PaxDb, a database of protein abundance averages across all three domains of life

On the specificity of protein-protein interactions in the context of disorder

With the increased focus on intrinsically disordered proteins (IDPs) and their large interactomes, the question about their specificity — or more so on their multispecificity — arise. Here we recapitulate how specificity and multispecificity are quantified and address through examples if IDPs in this respect differ from globular proteins. The conclusion is that quantitatively, globular proteins and IDPs are similar when it comes to specificity. However, compared with globular proteins, IDPs have larger interactome sizes, a phenomenon that is further enabled by their flexibility, repetitive binding motifs and propensity to adapt to different binding partners. For IDPs, this adaptability, interactome size and a higher degree of multivalency opens for new interaction mechanisms such as facilitated exchange through trimer formation and ultra-sensitivity via threshold effects and ensemble redistribution. IDPs and their interactions, thus, do not compromise the definition of specificity. Instead, it is the sheer size of their interactomes that complicates its calculation. More importantly, it is this size that challenges how we conceptually envision, interpret and speak about their specificity.

Abundance Imparts Evolutionary Constraints of Similar Magnitude on the Buried, Surface, and Disordered Regions of Proteins

An understanding of the forces shaping protein conservation is key, both for the fundamental knowledge it represents and to allow for optimal use of evolutionary information in practical applications. Sequence conservation is typically examined at one of two levels. The first is a residue-level, where intra-protein differences are analyzed and the second is a protein-level, where inter-protein differences are studied. At a residue level, we know that solvent-accessibility is a prime determinant of conservation. By inverting this logic, we inferred that disordered regions are slightly more solvent-accessible on average than the most exposed surface residues in domains. By integrating abundance information with evolutionary data within and across proteins, we confirmed a previously reported strong surface-core association in the evolution of structured regions, but we found a comparatively weak association between disordered and structured regions. The facts that disordered and structured regions experience different structural constraints and evolve independently provide a unique setup to examine an outstanding question: why is a protein’s abundance the main determinant of its sequence conservation? Indeed, any structural or biophysical property linked to the abundance-conservation relationship should increase the relative conservation of regions concerned with that property (e.g., disordered residues with mis-interactions, domain residues with misfolding). Surprisingly, however, we found the conservation of disordered and structured regions to increase in equal proportion with abundance. This observation implies that either abundance-related constraints are structure-independent, or multiple constraints apply to different regions and perfectly balance each other.

Proteomics of protein trafficking by in vivo tissue-specific labeling

Conventional approaches to identify secreted factors that regulate homeostasis are limited in their abilities to identify the tissues/cells of origin and destination. We established a platform to identify secreted protein trafficking between organs using an engineered biotin ligase (BirA*G3) that biotinylates, promiscuously, proteins in a subcellular compartment of one tissue. Subsequently, biotinylated proteins are affinity-enriched and identified from distal organs using quantitative mass spectrometry. Applying this approach in Drosophila, we identify 51 muscle-secreted proteins from heads and 269 fat body-secreted proteins from legs/muscles, including CG2145 (human ortholog ENDOU) that binds directly to muscles and promotes activity. In addition, in mice, we identify 291 serum proteins secreted from conditional BirA*G3 embryo stem cell-derived teratomas, including low-abundance proteins with hormonal properties. Our findings indicate that the communication network of secreted proteins is vast. This approach has broad potential across different model systems to identify cell-specific secretomes and mediators of interorgan communication in health or disease.

The Mechanism of SARS-CoV-2 Nucleocapsid Protein Recognition by the Human 14-3-3 Proteins

The coronavirus nucleocapsid protein (N) controls viral genome packaging and contains numerous phosphorylation sites located within unstructured regions. Binding of phosphorylated SARS-CoV N to the host 14-3-3 protein in the cytoplasm was reported to regulate nucleocytoplasmic N shuttling. All seven isoforms of the human 14-3-3 are abundantly present in tissues vulnerable to SARS-CoV-2, where N can constitute up to ~1% of expressed proteins during infection. Although the association between 14-3-3 and SARS-CoV-2 N proteins can represent one of the key host-pathogen interactions, its molecular mechanism and the specific critical phosphosites are unknown. Here, we show that phosphorylated SARS-CoV-2 N protein (pN) dimers, reconstituted via bacterial co-expression with protein kinase A, directly associate, in a phosphorylation-dependent manner, with the dimeric 14-3-3 protein, but not with its monomeric mutant. We demonstrate that pN is recognized by all seven human 14-3-3 isoforms with various efficiencies and deduce the apparent KD to selected isoforms, showing that these are in a low micromolar range. Serial truncations pinpointed a critical phosphorylation site to Ser197, which is conserved among related zoonotic coronaviruses and located within the functionally important, SR-rich region of N. The relatively tight 14-3-3/pN association could regulate nucleocytoplasmic shuttling and other functions of N via occlusion of the SR-rich region, and could also hijack cellular pathways by 14-3-3 sequestration. As such, the assembly may represent a valuable target for therapeutic intervention.

The dynamic mechanism of 4E-BP1 recognition and phosphorylation by mTORC1

The activation of cap-dependent translation in eukaryotes requires multisite, hierarchical phosphorylation of 4E-BP by the 1 MDa kinase mammalian target of rapamycin complex 1 (mTORC1). To resolve the mechanism of this hierarchical phosphorylation at the atomic level, we monitored by NMR spectroscopy the interaction of intrinsically disordered 4E binding protein isoform 1 (4E-BP1) with the mTORC1 subunit regulatory-associated protein of mTOR (Raptor). The N-terminal RAIP motif and the C-terminal TOR signaling (TOS) motif of 4E-BP1 bind separate sites in Raptor, resulting in avidity-based tethering of 4E-BP1. This tethering orients the flexible central region of 4E-BP1 toward the mTORC1 kinase site for phosphorylation. The structural constraints imposed by the two tethering interactions, combined with phosphorylation-induced conformational switching of 4E-BP1, explain the hierarchy of 4E-BP1 phosphorylation by mTORC1. Furthermore, we demonstrate that mTORC1 recognizes both free and eIF4E-bound 4E-BP1, allowing rapid phosphorylation of the entire 4E-BP1 pool and efficient activation of translation. Finally, our findings provide a mechanistic explanation for the differential rapamycin sensitivity of the 4E-BP1 phosphorylation sites.

Readthrough Errors Purge Deleterious Cryptic Sequences, Facilitating the Birth of Coding Sequences

De novo protein-coding innovations sometimes emerge from ancestrally noncoding DNA, despite the expectation that translating random sequences is overwhelmingly likely to be deleterious. The "preadapting selection" hypothesis claims that emergence is facilitated by prior, low-level translation of noncoding sequences via molecular errors. It predicts that selection on polypeptides translated only in error is strong enough to matter and is strongest when erroneous expression is high. To test this hypothesis, we examined noncoding sequences located downstream of stop codons (i.e., those potentially translated by readthrough errors) in Saccharomyces cerevisiae genes. We identified a class of "fragile" proteins under strong selection to reduce readthrough, which are unlikely substrates for co-option. Among the remainder, sequences showing evidence of readthrough translation, as assessed by ribosome profiling, encoded C-terminal extensions with higher intrinsic structural disorder, supporting the preadapting selection hypothesis. The cryptic sequences beyond the stop codon, rather than spillover effects from the regular C-termini, are primarily responsible for the higher disorder. Results are robust to controlling for the fact that stronger selection also reduces the length of C-terminal extensions. These findings indicate that selection acts on 3' UTRs in Saccharomyces cerevisiae to purge potentially deleterious variants of cryptic polypeptides, acting more strongly in genes that experience more readthrough errors.

Label-Free Quantitative Proteomic Analysis of Nitrogen Starvation in Arabidopsis Root Reveals New Aspects of H2S Signaling by Protein Persulfidation

Hydrogen sulfide (H2S)-mediated signaling pathways regulate many physiological and pathophysiological processes in mammalian and plant systems. The molecular mechanism by which hydrogen sulfide exerts its action involves the posttranslational modification of cysteine residues to form a persulfidated thiol motif. We developed a comparative and label-free quantitative proteomic analysis approach for the detection of endogenous persulfidated proteins in N-starved Arabidopsis thaliana roots by using the tag-switch method. In this work, we identified 5214 unique proteins from root tissue that were persulfidated, 1674 of which were quantitatively analyzed and found to show altered persulfidation levels in vivo under N deprivation. These proteins represented almost 13% of the entire annotated proteome in Arabidopsis. Bioinformatic analysis revealed that persulfidated proteins were involved in a wide range of biological functions, regulating important processes such as primary metabolism, plant responses to stresses, growth and development, RNA translation and protein degradation. Quantitative mass spectrometry analysis allowed us to obtain a comprehensive view of hydrogen sulfide signaling via changes in the persulfidation levels of key protein targets involved in ubiquitin-dependent protein degradation and autophagy, among others.

Proteome analysis using machine learning approaches and its applications to diseases

With the tremendous developments in the fields of biological and medical technologies, huge amounts of data are generated in the form of genomic data, images in medical databases or as data on protein sequences, and so on. Analyzing this data through different tools sheds light on the particulars of the disease and our body's reactions to it, thus, aiding our understanding of the human health. Most useful of these tools is artificial intelligence and deep learning (DL). The artificially created neural networks in DL algorithms help extract viable data from the datasets, and further, to recognize patters in these complex datasets. Therefore, as a part of machine learning, DL helps us face all the various challenges that come forth during protein prediction, protein identification and their quantification. Proteomics is the study of such proteins, their structures, features, properties and so on. As a form of data science, Proteomics has helped us progress excellently in the field of genomics technologies. One of the major techniques used in proteomics studies is mass spectrometry (MS). However, MS is efficient with analysis of large datasets only with the added help of informatics approaches for data analysis and interpretation; these mainly include machine learning and deep learning algorithms. In this chapter, we will discuss in detail the applications of deep learning and various algorithms of machine learning in proteomics.

The In Vitro Interaction of 12-Oxophytodienoic Acid and Related Conjugated Carbonyl Compounds with Thiol Antioxidants

α,β-unsaturated carbonyls interfere with numerous plant physiological processes. One mechanism of action is their reactivity toward thiols of metabolites like cysteine and glutathione (GSH). This work aimed at better understanding these interactions. Both 12-oxophytodienoic acid (12-OPDA) and abscisic acid (ABA) conjugated with cysteine. It was found that the reactivity of α,β-unsaturated carbonyls with GSH followed the sequence trans-2-hexenal < 12-OPDA ≈ 12-OPDA-ethylester < 2-cyclopentenone << methyl vinylketone (MVK). Interestingly, GSH, but not ascorbate (vitamin C), supplementation ameliorated the phytotoxic potential of MVK. In addition, 12-OPDA and 12-OPDA-related conjugated carbonyl compounds interacted with proteins, e.g., with members of the thioredoxin (TRX)-fold family. 12-OPDA modified two cysteinyl residues of chloroplast TRX-f1. The OPDAylated TRX-f1 lost its activity to activate the Calvin-Benson-cycle enzyme fructose-1,6-bisphosphatase (FBPase). Finally, we show that 12-OPDA interacts with cyclophilin 20-3 (Cyp20-3) non-covalently and affects its peptidyl-prolyl-cis/trans isomerase activity. The results demonstrate the high potential of 12-OPDA as a diverse interactor and cellular regulator and suggest that OPDAylation may occur in plant cells and should be investigated as novel regulatory mechanism.

Hierarchized phosphotarget binding by the seven human 14-3-3 isoforms

The seven 14-3-3 isoforms are highly abundant human proteins encoded by similar yet distinct genes. 14-3-3 proteins recognize phosphorylated motifs within numerous human and viral proteins. Here, we analyze by X-ray crystallography, fluorescence polarization, mutagenesis and fusicoccin-mediated modulation the structural basis and druggability of 14-3-3 binding to four E6 oncoproteins of tumorigenic human papillomaviruses. 14-3-3 isoforms bind variant and mutated phospho-motifs of E6 and unrelated protein RSK1 with different affinities, albeit following an ordered affinity ranking with conserved relative KD ratios. Remarkably, 14-3-3 isoforms obey the same hierarchy when binding to most of their established targets, as supported by literature and a recent human complexome map. This knowledge allows predicting proportions of 14-3-3 isoforms engaged with phosphoproteins in various tissues. Notwithstanding their individual functions, cellular concentrations of 14-3-3 may be collectively adjusted to buffer the strongest phosphorylation outbursts, explaining their expression variations in different tissues and tumors.

Genome-scale molecular principles of mRNA half-life regulation in yeast

Precise control of protein and messenger RNA (mRNA) degradation is essential for cellular metabolism and homeostasis. Controlled and specific degradation of both molecular species necessitates their engagements with the respective degradation machineries; this engagement involves a disordered/unstructured segment of the substrate traversing the degradation tunnel of the machinery and accessing the catalytic sites. However, while molecular factors influencing protein degradation have been extensively explored on a genome scale, and in multiple organisms, such a comprehensive understanding remains missing for mRNAs. Here, we analyzed multiple genome-scale experimental yeast mRNA half-life data in light of experimentally derived mRNA secondary structures and protein binding data, along with high-resolution X-ray crystallographic structures of the RNase machines. Results unraveled a consistent genome-scale trend that mRNAs comprising longer terminal and/or internal unstructured segments have significantly shorter half-lives; the lengths of the 5'-terminal, 3'-terminal, and internal unstructured segments that affect mRNA half-life are compatible with molecular structures of the 5' exo-, 3' exo-, and endoribonuclease machineries. Sequestration into ribonucleoprotein complexes elongates mRNA half-life, presumably by burying ribonuclease engagement sites under oligomeric interfaces. After gene duplication, differences in terminal unstructured lengths, proportions of internal unstructured segments, and oligomerization modes result in significantly altered half-lives of paralogous mRNAs. Side-by-side comparison of molecular principles underlying controlled protein and mRNA degradation in yeast unravels their remarkable mechanistic similarities and suggests how the intrinsic structural features of the two molecular species, at two different levels of the central dogma, regulate their half-lives on genome scale.

Proteostasis is adaptive: Balancing chaperone holdases against foldases

Because a cell must adapt to different stresses and growth rates, its proteostasis system must too. How do cells detect and adjust proteome folding to different conditions? Here, we explore a biophysical cost-benefit principle, namely that the cell should keep its proteome as folded as possible at the minimum possible energy cost. This can be achieved by differential expression of chaperones–balancing foldases (which accelerate folding) against holdases (which act as parking spots). The model captures changes in the foldase-holdase ratio observed both within organisms during aging and across organisms of varying metabolic rates. This work describes a simple biophysical mechanism by which cellular proteostasis adapts to meet the needs of a changing growth environment.

Cells must maintain low levels of protein unfolding to avoid deleterious outcomes such as protein aggregation, oxidative damage, or premature degradation. The proteins responsible for this, called chaperones, come in two main varieties: ATP-consuming “foldases” that help clients fold and ATP-independent “holdases” that hold unfolded proteins until a foldase arrives. While foldases are necessary for folding, they are expensive to have in high quantities. Given that chaperones are abundant and costly, cells are under strong selective pressure to find economical combinations of foldases and holdases for maintaining low levels of unfolded protein. Yet, it is presently unclear what the ideal combination is and how it varies with growth conditions. By examining a toy model of chaperone function and minimizing the total cost of folding at different rates of protein synthesis, we find that while foldases are necessary at fast growth, holdases become increasingly effective at slow growth. Unexpectedly, total chaperone requirements were predicted to increase as synthesis slows, consistent with observations across age in worms, as well as across species with varying metabolic rates. This work thus provides a general framework for understanding the chaperone requirements of a proteome in terms of an energy minimization principle.

Programmed Trade-offs in Protein Folding Networks

Molecular chaperones as specialized protein quality control enzymes form the core of cellular protein homeostasis. How chaperones selectively interact with their substrate proteins thus allocate their overall limited capacity remains poorly understood. Here, I present an integrated analysis of sequence and structural determinants that define interactions of protein domains as the basic protein folding unit with the Saccharomyces cerevisiae Hsp70 Ssb. Structural homologs of single-domain proteins that differentially interact with Ssb for de novo folding were found to systematically differ in complexity of their folding landscapes, selective use of nonoptimal codons, and presence of short discriminative sequences, thus highlighting pervasive trade-offs in chaperone-assisted protein folding landscapes. However, short discriminative sequences were found to contribute by far the strongest signal toward explaining Ssb interactions. This observation suggested that some chaperone interactions may be directly programmed in the amino acid sequences rather than responding to folding challenges, possibly for regulatory advantages.

A possible universal role for mRNA secondary structure in bacterial translation revealed using a synthetic operon

In bacteria, translation re-initiation is crucial for synthesizing proteins encoded by genes that are organized into operons. The mechanisms regulating translation re-initiation remain, however, poorly understood. We now describe the ribosome termination structure (RTS), a conserved and stable mRNA secondary structure localized immediately downstream of stop codons, and provide experimental evidence for its role in governing re-initiation efficiency in a synthetic Escherichia coli operon. We further report that RTSs are abundant, being associated with 18%–65% of genes in 128 analyzed bacterial genomes representing all phyla, and are selectively depleted when translation re-initiation is advantageous yet selectively enriched so as to insulate translation when re-initiation is deleterious. Our results support a potentially universal role for the RTS in controlling translation termination-insulation and re-initiation across bacteria.

The mechanisms for regulating translation re-initiation in bacteria remain poorly understood. Here, the authors screened a library of synthetic operons and identified a ribosome termination structure that modulates re-initiation efficiency and which is conserved across bacteria.

Increased supraorganization of respiratory complexes is a dynamic multistep remodelling in response to proteostasis stress

Proteasome-mediated degradation of misfolded proteins prevents aggregation inside and outside mitochondria. But how do cells safeguard the mitochondrial proteome and mitochondrial functions despite increased aggregation during proteasome inactivation? Here, using a novel two-dimensional complexome profiling strategy, we report increased supraorganization of respiratory complexes (RCs) in proteasome-inhibited cells that occurs simultaneously with increased pelletable aggregation of RC subunits inside mitochondria. Complex II (CII) and complex V (CV) subunits are increasingly incorporated into oligomers. Complex I (CI), complex III (CIII) and complex IV (CIV) subunits are engaged in supercomplex formation. We unravel unique quinary states of supercomplexes during early proteostatic stress that exhibit plasticity and inequivalence of constituent RCs. The core stoichiometry of CI and CIII is preserved, whereas the composition of CIV varies. These partially disintegrated supercomplexes remain functionally competent via conformational optimization. Subsequently, increased stepwise integration of RC subunits into holocomplexes and supercomplexes re-establishes steady-state stoichiometry. Overall, the mechanism of increased supraorganization of RCs mimics the cooperative unfolding and folding pathways for protein folding, but is restricted to RCs and is not observed for any other mitochondrial protein complexes.This article has an associated First Person interview with the first author of the paper.

Detection of Protein Aggregation in Live Plasmodium Parasites

The rapid evolution of resistance in the malaria parasite to every single drug developed against it calls for the urgent identification of new molecular targets. Using a stain specific for the detection of intracellular amyloid deposits in live cells, we have detected the presence of abundant protein aggregates in Plasmodium falciparum blood stages and female gametes cultured in vitro, in the blood stages of mice infected by Plasmodium yoelii, and in the mosquito stages of the murine malaria species Plasmodium berghei Aggregated proteins could not be detected in early rings, the parasite form that starts the intraerythrocytic cycle. A proteomics approach was used to pinpoint actual aggregating polypeptides in functional P. falciparum blood stages, which resulted in the identification of 369 proteins, with roles particularly enriched in nuclear import-related processes. Five aggregation-prone short peptides selected from this protein pool exhibited different aggregation propensity according to Thioflavin-T fluorescence measurements, and were observed to form amorphous aggregates and amyloid fibrils in transmission electron microscope images. The results presented suggest that generalized protein aggregation might have a functional role in malaria parasites. Future antimalarial strategies based on the upsetting of the pathogen's proteostasis and therefore affecting multiple gene products could represent the entry to new therapeutic approaches.

Edgetic perturbation signatures represent known and novel cancer biomarkers

Isoform switching is a recently characterized hallmark of cancer, and often translates to the loss or gain of domains mediating protein interactions and thus, the re-wiring of the interactome. Recent computational tools leverage domain-domain interaction data to resolve the condition-specific interaction networks from RNA-Seq data accounting for the domain content of the primary transcripts expressed. Here, we used The Cancer Genome Atlas RNA-Seq datasets to generate 642 patient-specific pairs of interactomes corresponding to both the tumor and the healthy tissues across 13 cancer types. The comparison of these interactomes provided a list of patient-specific edgetic perturbations of the interactomes associated with the cancerous state. We found that among the identified perturbations, select sets are robustly shared between patients at the multi-cancer, cancer-specific and cancer sub-type specific levels. Interestingly, the majority of the alterations do not directly involve significantly mutated genes, nevertheless, they strongly correlate with patient survival. The findings (available at EdgeExplorer: “http://webclu.bio.wzw.tum.de/EdgeExplorer”) are a new source of potential biomarkers for classifying cancer types and the proteins we identified are potential anti-cancer therapy targets.

Synthetic analogues of 2-oxo acids discriminate metabolic contribution of the 2-oxoglutarate and 2-oxoadipate dehydrogenases in mammalian cells and tissues

The biological significance of the DHTKD1-encoded 2-oxoadipate dehydrogenase (OADH) remains obscure due to its catalytic redundancy with the ubiquitous OGDH-encoded 2-oxoglutarate dehydrogenase (OGDH). In this work, metabolic contributions of OADH and OGDH are discriminated by exposure of cells/tissues with different DHTKD1 expression to the synthesized phosphonate analogues of homologous 2-oxodicarboxylates. The saccharopine pathway intermediates and phosphorylated sugars are abundant when cellular expressions of DHTKD1 and OGDH are comparable, while nicotinate and non-phosphorylated sugars are when DHTKD1 expression is order(s) of magnitude lower than that of OGDH. Using succinyl, glutaryl and adipoyl phosphonates on the enzyme preparations from tissues with varied DHTKD1 expression reveals the contributions of OADH and OGDH to oxidation of 2-oxoadipate and 2-oxoglutarate in vitro. In the phosphonates-treated cells with the high and low DHTKD1 expression, adipate or glutarate, correspondingly, are the most affected metabolites. The marker of fatty acid β-oxidation, adipate, is mostly decreased by the shorter, OGDH-preferring, phosphonate, in agreement with the known OGDH dependence of β-oxidation. The longest, OADH-preferring, phosphonate mostly affects the glutarate level. Coupled decreases in sugars and nicotinate upon the OADH inhibition link the perturbation in glucose homeostasis, known in OADH mutants, to the nicotinate-dependent NAD metabolism.

Receptor-based mechanism of relative sensing and cell memory in mammalian signaling networks

Detecting relative rather than absolute changes in extracellular signals enables cells to make decisions in constantly fluctuating environments. It is currently not well understood how mammalian signaling networks store the memories of past stimuli and subsequently use them to compute relative signals, that is perform fold change detection. Using the growth factor-activated PI3K-Akt signaling pathway, we develop here computational and analytical models, and experimentally validate a novel non-transcriptional mechanism of relative sensing in mammalian cells. This mechanism relies on a new form of cellular memory, where cells effectively encode past stimulation levels in the abundance of cognate receptors on the cell surface. The surface receptor abundance is regulated by background signal-dependent receptor endocytosis and down-regulation. We show the robustness and specificity of relative sensing for two physiologically important ligands, epidermal growth factor (EGF) and hepatocyte growth factor (HGF), and across wide ranges of background stimuli. Our results suggest that similar mechanisms of cell memory and fold change detection may be important in diverse signaling cascades and multiple biological contexts.

Model of a Kinetically Driven Crosstalk between Paralogous Protein Encounter Complexes

Proteins interact with one another across a broad spectrum of affinities. Our understanding of the low end of this spectrum, as characterized by millimolar dissociation constants, relies on a handful of cases in which weak encounters have experimentally been identified. These weak interactions away from the specific target binding site can lead toward a higher-affinity complex. Recently, we detected weak encounters between two paralogous phosphotransferase pathways of Escherichia coli, which regulate various metabolic processes and stress responses. In addition to encounters that are known to occur between cognate proteins, i.e., those that can exchange phosphate groups with each other, surprisingly, encounters involving noncognates were also observed. It is not clear whether these "futile" encounters have a cooperative or competitive role. Using agent-based simulations, we find that the encounter complexes can be cooperative or competitive so as to increase or lower the effective binding affinity of the specific complex under different circumstances. This finding invites further questions into how organisms might exploit such low affinities to connect their signaling components.

Cardiac mTORC1 Dysregulation Impacts Stress Adaptation and Survival in Huntington's Disease

Huntington’s disease (HD) is a dominantly inherited neurological disorder caused by CAG-repeat expansion in exon 1 of Huntingtin (HTT). But in addition to the neurological disease, mutant HTT (mHTT), which is ubiquitously expressed, impairs other organ systems. Indeed, epidemiological and animal model studies suggest higher incidence of and mortality from heart disease in HD. Here, we show that the protein complex mTORC1 is dysregulated in two HD mouse models through a mechanism that requires intrinsic mHTT expression. Moreover, restoring cardiac mTORC1 activity with constitutively active Rheb prevents mortality and relieves the mHTT-induced block to hypertrophic adaptation to cardiac stress. Finally, we show that chronic mTORC1 dysregulation is due in part to mislocalization of endogenous Rheb. These data provide insight into the increased cardiac-related mortality of HD patients, with cardiac mHTT expression inhibiting mTORC1 activity, limiting heart growth, and decreasing the heart’s ability to compensate to chronic stress.

Agent-based modeling and bifurcation analysis reveal mechanisms of macrophage polarization and phenotype pattern distribution

Macrophages play a key role in tissue regeneration by polarizing to different destinies and generating various phenotypes. Recognizing the underlying mechanisms is critical in designing therapeutic procedures targeting macrophage fate determination. Here, to investigate the macrophage polarization, a nonlinear mathematical model is proposed in which the effect of IL4, IFNγ and LPS, as external stimuli, on STAT1, STAT6, and NFκB is studied using bifurcation analysis. The existence of saddle-node bifurcations in these internal key regulators allows different combinations of steady state levels which are attributable to different fates. Therefore, we propose dynamic bifurcation as a crucial built-in mechanism of macrophage polarization. Next, in order to investigate the polarization of a population of macrophages, bifurcation analysis is employed aligned with agent-based approach and a two-layer model is proposed in which the information from single cells is exploited to model the behavior in tissue level. Also, in this model, a partial differential equation describes the diffusion of secreted cytokines in the medium. Finally, the model was validated against a set of experimental data. Taken together, we have here developed a cell and tissue level model of macrophage polarization behavior which can be used for designing therapeutic interventions.

Small-protein Enrichment Assay Enables the Rapid, Unbiased Analysis of Over 100 Low Abundance Factors from Human Plasma

Unbiased and sensitive quantification of low abundance small proteins in human plasma (e.g. hormones, immune factors, metabolic regulators) remains an unmet need. These small protein factors are typically analyzed individually and using antibodies that can lack specificity. Mass spectrometry (MS)-based proteomics has the potential to address these problems, however the analysis of plasma by MS is plagued by the extremely large dynamic range of this body fluid, with protein abundances spanning at least 13 orders of magnitude. Here we describe an enrichment assay (SPEA), that greatly simplifies the plasma dynamic range problem by enriching small-proteins of 2-10 kDa, enabling the rapid, specific and sensitive quantification of >100 small-protein factors in a single untargeted LC-MS/MS acquisition. Applying this method to perform deep-proteome profiling of human plasma we identify C5ORF46 as a previously uncharacterized human plasma protein. We further demonstrate the reproducibility of our workflow for low abundance protein analysis using a stable-isotope labeled protein standard of insulin spiked into human plasma. SPEA provides the ability to study numerous important hormones in a single rapid assay, which we applied to study the intermittent fasting response and observed several unexpected changes including decreased plasma abundance of the iron homeostasis regulator hepcidin.

The role of structural pleiotropy and regulatory evolution in the retention of heteromers of paralogs

Gene duplication is a driver of the evolution of new functions. The duplication of genes encoding homomeric proteins leads to the formation of homomers and heteromers of paralogs, creating new complexes after a single duplication event. The loss of these heteromers may be required for the two paralogs to evolve independent functions. Using yeast as a model, we find that heteromerization is frequent among duplicated homomers and correlates with functional similarity between paralogs. Using in silico evolution, we show that for homomers and heteromers sharing binding interfaces, mutations in one paralog can have structural pleiotropic effects on both interactions, resulting in highly correlated responses of the complexes to selection. Therefore, heteromerization could be preserved indirectly due to selection for the maintenance of homomers, thus slowing down functional divergence between paralogs. We suggest that paralogs can overcome the obstacle of structural pleiotropy by regulatory evolution at the transcriptional and post-translational levels.

Energy metabolism controls phenotypes by protein efficiency and allocation

Cells require energy for growth and maintenance and have evolved to have multiple pathways to produce energy in response to varying conditions. A basic question in this context is how cells organize energy metabolism, which is, however, challenging to elucidate due to its complexity, i.e., the energy-producing pathways overlap with each other and even intertwine with biomass formation pathways. Here, we propose a modeling concept that decomposes energy metabolism into biomass formation and ATP-producing pathways. The latter can be further decomposed into a high-yield and a low-yield pathway. This enables independent estimation of protein efficiency for each pathway. With this concept, we modeled energy metabolism for Escherichia coli and Saccharomyces cerevisiae and found that the high-yield pathway shows lower protein efficiency than the low-yield pathway. Taken together with a fixed protein constraint, we predict overflow metabolism in E. coli and the Crabtree effect in S. cerevisiae, meaning that energy metabolism is sufficient to explain the metabolic switches. The static protein constraint is supported by the findings that protein mass of energy metabolism is conserved across conditions based on absolute proteomics data. This also suggests that enzymes may have decreased saturation or activity at low glucose uptake rates. Finally, our analyses point out three ways to improve growth, i.e., increasing protein allocation to energy metabolism, decreasing ATP demand, or increasing activity for key enzymes.

ProteomicsDB

ProteomicsDB (https://www.ProteomicsDB.org) is a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. ProteomicsDB was first released in 2014 to enable the interactive exploration of the first draft of the human proteome. To date, it contains quantitative data from 78 projects totalling over 19k LC–MS/MS experiments. A standardized analysis pipeline enables comparisons between multiple datasets to facilitate the exploration of protein expression across hundreds of tissues, body fluids and cell lines. We recently extended the data model to enable the storage and integrated visualization of other quantitative omics data. This includes transcriptomics data from e.g. NCBI GEO, protein–protein interaction information from STRING, functional annotations from KEGG, drug-sensitivity/selectivity data from several public sources and reference mass spectra from the ProteomeTools project. The extended functionality transforms ProteomicsDB into a multi-purpose resource connecting quantification and meta-data for each protein. The rich user interface helps researchers to navigate all data sources in either a protein-centric or multi-protein-centric manner. Several options are available to download data manually, while our application programming interface enables accessing quantitative data systematically.

Computational Assessment of Bacterial Protein Structures Indicates a Selection Against Aggregation

The aggregation of proteins compromises cell fitness, either because it titrates functional proteins into non-productive inclusions or because it results in the formation of toxic assemblies. Accordingly, computational proteome-wide analyses suggest that prevention of aggregation upon misfolding plays a key role in sequence evolution. Most proteins spend their lifetimes in a folded state; therefore, it is conceivable that, in addition to sequences, protein structures would have also evolved to minimize the risk of aggregation in their natural environments. By exploiting the AGGRESCAN3D structure-based approach to predict the aggregation propensity of >600 Escherichia coli proteins, we show that the structural aggregation propensity of globular proteins is connected with their abundance, length, essentiality, subcellular location and quaternary structure. These data suggest that the avoidance of protein aggregation has contributed to shape the structural properties of proteins in bacterial cells.

FunCoup 4: new species, data, and visualization

This release of the FunCoup database (http://funcoup.sbc.su.se) is the fourth generation of one of the most comprehensive databases for genome-wide functional association networks. These functional associations are inferred via integrating various data types using a naive Bayesian algorithm and orthology based information transfer across different species. This approach provides high coverage of the included genomes as well as high quality of inferred interactions. In this update of FunCoup we introduce four new eukaryotic species: Schizosaccharomyces pombe, Plasmodium falciparum, Bos taurus, Oryza sativa and open the database to the prokaryotic domain by including networks for Escherichia coli and Bacillus subtilis. The latter allows us to also introduce a new class of functional association between genes - co-occurrence in the same operon. We also supplemented the existing classes of functional association: metabolic, signaling, complex and physical protein interaction with up-to-date information. In this release we switched to InParanoid v8 as the source of orthology and base for calculation of phylogenetic profiles. While populating all other evidence types with new data we introduce a new evidence type based on quantitative mass spectrometry data. Finally, the new JavaScript based network viewer provides the user an intuitive and responsive platform to further evaluate the results.

Defining Human Tyrosine Kinase Phosphorylation Networks Using Yeast as an In Vivo Model Substrate

Systematic assessment of tyrosine kinase-substrate relationships is fundamental to a better understanding of cellular signaling and its profound alterations in human diseases such as cancer. In human cells, such assessments are confounded by complex signaling networks, feedback loops, conditional activity, and intra-kinase redundancy. Here we address this challenge by exploiting the yeast proteome as an in vivo model substrate. We individually expressed 16 human non-receptor tyrosine kinases (NRTKs) in Saccharomyces cerevisiae and identified 3,279 kinase-substrate relationships involving 1,351 yeast phosphotyrosine (pY) sites. Based on the yeast data without prior information, we generated a set of linear kinase motifs and assigned ∼1,300 known human pY sites to specific NRTKs. Furthermore, experimentally defined pY sites for each individual kinase were shown to cluster within the yeast interactome network irrespective of linear motif information. We therefore applied a network inference approach to predict kinase-substrate relationships for more than 3,500 human proteins, providing a resource to advance our understanding of kinase biology.

Unexpected Roles of a Tether Harboring a Tyrosine Gatekeeper Residue in Modular Nitrite Reductase Catalysis

It is generally assumed that tethering enhances rates of electron harvesting and delivery to active sites in multidomain enzymes by proximity and sampling mechanisms. Here, we explore this idea in a tethered 3-domain, trimeric copper-containing nitrite reductase. By reverse engineering, we find that tethering does not enhance the rate of electron delivery from its pendant cytochrome c to the catalytic copper-containing core. Using a linker that harbors a gatekeeper tyrosine in a nitrite access channel, the tethered haem domain enables catalysis by other mechanisms. Tethering communicates the redox state of the haem to the distant T2Cu center that helps initiate substrate binding for catalysis. It also tunes copper reduction potentials, suppresses reductive enzyme inactivation, enhances enzyme affinity for substrate, and promotes intercopper electron transfer. Tethering has multiple unanticipated beneficial roles, the combination of which fine-tunes function beyond simplistic mechanisms expected from proximity and restrictive sampling models.

Regulation of the anaphase promoting complex/cyclosome by the degradation of its unassembled catalytic subunit, Apc11

One of the challenges encountered by the protein quality control machinery is the need to ensure that members of multiprotein complexes are available in the correct proportions. In this study, we demonstrate that the ubiquitin proteasome system (UPS) mediates the degradation of Apc11, the catalytic core subunit of the anaphase promoting complex/cyclosome (APC/C). In vitro studies have shown that Apc11, together with its E2 enzyme, is sufficient to ubiquitinate substrates independently of the APC/C. Here, we establish that this can occur in living yeast cells. We show that the tight controls regulating the function of the fully assembled APC/C can be circumvented when its substrates are ubiquitinated by the excess levels of Apc11 independently of the assembled complex. We thus suggest that the UPS-mediated degradation of Apc11 is an overlooked mechanism ensuring that proper function of the APC/C is limited to suitably delimited holoenzymes and that an imbalance in protein expression may result in detrimental gain-of-function activity, rather than merely the disruption of protein complex stoichiometry.-Volpe, M., Levinton, N., Rosenstein, N., Prag, G., Ben-Aroya, S. Regulation of the anaphase promoting complex/cyclosome by the degradation of its unassembled catalytic subunit, Apc11.

Optimizing the dynamics of protein expression

Heterologously expressed genes require adaptation to the host organism to ensure adequate levels of protein synthesis, which is typically approached by replacing codons by the target organism’s preferred codons. In view of frequently encountered suboptimal outcomes we introduce the codon-specific elongation model (COSEM) as an alternative concept. COSEM simulates ribosome dynamics during mRNA translation and informs about protein synthesis rates per mRNA in an organism- and context-dependent way. Protein synthesis rates from COSEM are integrated with further relevant covariates such as translation accuracy into a protein expression score that we use for codon optimization. The scoring algorithm further enables fine-tuning of protein expression including deoptimization and is implemented in the software OCTOPOS. The protein expression score produces competitive predictions on proteomic data from prokaryotic, eukaryotic, and human expression systems. In addition, we optimized and tested heterologous expression of manA and ova genes in Salmonella enterica serovar Typhimurium. Superiority over standard methodology was demonstrated by a threefold increase in protein yield compared to wildtype and commercially optimized sequences.

The disordered boundary of the cell: emerging properties of membrane-bound intrinsically disordered proteins

We define the disordered boundary of the cell (DBC) as the system formed by membrane tethered intrinsically disordered protein regions, dynamically coupled to the underlying membrane.

The emerging properties of the DBC makes it a global system of study, which cannot be understood from the individual properties of their components. Similarly, the properties of lipid bilayers cannot be understood from just the sum of the properties of individual lipid molecules.

The highly anisotropic confined environment, restricting the position and orientation of interacting sites, is affecting the properties of individual disordered proteins. In fact, the collective effect caused by high concentrations of disordered proteins extend beyond the sum of individual effects.

Examples of emerging properties of the DBC include enhanced protein-protein interactions, protein-driven phase separations, Z-compartmentalization, and protein modulated electrostatics.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

An "on-matrix" digestion procedure for AP-MS experiments dissects the interplay between complex-conserved and serotype-specific reactivities in Dengue virus-human plasma interactome

The interactions between the four Dengue virus (DENV) serotypes and plasma proteins are crucial in the initial steps of viral infection to humans. Affinity purification combined with quantitative mass spectrometry analysis, has become one of the most powerful tools for the investigation on novel protein-protein interactions. Using this approach, we report here that a significant number of bait-interacting proteins do not dissociate under standard elution conditions, i.e. acid pH and chaotropic agents, and that this problem can be circumvented by using the "on-matrix" digestion procedure described here. This procedure enabled the identification of 16 human plasma proteins interacting with domain III from the envelope protein of DENV serotypes 1, 3 and 4 that would have not been detected otherwise and increased the known DIIIE interactors in human plasma to 59 proteins. Selected Reaction Monitoring analysis evidenced DENV interactome in human plasma is rather conserved although significant differences on the reactivity of viral serotypes with specific proteins do exist. A comparison between the serotype-dependent profile of reactivity and the conservation pattern of amino acid residues suggests an evolutionary selection of highly conserved interactions with the host and other interactions mediated for surface regions of higher variability. SIGNIFICANCE: False negative results on the identification of interacting proteins in pull-down experiments compromise the subsequent interpretation of results and the formulation of a working hypothesis for the derived future work. In this study we demonstrate the presence of bait-interacting proteins reluctant to dissociate under elution conditions of acid pH and presence of chaotropics. We propose the direct proteolytic digestion of proteins while still bound to the affinity matrix ("on-matrix" digestion) and evaluate the impact of this methodology in the comparative study of the interactome of the four serotypes of Dengue virus mediated by the domain III of the viral envelope glycoprotein. Fifty nine proteins were identified as putative interaction partners of Dengue virus (IPs) either due to direct binding or by co-isolation with interacting proteins. Collectively the IPs identified from the pull-down with the recombinant domain III proteins representing the four viral serotypes, 29% were identified only after "on-matrix" digestion which demonstrate the usefulness of this method of recovering bait-bound proteins. Results highlight a particular importance of "on-matrix" digestion procedure for comparative studies where a stronger interaction with one of the interest baits could prevent a bound protein to elute under standard conditions thus leading to misinterpretation as absent in the interactome of this particular bait. The analysis of the Interaction Network indicates that Dengue virus interactome mediated by the domain III of the envelope protein is rather conserved in the viral complex suggesting a key role of these interactions for viral infection thus making candidates to explore for potential biomarkers of clinical outcome in DENV-caused disease. Interestingly, some particular IPs exhibit significant differences in the strength of the interaction with the viral serotypes representing interactions that involve more variable regions in the surface of the domain III. Since such variable regions are the consequence of the interaction with antibodies generated by human immune response; this result relates the interaction with proteins from human plasma with the interplay of the virus and the human immune system.

Proteome-wide Analysis of Protein Thermal Stability in the Model Higher Plant Arabidopsis thaliana

Modern tandem MS-based sequencing technologies allow for the parallel measurement of concentration and covalent modifications for proteins within a complex sample. Recently, this capability has been extended to probe a proteome's three-dimensional structure and conformational state by determining the thermal denaturation profile of thousands of proteins simultaneously. Although many animals and their resident microbes exist under a relatively narrow, regulated physiological temperature range, plants take on the often widely ranging temperature of their surroundings, possibly influencing the evolution of protein thermal stability. In this report we present the first in-depth look at the thermal proteome of a plant species, the model organism Arabidopsis thaliana By profiling the melting curves of over 1700 Arabidopsis proteins using six biological replicates, we have observed significant correlation between protein thermostability and several known protein characteristics, including molecular weight and the composition ratio of charged to polar amino acids. We also report on a divergence of the thermostability of the core and regulatory domains of the plant 26S proteasome that may reflect a unique property of the way protein turnover is regulated during temperature stress. Lastly, the highly replicated database of Arabidopsis melting temperatures reported herein provides baseline data on the variability of protein behavior in the assay. Unfolding behavior and experiment-to-experiment variability were observed to be protein-specific traits, and thus this data can serve to inform the design and interpretation of future targeted assays to probe the conformational status of proteins from plants exposed to different chemical, environmental and genetic challenges.

Integration of enzymatic data in Bacillus subtilis genome-scale metabolic model improves phenotype predictions and enables in silico design of poly-γ-glutamic acid production strains

Background

Genome-scale metabolic models (GEMs) allow predicting metabolic phenotypes from limited data on uptake and secretion fluxes by defining the space of all the feasible solutions and excluding physio-chemically and biologically unfeasible behaviors. The integration of additional biological information in genome-scale models, e.g., transcriptomic or proteomic profiles, has the potential to improve phenotype prediction accuracy. This is particularly important for metabolic engineering applications where more accurate model predictions can translate to more reliable model-based strain design.

Results

Here we present a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO) model of Bacillus subtilis, which uses publicly available proteomic data and enzyme kinetic parameters for central carbon (CC) metabolic reactions to constrain the flux solution space. This model allows more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to a standard genome-scale metabolic model. The flux prediction error decreased by 43% and 36% for wild-type and mutants respectively. The model additionally increased the number of correctly predicted essential genes in CC pathways by 2.5-fold and significantly decreased flux variability in more than 80% of the reactions with variable flux. Finally, the model was used to find new gene deletion targets to optimize the flux toward the biosynthesis of poly-γ-glutamic acid (γ-PGA) polymer in engineered B. subtilis. We implemented the single-reaction deletion targets identified by the model experimentally and showed that the new strains have a twofold higher γ-PGA concentration and production rate compared to the ancestral strain.

Conclusions

This work confirms that integration of enzyme constraints is a powerful tool to improve existing genome-scale models, and demonstrates the successful use of enzyme-constrained models in B. subtilis metabolic engineering. We expect that the new model can be used to guide future metabolic engineering efforts in the important industrial production host B. subtilis.

Quantitative Proteomics Data in the Public Domain: Challenges and Opportunities

Mass spectrometry based proteomics is no longer only a qualitative discipline, and can be successfully employed to obtain a truly multidimensional view of the proteome. In particular, systematic protein expression profiling is now a routine part of many studies in the field and beyond. The large growth in the number of quantitative studies is accompanied by a trend to share publicly the associated analysis results and the underlying raw data. This trend, established and strongly supported by public repositories such as the PRIDE database at the European Bioinformatics Institute, opens up enormous possibilities to explore the data beyond the original publications, for instance by reusing, reanalyzing, and performing different flavors of meta-analysis studies. To help researchers and scientists realize about this potential, here we describe the mainstream public proteomics resources containing quantitative proteomics data, including the processed analysis results and/or the underlying raw data. We then present and discuss the most important points to consider when attempting to (re)use proteomics data in the public domain. We conclude by highlighting potential pitfalls of (re)using quantitative data and discuss some of our own experiences in this context.

RNA-Seq-Based Analysis Reveals Heterogeneity in Mature 16S rRNA 3' Termini and Extended Anti-Shine-Dalgarno Motifs in Bacterial Species

We present an RNA-Seq based approach to map 3′ end sequences of mature 16S rRNA (3′ TAIL) in bacteria with single-base specificity. Our results show that 3′ TAILs are heterogeneous among species; they contain the core CCUCC anti-Shine-Dalgarno motif, but vary in downstream lengths. Importantly, our findings rectify the mis-annotated 16S rRNAs in 11 out of 13 bacterial species studied herein (covering Cyanobacteria, Deinococcus-Thermus, Firmicutes, Proteobacteria, Tenericutes, and Spirochaetes). Furthermore, our results show that species-specific 3′ TAIL boundaries are retained due to their high complementarity with preferred Shine-Dalgarno sequences, suggesting that 3′ TAIL bases downstream of the canonical CCUCC motif play a more important role in translation initiation than previously reported.

General decapping activators target different subsets of inefficiently translated mRNAs

The Dcp1-Dcp2 decapping enzyme and the decapping activators Pat1, Dhh1, and Lsm1 regulate mRNA decapping, but their mechanistic integration is unknown. We analyzed the gene expression consequences of deleting PAT1, LSM1, or DHH1, or the DCP2 C-terminal domain, and found that: i) the Dcp2 C-terminal domain is an effector of both negative and positive regulation; ii) rather than being global activators of decapping, Pat1, Lsm1, and Dhh1 directly target specific subsets of yeast mRNAs and loss of the functions of each of these factors has substantial indirect consequences for genome-wide mRNA expression; and iii) transcripts targeted by Pat1, Lsm1, and Dhh1 exhibit only partial overlap, are generally translated inefficiently, and, as expected, are targeted to decapping-dependent decay. Our results define the roles of Pat1, Lsm1, and Dhh1 in decapping of general mRNAs and suggest that these factors may monitor mRNA translation and target unique features of individual mRNAs.

A Model for Manganese interaction with Deinococcus radiodurans proteome network involved in ROS response and defense

A complex network of regulatory proteins takes part in the mechanism underlying the radioresistance of Deinoccocus radiodurans bacterium (DR). The interaction of Mn(II) ions with DR-proteins and peptides seems to be responsible for proteins protection from oxidative damage induced by Reactive Oxygen Species during irradiation. In the present work we describe a combined approach of bioinformatic strategies based on structural data and annotation to predict the Mn(II)-binding proteins encoded by the genome of DR and, in parallel, the same predictions for other bacteria were performed; the comparison revealed that, in most of the cases, the content of Mn(II)-binding proteins is significantly higher in radioresistant than in radiosensitive bacteria. Moreover, we report the in silico protein-protein interaction network of the putative Mn(II)-proteins, remodeled in order to enhance the knowledge about the impact of Mn-binding proteins in DR ability to protect also DNA from various damaging agents such as ionizing radiation, UV radiation and oxidative stress.

Membrane topologies of PEX13 and PEX14 provide new insights on the mechanism of protein import into peroxisomes

PEX13 and PEX14 are two core components of the so-called peroxisomal docking/translocation module, the transmembrane hydrophilic channel through which newly synthesized peroxisomal proteins are translocated into the organelle matrix. The two proteins interact with each other and with PEX5, the peroxisomal matrix protein shuttling receptor, through relatively well characterized domains. However, the topologies of these membrane proteins are still poorly defined. Here, we subjected proteoliposomes containing PEX13 or PEX14 and purified rat liver peroxisomes to protease-protection assays and analyzed the protected protein fragments by mass spectrometry, Edman degradation and western blotting using antibodies directed to specific domains of the proteins. Our results indicate that PEX14 is a bona fide intrinsic membrane protein with a N_in -C_out topology, and that PEX13 adopts a N_out -C_in topology, thus exposing its carboxy-terminal Src homology 3 [SH3] domain into the organelle matrix. These results reconcile several enigmatic findings previously reported on PEX13 and PEX14 and provide new insights into the organization of the peroxisomal protein import machinery. ENZYMES: Trypsin, EC3.4.21.4; Proteinase K, EC3.4.21.64; Tobacco etch virus protease, EC3.4.22.44.

The function of the oxylipin 12-oxophytodienoic acid in cell signaling, stress acclimation, and development

Forty years ago, 12-oxophytodienoic acid (12-OPDA) was reported as a prostaglandin (PG)-like metabolite of linolenic acid found in extracts of flaxseed. Since then, numerous studies have determined the role of 12-OPDA in regulating plant immunity, seed dormancy, and germination. This review summarizes our current knowledge of the regulation of 12-OPDA synthesis in the chloroplast and 12-OPDA-dependent signaling in gene expression and targeting protein functions. We describe the properties of OPDA that are linked to the activities of PGs, which are derived from arachidonic acid and act as tissue hormones in animals, including humans. The similarity of OPDA with bioactive PGs is particularly evident for the most-studied cyclopentenone, PG 15-dPGJ2. In addition to chemical approaches towards 12-OPDA synthesis, bio-organic synthesis strategies for 12-OPDA and analogous substances have recently been established. The resulting availability of OPDA will aid the identification of additional effector proteins, help in elucidating the mechanisms of OPDA sensing and transmission, and will foster the analysis of the physiological responses to OPDA in plants. There is a need to determine the compartmentation and transport of 12-OPDA and its conjugates, over long distances as well as short. It will be important to further study OPDA in animal and human cells, for example with respect to beneficial anti-inflammatory and anti-cancer activities.

Metabolic Determinants of Enzyme Evolution in a Genome-Scale Bacterial Metabolic Network

Different genes and proteins evolve at very different rates. To identify the factors that explain these differences is an important aspect of research in molecular evolution. One such factor is the role a protein plays in a large molecular network. Here, we analyze the evolutionary rates of enzyme-coding genes in the genome-scale metabolic network of Escherichia coli to find the evolutionary constraints imposed by the structure and function of this complex metabolic system. Central and highly connected enzymes appear to evolve more slowly than less connected enzymes, but we find that they do so as a by-product of their high abundance, and not because of their position in the metabolic network. In contrast, enzymes catalyzing reactions with high metabolic flux-high substrate to product conversion rates-evolve slowly even after we account for their abundance. Moreover, enzymes catalyzing reactions that are difficult to by-pass through alternative pathways, such that they are essential in many different genetic backgrounds, also evolve more slowly. Our analyses show that an enzyme's role in the function of a metabolic network affects its evolution more than its place in the network's structure. They highlight the value of a system-level perspective for studies of molecular evolution.

ProteomeVis: a web app for exploration of protein properties from structure to sequence evolution across organisms' proteomes

Motivation

Protein evolution spans time scales and its effects span the length of an organism. A web app named ProteomeVis is developed to provide a comprehensive view of protein evolution in the Saccharomyces cerevisiae and Escherichia coli proteomes. ProteomeVis interactively creates protein chain graphs, where edges between nodes represent structure and sequence similarities within user-defined ranges, to study the long time scale effects of protein structure evolution. The short time scale effects of protein sequence evolution are studied by sequence evolutionary rate (ER) correlation analyses with protein properties that span from the molecular to the organismal level.

Results

We demonstrate the utility and versatility of ProteomeVis by investigating the distribution of edges per node in organismal protein chain universe graphs (oPCUGs) and putative ER determinants. S.cerevisiae and E.coli oPCUGs are scale-free with scaling constants of 1.79 and 1.56, respectively. Both scaling constants can be explained by a previously reported theoretical model describing protein structure evolution. Protein abundance most strongly correlates with ER among properties in ProteomeVis, with Spearman correlations of -0.49 (P-value < 10-10) and -0.46 (P-value < 10-10) for S.cerevisiae and E.coli, respectively. This result is consistent with previous reports that found protein expression to be the most important ER determinant.

Availability and implementation

ProteomeVis is freely accessible at http://proteomevis.chem.harvard.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Human eIF5 and eIF1A Compete for Binding to eIF5B

Eukaryotic translation initiation is a multistep process requiring a number of eukaryotic translation initiation factors (eIFs). Two GTPases play key roles in the process. eIF2 brings the initiator Met-tRNA_i to the preinitiation complex (PIC). Upon start codon selection and GTP hydrolysis promoted by the GTPase-activating protein (GAP) eIF5, eIF2-GDP is displaced from Met-tRNA_i by eIF5B-GTP and is released in complex with eIF5. eIF5B promotes ribosomal subunit joining, with the help of eIF1A. Upon subunit joining, eIF5B hydrolyzes GTP and is released together with eIF1A. We found that human eIF5 interacts with eIF5B and may help recruit eIF5B to the PIC. An eIF5B-binding motif was identified at the C-terminus of eIF5, similar to that found in eIF1A. Indeed, eIF5 competes with eIF1A for binding and has an ∼100-fold higher affinity for eIF5B. Because eIF5 is the GAP of eIF2, the newly discovered interaction offers a possible mechanism for coordination between the two steps in translation initiation controlled by GTPases: start codon selection and ribosomal subunit joining. Our results indicate that in humans, eIF5B displacing eIF2 from Met-tRNA_i upon subunit joining may be coupled to eIF1A displacing eIF5 from eIF5B, allowing the eIF5:eIF2-GDP complex to leave the ribosome.

Context-dependent prediction of protein complexes by SiComPre

Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein-protein interactions and resulting in variations in the complexome. Quantitative and qualitative characterization of context-specific protein complexes will help to better understand context-dependent variations in the physiological behavior of cells. Here, we present SiComPre 1.0, a computational tool that predicts context-specific protein complexes by integrating multi-omics sources. SiComPre outperforms other protein complex prediction tools in qualitative predictions and is unique in giving quantitative predictions on the complexome depending on the specific interactions and protein abundances defined by the user. We provide tutorials and examples on the complexome prediction of common model organisms, various human tissues and how the complexome is affected by drug treatment.

Ezrin enrichment on curved membranes requires a specific conformation or interaction with a curvature-sensitive partner

One challenge in cell biology is to decipher the biophysical mechanisms governing protein enrichment on curved membranes and the resulting membrane deformation. The ERM protein ezrin is abundant and associated with cellular membranes that are flat, positively or negatively curved. Using in vitro and cell biology approaches, we assess mechanisms of ezrin’s enrichment on curved membranes. We evidence that wild-type ezrin (ezrinWT) and its phosphomimetic mutant T567D (ezrinTD) do not deform membranes but self-assemble anti-parallelly, zipping adjacent membranes. EzrinTD’s specific conformation reduces intermolecular interactions, allows binding to actin filaments, which reduces membrane tethering, and promotes ezrin binding to positively-curved membranes. While neither ezrinTD nor ezrinWT senses negative curvature alone, we demonstrate that interacting with curvature-sensing I-BAR-domain proteins facilitates ezrin enrichment in negatively-curved membrane protrusions. Overall, our work demonstrates that ezrin can tether membranes, or be targeted to curved membranes, depending on conformations and interactions with actin and curvature-sensing binding partners.

(Membrane) Protein Production in Context

Great progress has been made in elucidating the structural and mechanistic basis of (membrane) protein production. Here, we attempt to look ahead and indicate four directions in which our understanding of the protein production process can grow: (i) determine how the molecular mechanisms influence higher-level processes, such as the distribution of protein copy number over a population of cells or the cell growth rate; (ii) explore the functional landscape that the molecular mechanisms of protein production exist in, for instance by comparing membrane protein insertion mechanisms; (iii) uncover the life history of proteins - that is, what happens to them between their synthesis and degradation; and (iv) determine, and connect by calculation, the numbers that are associated with (membrane) protein production.