Evolution of structurally disordered proteins promotes neostructuralization

Protein structure is generally more conserved than sequence, but for regions that can adopt different structures in different environments, does this hold true? Understanding how structurally disordered regions evolve altered secondary structure element propensities as well as conformational flexibility among paralogs are fundamental questions for our understanding of protein structural evolution. We have investigated the evolutionary dynamics of structural disorder in protein families containing both orthologs and paralogs using phylogenetic tree reconstruction, protein structure disorder prediction, and secondary structure prediction in order to shed light upon these questions. Our results indicate that the extent and location of structurally disordered regions are not universally conserved. As structurally disordered regions often have high conformational flexibility, this is likely to have an effect on how protein structure evolves as spatially altered conformational flexibility can also change the secondary structure propensities for homologous regions in a protein family.     

Intrinsic Disorder in Protein Interactions: Insights From a Comprehensive Structural Analysis

We perform a large-scale study of intrinsically disordered regions in proteins and protein complexes using a non-redundant set of hundreds of different protein complexes. In accordance with the conventional view that folding and binding are coupled, in many of our cases the disorder-to-order transition occurs upon complex formation and can be localized to binding interfaces. Moreover, analysis of disorder in protein complexes depicts a significant fraction of intrinsically disordered regions, with up to one third of all residues being disordered. We find that the disorder in homodimers, especially in symmetrical homodimers, is significantly higher than in heterodimers and offer an explanation for this interesting phenomenon. We argue that the mechanisms of regulation of binding specificity through disordered regions in complexes can be as common as for unbound monomeric proteins. The fascinating diversity of roles of disordered regions in various biological processes and protein oligomeric forms shown in our study may be a subject of future endeavors in this area.     

An assignment of intrinsically disordered regions of proteins based on NMR structures

Intrinsically disordered proteins (IDPs) do not adopt stable three-dimensional structures in physiological conditions, yet these proteins play crucial roles in biological phenomena. In most cases, intrinsic disorder manifests itself in segments or domains of an IDP, called intrinsically disordered regions (IDRs), but fully disordered IDPs also exist. Although IDRs can be detected as missing residues in protein structures determined by X-ray crystallography, no protocol has been developed to identify IDRs from structures obtained by Nuclear Magnetic Resonance (NMR). Here, we propose a computational method to assign IDRs based on NMR structures. We compared missing residues of X-ray structures with residue-wise deviations of NMR structures for identical proteins, and derived a threshold deviation that gives the best correlation of ordered and disordered regions of both structures. The obtained threshold of 3.2 Å was applied to proteins whose structures were only determined by NMR, and the resulting IDRs were analyzed and compared to those of X-ray structures with no NMR counterpart in terms of sequence length, IDR fraction, protein function, cellular location, and amino acid composition, all of which suggest distinct characteristics. The structural knowledge of IDPs is still inadequate compared with that of structured proteins. Our method can collect and utilize IDRs from structures determined by NMR, potentially enhancing the understanding of IDPs.     

Characterization of molecular recognition features, MoRFs, and their binding partners

Molecular Recognition Features (MoRFs) are short, interaction-prone segments of protein disorder that undergo disorder-to-order transitions upon specific binding, representing a specific class of intrinsically disordered regions that exhibit molecular recognition and binding functions. MoRFs are common in various proteomes and occupy a unique structural and functional niche in which function is a direct consequence of intrinsic disorder. Example MoRFs collected from the Protein Data Bank (PDB) have been divided into three subtypes according to their structures in the bound state: alpha-MoRFs form alpha-helices, beta-MoRFs form beta-strands, and iota-MoRFs form structures without a regular pattern of backbone hydrogen bonds. These example MoRFs were indicated to be intrinsically disordered in the absence of their binding partners by several criteria. In this study, we used several geometric and physiochemical criteria to examine the properties of 62 alpha-, 20 beta-, and 176 iota-MoRF complex structures. Interface residues were examined by calculating differences in accessible surface area between the complex and isolated monomers. The compositions and physiochemical properties of MoRF and MoRF partner interface residues were compared to the interface residues of homodimers, heterodimers, and antigen-antibody complexes. Our analysis indicates that there are significant differences in residue composition and several geometric and physicochemical properties that can be used to discriminate, with a high degree of accuracy, between various interfaces in protein interaction data sets. Implications of these findings for the development of MoRF-partner interaction predictors are discussed. In addition, structural changes upon MoRF-to-partner complex formation were examined for several illustrative examples.     

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.     

The role of disorder in interaction networks: a structural analysis

Recent studies have emphasized the value of including structural information into the topological analysis of protein networks. Here, we utilized structural information to investigate the role of intrinsic disorder in these networks. Hub proteins tend to be more disordered than other proteins (i.e. the proteome average); however, we find this only true for those with one or two binding interfaces (‘single’‐interface hubs). In contrast, the distribution of disordered residues in multi‐interface hubs is indistinguishable from the overall proteome. Surprisingly, we find that the binding interfaces in single‐interface hubs are highly structured, as is the case for multi‐interface hubs. However, the binding partners of single‐interface hubs tend to have a higher level of disorder than the proteome average, suggesting that their binding promiscuity is related to the disorder of their binding partners. In turn, the higher level of disorder of single‐interface hubs can be partly explained by their tendency to bind to each other in a cascade. A good illustration of this trend can be found in signaling pathways and, more specifically, in kinase cascades. Finally, our findings have implications for the current controversy related to party and date‐hubs.     

Resolving the ambiguity: Making sense of intrinsic disorder when PDB structures disagree

Missing regions in X‐ray crystal structures in the Protein Data Bank (PDB) have played a foundational role in the study of intrinsically disordered protein regions (IDPRs), especially in the development of in silico predictors of intrinsic disorder. However, a missing region is only a weak indication of intrinsic disorder, and this uncertainty is compounded by the presence of ambiguous regions, where more than one structure of the same protein sequence “disagrees” in terms of the presence or absence of missing residues. The question is this: are these ambiguous regions intrinsically disordered, or are they the result of static disorder that arises from experimental conditions, ensembles of structures, or domain wobbling? A novel way of looking at ambiguous regions in terms of the pattern between multiple PDB structures has been demonstrated. It was found that the propensity for intrinsic disorder increases as the level of ambiguity decreases. However, it is also shown that ambiguity is more likely to occur as the protein region is placed within different environmental conditions, and even the most ambiguous regions as a set display compositional bias that suggests flexibility. The results suggested that ambiguity is a natural result for many IDPRs crystallized under different conditions and that static disorder and wobbling domains are relatively rare. Instead, it is more likely that ambiguity arises because many of these regions were conditionally or partially disordered.     

Long indels are disordered: A study of disorder and indels in homologous eukaryotic proteins

Proteins evolve through point mutations as well as by insertions and deletions (indels). During the last decade it has become apparent that protein regions that do not fold into three-dimensional structures, i.e. intrinsically disordered regions, are quite common. Here, we have studied the relationship between protein disorder and indels using HMM–HMM pairwise alignments in two sets of orthologous eukaryotic protein pairs. First, we show that disordered residues are much more frequent among indel residues than among aligned residues and, also are more prevalent among indels than in coils. Second, we observed that disordered residues are particularly common in longer indels. Disordered indels of short-to-medium size are prevalent in the non-terminal regions of proteins while the longest indels, ordered and disordered alike, occur toward the termini of the proteins where new structural units are comparatively well tolerated. Finally, while disordered regions often evolve faster than ordered regions and disorder is common in indels, there are some previously recognized protein families where the disordered region is more conserved than the ordered region. We find that these rare proteins are often involved in information processes, such as RNA processing and translation. This article is part of a Special Issue entitled: The emerging dynamic view of proteins: Protein plasticity in allostery, evolution and self-assembly.     

Serine/arginine-rich splicing factors belong to a class of intrinsically disordered proteins

Serine/arginine-rich (SR) splicing factors play an important role in constitutive and alternative splicing as well as during several steps of RNA metabolism. Despite the wealth of functional information about SR proteins accumulated to-date, structural knowledge about the members of this family is very limited. To gain a better insight into structure-function relationships of SR proteins, we performed extensive sequence analysis of SR protein family members and combined it with ordered/disordered structure predictions. We found that SR proteins have properties characteristic of intrinsically disordered (ID) proteins. The amino acid composition and sequence complexity of SR proteins were very similar to those of the disordered protein regions. More detailed analysis showed that the SR proteins, and their RS domains in particular, are enriched in the disorder-promoting residues and are depleted in the order-promoting residues as compared to the entire human proteome. Moreover, disorder predictions indicated that RS domains of SR proteins were completely unstructured. Two different classification methods, the charge-hydropathy measure and the cumulative distribution function (CDF) of the disorder scores, were in agreement with each other, and they both strongly predicted members of the SR protein family to be disordered. This study emphasizes the importance of the disordered structure for several functions of SR proteins, such as for spliceosome assembly and for interaction with multiple partners. In addition, it demonstrates the usefulness of order/disorder predictions for inferring protein structure from sequence.     

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.     

Distinct Types of Disorder in the Human Proteome: Functional Implications for Alternative Splicing

Intrinsically disordered regions have been associated with various cellular processes and are implicated in several human diseases, but their exact roles remain unclear. We previously defined two classes of conserved disordered regions in budding yeast, referred to as “flexible” and “constrained” conserved disorder. In flexible disorder, the property of disorder has been positionally conserved during evolution, whereas in constrained disorder, both the amino acid sequence and the property of disorder have been conserved. Here, we show that flexible and constrained disorder are widespread in the human proteome, and are particularly common in proteins with regulatory functions. Both classes of disordered sequences are highly enriched in regions of proteins that undergo tissue-specific (TS) alternative splicing (AS), but not in regions of proteins that undergo general (i.e., not tissue-regulated) AS. Flexible disorder is more highly enriched in TS alternative exons, whereas constrained disorder is more highly enriched in exons that flank TS alternative exons. These latter regions are also significantly more enriched in potential phosphosites and other short linear motifs associated with cell signaling. We further show that cancer driver mutations are significantly enriched in regions of proteins associated with TS and general AS. Collectively, our results point to distinct roles for TS alternative exons and flanking exons in the dynamic regulation of protein interaction networks in response to signaling activity, and they further suggest that alternatively spliced regions of proteins are often functionally altered by mutations responsible for cancer.     

A proteomic Ramachandran plot (PRplot)

Each protein structure can be characterized by the average values of the main chain torsion angles ? and ψ and, as a consequence, be plotted on a bidimensional diagram, which resembles the Ramachandran plot. Here, we describe a proteomic ?–ψ plot (PRplot) where each protein structure is associated with one point, allowing in this way to represent the entire protein structure universe. It was verified that the PRplot is a robust tool since it does not depend on the dimension of the proteins, on the crystallographic resolution of the structures, nor on the biological source; moreover, it is little affected by disordered and structurally uncharacterized residues. The proteins mapped on the PRplot tend to cluster in three regions that correspond to the structures rich in alpha-helices, in beta-strands, and in both helices and strands, and are distributed along a sigmoidal curve that connect these three highly populated regions. PRplots are a unique instrument to project all protein structures on a single bidimensional plane where the entire structural complexity is reduced to a striking simplicity, with the sigmoid curve clearly delineating the space fraction accessible to a stable protein.     

Dissecting Disease Inheritance Modes in a Three-Dimensional Protein Network Challenges the “Guilt-by-Association” Principle

To better understand different molecular mechanisms by which mutations lead to various human diseases, we classified 82,833 disease-associated mutations according to their inheritance modes (recessive versus dominant) and molecular types (in-frame [missense point mutations and in-frame indels] versus truncating [nonsense mutations and frameshift indels]) and systematically examined the effects of different classes of disease mutations in a three-dimensional protein interactome network with the atomic-resolution interface resolved for each interaction. We found that although recessive mutations affecting the interaction interface of two interacting proteins tend to cause the same disease, this widely accepted “guilt-by-association” principle does not apply to dominant mutations. Furthermore, recessive truncating mutations in regions encoding the same interface are much more likely to cause the same disease, even for interfaces close to the N terminus of the protein. Conversely, dominant truncating mutations tend to be enriched in regions encoding areas between interfaces. These results suggest that a significant fraction of truncating mutations can generate functional protein products. For example, TRIM27, a known cancer-associated protein, interacts with three proteins (MID2, TRIM42, and SIRPA) through two different interfaces. A dominant truncating mutation (c.1024delT [p.Tyr342Thrfs^∗30]) associated with ovarian carcinoma is located between the regions encoding the two interfaces; the altered protein retains its interaction with MID2 and TRIM42 through the first interface but loses its interaction with SIRPA through the second interface. Our findings will help clarify the molecular mechanisms of thousands of disease-associated genes and their tens of thousands of mutations, especially for those carrying truncating mutations, often erroneously considered “knockout” alleles.     

Phosphorylation Variation during the Cell Cycle Scales with Structural Propensities of Proteins

Phosphorylation at specific residues can activate a protein, lead to its localization to particular compartments, be a trigger for protein degradation and fulfill many other biological functions. Protein phosphorylation is increasingly being studied at a large scale and in a quantitative manner that includes a temporal dimension. By contrast, structural properties of identified phosphorylation sites have so far been investigated in a static, non-quantitative way. Here we combine for the first time dynamic properties of the phosphoproteome with protein structural features. At six time points of the cell division cycle we investigate how the variation of the amount of phosphorylation correlates with the protein structure in the vicinity of the modified site. We find two distinct phosphorylation site groups: intrinsically disordered regions tend to contain sites with dynamically varying levels, whereas regions with predominantly regular secondary structures retain more constant phosphorylation levels. The two groups show preferences for different amino acids in their kinase recognition motifs - proline and other disorder-associated residues are enriched in the former group and charged residues in the latter. Furthermore, these preferences scale with the degree of disorderedness, from regular to irregular and to disordered structures. Our results suggest that the structural organization of the region in which a phosphorylation site resides may serve as an additional control mechanism. They also imply that phosphorylation sites are associated with different time scales that serve different functional needs.     

SPINE-D: accurate prediction of short and long disordered regions by a single neural-network based method

Short and long disordered regions of proteins have different preference for different amino acid residues. Different methods often have to be trained to predict them separately. In this study, we developed a single neural-network-based technique called SPINE-D that makes a three-state prediction first (ordered residues and disordered residues in short and long disordered regions) and reduces it into a two-state prediction afterwards. SPINE-D was tested on various sets composed of different combinations of Disprot annotated proteins and proteins directly from the PDB annotated for disorder by missing coordinates in X-ray determined structures. While disorder annotations are different according to Disprot and X-ray approaches, SPINE-D's prediction accuracy and ability to predict disorder are relatively independent of how the method was trained and what type of annotation was employed but strongly depend on the balance in the relative populations of ordered and disordered residues in short and long disordered regions in the test set. With greater than 85% overall specificity for detecting residues in both short and long disordered regions, the residues in long disordered regions are easier to predict at 81% sensitivity in a balanced test dataset with 56.5% ordered residues but more challenging (at 65% sensitivity) in a test dataset with 90% ordered residues. Compared to eleven other methods, SPINE-D yields the highest area under the curve (AUC), the highest Mathews correlation coefficient for residue-based prediction, and the lowest mean square error in predicting disorder contents of proteins for an independent test set with 329 proteins. In particular, SPINE-D is comparable to a meta predictor in predicting disordered residues in long disordered regions and superior in short disordered regions. SPINE-D participated in CASP 9 blind prediction and is one of the top servers according to the official ranking. In addition, SPINE-D was examined for prediction of functional molecular recognition motifs in several case studies.     

Optimization of linear disorder predictors yields tight association between crystallographic disorder and hydrophobicity

X-ray crystallographic protein structures often contain disordered regions that are observed as missing electron density. Diffraction data may give little or no direct evidence as to the specific nature of disordered regions. We have developed a weighted window-based disorder predictor optimized using crystallographic data. Performance of a predictor is strongly influenced by chain termini. Optimized score adjustment values for amino- and carboxy-terminal positions demonstrate a simple, monotonic relationship between disorder and residue distance from termini. This optimized disorder predictor performs similarly to DISOPRED2 on crystallographically disordered regions. Data-optimized residue disorder propensities show strong linear correlation with experimentally determined amino acid transfer energies between water and hydrogen-bonding organic solvents, which primarily reflect residue hydrophobicity (exemplified by the Nozaki-Tanford hydrophobicity scale). Disorder propensities do not correlate as well with transfer energies between water and apolar solvents, which primarily reflect a different hydropathic property: residue hydrophilicity (also reflected by the Kyte-Doolittle hydropathy scale). Our results suggest that while hydrophobic side-chain interactions are primarily involved in determining stability of the folded conformation, hydrogen bonding, and similar polar interactions are primarily involved in conformational and interaction specificity.     

RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins

MOTIVATION: Recent studies have found many proteins containing regions that do not form well-defined three-dimensional structures in their native states. The study and detection of such disordered regions is important both for understanding protein function and for facilitating structural analysis since disordered regions may affect solubility and/or crystallizability. RESULTS: We have developed the regional order neural network (RONN) software as an application of our recently developed 'bio-basis function neural network' pattern recognition algorithm to the detection of natively disordered regions in proteins. The results of blind-testing a panel of nine disorder prediction tools (including RONN) against 80 protein sequences derived from the Protein Data Bank shows that, based on the probability excess measure, RONN performed the best.     

Analysis of molecular recognition features (MoRFs)

Several proteomic studies in the last decade revealed that many proteins are either completely disordered or possess long structurally flexible regions. Many such regions were shown to be of functional importance, often allowing a protein to interact with a large number of diverse partners. Parallel to these findings, during the last five years structural bioinformatics has produced an explosion of results regarding protein-protein interactions and their importance for cell signaling. We studied the occurrence of relatively short (10-70 residues), loosely structured protein regions within longer, largely disordered sequences that were characterized as bound to larger proteins. We call these regions molecular recognition features (MoRFs, also known as molecular recognition elements, MoREs). Interestingly, upon binding to their partner(s), MoRFs undergo disorder-to-order transitions. Thus, in our interpretation, MoRFs represent a class of disordered region that exhibits molecular recognition and binding functions. This work extends previous research showing the importance of flexibility and disorder for molecular recognition. We describe the development of a database of MoRFs derived from the RCSB Protein Data Bank and present preliminary results of bioinformatics analyses of these sequences. Based on the structure adopted upon binding, at least three basic types of MoRFs are found: α-MoRFs, β-MoRFs, and ι-MoRFs, which form α-helices, β-strands, and irregular secondary structure when bound, respectively. Our data suggest that functionally significant residual structure can exist in MoRF regions prior to the actual binding event. The contribution of intrinsic protein disorder to the nature and function of MoRFs has also been addressed. The results of this study will advance the understanding of protein-protein interactions and help towards the future development of useful protein-protein binding site predictors.