An atlas of protein-protein interactions across mouse tissues.

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options.

The DescribePROT database of amino acid-level descriptors of protein structures and functions was substantially expanded since its release in 2020. This expansion includes substantial increase in the size, scope, and quality of the underlying data, the addition of experimental structural information, the inclusion of new data download options, and an upgraded graphical interface. DescribePROT currently covers 19 structural and functional descriptors for proteins in 273 reference proteomes generated by 11 accurate and complementary predictive tools. Users can search our resource in multiple ways, interact with the data using the graphical interface, and download data at various scales including individual proteins, entire proteomes, and whole database. The annotations in DescribePROT are useful for a broad spectrum of studies that include investigations of protein structure and function, development and validation of predictive tools, and to support efforts in understanding molecular underpinnings of diseases and development of therapeutics. DescribePROT can be freely accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

Shift of the insoluble content of the proteome in the aging mouse brain.

During aging, the cellular response to unfolded proteins is believed to decline, resulting in diminished proteostasis. In model organisms, such as Caenorhabditis elegans, proteostatic decline with age has been linked to proteome solubility shifts and the onset of protein aggregation. However, this correlation has not been extensively characterized in aging mammals. To uncover age-dependent changes in the insoluble portion of a mammalian proteome, we analyzed the detergent-insoluble fraction of mouse brain tissue by mass spectrometry. We identified a group of 171 proteins, including the small heat shock protein α-crystallin, that become enriched in the detergent-insoluble fraction obtained from old mice. To enhance our ability to detect features associated with proteins in that fraction, we complemented our data with a meta-analysis of studies reporting the detergent-insoluble proteins in various mouse models of aging and neurodegeneration. Strikingly, insoluble proteins from young and old mice are distinct in several features in our study and across the collected literature data. In younger mice, proteins are more likely to be disordered, part of membraneless organelles, and involved in RNA binding. These traits become less prominent with age, as an increased number of structured proteins enter the pellet fraction. This analysis suggests that age-related changes to proteome organization lead a group of proteins with specific features to become detergent-insoluble. Importantly, these features are not consistent with those associated with proteins driving membraneless organelle formation. We see no evidence in our system of a general increase of condensate proteins in the detergent-insoluble fraction with age.

DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction.

Intrinsic disorder in proteins is relatively abundant in nature and essential for a broad spectrum of cellular functions. While disorder can be accurately predicted from protein sequences, as it was empirically demonstrated in recent community-organized assessments, it is rather challenging to collect and compile a comprehensive prediction that covers multiple disorder functions. To this end, we introduce the DEPICTER2 (DisorderEd PredictIon CenTER) webserver that offers convenient access to a curated collection of fast and accurate disorder and disorder function predictors. This server includes a state-of-the-art disorder predictor, flDPnn, and five modern methods that cover all currently predictable disorder functions: disordered linkers and protein, peptide, DNA, RNA and lipid binding. DEPICTER2 allows selection of any combination of the six methods, batch predictions of up to 25 proteins per request and provides interactive visualization of the resulting predictions. The webserver is freely available at http://biomine.cs.vcu.edu/servers/DEPICTER2/.

Dynamic phase separation of the androgen receptor and its coactivators key to regulate gene expression.

Numerous cancers, including prostate cancer (PCa), are addicted to transcription programs driven by specific genomic regions known as super-enhancers (SEs). The robust transcription of genes at such SEs is enabled by the formation of phase-separated condensates by transcription factors and coactivators with intrinsically disordered regions. The androgen receptor (AR), the main oncogenic driver in PCa, contains large disordered regions and is co-recruited with the transcriptional coactivator mediator complex subunit 1 (MED1) to SEs in androgen-dependent PCa cells, thereby promoting oncogenic transcriptional programs. In this work, we reveal that full-length AR forms foci with liquid-like properties in different PCa models. We demonstrate that foci formation correlates with AR transcriptional activity, as this activity can be modulated by changing cellular foci content chemically or by silencing MED1. AR ability to phase separate was also validated in vitro by using recombinant full-length AR protein. We also demonstrate that AR antagonists, which suppress transcriptional activity by targeting key regions for homotypic or heterotypic interactions of this receptor, hinder foci formation in PCa cells and phase separation in vitro. Our results suggest that enhanced compartmentalization of AR and coactivators may play an important role in the activation of oncogenic transcription programs in androgen-dependent PCa.

A multi-layered network model identifies Akt1 as a common modulator of neurodegeneration.

The accumulation of misfolded and aggregated proteins is a hallmark of neurodegenerative proteinopathies. Although multiple genetic loci have been associated with specific neurodegenerative diseases (NDs), molecular mechanisms that may have a broader relevance for most or all proteinopathies remain poorly resolved. In this study, we developed a multi-layered network expansion (MLnet) model to predict protein modifiers that are common to a group of diseases and, therefore, may have broader pathophysiological relevance for that group. When applied to the four NDs Alzheimer's disease (AD), Huntington's disease, and spinocerebellar ataxia types 1 and 3, we predicted multiple members of the insulin pathway, including PDK1, Akt1, InR, and sgg (GSK-3ß), as common modifiers. We validated these modifiers with the help of four Drosophila ND models. Further evaluation of Akt1 in human cell-based ND models revealed that activation of Akt1 signaling by the small molecule SC79 increased cell viability in all models. Moreover, treatment of AD model mice with SC79 enhanced their long-term memory and ameliorated dysregulated anxiety levels, which are commonly affected in AD patients. These findings validate MLnet as a valuable tool to uncover molecular pathways and proteins involved in the pathophysiology of entire disease groups and identify potential therapeutic targets that have relevance across disease boundaries. MLnet can be used for any group of diseases and is available as a web tool at http://ssbio.cau.ac.kr/software/mlnet.

GraPES: The Granule Protein Enrichment Server for prediction of biological condensate constituents.

Phase separation-based condensate formation is a novel working paradigm in biology, helping to rationalize many important cellular phenomena including the assembly of membraneless organelles. Uncovering the functional impact of cellular condensates requires a better knowledge of these condensates' constituents. Herein, we introduce the webserver GraPES (Granule Protein Enrichment Server), a user-friendly online interface containing the MaGS and MaGSeq predictors, which provide propensity scores for proteins' localization into cellular condensates. Our webpage contains models trained on human (Homo sapiens) and yeast (Saccharomyces cerevisiae) stress granule proteins. MaGS utilizes experimentally-based protein features for prediction, whereas MaGSeq is an entirely protein sequence-based implementation. GraPES is implemented in HTML/CSS and Javascript and is freely available for public use at https://grapes.msl.ubc.ca/. Documentation for using the provided webtools, descriptions of their methodology, and implementation notes can be found on the webpage.

DescribePROT: database of amino acid-level protein structure and function predictions.

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

LIST-S2: taxonomy based sorting of deleterious missense mutations across species.

The separation of deleterious from benign mutations remains a key challenge in the interpretation of genomic data. Computational methods used to sort mutations based on their potential deleteriousness rely largely on conservation measures derived from sequence alignments. Here, we introduce LIST-S2, a successor to our previously developed approach LIST, which aims to exploit local sequence identity and taxonomy distances in quantifying the conservation of human protein sequences. Unlike its predecessor, LIST-S2 is not limited to human sequences but can assess conservation and make predictions for sequences from any organism. Moreover, we provide a web-tool and downloadable software to compute and visualize the deleteriousness of mutations in user-provided sequences. This web-tool contains an HTML interface and a RESTful API to submit and manage sequences as well as a browsable set of precomputed predictions for a large number of UniProtKB protein sequences of common taxa. LIST-S2 is available at: https://list-s2.msl.ubc.ca/.

Phase separation and clustering of an ABC transporter in Mycobacterium tuberculosis.

Phase separation drives numerous cellular processes, ranging from the formation of membrane-less organelles to the cooperative assembly of signaling proteins. Features such as multivalency and intrinsic disorder that enable condensate formation are found not only in cytosolic and nuclear proteins, but also in membrane-associated proteins. The ABC transporter Rv1747, which is important for Mycobacterium tuberculosis (Mtb) growth in infected hosts, has a cytoplasmic regulatory module consisting of 2 phosphothreonine-binding Forkhead-associated domains joined by an intrinsically disordered linker with multiple phospho-acceptor threonines. Here we demonstrate that the regulatory modules of Rv1747 and its homolog in Mycobacterium smegmatis form liquid-like condensates as a function of concentration and phosphorylation. The serine/threonine kinases and sole phosphatase of Mtb tune phosphorylation-enhanced phase separation and differentially colocalize with the resulting condensates. The Rv1747 regulatory module also phase-separates on supported lipid bilayers and forms dynamic foci when expressed heterologously in live yeast and M. smegmatis cells. Consistent with these observations, single-molecule localization microscopy reveals that the endogenous Mtb transporter forms higher-order clusters within the Mycobacterium membrane. Collectively, these data suggest a key role for phase separation in the function of these mycobacterial ABC transporters and their regulation via intracellular signaling.

Conformational Plasticity and DNA-Binding Specificity of the Eukaryotic Transcription Factor Pax5.

The eukaryotic transcription factor Pax5 has a DNA-binding Paired domain composed of two independent helical bundle subdomains joined by a flexible linker. Previously, we showed distinct biophysical properties of the N-terminal (NTD) and C-terminal (CTD) subdomains, with implications for how these two regions cooperate to distinguish nonspecific and cognate DNA sites [Perez-Borrajero, C., et al. (2016) J. Mol. Biol. 428, 2372-2391]. In this study, we combined experimental methods and molecular dynamics (MD) simulations to dissect the mechanisms underlying the functional differences between the Pax5 subdomains. Both subdomains showed a similar dependence of DNA-binding affinity on ionic strength. However, due to a greater contribution of non-ionic interactions, the NTD bound its cognate DNA half-site with an affinity approximately 10-fold higher than that of the CTD with its half-site. These interactions involve base-mediated contacts as evidenced by nuclear magnetic resonance spectroscopy-monitored chemical shift perturbations. Isothermal titration calorimetry revealed that favorable enthalpic and compensating unfavorable entropic changes were substantially larger for DNA binding by the NTD than by the CTD. Complementary MD simulations indicated that the DNA recognition helix H3 of the NTD is particularly flexible in the absence of DNA and undergoes the largest changes in conformational dynamics upon binding. Overall, these data suggest that the differences observed for the subdomains of Pax5 are due to the coupling of DNA binding with dampening of motions in the NTD required for specific base contacts. Thus, the conformational plasticity of the Pax5 Paired domain underpins the differing roles of its subdomains in association with nonspecific versus cognate DNA sites.

Inverse Correlation between Amyloid Stiffness and Size.

We reveal that the axial stiffness of amyloid fibrils is inversely correlated with their cross-sectional area. Because amyloid fibrils' stiffness is determined by hydrogen bond (H-bond) density with a linear correlation, our finding implies that amyloid fibrils with larger radial sizes are generally softer and have lower density H-bond networks. In silico calculations show that the stiffness-size relationship of amyloid fibrils is, indeed, driven by the packing densities of residues and H-bonds. Our results suggest that polypeptide chains which form amyloid fibrils with narrow cross sections can optimize packing densities in the fibrillar core structure, in contrast to those forming wide amyloid fibrils. Consequently, the density of residues and H-bonds that contribute to mechanical stability is higher in amyloid fibrils with narrow cross sections. This size dependence of nanomechanics appears to be a global property of amyloid fibrils, just like the well-known cross-ß sheet topology.

MoRFchibi SYSTEM: software tools for the identification of MoRFs in protein sequences.

Molecular recognition features, MoRFs, are short segments within longer disordered protein regions that bind to globular protein domains in a process known as disorder-to-order transition. MoRFs have been found to play a significant role in signaling and regulatory processes in cells. High-confidence computational identification of MoRFs remains an important challenge. In this work, we introduce MoRFchibi SYSTEM that contains three MoRF predictors: MoRFCHiBi, a basic predictor best suited as a component in other applications, MoRFCHiBi_ Light, ideal for high-throughput predictions and MoRFCHiBi_ Web, slower than the other two but best for high accuracy predictions. Results show that MoRFchibi SYSTEM provides more than double the precision of other predictors. MoRFchibi SYSTEM is available in three different forms: as HTML web server, RESTful web server and downloadable software at: http://www.chibi.ubc.ca/faculty/joerg-gsponer/gsponer-lab/software/morf_chibi/.

Mapping the Broad Structural and Mechanical Properties of Amyloid Fibrils.

Amyloids are fibrillar nanostructures of proteins that are assembled in several physiological processes in human cells (e.g., hormone storage) but also during the course of infectious (prion) and noninfectious (nonprion) diseases such as Creutzfeldt-Jakob and Alzheimer's diseases, respectively. How the amyloid state, a state accessible to all proteins and peptides, can be exploited for functional purposes but also have detrimental effects remains to be determined. Here, we measure the nanomechanical properties of different amyloids and link them to features found in their structure models. Specifically, we use shape fluctuation analysis and sonication-induced scission in combination with full-atom molecular dynamics simulations to reveal that the amyloid fibrils of the mammalian prion protein PrP are mechanically unstable, most likely due to a very low hydrogen bond density in the fibril structure. Interestingly, amyloid fibrils formed by HET-s, a fungal protein that can confer functional prion behavior, have a much higher Young's modulus and tensile strength than those of PrP, i.e., they are much stiffer and stronger due to a tighter packing in the fibril structure. By contrast, amyloids of the proteins RIP1/RIP3 that have been shown to be of functional use in human cells are significantly stiffer than PrP fibrils but have comparable tensile strength. Our study demonstrates that amyloids are biomaterials with a broad range of nanomechanical properties, and we provide further support for the strong link between nanomechanics and ß-sheet characteristics in the amyloid core.

Free energies of solvation in the context of protein folding: Implications for implicit and explicit solvent models.

Implicit solvent models for biomolecular simulations have been developed to use in place of more expensive explicit models; however, these models make many assumptions and approximations that are likely to affect accuracy. Here, the changes in free energies of solvation upon folding ΔΔGsolv of several fast folding proteins are calculated from previously run µs-ms simulations with a number of implicit solvent models and compared to the values needed to be consistent with the explicit solvent model used in the simulations. In the majority of cases, there is a significant and substantial difference between the ΔΔGsolv values calculated from the two approaches that is robust to the details of the calculations. These differences could only be remedied by selecting values for the model parameters-the internal dielectric constant for the polar term and the surface tension coefficient for the nonpolar term-that were system-specific or physically unrealistic. We discuss the potential implications of our findings for both implicit and explicit solvent simulations. © 2015 Wiley Periodicals, Inc.

Computational identification of MoRFs in protein sequences.

MOTIVATION: Intrinsically disordered regions of proteins play an essential role in the regulation of various biological processes. Key to their regulatory function is the binding of molecular recognition features (MoRFs) to globular protein domains in a process known as a disorder-to-order transition. Predicting the location of MoRFs in protein sequences with high accuracy remains an important computational challenge. METHOD: In this study, we introduce MoRFCHiBi, a new computational approach for fast and accurate prediction of MoRFs in protein sequences. MoRFCHiBi combines the outcomes of two support vector machine (SVM) models that take advantage of two different kernels with high noise tolerance. The first, SVMS, is designed to extract maximal information from the general contrast in amino acid compositions between MoRFs, their surrounding regions (Flanks), and the remainders of the sequences. The second, SVMT, is used to identify similarities between regions in a query sequence and MoRFs of the training set. RESULTS: We evaluated the performance of our predictor by comparing its results with those of two currently available MoRF predictors, MoRFpred and ANCHOR. Using three test sets that have previously been collected and used to evaluate MoRFpred and ANCHOR, we demonstrate that MoRFCHiBi outperforms the other predictors with respect to different evaluation metrics. In addition, MoRFCHiBi is downloadable and fast, which makes it useful as a component in other computational prediction tools. AVAILABILITY AND IMPLEMENTATION: http://www.chibi.ubc.ca/morf/.

System-wide analysis reveals intrinsically disordered proteins are prone to ubiquitylation after misfolding stress.

Damaged and misfolded proteins that are no longer functional in the cell need to be eliminated. Failure to do so might lead to their accumulation and aggregation, a hallmark of many neurodegenerative diseases. Protein quality control pathways play a major role in the degradation of these proteins, which is mediated mainly by the ubiquitin proteasome system. Despite significant focus on identifying ubiquitin ligases involved in these pathways, along with their substrates, a systems-level understanding of these pathways has been lacking. For instance, as misfolded proteins are rapidly ubiquitylated, unconjugated ubiquitin is rapidly depleted from the cell upon misfolding stress; yet it is unknown whether certain targets compete more efficiently to be ubiquitylated. Using a system-wide approach, we applied statistical and computational methods to identify characteristics enriched among proteins that are further ubiquitylated after heat shock. We discovered that distinct populations of structured and, surprisingly, intrinsically disordered proteins are prone to ubiquitylation. Proteomic analysis revealed that abundant and highly structured proteins constitute the bulk of proteins in the low-solubility fraction after heat shock, but only a portion is ubiquitylated. In contrast, ubiquitylated, intrinsically disordered proteins are enriched in the low-solubility fraction after heat shock. These proteins have a very low abundance in the cell, are rarely encoded by essential genes, and are enriched in binding motifs. In additional experiments, we confirmed that several of the identified intrinsically disordered proteins were ubiquitylated after heat shock and demonstrated for two of them that their disordered regions are important for ubiquitylation after heat shock. We propose that intrinsically disordered regions may be recognized by the protein quality control machinery and thereby facilitate the ubiquitylation of proteins after heat shock.

Categorizer: a tool to categorize genes into user-defined biological groups based on semantic similarity.

BACKGROUND: Communalities between large sets of genes obtained from high-throughput experiments are often identified by searching for enrichments of genes with the same Gene Ontology (GO) annotations. The GO analysis tools used for these enrichment analyses assume that GO terms are independent and the semantic distances between all parent-child terms are identical, which is not true in a biological sense. In addition these tools output lists of often redundant or too specific GO terms, which are difficult to interpret in the context of the biological question investigated by the user. Therefore, there is a demand for a robust and reliable method for gene categorization and enrichment analysis. RESULTS: We have developed Categorizer, a tool that classifies genes into user-defined groups (categories) and calculates p-values for the enrichment of the categories. Categorizer identifies the biologically best-fit category for each gene by taking advantage of a specialized semantic similarity measure for GO terms. We demonstrate that Categorizer provides improved categorization and enrichment results of genetic modifiers of Huntington's disease compared to a classical GO Slim-based approach or categorizations using other semantic similarity measures. CONCLUSION: Categorizer enables more accurate categorizations of genes than currently available methods. This new tool will help experimental and computational biologists analyzing genomic and proteomic data according to their specific needs in a more reliable manner.

On the importance of polar interactions for complexes containing intrinsically disordered proteins.

There is a growing recognition for the importance of proteins with large intrinsically disordered (ID) segments in cell signaling and regulation. ID segments in these proteins often harbor regions that mediate molecular recognition. Coupled folding and binding of the recognition regions has been proposed to confer high specificity to interactions involving ID segments. However, researchers recently questioned the origin of the interaction specificity of ID proteins because of the overrepresentation of hydrophobic residues in their interaction interfaces. Here, we focused on the role of polar and charged residues in interactions mediated by ID segments. Making use of the extended nature of most ID segments when in complex with globular proteins, we first identified large numbers of complexes between globular proteins and ID segments by using radius-of-gyration-based selection criteria. Consistent with previous studies, we found the interfaces of these complexes to be enriched in hydrophobic residues, and that these residues contribute significantly to the stability of the interaction interface. However, our analyses also show that polar interactions play a larger role in these complexes than in structured protein complexes. Computational alanine scanning and salt-bridge analysis indicate that interfaces in ID complexes are highly complementary with respect to electrostatics, more so than interfaces of globular proteins. Follow-up calculations of the electrostatic contributions to the free energy of binding uncovered significantly stronger Coulombic interactions in complexes harbouring ID segments than in structured protein complexes. However, they are counter-balanced by even higher polar-desolvation penalties. We propose that polar interactions are a key contributing factor to the observed high specificity of ID segment-mediated interactions.

Promiscuity as a functional trait: intrinsically disordered regions as central players of interactomes.

Because of their pervasiveness in eukaryotic genomes and their unique properties, understanding the role that ID (intrinsically disordered) regions in proteins play in the interactome is essential for gaining a better understanding of the network. Especially critical in determining this role is their ability to bind more than one partner using the same region. Studies have revealed that proteins containing ID regions tend to take a central role in protein interaction networks; specifically, they act as hubs, interacting with multiple different partners across time and space, allowing for the co-ordination of many cellular activities. There appear to be three different modules within ID regions responsible for their functionally promiscuous behaviour: MoRFs (molecular recognition features), SLiMs (small linear motifs) and LCRs (low complexity regions). These regions allow for functionality such as engaging in the formation of dynamic heteromeric structures which can serve to increase local activity of an enzyme or store a collection of functionally related molecules for later use. However, the use of promiscuity does not come without a cost: a number of diseases that have been associated with ID-containing proteins seem to be caused by undesirable interactions occurring upon altered expression of the ID-containing protein.