A survey of human histone H1 subtypes interaction networks: Implications for histones H1 functioning.

To show a spectrum of histone H1 subtypes (H1.1-H1.5) activity realized through the protein-protein interactions, data selected from APID resources were processed with sequence-based bioinformatics approaches. Histone H1 subtypes participate in over half a thousand interactions with nuclear and cytosolic proteins (ComPPI database) engaged in the enzymatic activity and binding of nucleic acids and proteins (SIFTER tool). Small-scale networks of H1 subtypes (STRING network) have similar topological parameters (P > .05) which are, however, different for networks hubs between subtype H1.1 and H1.4 and subtype H1.3 and H1.5 (P < .05) (Cytoscape software). Based on enriched GO terms (g:Profiler toolset) of interacting proteins, molecular function and biological process of networks hubs is related to RNA binding and ribosome biogenesis (subtype H1.1 and H1.4), cell cycle and cell division (subtype H1.3 and H1.5) and protein ubiquitination and degradation (subtype H1.2). The residue propensity (BIPSPI predictor) and secondary structures of interacting surfaces (GOR algorithm) as well as a value of equilibrium dissociation constant (ISLAND predictor) indicate that a type of H1 subtypes interactions is transient in term of the stability and medium-strong in relation to the strength of binding. Histone H1 subtypes bind interacting partners in the intrinsic disorder-dependent mode (FoldIndex, PrDOS predictor), according to the coupled folding and binding and mutual synergistic folding mechanism. These results evidence that multifunctional H1 subtypes operate via protein interactions in the networks of crucial cellular processes and, therefore, confirm a new histone H1 paradigm relating to its functioning in the protein-protein interaction networks.

IDPology of the living cell: intrinsic disorder in the subcellular compartments of the human cell.

Intrinsic disorder can be found in all proteomes of all kingdoms of life and in viruses, being particularly prevalent in the eukaryotes. We conduct a comprehensive analysis of the intrinsic disorder in the human proteins while mapping them into 24 compartments of the human cell. In agreement with previous studies, we show that human proteins are significantly enriched in disorder relative to a generic protein set that represents the protein universe. In fact, the fraction of proteins with long disordered regions and the average protein-level disorder content in the human proteome are about 3 times higher than in the protein universe. Furthermore, levels of intrinsic disorder in the majority of human subcellular compartments significantly exceed the average disorder content in the protein universe. Relative to the overall amount of disorder in the human proteome, proteins localized in the nucleus and cytoskeleton have significantly increased amounts of disorder, measured by both high disorder content and presence of multiple long intrinsically disordered regions. We empirically demonstrate that, on average, human proteins are assigned to 2.3 subcellular compartments, with proteins localized to few subcellular compartments being more disordered than the proteins that are localized to many compartments. Functionally, the disordered proteins localized in the most disorder-enriched subcellular compartments are primarily responsible for interactions with nucleic acids and protein partners. This is the first-time disorder is comprehensively mapped into the human cell. Our observations add a missing piece to the puzzle of functional disorder and its organization inside the cell.

Mapping the transition state for a binding reaction between ancient intrinsically disordered proteins.

Intrinsically disordered protein domains often have multiple binding partners. It is plausible that the strength of pairing with specific partners evolves from an initial low affinity to a higher affinity. However, little is known about the molecular changes in the binding mechanism that would facilitate such a transition. We previously showed that the interaction between two intrinsically disordered domains, NCBD and CID, likely emerged in an ancestral deuterostome organism as a low-affinity interaction that subsequently evolved into a higher-affinity interaction before the radiation of modern vertebrate groups. Here we map native contacts in the transition states of the low-affinity ancestral and high-affinity human NCBD/CID interactions. We show that the coupled binding and folding mechanism is overall similar but with a higher degree of native hydrophobic contact formation in the transition state of the ancestral complex and more heterogeneous transient interactions, including electrostatic pairings, and an increased disorder for the human complex. Adaptation to new binding partners may be facilitated by this ability to exploit multiple alternative transient interactions while retaining the overall binding and folding pathway.

Sequence and Structure Properties Uncover the Natural Classification of Protein Complexes Formed by Intrinsically Disordered Proteins via Mutual Synergistic Folding.

Intrinsically disordered proteins mediate crucial biological functions through their interactions with other proteins. Mutual synergistic folding (MSF) occurs when all interacting proteins are disordered, folding into a stable structure in the course of the complex formation. In these cases, the folding and binding processes occur in parallel, lending the resulting structures uniquely heterogeneous features. Currently there are no dedicated classification approaches that take into account the particular biological and biophysical properties of MSF complexes. Here, we present a scalable clustering-based classification scheme, built on redundancy-filtered features that describe the sequence and structure properties of the complexes and the role of the interaction, which is directly responsible for structure formation. Using this approach, we define six major types of MSF complexes, corresponding to biologically meaningful groups. Hence, the presented method also shows that differences in binding strength, subcellular localization, and regulation are encoded in the sequence and structural properties of proteins. While current protein structure classification methods can also handle complex structures, we show that the developed scheme is fundamentally different, and since it takes into account defining features of MSF complexes, it serves as a better representation of structures arising through this specific interaction mode.

A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction.

Intrinsically disordered proteins and regions are widely distributed in proteins, which are associated with many biological processes and diseases. Accurate prediction of intrinsically disordered proteins and regions is critical for both basic research (such as protein structure and function prediction) and practical applications (such as drug development). During the past decades, many computational approaches have been proposed, which have greatly facilitated the development of this important field. Therefore, a comprehensive and updated review is highly required. In this regard, we give a review on the computational methods for intrinsically disordered protein and region prediction, especially focusing on the recent development in this field. These computational approaches are divided into four categories based on their methodologies, including physicochemical-based method, machine-learning-based method, template-based method and meta method. Furthermore, their advantages and disadvantages are also discussed. The performance of 40 state-of-the-art predictors is directly compared on the target proteins in the task of disordered region prediction in the 10th Critical Assessment of protein Structure Prediction. A more comprehensive performance comparison of 45 different predictors is conducted based on seven widely used benchmark data sets. Finally, some open problems and perspectives are discussed.

Conformational Characterization and Classification of Intrinsically Disordered Proteins by Native Mass Spectrometry and Charge-State Distribution Analysis.

Intrinsically disordered proteins (IDPs) are systematically under-represented in structural proteomics studies. Their structural characterization implies description of the dynamic conformational ensembles populated by these polymers in solution, posing major challenges to biophysical methods. "Native" MS (native-MS) has emerged as a central tool in this field, conjugating the unique MS analytical power with structurally meaningful descriptors, like solvent-accessible surface area (SASA) and collisional cross section (CCS). This review summarizes recently published papers comparing native-MS and solution methods, with a focus on charge-state-distribution (CSD) analysis for IDP conformational analysis. The results point to substantial agreement, supporting structural interpretation of native-MS spectra of IDPs. The discussion is integrated with data from our group on "synthetic" IDPs, obtained by reduction and alkylation of natively folded proteins, whose fold is stabilized by disulfide bridges. Finally, an MS-based compaction index (CI) is introduced, evaluating SASA with reference to globular and fully disorder proteins. Such a parameter can be calculated for single conformers or the whole conformational ensemble, offering a continuous index for IDP comparison and classification.

Exploring intrinsically disordered proteins in Chlamydomonas reinhardtii.

The content of intrinsically disordered protein (IDP) is related to organism complexity, evolution, and regulation. In the Plantae, despite their high complexity, experimental investigation of IDP content is lacking. We identified by mass spectrometry 682 heat-resistant proteins from the green alga, Chlamydomonas reinhardtii. Using a phosphoproteome database, we found that 331 of these proteins are targets of phosphorylation. We analyzed the flexibility propensity of the heat-resistant proteins and their specific features as well as those of predicted IDPs from the same organism. Their mean percentage of disorder was about 20%. Most of the IDPs (~70%) were addressed to other compartments than mitochondrion and chloroplast. Their amino acid composition was biased compared to other classic IDPs. Their molecular functions were diverse; the predominant ones were nucleic acid binding and unfolded protein binding and the less abundant one was catalytic activity. The most represented proteins were ribosomal proteins, proteins associated to flagella, chaperones and histones. We also found CP12, the only experimental IDP from C. reinhardtii that is referenced in disordered protein database. This is the first experimental investigation of IDPs in C. reinhardtii that also combines in silico analysis.

Prediction of Disordered Regions and Their Roles in the Anti-Pathogenic and Immunomodulatory Functions of Butyrophilins.

Butyrophilins (BTNs) are a group of the moonlighting proteins, some members of which are secreted in milk. They constitute a large family of structurally similar type 1 transmembrane proteins from the immunoglobulin superfamily. Although the founding member of this family is related to lactation, participating in the secretion, formation and stabilization of milk fat globules, it may also have a cell surface receptor function. Generally, the BTN family members are known to modulate co-stimulatory responses, T cell selection, differentiation, and cell fate determination. Polymorphism of these genes was shown to be associated with the pathology of several human diseases. Despite their biological significance, structural information on human butyrophilins is rather limited. Based on their remarkable multifunctionality, butyrophilins seem to belong to the category of moonlighting proteins, which are known to contain intrinsically disordered protein regions (IDPRs). However, the disorder status of human BTNs was not systematically investigated as of yet. The goal of this study is to fill this gap and to evaluate peculiarities of intrinsic disorder predisposition of the members of human BTN family, and to find if they have IDPRs that can be attributed to the multifunctionality of these important proteins.

Proteus: a random forest classifier to predict disorder-to-order transitioning binding regions in intrinsically disordered proteins.

The focus of the computational structural biology community has taken a dramatic shift over the past one-and-a-half decades from the classical protein structure prediction problem to the possible understanding of intrinsically disordered proteins (IDP) or proteins containing regions of disorder (IDPR). The current interest lies in the unraveling of a disorder-to-order transitioning code embedded in the amino acid sequences of IDPs/IDPRs. Disordered proteins are characterized by an enormous amount of structural plasticity which makes them promiscuous in binding to different partners, multi-functional in cellular activity and atypical in folding energy landscapes resembling partially folded molten globules. Also, their involvement in several deadly human diseases (e.g. cancer, cardiovascular and neurodegenerative diseases) makes them attractive drug targets, and important for a biochemical understanding of the disease(s). The study of the structural ensemble of IDPs is rather difficult, in particular for transient interactions. When bound to a structured partner, an IDPR adapts an ordered conformation in the complex. The residues that undergo this disorder-to-order transition are called protean residues, generally found in short contiguous stretches and the first step in understanding the modus operandi of an IDP/IDPR would be to predict these residues. There are a few available methods which predict these protean segments from their amino acid sequences; however, their performance reported in the literature leaves clear room for improvement. With this background, the current study presents 'Proteus', a random forest classifier that predicts the likelihood of a residue undergoing a disorder-to-order transition upon binding to a potential partner protein. The prediction is based on features that can be calculated using the amino acid sequence alone. Proteus compares favorably with existing methods predicting twice as many true positives as the second best method (55 vs. 27%) with a much higher precision on an independent data set. The current study also sheds some light on a possible 'disorder-to-order' transitioning consensus, untangled, yet embedded in the amino acid sequence of IDPs. Some guidelines have also been suggested for proceeding with a real-life structural modeling involving an IDPR using Proteus.

DisProt 7.0: a major update of the database of disordered proteins.

The Database of Protein Disorder (DisProt, URL: www.disprot.org) has been significantly updated and upgraded since its last major renewal in 2007. The current release holds information on more than 800 entries of IDPs/IDRs, i.e. intrinsically disordered proteins or regions that exist and function without a well-defined three-dimensional structure. We have re-curated previous entries to purge DisProt from conflicting cases, and also upgraded the functional classification scheme to reflect continuous advance in the field in the past 10 years or so. We define IDPs as proteins that are disordered along their entire sequence, i.e. entirely lack structural elements, and IDRs as regions that are at least five consecutive residues without well-defined structure. We base our assessment of disorder strictly on experimental evidence, such as X-ray crystallography and nuclear magnetic resonance (primary techniques) and a broad range of other experimental approaches (secondary techniques). Confident and ambiguous annotations are highlighted separately. DisProt 7.0 presents classified knowledge regarding the experimental characterization and functional annotations of IDPs/IDRs, and is intended to provide an invaluable resource for the research community for a better understanding structural disorder and for developing better computational tools for studying disordered proteins.

Detection of orphan domains in Drosophila using "hydrophobic cluster analysis".

INTRODUCTION: Comparative genomics has become an important strategy in life science research. While many genes, and the proteins they code for, can be well characterized by assigning orthologs, a significant amount of proteins or domains remain obscure "orphans". Some orphans are overlooked by current computational methods because they rapidly diverged, others emerged relatively recently (de novo). Recent research has demonstrated the importance of orphans, and of de novo proteins and domains for development of new phenotypic traits and adaptation. New approaches for detecting novel domains are thus of paramount importance. RESULTS: The hydrophobic cluster analysis (HCA) method delineates globular-like domains from the information of a protein sequence and thereby allows bypassing some of the established methods limitations based on conserved sequence similarity. In this study, HCA is tested for orphan domain detection on 12 Drosophila genomes. After their detection, the oprhan domains are classified into two categories, depending on their presence/absence in distantly related species. The two categories show significantly different physico-chemical properties when compared to previously characterized domains from the Pfam database. The newly detected domains have a higher degree of intrinsic disorder and a particular hydrophobic cluster composition. The older the domains are, the more similar their hydrophobic cluster content is to the cluster content of Pfam domains. The results suggest that, over time, newly created domains acquire a canonical set of hydrophobic clusters but conserve some features of intrinsically disordered regions. CONCLUSION: Our results agree with previous findings on orphan domains and suggest that the physico-chemical properties of domains change over evolutionary long time scale. The presented HCA-based method is able to detect domains with unusual properties without relying on prior knowledge, such as the availability of homologs. Therefore, the method has large potential for complementing existing strategies to annotate genomes, and for better understanding how molecular features emerge.