Biology subjects

Juri Rappsilber

Publications and source records attributed to Juri Rappsilber.

5 recordsLinked to original sources

bioRxiv2016

Structure of complement C3(H2O) revealed by quantitative cross-linking/mass spectrometry and modeling

The slow but spontaneous and ubiquitous formation of C3(H2O), the hydrolytic and conformationally rearranged product of C3, initiates antibody-independent activation of the complement system that is a key first line of antimicrobial defense. The structure of C3(H2O) has not been determined. Here we subjected C3(H2O) to quantitative cross-linking/mass spectrometry (QCLMS). This revealed details of the structural differences and similarities between C3(H2O) and C3, as well as between C3(H2O) and its pivotal proteolytic cleavage product, C3b, which shares functionally similarity with C3(H2O). Considered in combination with the crystal structures of C3 and C3b, the QCMLS data suggest that C3(H2O) generation is accompanied by the migration of the thioester-containing domain of C3 from one end of the molecule to the other. This creates a stable C3b-like platform able to bind the zymogen, factor B, or the regulator, factor H. Integration of available crystallographic and QCLMS data allowed the determination of a 3D model of the C3(H2O) domain architecture. The unique arrangement of domains thus observed in C3(H2O), which retains the anaphylatoxin domain (that is excised when C3 is enzymatically activated to C3b), can be used to rationalize observed differences between C3(H2O) and C3b in terms of complement activation and regulation.

Biochemistry↗

bioRxiv2016

Quantitative cross-linking/mass spectrometry using isotope-labeled cross-linkers and MaxQuant

AbstractThe conceptually simple step from cross-linking/mass spectrometry (CLMS) to quantitative cross-linking/mass spectrometry (QCLMS) is compounded by technical challenges. Currently, quantitative proteomics software is tightly integrated with the protein identification workflow. This prevents automatically quantifying other m/z features in a targeted manner including those associated with cross-linked peptides. Here we present a new release of MaxQuant that permits starting the quantification process from an m/z feature list. Comparing the automated quantification to a carefully manually curated test set of cross-linked peptides obtained by cross-linking C3 and C3b with BS3 and isotope-labeled BS3-d4 revealed a number of observations: 1) Fully automated process using MaxQuant can quantify cross-links in our reference dataset with 68% recall rate and 88% accuracy. 2) Hidden quantification errors can be converted into exposed failures by label-swap replica, which makes label-swap replica an essential part of QCLMS. 3) Cross-links that failed during automated quantification can be recovered by semi-automated re-quantification. The integrated workflow of MaxQuant and semi-automated assessment provides the maximum of quantified cross-links. In contrast, work on larger data sets or by less experienced users will benefit from full automation in MaxQuant.\n\nAbbreviations

Biochemistry↗

bioRxiv2016

Quantitative cross-linking/mass spectrometry reveals subtle protein conformational changes

We have developed quantitative cross-linking/mass spectrometry (QCLMS) to interrogate conformational rearrangements of proteins in solution. Our workflow was tested using a structurally well-described reference system, the human complement protein C3 and its activated cleavage product C3b. We found that small local conformational changes affect the yields of cross-linking residues that are near in space while larger conformational changes affect the detectability of cross-links. Distinguishing between minor and major changes required robust analysis based on replica analysis and a label-swapping procedure. By providing workflow, code of practice and a framework for semi-automated data processing, we lay the foundation for QCLMS as a tool to monitor the domain choreography that drives binary switching in many protein-protein interaction networks.\n\nAbbreviations

Biochemistry↗

bioRxiv2016

Blind testing cross-linking/mass spectrometry under the auspices of the 11th critical assessment of methods of protein structure prediction (CASP11)

Determining the structure of a protein by any method requires varies contributions from experimental and computational sides. In a recent study, high-density cross-linking/mass spectrometry data in combination with ab initio structure prediction by conformational space search determined the structure of human serum albumin (HSA) domains, with an RMSD to X-ray structure of up to 2.53 [A], or 3.38 [A] in the context of blood serum. This paper reports the blind test on the readiness of this technology through the help of Critical Assessment of protein Structure Prediction (CASP). We identified between 201-381 unique residue pairs at an estimated 5% FDR (at link level albeit with missing site assignment precision evaluation), for the four proteins that we provided data for. This equates to between 0.63-1.20 proximal residues per residue, which is comparable to that obtained in the HSA study (0.85 links per residue at 5% FDR). Nevertheless, initial results of CASP11 have suggested that improvements in structure prediction using cross-link data are slight. Most significantly, however, CASP11 revealed to us some of the current limitations of cross-linking, spelling out areas in which the method must develop in future: links spread unevenly over sequence and beta sheets both lacked links and suffered from weak definition of observed links over structure. With CASP12 taking place this year and biannually in the future, blind testing low-resolution structure analysis tools is a worthwhile and feasible undertaking. Data are available via ProteomeXchange with identifier PXD003643.\n\nThe abbreviations used are

Biochemistry↗

bioRxiv2016

Nano Random Forests to mine protein complexes and their relationships in quantitative proteomics data

The large and ever increasing numbers of quantitative proteomics datasets constitute a currently underexploited resource for drawing biological insights on proteins and their functions. Multiple observations by different laboratories indicate that protein complexes often follow similar trends. However, proteomic data is often noisy and incomplete - members of a complex may correlate weakly or only in a fraction of all experiments, or may not be observed in all experiments. We have previously used the Random Forest (RF) machine-learning algorithm to distinguish functional chromosomal proteins from hitchhikers in an analysis of mitotic chromosomes. Even though it is assumed that RFs need large training sets, in this technical note we show that RFs also are able to detect small protein complexes and relationships between them. We use artificial datasets to demonstrate the robustness of RFs to identify small groups even when working with mixes of noisy and apparently uninformative experiments. We then use our procedure to retrieve a number of chromosomal complexes from real quantitative proteomics datasets, comparing wild-type and multiple different knock-out mitotic chromosomes. The procedure also revealed other proteins that covary strongly with these complexes suggesting novel functional links. Integrating the RF analysis for several complexes revealed the known interdependency of kinetochore subcomplexes, as well as an unexpected dependency between the Constitutive-Centromere-Associated Network (CCAN) and the condensin (SMC 2/4) complex. Serving as negative control, ribosomal proteins remained independent of kinetochore complexes. Together, these results show that this complex-oriented RF (nanoRF) can uncover subtle protein relationships and higher-order dependencies in integrated proteomics data.

Cell Biology↗