Autoinhibited Protein Database: a curated database of autoinhibitory domains and their autoinhibition mechanisms.

Autoinhibition, a crucial allosteric self-regulation mechanism in cell signaling, ensures signal propagation exclusively in the presence of specific molecular inputs. The heightened focus on autoinhibited proteins stems from their implication in human diseases, positioning them as potential causal factors or therapeutic targets. However, the absence of a comprehensive knowledgebase impedes a thorough understanding of their roles and applications in drug discovery. Addressing this gap, we introduce Autoinhibited Protein Database (AiPD), a curated database standardizing information on autoinhibited proteins. AiPD encompasses details on autoinhibitory domains (AIDs), their targets, regulatory mechanisms, experimental validation methods, and implications in diseases, including associated mutations and post-translational modifications. AiPD comprises 698 AIDs from 532 experimentally characterized autoinhibited proteins and 2695 AIDs from their 2096 homologs, which were retrieved from 864 published articles. AiPD also includes 42 520 AIDs of computationally predicted autoinhibited proteins. In addition, AiPD facilitates users in investigating potential AIDs within a query sequence through comparisons with documented autoinhibited proteins. As the inaugural autoinhibited protein repository, AiPD significantly aids researchers studying autoinhibition mechanisms and their alterations in human diseases. It is equally valuable for developing computational models, analyzing allosteric protein regulation, predicting new drug targets, and understanding intervention mechanisms AiPD serves as a valuable resource for diverse researchers, contributing to the understanding and manipulation of autoinhibition in cellular processes. Database URL: http://ssbio.cau.ac.kr/databases/AiPD.

Widespread alteration of protein autoinhibition in human cancers.

Autoinhibition is a prevalent allosteric regulatory mechanism in signaling proteins. Reduced autoinhibition underlies the tumorigenic effect of some known cancer drivers, but whether autoinhibition is altered generally in cancer remains elusive. Here, we demonstrate that cancer-associated missense mutations, in-frame insertions/deletions, and fusion breakpoints are enriched within inhibitory allosteric switches (IASs) across all cancer types. Selection for IASs that are recurrently mutated in cancers identifies established and unknown cancer drivers. Recurrent missense mutations in IASs of these drivers are associated with distinct, cancer-specific changes in molecular signaling. For the specific case of PPP3CA, the catalytic subunit of calcineurin, we provide insights into the molecular mechanisms of altered autoinhibition by cancer mutations using biomolecular simulations, and demonstrate that such mutations are associated with transcriptome changes consistent with increased calcineurin signaling. Our integrative study shows that autoinhibition-modulating genetic alterations are positively selected for by cancer cells.

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/.

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.

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.

Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins.

Intrinsic disorder is instrumental for a wide range of protein functions, and its analysis, using computational predictions from primary structures, complements secondary and tertiary structure-based approaches. In this Tutorial, we provide an overview and comparison of 23 publicly available computational tools with complementary parameters useful for intrinsic disorder prediction, partly relying on results from the Critical Assessment of protein Intrinsic Disorder prediction experiment. We consider factors such as accuracy, runtime, availability and the need for functional insights. The selected tools are available as web servers and downloadable programs, offer state-of-the-art predictions and can be used in a high-throughput manner. We provide examples and instructions for the selected tools to illustrate practical aspects related to the submission, collection and interpretation of predictions, as well as the timing and their limitations. We highlight two predictors for intrinsically disordered proteins, flDPnn as accurate and fast and IUPred as very fast and moderately accurate, while suggesting ANCHOR2 and MoRFchibi as two of the best-performing predictors for intrinsically disordered region binding. We link these tools to additional resources, including databases of predictions and web servers that integrate multiple predictive methods. Altogether, this Tutorial provides a hands-on guide to comparatively evaluating multiple predictors, submitting and collecting their own predictions, and reading and interpreting results. It is suitable for experimentalists and computational biologists interested in accurately and conveniently identifying intrinsic disorder, facilitating the functional characterization of the rapidly growing collections of protein sequences.

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/.

Comparison of Biomolecular Condensate Localization and Protein Phase Separation Predictors.

Research in the field of biochemistry and cellular biology has entered a new phase due to the discovery of phase separation driving the formation of biomolecular condensates, or membraneless organelles, in cells. The implications of this novel principle of cellular organization are vast and can be applied at multiple scales, spawning exciting research questions in numerous directions. Of fundamental importance are the molecular mechanisms that underly biomolecular condensate formation within cells and whether insights gained into these mechanisms provide a gateway for accurate predictions of protein phase behavior. Within the last six years, a significant number of predictors for protein phase separation and condensate localization have emerged. Herein, we compare a collection of state-of-the-art predictors on different tasks related to protein phase behavior. We show that the tested methods achieve high AUCs in the identification of biomolecular condensate drivers and scaffolds, as well as in the identification of proteins able to phase separate in vitro. However, our benchmark tests reveal that their performance is poorer when used to predict protein segments that are involved in phase separation or to classify amino acid substitutions as phase-separation-promoting or -inhibiting mutations. Our results suggest that the phenomenological approach used by most predictors is insufficient to fully grasp the complexity of the phenomenon within biological contexts and make reliable predictions related to protein phase behavior at the residue level.

Phase Separation in Biology and Disease; Current Perspectives and Open Questions.

In the past almost 15 years, we witnessed the birth of a new scientific field focused on the existence, formation, biological functions, and disease associations of membraneless bodies in cells, now referred to as biomolecular condensates. Pioneering studies from several laboratories [reviewed in1-3] supported a model wherein biomolecular condensates associated with diverse biological processes form through the process of phase separation. These and other findings that followed have revolutionized our understanding of how biomolecules are organized in space and time within cells to perform myriad biological functions, including cell fate determination, signal transduction, endocytosis, regulation of gene expression and protein translation, and regulation of RNA metabolism. Further, condensates formed through aberrant phase transitions have been associated with numerous human diseases, prominently including neurodegeneration and cancer. While in some cases, rigorous evidence supports links between formation of biomolecular condensates through phase separation and biological functions, in many others such links are less robustly supported, which has led to rightful scrutiny of the generality of the roles of phase separation in biology and disease.4-7 During a week-long workshop in March 2022 at the Telluride Science Research Center (TSRC) in Telluride, Colorado, ∼25 scientists addressed key questions surrounding the biomolecular condensates field. Herein, we present insights gained through these discussions, addressing topics including, roles of condensates in diverse biological processes and systems, and normal and disease cell states, their applications to synthetic biology, and the potential for therapeutically targeting biomolecular condensates.

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.

Pulse labeling reveals the tail end of protein folding by proteome profiling.

Accurate and efficient folding of nascent protein sequences into their native states requires support from the protein homeostasis network. Herein we probe which newly translated proteins are thermo-sensitive, making them susceptible to misfolding and aggregation under heat stress using pulse-SILAC mass spectrometry. We find a distinct group of proteins that is highly sensitive to this perturbation when newly synthesized but not once matured. These proteins are abundant and highly structured. Notably, they display a tendency to form ß sheet secondary structures, have more complex folding topology, and are enriched for chaperone-binding motifs, suggesting a higher demand for chaperone-assisted folding. These polypeptides are also more often components of stable protein complexes in comparison with other proteins. Combining these findings suggests the existence of a specific subset of proteins in the cell that is particularly vulnerable to misfolding and aggregation following synthesis before reaching the native state.

Where protein structure and cell diversity meet.

Protein-protein interaction networks - interactomes - are charted with the hope to understand how phenotypes emerge and how they are altered in disease states. Early efforts to map interactomes have focused on the assembly of context agnostic, reference networks. However, recent studies have mapped interactomes across different cell lines and tissues, finding highly variable interactomes due to the rewiring of protein-protein interactions in different contexts. Increasing evidence points to significant links between protein structure and interactome diversity seen across cell types and tissues. We discuss how recent findings support the key role of alternative splicing and phosphorylation, two well-established regulators of protein structural and functional diversity, in defining cell type- and tissue-specific interactomes. Moreover, we show that intrinsically disordered protein regions are most favorably equipped to support interactome rewiring by acting as hubs of protein structure and function regulation.

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.

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.

Transient Tertiary Structures of Disordered Dynein Intermediate Chain Regulate its Interactions with Multiple Partners.

The N-terminal domain of dynein intermediate chain (N-IC) is central to the cytoplasmic dynein 'cargo attachment subcomplex' and regulation of motor activity. It is a prototypical intrinsically disordered protein (IDP), serving as a primarily disordered polybivalent molecular scaffold for numerous binding partners, including three dimeric dynein light chains and coiled coil domains of dynein partners dynactin p150Glued and NudE. At the very N-terminus, a 40 amino acid single alpha helix (SAH) forms the major binding site for both p150Glued and NudE, while a shorter nascent helix (H2) separated from SAH by a disordered linker, is necessary for tight binding to dynactin p150Glued but not to NudE. Here we demonstrate that transient tertiary interactions in this highly dynamic protein underlie the differences in its interactions with p150Glued and NudE. NMR paramagnetic relaxation enhancement experiments and restrained molecular dynamics simulations identify interactions between the two non-contiguous SAH and H2 helical regions, the extent of which correlates with the length and stability of H2, showing clearly that tertiary and secondary structure formation are coupled in IDPs. These interactions are significantly attenuated when N-IC is bound to NudE, suggesting that NudE binding shifts the conformational ensemble to one that is more extended and with less structure in H2. While the intrinsic disorder and flexibility in N-IC modulate its ability to serve as a binding platform for numerous partners, deviations of this protein from random-coil behavior provide a process for regulating these binding interactions and potentially the dynein motor.

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.

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/.

Protein-Protein Interactions Mediated by Intrinsically Disordered Protein Regions Are Enriched in Missense Mutations.

Because proteins are fundamental to most biological processes, many genetic diseases can be traced back to single nucleotide variants (SNVs) that cause changes in protein sequences. However, not all SNVs that result in amino acid substitutions cause disease as each residue is under different structural and functional constraints. Influential studies have shown that protein-protein interaction interfaces are enriched in disease-associated SNVs and depleted in SNVs that are common in the general population. These studies focus primarily on folded (globular) protein domains and overlook the prevalent class of protein interactions mediated by intrinsically disordered regions (IDRs). Therefore, we investigated the enrichment patterns of missense mutation-causing SNVs that are associated with disease and cancer, as well as those present in the healthy population, in structures of IDR-mediated interactions with comparisons to classical globular interactions. When comparing the different categories of interaction interfaces, division of the interface regions into solvent-exposed rim residues and buried core residues reveal distinctive enrichment patterns for the various types of missense mutations. Most notably, we demonstrate a strong enrichment at the interface core of interacting IDRs in disease mutations and its depletion in neutral ones, which supports the view that the disruption of IDR interactions is a mechanism underlying many diseases. Intriguingly, we also found an asymmetry across the IDR interaction interface in the enrichment of certain missense mutation types, which may hint at an increased variant tolerance and urges further investigations of IDR interactions.

Multiple Sequence Variants in STAC3 Affect Interactions with CaV1.1 and Excitation-Contraction Coupling.

STAC3 is a soluble protein essential for skeletal muscle excitation-contraction (EC) coupling. Through its tandem SH3 domains, it interacts with the cytosolic II-III loop of the skeletal muscle voltage-gated calcium channel. STAC3 is the target for a mutation (W284S) that causes Native American myopathy, but multiple other sequence variants have been reported. Here, we report a crystal structure of the human STAC3 tandem SH3 domains. We analyzed the effect of five disease-associated variants, spread over both SH3 domains, on their ability to bind to the CaV1.1 II-III loop and on muscle EC coupling. In addition to W284S, we find the F295L and K329N variants to affect both binding and EC coupling. The ability of the K329N variant, located in the second SH3 domain, to affect the interaction highlights the importance of both SH3 domains in association with CaV1.1. Our results suggest that multiple STAC3 variants may cause myopathy.

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/.