Large-scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe

Taxonomy-specific assessment of intrinsic disorder predictions at residue and region levels in higher eukaryotes, protists, archaea, bacteria and viruses

Intrinsic disorder predictors were evaluated in several studies including the two large CAID experiments. However, these studies are biased towards eukaryotic proteins and focus primarily on the residue-level predictions. We provide first-of-its-kind assessment that comprehensively covers the taxonomy and evaluates predictions at the residue and disordered region levels. We curate a benchmark dataset that uniformly covers eukaryotic, archaeal, bacterial, and viral proteins. We find that predictive performance differs substantially across taxonomy, where viruses are predicted most accurately, followed by protists and higher eukaryotes, while bacterial and archaeal proteins suffer lower levels of accuracy. These trends are consistent across predictors. We also find that current tools, except for flDPnn, struggle with reproducing native distributions of the numbers and sizes of the disordered regions. Moreover, analysis of two variants of disorder predictions derived from the AlphaFold2 predicted structures reveals that they produce accurate residue-level propensities for archaea, bacteria and protists. However, they underperform for higher eukaryotes and generally struggle to accurately identify disordered regions. Our results motivate development of new predictors that target bacteria and archaea and which produce accurate results at both residue and region levels. We also stress the need to include the region-level assessments in future assessments.

DisProt in 2024: improving function annotation of intrinsically disordered proteins

DisProt (URL: https://disprot.org) is the gold standard database for intrinsically disordered proteins and regions, providing valuable information about their functions. The latest version of DisProt brings significant advancements, including a broader representation of functions and an enhanced curation process. These improvements aim to increase both the quality of annotations and their coverage at the sequence level. Higher coverage has been achieved by adopting additional evidence codes. Quality of annotations has been improved by systematically applying Minimum Information About Disorder Experiments (MIADE) principles and reporting all the details of the experimental setup that could potentially influence the structural state of a protein. The DisProt database now includes new thematic datasets and has expanded the adoption of Gene Ontology terms, resulting in an extensive functional repertoire which is automatically propagated to UniProtKB. Finally, we show that DisProt's curated annotations strongly correlate with disorder predictions inferred from AlphaFold2 pLDDT (predicted Local Distance Difference Test) confidence scores. This comparison highlights the utility of DisProt in explaining apparent uncertainty of certain well-defined predicted structures, which often correspond to folding-upon-binding fragments. Overall, DisProt serves as a comprehensive resource, combining experimental evidence of disorder information to enhance our understanding of intrinsically disordered proteins and their functional implications.

Uncovering the structural stability of Magnaporthe oryzae effectors: a secretome-wide in silico analysis

Rice blast, caused by the ascomycete fungus Magnaporthe oryzae, is a deadly disease and a major threat to global food security. The pathogen secretes small proteinaceous effectors, virulence factors, inside the host to manipulate and perturb the host immune system, allowing the pathogen to colonize and establish a successful infection. While the molecular functions of several effectors are characterized, very little is known about the structural stability of these effectors. We analyzed a total of 554 small secretory proteins (SSPs) from the M. oryzae secretome to decipher key features of intrinsic disorder (ID) and the structural dynamics of the selected putative effectors through thorough and systematic in silico studies. Our results suggest that out of the total SSPs, 66% were predicted as effector proteins, released either into the apoplast or cytoplasm of the host cell. Of these, 68% were found to be intrinsically disordered effector proteins (IDEPs). Among the six distinct classes of disordered effectors, we observed peculiar relationships between the localization of several effectors in the apoplast or cytoplasm and the degree of disorder. We determined the degree of structural disorder and its impact on protein foldability across all the putative small secretory effector proteins from the blast pathogen, further validated by molecular dynamics simulation studies. This study provides definite clues toward unraveling the mystery behind the importance of structural distortions in effectors and their impact on plant-pathogen interactions. The study of these dynamical segments may help identify new effectors as well.Communicated by Ramaswamy H. Sarma.

Protein intrinsically disordered regions have a non-random, modular architecture

MOTIVATION

Protein sequences can be broadly categorized into two classes: those which adopt stable secondary structure and fold into a domain (i.e. globular proteins), and those that do not. The sequences belonging to this latter class are conformationally heterogeneous and are described as being intrinsically disordered. Decades of investigation into the structure and function of globular proteins has resulted in a suite of computational tools that enable their sub-classification by domain type, an approach that has revolutionized how we understand and predict protein functionality. Conversely, it is unknown if sequences of disordered protein regions are subject to broadly generalizable organizational principles that would enable their sub-classification.

RESULTS

Here, we report the development of a statistical approach that quantifies linear variance in amino acid composition across a sequence. With multiple examples, we provide evidence that intrinsically disordered regions are organized into statistically non-random modules of unique compositional bias. Modularity is observed for both low and high-complexity sequences and, in some cases, we find that modules are organized in repetitive patterns. These data demonstrate that disordered sequences are non-randomly organized into modular architectures and motivate future experiments to comprehensively classify module types and to determine the degree to which modules constitute functionally separable units analogous to the domains of globular proteins.

AVAILABILITY AND IMPLEMENTATION

The source code, documentation, and data to reproduce all figures are freely available at https://github.com/MWPlabUTSW/Chi-Score-Analysis.git. The analysis is also available as a Google Colab Notebook (https://colab.research.google.com/github/MWPlabUTSW/Chi-Score-Analysis/blob/main/ChiScore_Analysis.ipynb).

Antimicrobial Peptide Arsenal Predicted from the Venom Gland Transcriptome of the Tropical Trap-Jaw Ant Odontomachus chelifer

With about 13,000 known species, ants are the most abundant venomous insects. Their venom consists of polypeptides, enzymes, alkaloids, biogenic amines, formic acid, and hydrocarbons. In this study, we investigated, using in silico techniques, the peptides composing a putative antimicrobial arsenal from the venom gland of the neotropical trap-jaw ant Odontomachus chelifer. Focusing on transcripts from the body and venom gland of this insect, it was possible to determine the gland secretome, which contained about 1022 peptides with putative signal peptides. The majority of these peptides (75.5%) were unknown, not matching any reference database, motivating us to extract functional insights via machine learning-based techniques. With several complementary methodologies, we investigated the existence of antimicrobial peptides (AMPs) in the venom gland of O. chelifer, finding 112 non-redundant candidates. Candidate AMPs were predicted to be more globular and hemolytic than the remaining peptides in the secretome. There is evidence of transcription for 97% of AMP candidates across the same ant genus, with one of them also verified as translated, thus supporting our findings. Most of these potential antimicrobial sequences (94.8%) matched transcripts from the ant's body, indicating their role not solely as venom toxins.

Mapping the Constrained Coding Regions in the Human Genome to Their Corresponding Proteins

Constrained Coding Regions (CCRs) in the human genome have been derived from DNA sequencing data of large cohorts of healthy control populations, available in the Genome Aggregation Database (gnomAD) [1]. They identify regions depleted of protein-changing variants and thus identify segments of the genome that have been constrained during human evolution. By mapping these DNA-defined regions from genomic coordinates onto the corresponding protein positions and combining this information with protein annotations, we have explored the distribution of CCRs and compared their co-occurrence with different protein functional features, previously annotated at the amino acid level in public databases. As expected, our results reveal that functional amino acids involved in interactions with DNA/RNA, protein-protein contacts and catalytic sites are the protein features most likely to be highly constrained for variation in the control population. More surprisingly, we also found that linear motifs, linear interacting peptides (LIPs), disorder-order transitions upon binding with other protein partners and liquid-liquid phase separating (LLPS) regions are also strongly associated with high constraint for variability. We also compared intra-species constraints in the human CCRs with inter-species conservation and functional residues to explore how such CCRs may contribute to the analysis of protein variants. As has been previously observed, CCRs are only weakly correlated with conservation, suggesting that intraspecies constraints complement interspecies conservation and can provide more information to interpret variant effects.

Intrinsically Disordered Proteins: An Overview

Many proteins and protein segments cannot attain a single stable three-dimensional structure under physiological conditions; instead, they adopt multiple interconverting conformational states. Such intrinsically disordered proteins or protein segments are highly abundant across proteomes, and are involved in various effector functions. This review focuses on different aspects of disordered proteins and disordered protein regions, which form the basis of the so-called "Disorder-function paradigm" of proteins. Additionally, various experimental approaches and computational tools used for characterizing disordered regions in proteins are discussed. Finally, the role of disordered proteins in diseases and their utility as potential drug targets are explored.

BoMiProt 2.0: An update of the bovine milk protein database

Milk is a biofluid with various functions, containing carbohydrates, lipids, proteins, vitamins, and minerals. Owing to its importance and availability of vast proteomics information, our research group designed a database for bovine milk proteins (N = 3159) containing the primary and secondary information called BoMiProt. Due to the gaining interest and intensively published literature in the last three years, BoMiProt has been upgraded with newer identified proteins (N = 7459) from peer-reviewed journals, significantly expanding the database from different milk fractions (e.g., whey, fat globule membranes, and exosomes). Additionally, class, architecture, topology, and homology, structural classification of proteins, known and predicted disorder, predicted transmembrane helices, and structures have been included. Each protein entry in the database is thoroughly cross-referenced, including 1392 BoMiProt defined proteins provided with secondary information, such as protein function, biochemical properties, post-translational modifications, significance in milk, domains, fold, AlphaFold predicted models and crystal structures. The proteome data in the database can be retrieved using several search parameters using protein name, accession IDs, and FASTA sequence. Overall, BoMiProt represents an extensive compilation of newer proteins, including structural, functional, and hierarchical information, to help researchers better understand mammary gland pathophysiology, including their potential application in improving the nutritional quality of dairy products.

Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning

A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features that mediate functions of these regions, such as short motifs, amino acid repeats and physicochemical properties. Here, we introduce a proteome-scale feature discovery approach for IDRs. Our approach, which we call “reverse homology”, exploits the principle that important functional features are conserved over evolution. We use this as a contrastive learning signal for deep learning: given a set of homologous IDRs, the neural network has to correctly choose a held-out homolog from another set of IDRs sampled randomly from the proteome. We pair reverse homology with a simple architecture and standard interpretation techniques, and show that the network learns conserved features of IDRs that can be interpreted as motifs, repeats, or bulk features like charge or amino acid propensities. We also show that our model can be used to produce visualizations of what residues and regions are most important to IDR function, generating hypotheses for uncharacterized IDRs. Our results suggest that feature discovery using unsupervised neural networks is a promising avenue to gain systematic insight into poorly understood protein sequences.

Intrinsically disordered regions (IDRs) are widespread in proteins but are poorly understood on a systematic level because they evolve too rapidly for classic bioinformatics methods to be effective. We designed a neural network that learns what features (for example, electrostatic charge, or the presence of certain motifs) might be important to the function of IDRs, even when we don’t have prior knowledge of function. Our neural network learns by exploiting principles of evolution. Important features tend to be conserved over species, so guessing what sequences evolved from the same common ancestor helps the neural network identify these features. Importantly, training a neural network this way can be defined as a fully automatic operation, so no manual effort is required. After our neural network is trained, we can apply interpretation techniques to understand what kinds of features are important to IDRs globally in the proteome, and to form hypotheses about specific IDRs. We show that many of the features our neural network learns are consistent with features we already know to be important to IDRs. We hope that our neural network can be applied to help biologists form hypotheses about poorly characterized IDRs.

Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions

Intrinsically disordered regions (IDRs) carry out many cellular functions and vary in length and placement in protein sequences. This diversity leads to variations in the underlying compositional biases, which were demonstrated for the short vs. long IDRs. We analyze compositional biases across four classes of disorder: fully disordered proteins; short IDRs; long IDRs; and binding IDRs. We identify three distinct biases: for the fully disordered proteins, the short IDRs and the long and binding IDRs combined. We also investigate compositional bias for putative disorder produced by leading disorder predictors and find that it is similar to the bias of the native disorder. Interestingly, the accuracy of disorder predictions across different methods is correlated with the correctness of the compositional bias of their predictions highlighting the importance of the compositional bias. The predictive quality is relatively low for the disorder classes with compositional bias that is the most different from the “generic” disorder bias, while being much higher for the classes with the most similar bias. We discover that different predictors perform best across different classes of disorder. This suggests that no single predictor is universally best and motivates the development of new architectures that combine models that target specific disorder classes.

Characterization of intrinsically disordered regions in proteins informed by human genetic diversity

All proteomes contain both proteins and polypeptide segments that don’t form a defined three-dimensional structure yet are biologically active—called intrinsically disordered proteins and regions (IDPs and IDRs). Most of these IDPs/IDRs lack useful functional annotation limiting our understanding of their importance for organism fitness. Here we characterized IDRs using protein sequence annotations of functional sites and regions available in the UniProt knowledgebase (“UniProt features”: active site, ligand-binding pocket, regions mediating protein-protein interactions, etc.). By measuring the statistical enrichment of twenty-five UniProt features in 981 IDRs of 561 human proteins, we identified eight features that are commonly located in IDRs. We then collected the genetic variant data from the general population and patient-based databases and evaluated the prevalence of population and pathogenic variations in IDPs/IDRs. We observed that some IDRs tolerate 2 to 12-times more single amino acid-substituting missense mutations than synonymous changes in the general population. However, we also found that 37% of all germline pathogenic mutations are located in disordered regions of 96 proteins. Based on the observed-to-expected frequency of mutations, we categorized 34 IDRs in 20 proteins (DDX3X, KIT, RB1, etc.) as intolerant to mutation. Finally, using statistical analysis and a machine learning approach, we demonstrate that mutation-intolerant IDRs carry a distinct signature of functional features. Our study presents a novel approach to assign functional importance to IDRs by leveraging the wealth of available genetic data, which will aid in a deeper understating of the role of IDRs in biological processes and disease mechanisms.

Databases for intrinsically disordered proteins

Intrinsically disordered regions (IDRs) lacking a fixed three-dimensional protein structure are widespread and play a central role in cell regulation. Only a small fraction of IDRs have been functionally characterized, with heterogeneous experimental evidence that is largely buried in the literature. Predictions of IDRs are still difficult to estimate and are poorly characterized. Here, an overview of the publicly available knowledge about IDRs is reported, including manually curated resources, deposition databases and prediction repositories. The types, scopes and availability of the various resources are analyzed, and their complementarity and overlap are highlighted. The volume of information included and the relevance to the field of structural biology are compared.

Assembly-Line Catalysis in Bifunctional Terpene Synthases

The magnificent chemodiversity of more than 95 000 terpenoid natural products identified to date largely originates from catalysis by two types of terpene synthases, prenyltransferases and cyclases. Prenyltransferases utilize 5-carbon building blocks in processive chain elongation reactions to generate linear C_5n isoprenoid diphosphates (n ≥ 2), which in turn serve as substrates for terpene cyclases that convert these linear precursors into structurally complex hydrocarbon products containing multiple rings and stereocenters. Terpene cyclization reactions are the most complex organic transformations found in nature in that more than half of the substrate carbon atoms undergo changes in chemical bonding during a multistep reaction sequence proceeding through several carbocation intermediates. Two general classes of cyclases are established on the basis of the chemistry of initial carbocation formation, and structural studies from our laboratory and others show that three fundamental protein folds designated α, β, and γ govern this chemistry. Catalysis by a class I cyclase occurs in an α domain, where a trinuclear metal cluster activates the substrate diphosphate leaving group to generate an allylic cation. Catalysis by a class II cyclase occurs in a β domain or at the interface of β and γ domains, where an aspartic acid protonates the terminal π bond of the substrate to yield a tertiary carbocation. Crystal structures reveal domain architectures of α, αβ, αβγ, βγ, and β.In some terpene synthases, these domains are combined to yield bifunctional enzymes that catalyze successive biosynthetic steps in assembly line fashion. Structurally characterized examples include bacterial geosmin synthase, an αα domain enzyme that catalyzes a class I cyclization reaction of C₁₅ farnesyl diphosphate in one active site and a transannulation-fragmentation reaction in the other to yield C₁₂ geosmin and C₃ acetone products. In comparison, plant abietadiene synthase is an αβγ domain enzyme in which C₂₀ geranylgeranyl diphosphate undergoes tandem class II-class I cyclization reactions to yield the tricyclic product. Recent structural studies from our laboratory show that bifunctional fungal cyclases form oligomeric complexes for assembly line catalysis. Bifunctional (+)-copalyl diphosphate synthase adopts (αβγ)₆ architecture in which the α domain generates geranylgeranyl diphosphate, which then undergoes class II cyclization in the βγ domains to yield the bicyclic product. Bifunctional fusicoccadiene synthase adopts (αα)₆ or (αα)₈ architecture in which one α domain generates geranylgeranyl diphosphate, which then undergoes class I cyclization in the other α domain to yield the tricyclic product. The prenyltransferase α domain mediates oligomerization in these systems. Attached by flexible polypeptide linkers, cyclase domains splay out from oligomeric prenyltransferase cores.In this Account, we review structure-function relationships for these bifunctional terpene synthases, with a focus on the oligomeric systems studied in our laboratory. The observation of substrate channeling for fusicoccadiene synthase suggests a model for dynamic cluster channeling in catalysis by oligomeric assembly line terpenoid synthases. Resulting efficiencies in carbon management suggest that such systems could be particularly attractive for use in synthetic biology approaches to generate high-value terpenoid natural products.

flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions

Identification of intrinsic disorder in proteins relies in large part on computational predictors, which demands that their accuracy should be high. Since intrinsic disorder carries out a broad range of cellular functions, it is desirable to couple the disorder and disorder function predictions. We report a computational tool, flDPnn, that provides accurate, fast and comprehensive disorder and disorder function predictions from protein sequences. The recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment and results on other test datasets demonstrate that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions. These predictions are substantially better than the results of the existing disorder predictors and methods that predict functions of disorder. Ablation tests reveal that the high predictive performance stems from innovative ways used in flDPnn to derive sequence profiles and encode inputs. flDPnn's webserver is available at http://biomine.cs.vcu.edu/servers/flDPnn/.

FLIPPER: Predicting and Characterizing Linear Interacting Peptides in the Protein Data Bank

A large fraction of peptides or protein regions are disordered in isolation and fold upon binding. These regions, also called MoRFs, SLiMs or LIPs, are often associated with signaling and regulation processes. However, despite their importance, only a limited number of examples are available in public databases and their automatic detection at the proteome level is problematic. Here we present FLIPPER, an automatic method for the detection of structurally linear sub-regions or peptides that interact with another chain in a protein complex. FLIPPER is a random forest classification that takes the protein structure as input and provides the propensity of each amino acid to be part of a LIP region. Models are built taking into consideration structural features such as intra- and inter-chain contacts, secondary structure, solvent accessibility in both bound and unbound state, structural linearity and chain length. FLIPPER is accurate when evaluated on non-redundant independent datasets, 99% precision and 99% sensitivity on PixelDB-25 and 87% precision and 88% sensitivity on DIBS-25. Finally, we used FLIPPER to process the entire Protein Data Bank and identified different classes of LIPs based on different binding modes and partner molecules. We provide a detailed description of these LIP categories and show that a large fraction of these regions are not detected by disorder predictors. All FLIPPER predictions are integrated in the MobiDB 4.0 database.

MobiDB: intrinsically disordered proteins in 2021

The MobiDB database (URL: https://mobidb.org/) provides predictions and annotations for intrinsically disordered proteins. Here, we report recent developments implemented in MobiDB version 4, regarding the database format, with novel types of annotations and an improved update process. The new website includes a re-designed user interface, a more effective search engine and advanced API for programmatic access. The new database schema gives more flexibility for the users, as well as simplifying the maintenance and updates. In addition, the new entry page provides more visualisation tools including customizable feature viewer and graphs of the residue contact maps. MobiDB v4 annotates the binding modes of disordered proteins, whether they undergo disorder-to-order transitions or remain disordered in the bound state. In addition, disordered regions undergoing liquid-liquid phase separation or post-translational modifications are defined. The integrated information is presented in a simplified interface, which enables faster searches and allows large customized datasets to be downloaded in TSV, Fasta or JSON formats. An alternative advanced interface allows users to drill deeper into features of interest. A new statistics page provides information at database and proteome levels. The new MobiDB version presents state-of-the-art knowledge on disordered proteins and improves data accessibility for both computational and experimental users.

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

Biomedical institutions rely on data evaluation and are turning into data factories. Big-data storage centers, supercomputing systems, and increased algorithmic efficiency allow us to analyze the ever-increasing amount of data generated every day in biomedical research centers. In network science, the principal intrinsic problem is how to integrate the data and information from different experiments on genes or proteins. Data curation is an essential process in annotating new functional data to known genes or proteins, undertaken by a biobank curator, which is then reflected in the calculated networks. We provide an example of how protein-protein networks today have space-time limits. The next step is the integration of data and information from different biobanks. Omics data and networks are essential parts of this step but also have flawed protocols and errors. Consider data from patients with cancer: from biopsy procedures to experimental tests, to archiving methods and computational algorithms, these are continuously handled so require critical and continuous "updates" to obtain reproducible, reliable, and correct results. We show, as a second example, how all this distorts studies in cellular hepatocellular carcinoma. It is not unlikely that these flawed data have been polluting biobanks for some time before stringent conditions for the veracity of data were implemented in Big data. Therefore, all this could contribute to errors in future medical decisions.

Critical assessment of protein intrinsic disorder prediction

Intrinsically disordered proteins, defying the traditional protein structure-function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has F_max = 0.483 on the full dataset and F_max = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with F_max = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude.

MobiDB-lite 3.0: fast consensus annotation of intrinsic disorder flavours in proteins

MOTIVATION

The earlier version of MobiDB-lite is currently used in large-scale proteome annotation platforms to detect intrinsic disorder. However, new theoretical models allow for the classification of intrinsically disordered regions into subtypes from sequence features associated with specific polymeric properties or compositional bias.

RESULTS

MobiDB-lite 3.0 maintains its previous speed and performance but also provides a finer classification of disorder by identifying regions with characteristics of polyolyampholytes, positive or negative polyelectrolytes, low complexity regions or enriched in cysteine, proline or glycine or polar residues. Sub-regions are abundantly detected in IDRs of the human proteome. The new version of MobiDB-lite represents a new step for the proteome level analysis of protein disorder.

AVAILABILITY

Both the MobiDB-lite 3.0 source code and a docker container are available from the GitHub repository: https://github.com/BioComputingUP/MobiDB-lite.

Intrinsically Disordered Protein Ensembles Shape Evolutionary Rates Revealing Conformational Patterns

Intrinsically disordered proteins (IDPs) lack stable tertiary structure under physiological conditions. The unique composition and complex dynamical behaviour of IDPs make them a challenge for structural biology and molecular evolution studies. Using NMR ensembles, we found that IDPs evolve under a strong site-specific evolutionary rate heterogeneity, mainly originated by different constraints derived from their inter-residue contacts. Evolutionary rate profiles correlate with the experimentally observed conformational diversity of the protein, allowing the description of different conformational patterns possibly related to their structure-function relationships. The correlation between evolutionary rates and contact information improves when structural information is taken not from any individual conformer or the whole ensemble, but from combining a limited number of conformers. Our results suggest that residue contacts in disordered regions constrain evolutionary rates to conserve the dynamic behaviour of the ensemble and that evolutionary rates can be used as a proxy for the conformational diversity of IDPs.

The E3 ubiquitin-protein ligase MDM2 is a novel interactor of the von Hippel-Lindau tumor suppressor

Mutations of the von Hippel-Lindau (pVHL) tumor suppressor are causative of a familiar predisposition to develop different types of cancer. pVHL is mainly known for its role in regulating hypoxia-inducible factor 1 α (HIF-1α) degradation, thus modulating the hypoxia response. There are different pVHL isoforms, including pVHL30 and pVHL19. However, little is known about isoform-specific functions and protein-protein interactions. Integrating in silico predictions with in vitro and in vivo assays, we describe a novel interaction between pVHL and mouse double minute 2 homolog (MDM2). We found that pVHL30, and not pVHL19, forms a complex with MDM2, and that the N-terminal acidic tail of pVHL30 is required for its association with MDM2. Further, we demonstrate that an intrinsically disordered region upstream of the tetramerization domain of MDM2 is responsible for its isoform-specific association with pVHL30. This region is highly conserved in higher mammals, including primates, similarly to what has been already shown for the N-terminal tail of pVHL30. Finally, we show that overexpression of pVHL30 and MDM2 together reduces cell metabolic activity and necrosis, suggesting a synergistic effect of these E3 ubiquitin ligases. Collectively, our data show an isoform-specific interaction of pVHL with MDM2, suggesting an interplay between these two E3 ubiquitin ligases.

Disorder in a two-domain neuronal Ca2+-binding protein regulates domain stability and dynamics using ligand mimicry

Understanding the interplay between sequence, structure and function of proteins has been complicated in recent years by the discovery of intrinsically disordered proteins (IDPs), which perform biological functions in the absence of a well-defined three-dimensional fold. Disordered protein sequences account for roughly 30% of the human proteome and in many proteins, disordered and ordered domains coexist. However, few studies have assessed how either feature affects the properties of the other. In this study, we examine the role of a disordered tail in the overall properties of the two-domain, calcium-sensing protein neuronal calcium sensor 1 (NCS-1). We show that loss of just six of the 190 residues at the flexible C-terminus is sufficient to severely affect stability, dynamics, and folding behavior of both ordered domains. We identify specific hydrophobic contacts mediated by the disordered tail that may be responsible for stabilizing the distal N-terminal domain. Moreover, sequence analyses indicate the presence of an LSL-motif in the tail that acts as a mimic of native ligands critical to the observed order-disorder communication. Removing the disordered tail leads to a shorter life-time of the ligand-bound complex likely originating from the observed destabilization. This close relationship between order and disorder may have important implications for how investigations into mixed systems are designed and opens up a novel avenue of drug targeting exploiting this type of behavior.

Experimentally Determined Long Intrinsically Disordered Protein Regions Are Now Abundant in the Protein Data Bank

Intrinsically disordered protein regions are commonly defined from missing electron density in X-ray structures. Experimental evidence for long disorder regions (LDRs) of at least 30 residues was so far limited to manually curated proteins. Here, we describe a comprehensive and large-scale analysis of experimental LDRs for 3133 unique proteins, demonstrating an increasing coverage of intrinsic disorder in the Protein Data Bank (PDB) in the last decade. The results suggest that long missing residue regions are a good quality source to annotate intrinsically disordered regions and perform functional analysis in large data sets. The consensus approach used to define LDRs allows to evaluate context dependent disorder and provide a common definition at the protein level.

DisProt: intrinsic protein disorder annotation in 2020

The Database of Protein Disorder (DisProt, URL: https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the ‘dark’ proteome.

INGA 2.0: improving protein function prediction for the dark proteome

Our current knowledge of complex biological systems is stored in a computable form through the Gene Ontology (GO) which provides a comprehensive description of genes function. Prediction of GO terms from the sequence remains, however, a challenging task, which is particularly critical for novel genomes. Here we present INGA 2.0, a new version of the INGA software for protein function prediction. INGA exploits homology, domain architecture, interaction networks and information from the ‘dark proteome’, like transmembrane and intrinsically disordered regions, to generate a consensus prediction. INGA was ranked in the top ten methods on both CAFA2 and CAFA3 blind tests. The new algorithm can process entire genomes in a few hours or even less when additional input files are provided. The new interface provides a better user experience by integrating filters and widgets to explore the graph structure of the predicted terms. The INGA web server, databases and benchmarking are available from URL: https://inga.bio.unipd.it/.

Flexible and Extended Linker Domains Support Efficient Targeting of Heh2 to the Inner Nuclear Membrane

The nuclear pore complex (NPC) is embedded in the nuclear envelope and forms the main gateway to the nuclear interior including the inner nuclear membrane (INM). Two INM proteins in yeast are selectively imported. Their sorting signals consist of a nuclear localization signal, separated from the transmembrane domain by a long intrinsically disordered (ID) linker. We used computational models to predict the dynamic conformations of ID linkers and analyzed the INM targeting efficiency of proteins with linker regions with altered Stokes radii and decreased flexibilities. We find that flexibility, Stokes radius, and the frequency at which the linkers are at an extended end-to-end distance larger than 25 nm are good predictors for the targeting of the proteins. The data are consistent with a transport mechanism in which INM targeting of Heh2 is dependent on an ID linker that facilitates the crossing of the approximately 25-nm thick NPC scaffold.

Intrinsically disordered proteins and structured proteins with intrinsically disordered regions have different functional roles in the cell

Many studies about classification and the functional annotation of intrinsically disordered proteins (IDPs) are based on either the occurrence of long disordered regions or the fraction of disordered residues in the sequence. Taking into account both criteria we separate the human proteome, taken as a case study, into three variants of proteins: i) ordered proteins (ORDPs), ii) structured proteins with intrinsically disordered regions (IDPRs), and iii) intrinsically disordered proteins (IDPs). The focus of this work is on the different functional roles of IDPs and IDPRs, which up until now have been generally considered as a whole. Previous studies assigned a large set of functional roles to the general category of IDPs. We show here that IDPs and IDPRs have non-overlapping functional spectra, play different roles in human diseases, and deserve to be treated as distinct categories of proteins. IDPs enrich only a few classes, functions, and processes: nucleic acid binding proteins, chromatin binding proteins, transcription factors, and developmental processes. In contrast, IDPRs are spread over several functional protein classes and GO annotations which they partly share with ORDPs. As regards to diseases, we observe that IDPs enrich only cancer-related proteins, at variance with previous results reporting that IDPs are widespread also in cardiovascular and neurodegenerative pathologies. Overall, the operational separation of IDPRs from IDPs is relevant towards correct estimates of the occurrence of intrinsically disordered proteins in genome-wide studies and in the understanding of the functional spectra associated to different flavors of protein disorder.

Structural Basis of the Subcellular Topology Landscape of Escherichia coli

Cellular proteomes are distributed in multiple compartments: on DNA, ribosomes, on and inside membranes, or they become secreted. Structural properties that allow polypeptides to occupy subcellular niches, particularly to after crossing membranes, remain unclear. We compared intrinsic and extrinsic features in cytoplasmic and secreted polypeptides of the Escherichia coli K-12 proteome. Structural features between the cytoplasmome and secretome are sharply distinct, such that a signal peptide-agnostic machine learning tool distinguishes cytoplasmic from secreted proteins with 95.5% success. Cytoplasmic polypeptides are enriched in aliphatic, aromatic, charged and hydrophobic residues, unique folds and higher early folding propensities. Secretory polypeptides are enriched in polar/small amino acids, β folds, have higher backbone dynamics, higher disorder and contact order and are more often intrinsically disordered. These non-random distributions and experimental evidence imply that evolutionary pressure selected enhanced secretome flexibility, slow folding and looser structures, placing the secretome in a distinct protein class. These adaptations protect the secretome from premature folding during its cytoplasmic transit, optimize its lipid bilayer crossing and allowed it to acquire cell envelope specific chemistries. The latter may favor promiscuous multi-ligand binding, sensing of stress and cell envelope structure changes. In conclusion, enhanced flexibility, slow folding, looser structures and unique folds differentiate the secretome from the cytoplasmome. These findings have wide implications on the structural diversity and evolution of modern proteomes and the protein folding problem.

The Balancing Act of Intrinsically Disordered Proteins: Enabling Functional Diversity while Minimizing Promiscuity

Intrinsically disordered proteins (IDPs) or regions (IDRs) perform diverse cellular functions, but are also prone to forming promiscuous and potentially deleterious interactions. We investigate the extent to which the properties of, and content in, IDRs have adapted to enable functional diversity while limiting interference from promiscuous interactions in the crowded cellular environment. Information on protein sequences, their predicted intrinsic disorder, and 3D structure contents is related to data on protein cellular concentrations, gene co-expression, and protein-protein interactions in the well-studied yeast Saccharomyces cerevisiae. Results reveal that both the protein IDR content and the frequency of "sticky" amino acids in IDRs (those more frequently involved in protein interfaces) decrease with increasing protein cellular concentration. This implies that the IDR content and the amino acid composition of IDRs experience negative selection as the protein concentration increases. In the S. cerevisiae protein-protein interaction network, the higher a protein's IDR content, the more frequently it interacts with IDR-containing partners, and the more functionally diverse the partners are. Employing a clustering analysis of Gene Ontology terms, we newly identify ~600 putative multifunctional proteins in S. cerevisiae. Strikingly, these proteins are enriched in IDRs and contribute significantly to all the observed trends. In particular, IDRs of multi-functional proteins feature more sticky amino acids than IDRs of their non-multifunctional counterparts, or the surfaces of structured yeast proteins. This property likely affords sufficient binding affinity for the functional interactions, commonly mediated by short IDR segments, thereby counterbalancing the loss in overall IDR conformational entropy upon binding.

InterPro in 2019: improving coverage, classification and access to protein sequence annotations

The InterPro database (http://www.ebi.ac.uk/interpro/) classifies protein sequences into families and predicts the presence of functionally important domains and sites. Here, we report recent developments with InterPro (version 70.0) and its associated software, including an 18% growth in the size of the database in terms on new InterPro entries, updates to content, the inclusion of an additional entry type, refined modelling of discontinuous domains, and the development of a new programmatic interface and website. These developments extend and enrich the information provided by InterPro, and provide greater flexibility in terms of data access. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB, and discuss how our evaluation of residue coverage may help guide future curation activities.

Where differences resemble: sequence-feature analysis in curated databases of intrinsically disordered proteins

Intrinsic disorder (ID) in proteins is involved in crucial interactions in the living cell. As the importance of ID is increasingly recognized, so are detailed analyses aimed at its identification and characterization. An open question remains the existence of ID `flavors’ representing different sub-phenomena. Several databases collect manually curated examples of experimentally validated ID, focusing on apparently different aspects of this phenomenon. The recent update of MobiDB presented the opportunity to carry out an in-depth comparison of the content of these validated ID collections, namely DIBS, DisProt, IDEAL, MFIB, FuzDB, ELM and UniProt. In order to assess what is specific to different ID flavors, we analyzed relevant sequence-based features, such as amino acid composition, length, taxa and gene ontology terms, highlighting differences and similarities among datasets. Despite that, the majority of the considered features are not statistically different across databases, with the exception of ELM. FuzDB also shares half of its entries with DisProt. In general, different ID databases describe similar phenomena. DisProt, which is the largest database, better represents the entire spectrum of different disorder flavors and the corresponding sequence diversity.

CIDER: Resources to Analyze Sequence-Ensemble Relationships of Intrinsically Disordered Proteins

Intrinsically disordered proteins and regions (IDPs) represent a large class of proteins that are defined by conformational heterogeneity and lack of persistent tertiary/secondary structure. IDPs play important roles in a range of biological functions, and their dysregulation is central to numerous diseases, including neurodegeneration and cancer. The conformational ensembles of IDPs are encoded by their amino acid sequences. Here, we present two computational tools that are designed to enable rapid and high-throughput analyses of a wide range of physicochemical properties encoded by IDP sequences. The first, CIDER, is a user-friendly webserver that enables rapid analysis of IDP sequences. The second, localCIDER, is a high-performance software package that enables a wide range of analyses relevant to IDP sequences. In addition to introducing the two packages, we demonstrate the utility of these resources using examples where sequence analysis offers biophysical insights.

A mechanism for lipid binding to apoE and the role of intrinsically disordered regions coupled to domain-domain interactions

Relative to the apolipoprotein E (apoE) E3 allele of the APOE gene, apoE4 strongly increases the risk for the development of late-onset Alzheimer's disease. However, apoE4 differs from apoE3 by only a single amino acid at position 112, which is arginine in apoE4 and cysteine in apoE3. It remains unclear why apoE3 and apoE4 are functionally different. Described here is a proposal for understanding the functional differences between these two isoforms with respect to lipid binding. A mechanism is proposed that is based on the full-length monomeric structure of the protein, on hydrogen-deuterium exchange mass spectrometry data, and on the role of intrinsically disordered regions to control protein motions. It is proposed that lipid binds between the N-terminal and C-terminal domains and that separation of the two domains, along with the presence of intrinsically disordered regions, controls this process. The mechanism explains why apoE3 differs from apoE4 with respect to different lipid-binding specificities, why lipid increases the binding of apoE to its receptor, and why specific residues are conserved.

Comparison of the intrinsic disorder propensities of the RuBisCO activase enzyme from the motile and non-motile oceanic green microalgae

Green oceanic microalgae are efficient converters of solar energy into the biomass via the photosynthesis process, with the first step of carbon fixation in the photosynthesis being controlled by the enzyme ribulose-1, 5-bisphosphate carboxylase/oxygenase (RuBisCO), which is a large proteinaceous machine composed of large (L, 52 kDa) and small (S, 12 kDa) subunits arranged as a L8S8 hexadecamer that catalyzes the formation of 2 phosphoglyceric acid molecules from one ribulose 1,5-bisphosphate (RuBP) molecule and one of carbon dioxide (CO2) and that is considered as the most abundant protein on Earth. The catalytic efficiency of this protein is controlled by the RuBisCO activase (RCA) that interacts with RuBisCO and promotes the CO2 entrance to the active site of RuBisCO by removing RuBP. One of the peculiar features of RCA is the presence of functional disordered tails that might play a role in RCA-RuBisCO interaction. Based on their ability to move, microalgae are grouped into 2 major class, motile and non-motile. Motile microalgae have an obvious advantage over their non-motile counterparts because of their ability to actively migrate within the water column to find the most optimal environmental conditions. We hypothesizes that the RCA could be functionally different in the non-motile and motile microalgae. To check this hypothesis, we conducted a comparative computational analysis of the RCAs from the representatives of the non-motile (Ostreococcus tauri) and motile (Tetraselmis sp. GSL018) green oceanic microalgae.

InterPro in 2017-beyond protein family and domain annotations

InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPro's predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences.