements using fluorescence microscopy. Proteins binding nonspecifically to the most commonly used affinity matrices were determined using quantitative mass spectrometry, revealing important differences that affect experimental design. These data provide a specificity filter to distinguish specific protein binding partners in both quantitative and nonquantitative pull-down and immunoprecipitation experiments

Proteomic and 3D structure analyses highlight the C/D box snoRNP assembly mechanism and its control

International audienceIn vitro, assembly of box C/D small nucleolar ribonucleoproteins (snoRNPs) involves the sequential recruitment of core proteins to snoRNAs. In vivo, however, assembly factors are required (NUFIP, BCD1, and the FISP90-R2TP complex), and it is unknown whether a similar sequential scheme applies. In this paper, we describe systematic quantitative stable isotope labeling by amino acids in cell culture proteomic experiments and the crystal structure of the core protein Snu 13p/15.5K bound to a fragment of the assembly factor Rsa1p/NUFIP. This revealed several unexpected features: (a) the existence of a protein-only pre-snoRNP complex containing five assembly factors and two core proteins, 15.5K and Nop58; (b) the characterization of ZNHIT3, which is present in the protein-only complex but gets released upon binding to C/D snoRNAs; (c) the dynamics of the R2TP complex, which,appears a to load/unload RuvBL AAA(+) adenosine triphosphatase from pre-snoRNPs; and (d) a potential mechanism for preventing premature activation of snoRNP catalytic activity. These data provide a framework for understanding the assembly of box C/D snoRNPs

The Hsp90 chaperone controls the biogenesis of L7Ae RNPs through conserved machinery

RNA-binding proteins of the L7Ae family are at the heart of many essential ribonucleoproteins (RNPs), including box C/D and H/ACA small nucleolar RNPs, U4 small nuclear RNP, telomerase, and messenger RNPs coding for selenoproteins. In this study, we show that Nufip and its yeast homologue Rsa1 are key components of the machinery that assembles these RNPs. We observed that Rsa1 and Nufip bind several L7Ae proteins and tether them to other core proteins in the immature particles. Surprisingly, Rsa1 and Nufip also link assembling RNPs with the AAA + adenosine triphosphatases hRvb1 and hRvb2 and with the Hsp90 chaperone through two conserved adaptors, Tah1/hSpagh and Pih1. Inhibition of Hsp90 in human cells prevents the accumulation of U3, U4, and telomerase RNAs and decreases the levels of newly synthesized hNop58, hNHP2, 15.5K, and SBP2. Thus, Hsp90 may control the folding of these proteins during the formation of new RNPs. This suggests that Hsp90 functions as a master regulator of cell proliferation by allowing simultaneous control of cell signaling and cell growth

PIP30/FAM192A is a novel regulator of the nuclear proteasome activator PA28γ

PA28γ is a nuclear activator of the 20S proteasome involved in the regulation of several essential cellular processes, such as cell proliferation, apoptosis, nuclear dynamics, and cellular stress response. Unlike the 19S regulator of the proteasome, which specifically recognizes ubiquitylated proteins, PA28γ promotes the degradation of several substrates by the proteasome in an ATP- and ubiquitin-independent manner. However, its exact mechanisms of action are unclear and likely involve additional partners that remain to be identified. Here we report the identification of a cofactor of PA28γ, PIP30/FAM192A. PIP30 binds directly and specifically via its C-terminal end and in an interaction stabilized by casein kinase 2 phosphorylation to both free and 20S proteasome-associated PA28γ. Its recruitment to proteasome-containing complexes depends on PA28γ and its expression increases the association of PA28γ with the 20S proteasome in cells. Further dissection of its possible roles shows that PIP30 alters PA28γ-dependent activation of peptide degradation by the 20S proteasome in vitro and negatively controls in cells the presence of PA28γ in Cajal bodies by inhibition of its association with the key Cajal body component coilin. Taken together, our data show that PIP30 deeply affects PA28γ interactions with cellular proteins, including the 20S proteasome, demonstrating that it is an important regulator of PA28γ in cells and thus a new player in the control of the multiple functions of the proteasome within the nucleus

Inverse analysis in geotechnics: soil parameter identification by genetic algorithm

peer reviewe

Trafic et biogenèse des petits ARNs nucléolaires à boîte C/D [sic]

MONTPELLIER-BU Sciences (341722106) / SudocSudocFranceF

Analyse inverse en géotechnique (développement d'une méthode à base d'algorithmes génétiques)

L'objectif de ce travail est de développer une méthode d'analyse inverse permettant d'identifier l'ensemble des paramètres constitutifs d'un sol à partir de mesures géotechniques in situ. La procédure est basée sur un processus d'optimisation par algorithme génétique. L'ensemble des solutions identifiées sont ensuite décrites mathématiquement par une étude statistique de type analyse en composantes principales. Cette étude montre que ce type d'optimisation par algorithme génétique permet d'estimer différentes solutions approchées pour les problèmes inverses de géotechnique. Si toutes les solutions d'un problème ne sont pas identifiées directement par l'algorithme génétique, leur exploitation par une analyse en composantes principales permet d'estimer l'ensemble des solutions du problème inverse. Cette méthode est développée sur des exemples d'ouvrages de soutènemen et d'essais pressiométriques.This study is dedicated to the identification of parameters of soil constitutive models by inverse analysis from in situ geotechnical measurements. The inverse analysis is based on a genetic algorithm optimization process. Furthermore, to describe the set of solutions identified from a genetic algorithm optimization, a statistical analysis based on Principle Component Analysis is introduced. This analysis provides a set of solutions of the inverse problem. If aIl solutions are not directly identify by genetic algorithm, a principle component analysis on these solutions permits to define an envelop in which aIl the parameter values satisfy the inverse problem. These developments are based on sorne synthetic excavation problems and pressuremeter tests.GRENOBLE1-BU Sciences (384212103) / SudocSudocFranceF

Statistical inverse analysis based on genetic algorithm and principal component analysis: Applications to excavation problems and pressuremeter tests

This study concerns the identification of constitutive models from geotechnical measurements by inverse analysis. Soil parameters are identified from measured horizontal displacements of sheet pile walls and from a measured pressuremeter curve. An optimization method based on a genetic algorithm and a principal component analysis, developed and tested on synthetic data in a previous paper, is applied. These applications show that the conclusions deduced from synthetic problems can be extrapolated to real problems. The genetic algorithm is a robust optimization method which is able to deal with the non-uniqueness of the solution in identifying a set of solutions for a given uncertainty on the measurements. This set is then characterized by a principal component analysis (PCA) which gives a fi rst order approximation of the solution as an ellipsoid. When the solution set is not too curved in the research space, this ellipsoid characterizes the soil properties considering the measured data and the tolerate margins for the response of the numerical model. Besides, optimizations from di fferent measurements provide solution sets with a common area in the research space. This intersection gives a more relevant and accurate identification of parameters. Finally, we show that these identified parameters permit to reproduce geotechnical measurements not used in the identification process

Inverse analysis on in situ geotechnical measurement using a genetic algorithm

peer reviewedThis paper is dedicated to the identification of constitutive parameters of the Mohr-Coulomb constitutive model from in situ measurements. A general definition of an objective function is proposed. A direct approach of inverse analysis is used to identify the shear modulus and the friction angle in four different situations. The first two examples deal with a “numerical” and with a real pressuremeter curve. A difficult convergence and a strong non unicity of solution is observed, which is classical in inverse analysis (ill posed problems). In a second stage, the horizontal displacements related to two excavation problems are used for identifying the two mechanical parameters. A clear minimum of the objective function is detected, giving a unique solution. The reasons of these differences are discussed and some ways of improving the interpretation of the pressuremeter test results are proposed