Conservation of intrinsic disorder in protein domains and families: I. A database of conserved predicted disordered regions

Many protein regions have been shown to be intrinsically disordered, lacking unique structure under physiological conditions. These intrinsically disordered regions are not only very common in proteomes, but also crucial to the function of many proteins, especially those involved in signaling, recognition, and regulation. The goal of this work was to identify the prevalence, characteristics, and functions of conserved disordered regions within protein domains and families. A database was created to store the amino acid sequences of nearly one million proteins and their domain matches from the InterPro database, a resource integrating eight different protein family and domain databases. Disorder prediction was performed on these protein sequences. Regions of sequence corresponding to domains were aligned using a multiple sequence alignment tool. From this initial information, regions of conserved predicted disorder were found within the domains. The methodology for this search consisted of finding regions of consecutive positions in the multiple sequence alignments in which a 90% or more of the sequences were predicted to be disordered. This procedure was constrained to find such regions of conserved disorder prediction that were at least 20 amino acids in length. The results of this work included 3,653 regions of conserved disorder prediction, found within 2,898 distinct InterPro entries. Most regions of conserved predicted disorder detected were short, with less than 10% of those found exceeding 30 residues in length.     

Intrinsic disorder in the Protein Data Bank

The Protein Data Bank (PDB) is the preeminent source of protein structural information. PDB contains over 32,500 experimentally determined 3-D structures solved using X-ray crystallography or nuclear magnetic resonance spectroscopy. Intrinsically disordered regions fail to form a fixed 3-D structure under physiological conditions. In this study, we compare the amino-acid sequences of proteins whose structures are determined by X-ray crystallography with the corresponding sequences from the Swiss-Prot database. The analyzed dataset includes 16,370 structures, which represent 18,101 PDB chains and 5,434 different proteins from 910 different organisms (2,793 eukaryotic, 2,109 bacterial, 288 viral, and 244 archaeal). In this dataset, on average, each Swiss-Prot protein is represented by 7 PDB chains with 76% of the crystallized regions being represented by more than one structure. Intriguingly, the complete sequences of only approximately 7% of proteins are observed in the corresponding PDB structures, and only approximately 25% of the total dataset have >95% of their lengths observed in the corresponding PDB structures. This suggests that the vast majority of PDB proteins is shorter than their corresponding Swiss-Prot sequences and/or contain numerous residues, which are not observed in maps of electron density. To determine the prevalence of disordered regions in PDB, the residues in the Swiss-Prot sequences were grouped into four general categories, "Observed" (which correspond to structured regions), "Not observed" (regions with missing electron density, potentially disordered), "Uncharacterized," and "Ambiguous," depending on their appearance in the corresponding PDB entries. This non-redundant set of residues can be viewed as a 'fragment' or empirical domain database that contains a set of experimentally determined structured regions or domains and a set of experimentally verified disordered regions or domains. We studied the propensities and properties of residues in these four categories and analyzed their relations to the predictions of disorder using several algorithms. "Non-observed," "Ambiguous," and "Uncharacterized" regions were shown to possess the amino acid compositional biases typical of intrinsically disordered proteins. The application of four different disorder predictors (PONDR(R) VL-XT, VL3-BA, VSL1P, and IUPred) revealed that the vast majority of residues in the "Observed" dataset are ordered, and that the "Not observed" regions are mostly disordered. The "Uncharacterized" regions possess some tendency toward order, whereas the predictions for the short "Ambiguous" regions are really ambiguous. Long "Ambiguous" regions (>70 amino acid residues) are mostly predicted to be ordered, suggesting that they are likely to be "wobbly" domains. Overall, we showed that completely ordered proteins are not highly abundant in PDB and many PDB sequences have disordered regions. In fact, in the analyzed dataset approximately 10% of the PDB proteins contain regions of consecutive missing or ambiguous residues longer than 30 amino-acids and approximately 40% of the proteins possess short regions (> or =10 and < 30 amino-acid long) of missing and ambiguous residues.     

Intrinsic Disorder in the Protein Data Bank

Abstract

The Protein Data Bank (PDB) is the preeminent source of protein structural information. PDB contains over 32,500 experimentally determined 3-D structures solved using X-ray crystallography or nuclear magnetic resonance spectroscopy. Intrinsically disordered regions fail to form a fixed 3-D structure under physiological conditions. In this study, we compare the amino-acid sequences of proteins whose structures are determined by X-ray crystallography with the corresponding sequences from the Swiss-Prot database. The analyzed dataset includes 16,370 structures, which represent 18,101 PDB chains and 5,434 different proteins from 910 different organisms (2,793 eukaryotic, 2,109 bacterial, 288 viral, and 244 archaeal). In this dataset, on average, each Swiss-Prot protein is represented by 7 PDB chains with 76% of the crystallized regions being represented by more than one structure. Intriguingly, the complete sequences of only ~7% of proteins are observed in the corresponding PDB structures, and only ~25% of the total dataset have >95% of their lengths observed in the corresponding PDB structures. This suggests that the vast majority of PDB proteins is shorter than their corresponding Swiss-Prot sequences and/or contain numerous residues, which are not observed in maps of electron density. To determine the prevalence of disordered regions in PDB, the residues in the Swiss-Prot sequences were grouped into four general categories, “Observed” (which correspond to structured regions), “Not observed” (regions with missing electron density, potentially disordered), “Uncharacterized,” and “Ambiguous,” depending on their appearance in the corresponding PDB entries. This non-redundant set of residues can be viewed as a ‘fragment’ or empirical domain database that contains a set of experimentally determined structured regions or domains and a set of experimentally verified disordered regions or domains. We studied the propensities and properties of residues in these four categories and analyzed their relations to the predictions of disorder using several algorithms. “Non-observed,” “Ambiguous,” and “Uncharacterized” regions were shown to possess the amino acid compositional biases typical of intrinsically disordered proteins. The application of four different disorder predictors (PONDR® VL-XT, VL3-BA, VSL1P, and IUPred) revealed that the vast majority of residues in the “Observed” dataset are ordered, and that the “Not observed” regions are mostly disordered. The “Uncharacterized” regions possess some tendency toward order, whereas the predictions for the short “Ambiguous” regions are really ambiguous. Long “Ambiguous” regions (>70 amino acid residues) are mostly predicted to be ordered, suggesting that they are likely to be “wobbly” domains.

Overall, we showed that completely ordered proteins are not highly abundant in PDB and many PDB sequences have disordered regions. In fact, in the analyzed dataset ~10% of the PDB proteins contain regions of consecutive missing or ambiguous residues longer than 30 amino-acids and ~40% of the proteins possess short regions (≥10 and <30 amino-acid long) of missing and ambiguous residues.     

Deciphering the cause of evolutionary variance within intrinsically disordered regions in human proteins

Why the intrinsically disordered regions evolve within human proteome has became an interesting question for a decade. Till date, it remains an unsolved yet an intriguing issue to investigate why some of the disordered regions evolve rapidly while the rest are highly conserved across mammalian species. Identifying the key biological factors, responsible for the variation in the conservation rate of different disordered regions within the human proteome, may revisit the above issue. We emphasized that among the other biological features (multifunctionality, gene essentiality, protein connectivity, number of unique domains, gene expression level and expression breadth) considered in our study, the number of unique protein domains acts as a strong determinant that negatively influences the conservation of disordered regions. In this context, we justified that proteins having a fewer types of domains preferably need to conserve their disordered regions to enhance their structural flexibility which in turn will facilitate their molecular interactions. In contrast, the selection pressure acting on the stretches of disordered regions is not so strong in the case of multi-domains proteins. Therefore, we reasoned that the presence of conserved disordered stretches may compensate the functions of multiple domains within a single domain protein. Interestingly, we noticed that the influence of the unique domain number and expression level acts differently on the evolution of disordered regions from that of well-structured ones.     

Kinesin tail domains are intrinsically disordered

Kinesin motor proteins transport a wide variety of molecular cargoes in a spatially and temporally regulated manner. Kinesin motor domains, which hydrolyze ATP to produce a directed mechanical force along a microtubule, are well conserved throughout the entire superfamily. Outside of the motor domains, kinesin sequences diverge along with their transport functions. The nonmotor regions, particularly the tails, respond to a wide variety of structural and molecular cues that enable kinesins to carry specific cargoes in response to particular cellular signals. Here, we demonstrate that intrinsic disorder is a common structural feature of kinesins. A bioinformatics survey of the full‐length sequences of all 43 human kinesins predicts that significant regions of intrinsically disordered residues are present in all kinesins. These regions are concentrated in the nonmotor domains, particularly in the tails and near sites for ligand binding or post‐translational modifications. In order to experimentally verify these predictions, we expressed and purified the tail domains of kinesins representing three different families (Kif5B, Kif10, and KifC3). Circular dichroism and NMR spectroscopy experiments demonstrate that the isolated tails are disordered in vitro, yet they retain their functional microtubule‐binding activity. On the basis of these results, we propose that intrinsic disorder is a common structural feature that confers functional specificity to kinesins. Proteins 2012;. © 2012 Wiley Periodicals, Inc.     

Intrinsically disordered domains: Sequence ➔ disorder ➔ function relationships

Disordered domains are long regions of intrinsic disorder that ideally have conserved sequences, conserved disorder, and conserved functions. These domains were first noticed in protein–protein interactions that are distinct from the interactions between two structured domains and the interactions between structured domains and linear motifs or molecular recognition features (MoRFs). So far, disordered domains have not been systematically characterized. Here, we present a bioinformatics investigation of the sequence–disorder–function relationships for a set of probable disordered domains (PDDs) identified from the Pfam database. All the Pfam seed proteins from those domains with at least one PDD sequence were collected. Most often, if a set contains one PDD sequence, then all members of the set are PDDs or nearly so. However, many seed sets have sequence collections that exhibit diverse proportions of predicted disorder and structure, thus giving the completely unexpected result that conserved sequences can vary substantially in predicted disorder and structure. In addition to the induction of structure by binding to protein partners, disordered domains are also induced to form structure by disulfide bond formation, by ion binding, and by complex formation with RNA or DNA. The two new findings, (a) that conserved sequences can vary substantially in their predicted disorder content and (b) that homologues from a single domain can evolve from structure to disorder (or vice versa), enrich our understanding of the sequence ? disorder ensemble ? function paradigm.     

Intrinsic disorder of the extracellular matrix

The extracellular matrix is very well organized at the supramolecular and tissue levels and little is known on the potential role of intrinsic disorder in promoting its organization. We predicted the amount of disorder and identified disordered regions in the human extracellular proteome with established computational tools. The extracellular proteome is significantly enriched in proteins comprising more than 50% of disorder compared to the complete human proteome. The enrichment is mostly due to long disordered regions containing at least 100 consecutive disordered residues. The amount of intrinsic disorder is heterogeneous in the extracellular protein families, with the most disordered being collagens and the small integrin-binding ligand N-linked glycoproteins. Although most domains found in extracellular proteins are structured, the fibronectin III domains contain a variable amount of disordered residues (up to 92%). Binding sites for heparin and integrins are found in disordered sequences of extracellular proteins. Intrinsic disorder is evenly distributed in hubs and ends in the interaction network of extracellular proteins with their extracellular partners. In contrast, extracellular hubs are significantly enriched in disorder in the network of extracellular proteins with their extracellular, membrane and intracellular partners. Disorder could thus provide the structural plasticity required for the hubs to interact with membrane and intracellular proteins. Organization and assembly of the extracellular matrix, development of mineralized tissues and cell-matrix adhesion are the biological processes overrepresented in the most disordered extracellular proteins. Extracellular disorder is associated with binding to growth factors, glycosaminoglycans and integrins at the molecular level.     

Exploring Potential Signals of Selection for Disordered Residues in Prokaryotic and Eukaryotic Proteins

Intrinsically disordered proteins (IDPs) are an important class of proteins in all domains of life for their functional importance. However, how nature has shaped the disorder potential of prokaryotic and eukaryotic proteins is still not clearly known. Randomly generated sequences are free of any selective constraints, thus these sequences are commonly used as null models. Considering different types of random protein models, here we seek to understand how the disorder potential of natural eukaryotic and prokaryotic proteins differs from random sequences. Comparing proteome-wide disorder content between real and random sequences of 12 model organisms, we noticed that eukaryotic proteins are enriched in disordered regions compared to random sequences, but in prokaryotes such regions are depleted. By analyzing the position-wise disorder profile, we show that there is a generally higher disorder near the N- and C-terminal regions of eukaryotic proteins as compared to the random models; however, either no or a weak such trend was found in prokaryotic proteins. Moreover, here we show that this preference is not caused by the amino acid or nucleotide composition at the respective sites. Instead, these regions were found to be endowed with a higher fraction of protein–protein binding sites, suggesting their functional importance. We discuss several possible explanations for this pattern, such as improving the efficiency of protein–protein interaction, ribosome movement during translation, and post-translational modification. However, further studies are needed to clearly understand the biophysical mechanisms causing the trend.     

NMR illuminates intrinsic disorder

Nuclear magnetic resonance (NMR) has long been instrumental in the characterization of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs). This method continues to offer rich insights into the nature of IDPs in solution, especially in combination with other biophysical methods such as small-angle scattering, single-molecule fluorescence, electron paramagnetic resonance (EPR), and mass spectrometry. Substantial advances have been made in recent years in studies of proteins containing both ordered and disordered domains and in the characterization of problematic sequences containing repeated tracts of a single or a few amino acids. These sequences are relevant to disease states such as Alzheimer's, Parkinson's, and Huntington's diseases, where disordered proteins misfold into harmful amyloid. Innovative applications of NMR are providing novel insights into mechanisms of protein aggregation and the complexity of IDP interactions with their targets. As a basis for understanding the solution structural ensembles, dynamic behavior, and functional mechanisms of IDPs and IDRs, NMR continues to prove invaluable.     

Attributes of short linear motifs

Traditionally, protein-protein interactions were thought to be mediated by large, structured domains. However, it has become clear that the interactome comprises a wide range of binding interfaces with varying degrees of flexibility, ranging from rigid globular domains to disordered regions that natively lack structure. Enrichment for disorder in highly connected hub proteins and its correlation with organism complexity hint at the functional importance of disordered regions. Nevertheless, they have not yet been extensively characterised. Shifting the attention from globular domains to disordered regions of the proteome might bring us closer to elucidating the dense and complex connectivity of the interactome. An important class of disordered interfaces are the compact mono-partite, short linear motifs (SLiMs, or eukaryotic linear motifs (ELMs)). They are evolutionarily plastic and interact with relatively low affinity due to the limited number of residues that make direct contact with the binding partner. These features confer to SLiMs the ability to evolve convergently and mediate transient interactions, which is imperative to network evolution and to maintain robust cell signalling, respectively. The ability to discriminate biologically relevant SLiMs by means of different attributes will improve our understanding of the complexity of the interactome and aid development of bioinformatics tools for motif discovery. In this paper, the curated instances currently available in the Eukaryotic Linear Motif (ELM) database are analysed to provide a clear overview of the defining attributes of SLiMs. These analyses suggest that functional SLiMs have higher levels of conservation than their surrounding residues, frequently evolve convergently, preferentially occur in disordered regions and often form a secondary structure when bound to their interaction partner. These results advocate searching for small groupings of residues in disordered regions with higher relative conservation and a propensity to form the secondary structure. Finally, the most interesting conclusions are examined in regard to their functional consequences.     

Functional anthology of intrinsic disorder. 2. Cellular components, domains, technical terms, developmental processes, and coding sequence diversities correlated with long disordered regions

Biologically active proteins without stable ordered structure (i.e., intrinsically disordered proteins) are attracting increased attention. Functional repertoires of ordered and disordered proteins are very different, and the ability to differentiate whether a given function is associated with intrinsic disorder or with a well-folded protein is crucial for modern protein science. However, there is a large gap between the number of proteins experimentally confirmed to be disordered and their actual number in nature. As a result, studies of functional properties of confirmed disordered proteins, while helpful in revealing the functional diversity of protein disorder, provide only a limited view. To overcome this problem, a bioinformatics approach for comprehensive study of functional roles of protein disorder was proposed in the first paper of this series (Xie, H.; Vucetic, S.; Iakoucheva, L. M.; Oldfield, C. J.; Dunker, A. K.; Obradovic, Z.; Uversky, V. N. Functional anthology of intrinsic disorder. 1. Biological processes and functions of proteins with long disordered regions. J. Proteome Res. 2007, 5, 1882-1898). Applying this novel approach to Swiss-Prot sequences and functional keywords, we found over 238 and 302 keywords to be strongly positively or negatively correlated, respectively, with long intrinsically disordered regions. This paper describes approximately 90 Swiss-Prot keywords attributed to the cellular components, domains, technical terms, developmental processes, and coding sequence diversities possessing strong positive and negative correlation with long disordered regions.     

Prelude and Fugue, predicting local protein structure, early folding regions and structural weaknesses

Prelude&Fugue are bioinformatics tools aiming at predicting the local 3D structure of a protein from its amino acid sequence in terms of seven backbone torsion angle domains, using database-derived potentials. Prelude(&Fugue) computes all lowest free energy conformations of a protein or protein region, ranked by increasing energy, and possibly satisfying some interresidue distance constraints specified by the user. (Prelude&)Fugue detects sequence regions whose predicted structure is significantly preferred relative to other conformations in the absence of tertiary interactions. These programs can be used for predicting secondary structure, tertiary structure of short peptides, flickering early folding sequences and peptides that adopt a preferred conformation in solution. They can also be used for detecting structural weaknesses, i.e. sequence regions that are not optimal with respect to the tertiary fold. AVAILABILITY: http://babylone.ulb.ac.be/Prelude_and_Fugue.     

Identification and analysis of polyserine linker domains in prokaryotic proteins with emphasis on the marine bacterium Microbulbifer degradans

Polyserine linkers (PSLs) are interdomain, serine-rich sequences found in modular proteins. Though common among eukaryotes, their presence in prokaryotic enzymes is limited. We identified 46 extracellular proteins involved in complex carbohydrate degradation from Microbulbifer degradans that contain PSLs that separate carbohydrate-binding domains or catalytic domains from other binding domains. In nine M. degradans proteins, PSLs also separated amino-terminal lipoprotein acylation sites from the remainder of the polypeptide. Furthermore, among the 76 PSL proteins identified in sequence repositories, 65 are annotated as proteins involved in complex carbohydrate degradation. We discuss the notion that PSLs are flexible, disordered spacer regions that enhance substrate accessibility.     

Conservation of intrinsic disorder in protein domains and families: II. functions of conserved disorder

Regions of conserved disorder prediction (CDP) were found in protein domains from all available InterPro member databases, although with varying frequency. These CDP regions were found in proteins from all kingdoms of life, including viruses. However, eukaryotes had 1 order of magnitude more proteins containing long disordered regions than did archaea and bacteria. Sequence conservation in CDP regions varied, but was on average slightly lower than in regions of conserved order. In some cases, disordered regions evolve faster than ordered regions, in others they evolve slower, and in the rest they evolve at roughly the same rate. A variety of functions were found to be associated with domains containing conserved disorder. The most common were DNA/RNA binding, and protein binding. Many ribosomal proteins also were found to contain conserved disordered regions. Other functions identified included membrane translocation and amino acid storage for germination. Due to limitations of current knowledge as well as the methodology used for this work, it was not determined whether these functions were directly associated with the predicted disordered region. However, the functions associated with conserved disorder in this work are in agreement with the functions found in other studies to correlate to disordered regions. We have established that intrinsic disorder may be more common in bacterial and archaeal proteins than previously thought, but this disorder is likely to be used for different purposes than in eukaryotic proteins, as well as occurring in shorter stretches of protein. Regions of predicted disorder were found to be conserved within a large number of protein families and domains. Although many think of such conserved domains as being ordered, in fact a significant number of them contain regions of disorder that are likely to be crucial to their functions.     

A practical overview of protein disorder prediction methods

In the past few years there has been a growing awareness that a large number of proteins contain long disordered (unstructured) regions that often play a functional role. However, these disordered regions are still poorly detected. Recognition of disordered regions in a protein is important for two main reasons: reducing bias in sequence similarity analysis by avoiding alignment of disordered regions against ordered ones, and helping to delineate boundaries of protein domains to guide structural and functional studies. As none of the available method for disorder prediction can be taken as fully reliable on its own, we present an overview of the methods currently employed highlighting their advantages and drawbacks. We show a few practical examples of how they can be combined to avoid pitfalls and to achieve more reliable predictions.