The mitochondrial complexome of Arabidopsis thaliana

Mitochondria are central to cellular metabolism and energy conversion. In plants they also enable photosynthesis through additional components and functional flexibility. A majority of those processes relies on the assembly of individual proteins to larger protein complexes, some of which operate as large molecular machines. There has been a strong interest in the makeup and function of mitochondrial protein complexes and protein–protein interactions in plants, but the experimental approaches used typically suffer from selectivity or bias. Here, we present a complexome profiling analysis for leaf mitochondria of the model plant Arabidopsis thaliana for the systematic characterization of protein assemblies. Purified organelle extracts were separated by 1D Blue native (BN) PAGE, a resulting gel lane was dissected into 70 slices (complexome fractions) and proteins in each slice were identified by label free quantitative shot‐gun proteomics. Overall, 1359 unique proteins were identified, which were, on average, present in 17 complexome fractions each. Quantitative profiles of proteins along the BN gel lane were aligned by similarity, allowing us to visualize protein assemblies. The data allow re‐annotating the subunit compositions of OXPHOS complexes, identifying assembly intermediates of OXPHOS complexes and assemblies of alternative respiratory oxidoreductases. Several protein complexes were discovered that have not yet been reported in plants, such as a 530 kDa Tat complex, 460 and 1000 kDa SAM complexes, a calcium ion uniporter complex (150 kDa) and several PPR protein complexes. We have set up a tailored online resource ( https://complexomemap.de/at_mito_leaves ) to deposit the data and to allow straightforward access and custom data analyses.     

Determination of protein complex stoichiometry through multisignal sedimentation velocity experiments

Determination of the stoichiometry of macromolecular assemblies is fundamental to an understanding of how they function. Many different biophysical methodologies may be used to determine stoichiometry. In the past, both sedimentation equilibrium and sedimentation velocity analytical ultracentrifugation have been employed to determine component stoichiometries. Recently, a method of globally analyzing multisignal sedimentation velocity data was introduced by Schuck and coworkers. This global analysis removes some of the experimental inconveniences and inaccuracies that could occur in the previously used strategies. This method uses spectral differences between the macromolecular components to decompose the well-known c(s) distribution into component distributions c_k(s); that is, each component k has its own c_k(s) distribution. Integration of these distributions allows the calculation of the populations of each component in cosedimenting complexes, yielding their stoichiometry. In our laboratories, we have used this method extensively to determine the component stoichiometries of several protein-protein complexes involved in cytoskeletal remodeling, sugar metabolism, and host-pathogen interactions. The overall method is described in detail in this work, as are experimental examples and caveats.     

An empirical approach for structure-based prediction of carbohydrate-binding sites on proteins

A computer program system was developed to predict carbohydrate-binding sites on three-dimensional (3D) protein structures. The programs search for binding sites by referring to the empirical rules derived from the known 3D structures of carbohydrate-protein complexes. A total of 80 non-redundant carbohydrate-protein complex structures were selected from the Protein Data Bank for the empirical rule construction. The performance of the prediction system was tested on 50 known complex structures to determine whether the system could detect the known binding sites. The known monosaccharide-binding sites were detected among the best three predictions in 59% of the cases, which covered 69% of the polysaccharide-binding sites in the target proteins, when the performance was evaluated by the overlap between residue patches of predicted and known binding sites.     

Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces

Macromolecular oligomeric assemblies are involved in many biochemical processes of living organisms. The benefits of such assemblies in crowded cellular environments include increased reaction rates, efficient feedback regulation, cooperativity and protective functions. However, an atom‐level structural determination of large assemblies is challenging due to the size of the complex and the difference in binding affinities of the involved proteins. In this study, we propose a novel combinatorial greedy algorithm for assembling large oligomeric complexes from information on the approximate position of interaction interfaces of pairs of monomers in the complex. Prior information on complex symmetry is not required but rather the symmetry is inferred during assembly. We implement an efficient geometric score, the transformation match score, that bypasses the model ranking problems of state‐of‐the‐art scoring functions by scoring the similarity between the inferred dimers of the same monomer simultaneously with different binding partners in a (sub)complex with a set of pregenerated docking poses. We compiled a diverse benchmark set of 308 homo and heteromeric complexes containing 6 to 60 monomers. To explore the applicability of the method, we considered 48 sets of parameters and selected those three sets of parameters, for which the algorithm can correctly reconstruct the maximum number, namely 252 complexes (81.8%) in, at least one of the respective three runs. The crossvalidation coverage, that is, the mean fraction of correctly reconstructed benchmark complexes during crossvalidation, was 78.1%, which demonstrates the ability of the presented method to correctly reconstruct topology of a large variety of biological complexes. Proteins 2015; 83:1887–1899. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

Fast fitting of atomic structures to low-resolution electron density maps by surface overlap maximization

The complexities of X-ray crystallography and NMR spectroscopy for large protein complexes, and the comparative ease of approaches such as electron microscopy mean that low-resolution structures are often available long before their atomic resolution equivalents. To help bridge this gap in knowledge, we present 3SOM: an approach for finding the best fit of atomic resolution structures into lower-resolution density maps through surface overlap maximization. High-resolution templates (i.e. partial structures or models for multi-subunit complexes) and targets (lower-resolution maps) are initially represented as iso-surfaces. The latter are used first in a fast search for transformations that superimpose a significant portion of the target surface onto the template surface, which is quantified as surface overlap. The vast search space is reduced by considering key vectors that capture local surface information. The set of transformations with the highest surface overlap scores are then re-ranked by using more sophisticated scores including cross-correlation. We give a number of examples to illustrate the efficiency of the method and its restrictions. For targets for which partial complexes are available, the speed and performance of the method make it an attractive complement to existing methods, as many different hypotheses can be tested quickly on a single processor.     

Interactions between semaphorins and plexin–neuropilin receptor complexes in the membranes of live cells

Signaling of semaphorin ligands via their plexin–neuropilin receptors is involved in tissue patterning in the developing embryo. These proteins play roles in cell migration and adhesion but are also important in disease etiology, including in cancer angiogenesis and metastasis. While some structures of the soluble domains of these receptors have been determined, the conformations of the full-length receptor complexes are just beginning to be elucidated, especially within the context of the plasma membrane. Pulsed-interleaved excitation fluorescence cross-correlation spectroscopy allows direct insight into the formation of protein–protein interactions in the membranes of live cells. Here, we investigated the homodimerization of neuropilin-1 (Nrp1), plexin A2, plexin A4, and plexin D1 using pulsed-interleaved excitation fluorescence cross-correlation spectroscopy. Consistent with previous studies, we found that Nrp1, plexin A2, and plexin A4 are present as dimers in the absence of exogenous ligand. Plexin D1, on the other hand, was monomeric under similar conditions, which had not been previously reported. We also found that plexin A2 and A4 assemble into a heteromeric complex. Stimulation with semaphorin 3A or semaphorin 3C neither disrupts nor enhances the dimerization of the receptors when expressed alone, suggesting that activation involves a conformational change rather than a shift in the monomer–dimer equilibrium. However, upon stimulation with semaphorin 3C, plexin D1 and Nrp1 form a heteromeric complex. This analysis of interactions provides a complementary approach to the existing structural and biochemical data that will aid in the development of new therapeutic strategies to target these receptors in cancer.     

MR microscopy of the human fetal upper extremity – a proof-of-principle study

Background

Bone morphogenetic proteins regulate multiple processes in embryonic development, including early dorso-ventral patterning and neural crest development. BMPs activate heteromeric receptor complexes consisting of type I and type II receptor-serine/threonine kinases. BMP receptors Ia and Ib, also known as ALK3 and ALK6 respectively, are the most common type I receptors that likely mediate most BMP signaling events. Since early expression patterns and functions in Xenopus laevis development have not been described, we have addressed these questions in the present study.

Results

Here we have analyzed the temporal and spatial expression patterns of ALK3 and ALK6; we have also carried out loss-of-function studies to define the function of these receptors in early Xenopus development. We detected both redundant and non-redundant roles of ALK3 and ALK6 in dorso-ventral patterning. From late gastrula stages onwards, their expression patterns diverged, which correlated with a specific, non-redundant requirement of ALK6 in post-gastrula neural crest cells. ALK6 was essential for induction of neural crest cell fate and further development of the neural crest and its derivatives.

Conclusions

ALK3 and ALK6 both contribute to the gene regulatory network that regulates dorso-ventral patterning; they play partially overlapping and partially non-redundant roles in this process. ALK3 and ALK6 are independently required for the spatially restricted activation of BMP signaling and msx2 upregulation at the neural plate border, whereas in post-gastrula development ALK6 exerts a highly specific, conserved function in neural crest development.

     

Formation of reactive oxygen species by human and bacterial pyruvate and 2-oxoglutarate dehydrogenase multienzyme complexes reconstituted from recombinant components

Individual recombinant components of pyruvate and 2-oxoglutarate dehydrogenase multienzyme complexes (PDHc, OGDHc) of human and Escherichia coli (E. coli) origin were expressed and purified from E. coli with optimized protocols. The four multienzyme complexes were each reconstituted under optimal conditions at different stoichiometric ratios. Binding stoichiometries for the highest catalytic efficiency were determined from the rate of NADH generation by the complexes at physiological pH. Since some of these complexes were shown to possess ‘moonlighting’ activities under pathological conditions often accompanied by acidosis, activities were also determined at pH 6.3. As reactive oxygen species (ROS) generation by the E3 component of hOGDHc is a pathologically relevant feature, superoxide generation by the complexes with optimal stoichiometry was measured by the acetylated cytochrome c reduction method in both the forward and the reverse catalytic directions. Various known affectors of physiological activity and ROS production, including Ca²⁺, ADP, lipoylation status or pH, were investigated. The human complexes were also reconstituted with the most prevalent human pathological mutant of the E3 component, G194C and characterized; isolated human E3 with the G194C substitution was previously reported to have an enhanced ROS generating capacity. It is demonstrated that: i. PDHc, similarly to OGDHc, is able to generate ROS and this feature is displayed by both the E. coli and human complexes, ii. Reconstituted hPDHc generates ROS at a significantly higher rate as compared to hOGDHc in both the forward and the reverse reactions when ROS generation is calculated for unit mass of their common E3 component, iii. The E1 component or E1-E2 subcomplex generates significant amount of ROS only in hOGDHc; iv. Incorporation of the G194C variant of hE3, the result of a disease-causing mutation, into reconstituted hOGDHc and hPDHc indeed leads to a decreased activity of both complexes and higher ROS generation by only hOGDHc and only in its reverse reaction.     

Sampling Native-like Structures of RNA-Protein Complexes through Rosetta Folding and Docking

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Facilitated Protein Association via Engineered Target Search Pathways Visualized by Paramagnetic NMR Spectroscopy

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Membranome 3.0: Database of single‐pass membrane proteins with AlphaFold models

The Membranome database provides comprehensive structural information on single‐pass (i.e., bitopic) membrane proteins from six evolutionarily distant organisms, including protein–protein interactions, complexes, mutations, experimental structures, and models of transmembrane α‐helical dimers. We present a new version of this database, Membranome 3.0, which was significantly updated by revising the set of 5,758 bitopic proteins and incorporating models generated by AlphaFold 2 in the database. The AlphaFold models were parsed into structural domains located at the different membrane sides, modified to exclude low‐confidence unstructured terminal regions and signal sequences, validated through comparison with available experimental structures, and positioned with respect to membrane boundaries. Membranome 3.0 was re‐developed to facilitate visualization and comparative analysis of multiple 3D structures of proteins that belong to a specified family, complex, biological pathway, or membrane type. New tools for advanced search and analysis of proteins, their interactions, complexes, and mutations were included. The database is freely accessible at https://membranome.org.     

Identification and characterization of protein subcomplexes in yeast

Protein complexes are major components of cellular organization. Based on large-scale protein complex data, we present the first statistical procedure to find insightful substructures in protein complexes: we identify protein subcomplexes (SCs), i.e., multiprotein assemblies residing in different protein complexes. Four protein complex datasets with different origins and variable reliability are separately analyzed. Our method identifies well-characterized protein assemblies with known functions, thereby confirming the utility of the procedure. In addition, we also identify hitherto unknown functional entities consisting of either functionally unknown proteins or proteins with different functional annotation. We show that SCs represent more reliable protein assemblies than the original complexes. Finally, we demonstrate unique properties of subcomplex proteins that underline the distinct roles of SCs: (i) SCs are functionally and spatially more homogeneous than complete protein complexes (this fact is utilized to predict functional roles and subcellular localizations for so far unannotated proteins); (ii) the abundance of subcomplex proteins is less variable than the abundance of other proteins; (iii) SCs are enriched with essential and synthetic lethal proteins; and (iv) mutations in SC-proteins have higher fitness effects than mutations in other proteins.     

Detecting neural assemblies in calcium imaging data

Background

Activity in populations of neurons often takes the form of assemblies, where specific groups of neurons tend to activate at the same time. However, in calcium imaging data, reliably identifying these assemblies is a challenging problem, and the relative performance of different assembly-detection algorithms is unknown.

Results

To test the performance of several recently proposed assembly-detection algorithms, we first generated large surrogate datasets of calcium imaging data with predefined assembly structures and characterised the ability of the algorithms to recover known assemblies. The algorithms we tested are based on independent component analysis (ICA), principal component analysis (Promax), similarity analysis (CORE), singular value decomposition (SVD), graph theory (SGC), and frequent item set mining (FIM-X). When applied to the simulated data and tested against parameters such as array size, number of assemblies, assembly size and overlap, and signal strength, the SGC and ICA algorithms and a modified form of the Promax algorithm performed well, while PCA-Promax and FIM-X did less well, for instance, showing a strong dependence on the size of the neural array. Notably, we identified additional analyses that can improve their importance. Next, we applied the same algorithms to a dataset of activity in the zebrafish optic tectum evoked by simple visual stimuli, and found that the SGC algorithm recovered assemblies closest to the averaged responses.

Conclusions

Our findings suggest that the neural assemblies recovered from calcium imaging data can vary considerably with the choice of algorithm, but that some algorithms reliably perform better than others. This suggests that previous results using these algorithms may need to be reevaluated in this light.

     

New thiocyanato and azido adducts of the redox-active Fe(η-C5Me5)(η-dppe) center: Synthesis and study of the Fe(II) and Fe(III) complexes

The new thiocyanato- (5) and azido- (6) complexes were synthesized and studied under their Fe(II) and Fe(III) redox states. For the first time among the various [Fe(η⁵-C₅Me₅)(η²-dppe)]-based cationic radicals studied so far, the magnitude and spatial orientation of the g-tensor diagonal values were experimentally determined for 5[PF₆]. These data are in good agreement with those issued from a DFT modelization. The changes experienced by the electronic structure of the Fe(II) complexes subsequent to oxidation are reminiscent of these previously observed for the known arylalkynyl analogues, albeit some differences can be pointed out. Thus, the differences observed in the ¹H NMR spectra of 5[PF₆] and 6[PF₆] are attributed to a slower electronic spin relaxation and to the differently oriented magnetic anisotropy. The sizeable spin density evidenced by DFT on the terminal atom of the ligands of the Fe(III) complexes renders these new paramagnetic metallo-ligands quite appealing for accessing larger polynuclear molecular assemblies with magnetically interacting centers.     

Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery

Background

Although high-throughput microarray based molecular diagnostic technologies show a great promise in cancer diagnosis, it is still far from a clinical application due to its low and instable sensitivities and specificities in cancer molecular pattern recognition. In fact, high-dimensional and heterogeneous tumor profiles challenge current machine learning methodologies for its small number of samples and large or even huge number of variables (genes). This naturally calls for the use of an effective feature selection in microarray data classification.

Methods

We propose a novel feature selection method: multi-resolution independent component analysis (MICA) for large-scale gene expression data. This method overcomes the weak points of the widely used transform-based feature selection methods such as principal component analysis (PCA), independent component analysis (ICA), and nonnegative matrix factorization (NMF) by avoiding their global feature-selection mechanism. In addition to demonstrating the effectiveness of the multi-resolution independent component analysis in meaningful biomarker discovery, we present a multi-resolution independent component analysis based support vector machines (MICA-SVM) and linear discriminant analysis (MICA-LDA) to attain high-performance classifications in low-dimensional spaces.

Results

We have demonstrated the superiority and stability of our algorithms by performing comprehensive experimental comparisons with nine state-of-the-art algorithms on six high-dimensional heterogeneous profiles under cross validations. Our classification algorithms, especially, MICA-SVM, not only accomplish clinical or near-clinical level sensitivities and specificities, but also show strong performance stability over its peers in classification. Software that implements the major algorithm and data sets on which this paper focuses are freely available at https://sites.google.com/site/heyaumapbc2011/.

Conclusions

This work suggests a new direction to accelerate microarray technologies into a clinical routine through building a high-performance classifier to attain clinical-level sensitivities and specificities by treating an input profile as a ‘profile-biomarker’. The multi-resolution data analysis based redundant global feature suppressing and effective local feature extraction also have a positive impact on large scale ‘omics’ data mining.

     

Apparent homomeric NR1 currents observed in Xenopus oocytes are caused by an endogenous NR2 subunit

Functional N-methyl-d-aspartate receptors NMDARs are thought to be heteromeric receptor complexes consisting of NR1 and NR2 subunits. However, recombinant NR1 subunits expressed in Xenopus oocytes assemble functional ion channels even without exogenous NR2 subunits and with a different pharmacology, suggesting a homomeric subunit stoichiometry. To explain this phenomenon, we screened oocytes for Xenopus NR2 subunits and found all four subunit-encoding mRNAs (XenNR2A-XenNR2D) to be present endogenously, with those encoding the XenNR2B subunit being particularly abundant. We cloned the full-length XenNR2B cDNA and co-expressed it with NR1 in oocytes. A detailed electrophysiological characterization revealed that the pharmacology of NR1/XenNR2B was identical with that of the presumed homomeric NMDARs expressed from NR1 subunits. By contrast, heteromeric receptors containing the rat NR2B subunit showed significant pharmacological differences compared with NR1/XenNR2B receptors. These results demonstrate that recombinant NR1 subunits expressed in Xenopus oocytes interact with an endogenously expressed NR2B subunit and form hybrid heteromeric NMDARs. These findings confirm the current views that NMDARs are obligatory heteromeric complexes and that functional homomeric NMDARs do not exist.     

Steps towards flexible docking: modeling of three-dimensional structures of the nuclear receptors bound with peptide ligands mimicking co-activators' sequences

We developed a fully flexible docking method that uses a reduced lattice representation of protein molecules, adapted for modeling peptide–protein complexes. The CABS model (Carbon Alpha, Carbon Beta, Side Group) employed here, incorporates three pseudo-atoms per residue—C, Cβ and the center of the side group instead of full-atomic protein representation. Force field used by CABS was derived from statistical analysis of non-redundant database of protein structures. Application of our method included modeling of the complexes between various nuclear receptors (NRs) and peptide co-activators, for which three-dimensional structures are known. We tried to rebuild the native state of the complexes, starting from separated components. Accuracy of the best obtained models, calculated as coordinate root-mean-square deviation (cRMSD) between the target and the modeled structures, was under 1 Å, which is competitive with experimental methods, such as crystallography or NMR. Forthcoming modeling study should lead to better understanding of mechanisms of macromolecular assembly and will explain co-activators’ effects on receptors activity, especially on vitamin D receptor and other nuclear receptors.     

Analysing the yeast complexome—the Complex Portal rising to the challenge

The EMBL-EBI Complex Portal is a knowledgebase of macromolecular complexes providing persistent stable identifiers. Entries are linked to literature evidence and provide details of complex membership, function, structure and complex-specific Gene Ontology annotations. Data are freely available and downloadable in HUPO-PSI community standards and missing entries can be requested for curation. In collaboration with Saccharomyces Genome Database and UniProt, the yeast complexome, a compendium of all known heteromeric assemblies from the model organism Saccharomyces cerevisiae, was curated. This expansion of knowledge and scope has led to a 50% increase in curated complexes compared to the previously published dataset, CYC2008. The yeast complexome is used as a reference resource for the analysis of complexes from large-scale experiments. Our analysis showed that genes coding for proteins in complexes tend to have more genetic interactions, are co-expressed with more genes, are more multifunctional, localize more often in the nucleus, and are more often involved in nucleic acid-related metabolic processes and processes where large machineries are the predominant functional drivers. A comparison to genetic interactions showed that about 40% of expanded co-complex pairs also have genetic interactions, suggesting strong functional links between complex members.     

Structural modeling of heteromeric protein complexes from disassembly pathways and ion mobility-mass spectrometry

Structure determination of macromolecular protein assemblies remains a challenge for well-established methods. Here, we provide an assessment of an emerging structural technique, ion mobility-mass spectrometry (IM-MS), and examine the use of collision cross-sections (CCSs), derived from IM-MS, as restraints for structure characterization of heteromeric protein assemblies. Using 15 complexes selected from the Protein Data Bank, we validate the use of low-resolution models by comparing their CCSs with those calculated for all-atom structures. We then select six heteromeric complexes, disrupting them in solution to form subcomplexes. Experimental and calculated CCSs reveal close similarity for 18 of the 21 (sub)complexes. Exploring the use of CCS as a restraint, we incorporate it into a scoring function and show good correlation between the score and similarity to the native structure for heteromers, especially when an additional symmetry restraint was introduced.