A cyclin D1 intrinsically disordered domain accesses modified histone motifs to govern gene transcription.

The essential G1-cyclin, CCND1, is frequently overexpressed in cancer, contributing to tumorigenesis by driving cell-cycle progression. D-type cyclins are rate-limiting regulators of G1-S progression in mammalian cells via their ability to bind and activate CDK4 and CDK6. In addition, cyclin D1 conveys kinase-independent transcriptional functions of cyclin D1. Here we report that cyclin D1 associates with H2BS14 via an intrinsically disordered domain (IDD). The same region of cyclin D1 was necessary for the induction of aneuploidy, induction of the DNA damage response, cyclin D1-mediated recruitment into chromatin, and CIN gene transcription. In response to DNA damage H2BS14 phosphorylation occurs, resulting in co-localization with γH2AX in DNA damage foci. Cyclin D1 ChIP seq and γH2AX ChIP seq revealed ~14% overlap. As the cyclin D1 IDD functioned independently of the CDK activity to drive CIN, the IDD domain may provide a rationale new target to complement CDK-extinction strategies.

PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins.

The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version of PED reflects advancements in the field, denoting a continual expansion with a total of 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques. With this significant increment in the number of ensembles, a few yet-unprecedented new entries entered the database, including those also determined or refined by electron paramagnetic resonance or circular dichroism data. In addition, PED was enriched with several new features, including a novel deposition service, improved user interface, new database cross-referencing options and integration with the 3D-Beacons network-all representing efforts to improve the FAIRness of the database. Foreseeably, PED will keep growing in size and expanding with new types of ensembles generated by accurate and fast ML-based generative models and coarse-grained simulations. Therefore, among future efforts, priority will be given to further develop the database to be compatible with ensembles modeled at a coarse-grained level.

ELM-the Eukaryotic Linear Motif resource-2024 update.

Short Linear Motifs (SLiMs) are the smallest structural and functional components of modular eukaryotic proteins. They are also the most abundant, especially when considering post-translational modifications. As well as being found throughout the cell as part of regulatory processes, SLiMs are extensively mimicked by intracellular pathogens. At the heart of the Eukaryotic Linear Motif (ELM) Resource is a representative (not comprehensive) database. The ELM entries are created by a growing community of skilled annotators and provide an introduction to linear motif functionality for biomedical researchers. The 2024 ELM update includes 346 novel motif instances in areas ranging from innate immunity to both protein and RNA degradation systems. In total, 39 classes of newly annotated motifs have been added, and another 17 existing entries have been updated in the database. The 2024 ELM release now includes 356 motif classes incorporating 4283 individual motif instances manually curated from 4274 scientific publications and including >700 links to experimentally determined 3D structures. In a recent development, the InterPro protein module resource now also includes ELM data. ELM is available at: http://elm.eu.org.

In Silico Structural Analysis Exploring Conformational Folding of Protein Variants in Alzheimer's Disease.

Accurate protein structure prediction using computational methods remains a challenge in molecular biology. Recent advances in AI-powered algorithms provide a transformative effect in solving this problem. Even though AlphaFold's performance has improved since its release, there are still limitations that apply to its efficacy. In this study, a selection of proteins related to the pathology of Alzheimer's disease was modeled, with Presenilin-1 (PSN1) and its mutated variants in the foreground. Their structural predictions were evaluated using the ColabFold implementation of AlphaFold, which utilizes MMseqs2 for the creation of multiple sequence alignments (MSAs). A higher number of recycles than the one used in the AlphaFold DB was selected, and no templates were used. In addition, prediction by RoseTTAFold was also applied to address how structures from the two deep learning frameworks match reality. The resulting conformations were compared with the corresponding experimental structures, providing potential insights into the predictive ability of this approach in this particular group of proteins. Furthermore, a comprehensive examination was performed on features such as predicted regions of disorder and the potential effect of mutations on PSN1. Our findings consist of highly accurate superpositions with little or no deviation from experimentally determined domain-level models.

YbiB: a novel interactor of the GTPase ObgE.

Obg is a widely conserved and essential GTPase in bacteria, which plays a central role in a large range of important cellular processes, such as ribosome biogenesis, DNA replication, cell division and bacterial persistence. Nevertheless, the exact function of Obg in these processes and the interactions it makes within the associated pathways remain largely unknown. Here, we identify the DNA-binding TrpD2 protein YbiB as an interactor of the Escherichia coli Obg (ObgE). We show that both proteins interact with high affinity in a peculiar biphasic fashion, and pinpoint the intrinsically disordered and highly negatively charged C-terminal domain of ObgE as a main driver for this interaction. Molecular docking and X-ray crystallography, together with site-directed mutagenesis, are used to map the binding site of this ObgE C-terminal domain within a highly positively charged groove on the surface of the YbiB homodimer. Correspondingly, ObgE efficiently inhibits the binding of DNA to YbiB, indicating that ObgE competes with DNA for binding in the positive clefts of YbiB. This study thus forms an important step for the further elucidation of the interactome and cellular role of the essential bacterial protein Obg.

The evolution and polymorphism of mono-amino acid repeats in androgen receptor and their regulatory role in health and disease.

Androgen receptor (AR) is a key member of nuclear hormone receptors with the longest intrinsically disordered N-terminal domain (NTD) in its protein family. There are four mono-amino acid repeats (polyQ1, polyQ2, polyG, and polyP) located within its NTD, of which two are polymorphic (polyQ1 and polyG). The length of both polymorphic repeats shows clinically important correlations with disease, especially with cancer and neurodegenerative diseases, as shorter and longer alleles exhibit significant differences in expression, activity and solubility. Importantly, AR has also been shown to undergo condensation in the nucleus by liquid-liquid phase separation, a process highly sensitive to protein solubility and concentration. Nonetheless, in prostate cancer cells, AR variants also partition into transcriptional condensates, which have been shown to alter the expression of target gene products. In this review, we summarize current knowledge on the link between AR repeat polymorphisms and cancer types, including mechanistic explanations and models comprising the relationship between condensate formation, polyQ1 length and transcriptional activity. Moreover, we outline the evolutionary paths of these recently evolved amino acid repeats across mammalian species, and discuss new research directions with potential breakthroughs and controversies in the literature.

Intrinsic protein disorder uncouples affinity from binding specificity.

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins often function by molecular recognition, in which they undergo induced folding. Based on prior generalizations, the idea prevails in the IDP field that due to the entropic penalty of induced folding, the major functional advantage associated with this binding mode is "uncoupling" specificity from binding strength. Nevertheless, both weaker binding and high specificity of IDPs/IDRs rest on limited experimental observations, making these assumptions more speculations than evidence-supported facts. The issue is also complicated by the rather vague concept of specificity that lacks an exact measure, such as the Kd for binding strength. We addressed these issues by creating and analyzing a comprehensive dataset of well-characterized ID/globular protein complexes, for which both the atomic structure of the complex and free energy (ΔG, Kd ) of interaction is known. Through this analysis, we provide evidence that the affinity distributions of IDP/globular and globular/globular complexes show different trends, whereas specificity does not connote to weaker binding strength of IDPs/IDRs. Furthermore, protein disorder extends the spectrum in the direction of very weak interactions, which may have important regulatory consequences and suggest that, in a biological sense, strict correlation of specificity and binding strength are uncoupled by structural disorder.

Challenges in describing the conformation and dynamics of proteins with ambiguous behavior.

Traditionally, our understanding of how proteins operate and how evolution shapes them is based on two main data sources: the overall protein fold and the protein amino acid sequence. However, a significant part of the proteome shows highly dynamic and/or structurally ambiguous behavior, which cannot be correctly represented by the traditional fixed set of static coordinates. Representing such protein behaviors remains challenging and necessarily involves a complex interpretation of conformational states, including probabilistic descriptions. Relating protein dynamics and multiple conformations to their function as well as their physiological context (e.g., post-translational modifications and subcellular localization), therefore, remains elusive for much of the proteome, with studies to investigate the effect of protein dynamics relying heavily on computational models. We here investigate the possibility of delineating three classes of protein conformational behavior: order, disorder, and ambiguity. These definitions are explored based on three different datasets, using interpretable machine learning from a set of features, from AlphaFold2 to sequence-based predictions, to understand the overlap and differences between these datasets. This forms the basis for a discussion on the current limitations in describing the behavior of dynamic and ambiguous proteins.

Degron masking outlines degronons, co-degrading functional modules in the proteome.

Effective organization of proteins into functional modules (networks, pathways) requires systems-level coordination between transcription, translation and degradation. Whereas the cooperation between transcription and translation was extensively studied, the cooperative degradation regulation of protein complexes and pathways has not been systematically assessed. Here we comprehensively analyzed degron masking, a major mechanism by which cellular systems coordinate degron recognition and protein degradation. For over 200 substrates with characterized degrons (E3 ligase targeting motifs, ubiquitination sites and disordered proteasomal entry sequences), we demonstrate that degrons extensively overlap with protein-protein interaction sites. Analysis of binding site information and protein abundance comparisons show that regulatory partners effectively outcompete E3 ligases, masking degrons from the ubiquitination machinery. Protein abundance variations between normal and cancer cells highlight the dynamics of degron masking components. Finally, integrative analysis of gene co-expression, half-life correlations and functional relationships between interacting proteins point towards higher-order, co-regulated degradation modules ('degronons') in the proteome.

F/YGG-motif is an intrinsically disordered nucleic-acid binding motif.

Heterogeneous nuclear ribonucleoproteins (hnRNP) function in RNA processing, have RNA-recognition motifs (RRMs) and intrinsically disordered, low-complexity domains (LCDs). While RRMs are drivers of RNA binding, there is only limited knowledge about the RNA interaction by the LCD of some hnRNPs. Here, we show that the LCD of hnRNPA2 interacts with RNA via an embedded Tyr/Gly-rich region which is a disordered RNA-binding motif. RNA binding is maintained upon mutating tyrosine residues to phenylalanines, but abrogated by mutating to alanines, thus we term the RNA-binding region 'F/YGG motif'. The F/YGG motif can bind a broad range of structured (e.g. tRNA) and disordered (e.g. polyA) RNAs, but not rRNA. As the F/YGG otif can also interact with DNA, we consider it a general nucleic acid-binding motif. hnRNPA2 LCD can form dense droplets, by liquid-liquid phase separation (LLPS). Their formation is inhibited by RNA binding, which is mitigated by salt and 1,6-hexanediol, suggesting that both electrostatic and hydrophobic interactions feature in the F/YGG motif. The D290V mutant also binds RNA, which interferes with both LLPS and aggregation thereof. We found homologous regions in a broad range of RNA- and DNA-binding proteins in the human proteome, suggesting that the F/YGG motif is a general nucleic acid-interaction motif.

DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation.

The Database of Intrinsically Disordered Proteins (DisProt, URL: https://disprot.org) is the major repository of manually curated annotations of intrinsically disordered proteins and regions from the literature. We report here recent updates of DisProt version 9, including a restyled web interface, refactored Intrinsically Disordered Proteins Ontology (IDPO), improvements in the curation process and significant content growth of around 30%. Higher quality and consistency of annotations is provided by a newly implemented reviewing process and training of curators. The increased curation capacity is fostered by the integration of DisProt with APICURON, a dedicated resource for the proper attribution and recognition of biocuration efforts. Better interoperability is provided through the adoption of the Minimum Information About Disorder (MIADE) standard, an active collaboration with the Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO) consortia and the support of the ELIXIR infrastructure.

The Eukaryotic Linear Motif resource: 2022 release.

Almost twenty years after its initial release, the Eukaryotic Linear Motif (ELM) resource remains an invaluable source of information for the study of motif-mediated protein-protein interactions. ELM provides a comprehensive, regularly updated and well-organised repository of manually curated, experimentally validated short linear motifs (SLiMs). An increasing number of SLiM-mediated interactions are discovered each year and keeping the resource up-to-date continues to be a great challenge. In the current update, 30 novel motif classes have been added and five existing classes have undergone major revisions. The update includes 411 new motif instances mostly focused on cell-cycle regulation, control of the actin cytoskeleton, membrane remodelling and vesicle trafficking pathways, liquid-liquid phase separation and integrin signalling. Many of the newly annotated motif-mediated interactions are targets of pathogenic motif mimicry by viral, bacterial or eukaryotic pathogens, providing invaluable insights into the molecular mechanisms underlying infectious diseases. The current ELM release includes 317 motif classes incorporating 3934 individual motif instances manually curated from 3867 scientific publications. ELM is available at: http://elm.eu.org.

Thiol-disulphide independent in-cell trapping for the identification of peroxiredoxin 2 interactors.

Hydrogen peroxide (H2O2) acts as a signalling molecule by oxidising cysteine thiols in proteins. Recent evidence has established a role for cytosolic peroxiredoxins in transmitting H2O2-based oxidation to a multitude of target proteins. Moreover, it is becoming clear that peroxiredoxins fulfil their function in organised microdomains, where not all interactors are covalently bound. However, most studies aimed at identifying peroxiredoxin interactors were based on methods that only detect covalently linked partners. Here, we explore the applicability of two thiol-disulphide independent in-cell trapping methodological approaches in combination with mass spectrometry for the identification of interaction partners of peroxiredoxin 2 (Prdx2). The first is biotin-dependent proximity-labelling (BioID) with a biotin ligase A (BirA*)-fused Prdx2, which has never been applied on redox-active proteins. The second is crosslinker co-immunoprecipitation with an N-terminally His-tagged Prdx2. During the initial characterisation of the tagged Prdx2 constructs, we found that the His-tag, but not BirA*, compromises the peroxidase and signalling activities of Prdx2. Further, the Prdx2 interactors identified with each approach showed little overlap. We therefore concluded that BioID is a more reliable method than crosslinker co-immunoprecipitation. After a stringent mass spec data filtering, BioID identified 13 interactors under elevated H2O2 conditions, including subunit five of the COP9 signalosome complex (CSN5). The Prdx2:CSN5 interaction was further confirmed in a proximity ligation assay. Taken together, our results demonstrate that BioID can be used as a method for the identification of interactors of Prdxs, and that caution should be exercised when interpreting protein-protein interaction results using tagged Prdxs.

Exploring Curated Conformational Ensembles of Intrinsically Disordered Proteins in the Protein Ensemble Database.

The Protein Ensemble Database (PED; https://proteinensemble.org/) is the major repository of conformational ensembles of intrinsically disordered proteins (IDPs). Conformational ensembles of IDPs are primarily provided by their authors or occasionally collected from literature, and are subsequently deposited in PED along with the corresponding structured, manually curated metadata. The modeling of conformational ensembles usually relies on experimental data from small-angle X-ray scattering (SAXS), fluorescence resonance energy transfer (FRET), NMR spectroscopy, and molecular dynamics (MD) simulations, or a combination of these techniques. The growing number of scientific studies based on these data, along with the astounding and swift progress in the field of protein intrinsic disorder, has required a significant update and upgrade of PED, first published in 2014. To this end, the database was entirely renewed in 2020 and now has a dedicated team of biocurators providing manually curated descriptions of the methods and conditions applied to generate the conformational ensembles and for checking consistency of the data. Here, we present a detailed description on how to explore PED with its protein pages and experimental pages, and how to interpret entries of conformational ensembles. We describe how to efficiently search conformational ensembles deposited in PED by means of its web interface and API. We demonstrate how to make sense of the PED protein page and its associated experimental entry pages with reference to the yeast Sic1 use case. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Performing a search in PED Support Protocol 1: Programmatic access with the PED API Basic Protocol 2: Interpreting the protein page and the experimental entry page-the Sic1 use case Support Protocol 2: Downloading options Support Protocol 3: Understanding the validation report-the Sic1 use case Basic Protocol 3: Submitting new conformational ensembles to PED Basic Protocol 4: Providing feedback in PED.

DNA-binding domain as the minimal region driving RNA-dependent liquid-liquid phase separation of androgen receptor.

Androgen receptor (AR) is a nuclear hormone receptor that regulates the transcription of genes involved in the development of testis, prostate and the nervous system. Misregulation of AR is a major driver of prostate cancer (PC). The primary agonist of full-length AR is testosterone, whereas its splice variants, for example, AR-v7 implicated in cancer may lack a ligand-binding domain and are thus devoid of proper hormonal control. Recently, it was demonstrated that full-length AR, but not AR-v7, can undergo liquid-liquid phase separation (LLPS) in a cellular model of PC. In a detailed bioinformatics and deletion analysis, we have analyzed which AR region is responsible for LLPS. We found that its DNA-binding domain (DBD) can bind RNA and can undergo RNA-dependent LLPS. RNA regulates its LLPS in a reentrant manner, that is, it has an inhibitory effect at higher concentrations. As RNA binds DBD more weakly than DNA, while both RNA and DNA localizes into AR droplets, its LLPS depends on the relative concentration of the two nucleic acids. The region immediately preceding DBD has no effect on the LLPS propensity of AR, whereas the functional part of its long N-terminal disordered transactivation domain termed activation function 1 (AF1) inhibits AR-v7 phase separation. We suggest that the resulting diminished LLPS tendency of AR-v7 may contribute to the misregulation of the transcription function of AR in prostate cancer.

Integration of Data from Liquid-Liquid Phase Separation Databases Highlights Concentration and Dosage Sensitivity of LLPS Drivers.

Liquid-liquid phase separation (LLPS) is a molecular process that leads to the formation of membraneless organelles, representing functionally specialized liquid-like cellular condensates formed by proteins and nucleic acids. Integrating the data on LLPS-associated proteins from dedicated databases revealed only modest agreement between them and yielded a high-confidence dataset of 89 human LLPS drivers. Analysis of the supporting evidence for our dataset uncovered a systematic and potentially concerning difference between protein concentrations used in a good fraction of the in vitro LLPS experiments, a key parameter that governs the phase behavior, and the proteomics-derived cellular abundance levels of the corresponding proteins. Closer scrutiny of the underlying experimental data enabled us to offer a sound rationale for this systematic difference, which draws on our current understanding of the cellular organization of the proteome and the LLPS process. In support of this rationale, we find that genes coding for our human LLPS drivers tend to be dosage-sensitive, suggesting that their cellular availability is tightly regulated to preserve their functional role in direct or indirect relation to condensate formation. Our analysis offers guideposts for increasing agreement between in vitro and in vivo studies, probing the roles of proteins in LLPS.

PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins.

The Protein Ensemble Database (PED) (https://proteinensemble.org), which holds structural ensembles of intrinsically disordered proteins (IDPs), has been significantly updated and upgraded since its last release in 2016. The new version, PED 4.0, has been completely redesigned and reimplemented with cutting-edge technology and now holds about six times more data (162 versus 24 entries and 242 versus 60 structural ensembles) and a broader representation of state of the art ensemble generation methods than the previous version. The database has a completely renewed graphical interface with an interactive feature viewer for region-based annotations, and provides a series of descriptors of the qualitative and quantitative properties of the ensembles. High quality of the data is guaranteed by a new submission process, which combines both automatic and manual evaluation steps. A team of biocurators integrate structured metadata describing the ensemble generation methodology, experimental constraints and conditions. A new search engine allows the user to build advanced queries and search all entry fields including cross-references to IDP-related resources such as DisProt, MobiDB, BMRB and SASBDB. We expect that the renewed PED will be useful for researchers interested in the atomic-level understanding of IDP function, and promote the rational, structure-based design of IDP-targeting drugs.

Chasing coevolutionary signals in intrinsically disordered proteins complexes.

Intrinsically disordered proteins/regions (IDPs/IDRs) are crucial components of the cell, they are highly abundant and participate ubiquitously in a wide range of biological functions, such as regulatory processes and cell signaling. Many of their important functions rely on protein interactions, by which they trigger or modulate different pathways. Sequence covariation, a powerful tool for protein contact prediction, has been applied successfully to predict protein structure and to identify protein-protein interactions mostly of globular proteins. IDPs/IDRs also mediate a plethora of protein-protein interactions, highlighting the importance of addressing sequence covariation-based inter-protein contact prediction of this class of proteins. Despite their importance, a systematic approach to analyze the covariation phenomena of intrinsically disordered proteins and their complexes is still missing. Here we carry out a comprehensive critical assessment of coevolution-based contact prediction in IDP/IDR complexes and detail the challenges and possible limitations that emerge from their analysis. We found that the coevolutionary signal is faint in most of the complexes of disordered proteins but positively correlates with the interface size and binding affinity between partners. In addition, we discuss the state-of-art methodology by biological interpretation of the results, formulate evaluation guidelines and suggest future directions of development to the field.

Distance-Based Metrics for Comparing Conformational Ensembles of Intrinsically Disordered Proteins.

Intrinsically disordered proteins are proteins whose native functional states represent ensembles of highly diverse conformations. Such ensembles are a challenge for quantitative structure comparisons because their conformational diversity precludes optimal superimposition of the atomic coordinates necessary for deriving common similarity measures such as the root mean-square deviation of these coordinates. Here, we introduce superimposition-free metrics that are based on computing matrices of the Cα-Cα distance distributions within ensembles and comparing these matrices between ensembles. Differences between two matrices yield information on the similarity between specific regions of the polypeptide, whereas the global structural similarity is captured by the root mean-square difference between the medians of the Cα-Cα distance distributions of two ensembles. Together, our metrics enable rigorous investigations of structure-function relationships in conformational ensembles of intrinsically disordered proteins derived using experimental restraints or by molecular simulations and for proteins containing both structured and disordered regions.