Editorial: Function and Flexibility: Friend or Foe?

Intrinsic disorder (ID) in proteins is well-established in structural biology, with increasing evidence for its involvement in essential biological processes. As measuring dynamic ID behavior experimentally on a large scale remains difficult, scores of published ID predictors have tried to fill this gap. Unfortunately, their heterogeneity makes it difficult to compare performance, confounding biologists wanting to make an informed choice. To address this issue, the Critical Assessment of protein Intrinsic Disorder (CAID) benchmarks predictors for ID and binding regions as a community blind-test in a standardized computing environment. Here we present the CAID Prediction Portal, a web server executing all CAID methods on user-defined sequences. The server generates standardized output and facilitates comparison between methods, producing a consensus prediction highlighting high-confidence ID regions. The website contains extensive documentation explaining the meaning of different CAID statistics and providing a brief description of all methods. Predictor output is visualized in an interactive feature viewer and made available for download in a single table, with the option to recover previous sessions via a private dashboard. The CAID Prediction Portal is a valuable resource for researchers interested in studying ID in proteins. The server is available at the URL: https://caid.idpcentral.org.

ABSTRACTIntroductionThe human retina relies on a complex network of proteins, many of which exhibit intrinsic disorder and liquid‐liquid phase separation (LLPS), enabling dynamic interactions for retinal function. Disruptions in these properties, along with missense mutations, have been linked to retinal diseases. This study aims to characterize and compare retinal proteins categorized by their expression specificity and tissue distribution using bioinformatics tools to explore relationships between intrinsic protein disorder, phase separation potential, and mutation pathogenicity.MethodsWe analyzed retinal proteins classified by the Human Protein Atlas (HPA) into two major groups based on gene expression specificity (degree of unique retinal expression) and gene expression distribution (extent of expression across tissues). We analyzed nine retinal proteomes categorized by gene expression specificity and distribution. Intrinsic protein disorder was assessed using per‐residue and global disorder predictors from the Rapid Intrinsic Disorder Analysis Online (RIDAO) platform, LLPS potential was evaluated with ParSe v2, and missense mutation pathogenicity was predicted using AlphaMissense.ResultsSignificant differences in per‐residue intrinsic protein disorder were found within the specificity and distribution subgroups (p < 0.0001). In addition, global disorder predictions from the RIDAO platform demonstrated non‐random distributions of protein species across the proteomes analyzed in both subgroups (p < 0.0001). Furthermore, proteins specifically elevated in the retina exhibited higher intrinsic disorder and greater phase separation propensity (ParSe v2, AUC up to 0.650), compared to those more broadly expressed. Lastly, AlphaMissense analysis showed significant variations in the average pathogenicity scores of missense mutations within subgroups (p < 0.0001).ConclusionOur results show that intrinsic disorder, LLPS, and mutational tendencies are not evenly distributed among retinal proteomes. Our study demonstrates a link between intrinsic disorder, LLPS potential, and pathogenic vulnerability among retinal proteins, underscoring the unique structural and functional landscape of retinal proteomes. Proteins with higher specificity to the retina exhibit greater disorder and phase separation potential, highlighting their potential role in dynamic cellular processes that support retinal function. Conversely, proteins widely distributed across multiple tissues tend to be more ordered, suggesting a need for structural stability in their broader functional roles.

The structure–function paradigm, i.e. the concept that it is the three-dimensional structure of a protein that determines its function, has been partially modified by the discovery that a significant portion of the eukaryotic proteome is disordered and that this disorder is often functional. The presence of disorder is the origin of several issues, but the most relevant, at least from the biomedical point of view, is the difficulty of designing drugs in absence of a well-defined conformation of the target. To make the problem worse, we have to consider that often the disorder concerns proteins involved in diseases very relevant for human health, as cancer or neurodegenerative disorders. This review tries to summarize the state of the art of our knowledge on the subject and to describe the tools used to detect disorder and how drug design techniques used for well-folded proteins have been adjusted to this more challenging situation. Abbreviations: AD: Alzheimer’s disease; CAID: Critical assessment of intrinsic protein disorder; CASP: Critical assessment of protein structure prediction; CD: circular dichroism; Cryo-EM: cryo-electron microscopy; DIBS: differential binding score; FRET: Förster resonance energy transfer; HD: Huntington’s disease; IDR: Intrinsically disordered regions; IDP: intrinsically disordered proteins; LDR: long intrinsically disordered regions; MG: Molten globule; MoRF: Molecular recognition feature; NMR: Nuclear magnetic resonance; PDB: Protein Data Bank; PD: Parkinson’s disease; POMS: polyoxometalates; SAXS: Small-angle X-ray scattering; SLiMS: short linear motifs; TFs: Transcription factors.

Intrinsic disorder (ID) in proteins has been extensively described for the last decade; a large-scale classification of ID in proteins is mostly missing. Here, we provide an extensive analysis of ID in the protein universe on the UniProt database derived from sequence-based predictions in MobiDB. Almost half the sequences contain an ID region of at least five residues. About 9% of proteins have a long ID region of over 20 residues which are more abundant in Eukaryotic organisms and most frequently cover less than 20% of the sequence. A small subset of about 67,000 (out of over 80 million) proteins is fully disordered and mostly found in Viruses. Most proteins have only one ID, with short ID evenly distributed along the sequence and long ID overrepresented in the center. The charged residue composition of Das and Pappu was used to classify ID proteins by structural propensities and corresponding functional enrichment. Swollen Coils seem to be used mainly as structural components and in biosynthesis in both Prokaryotes and Eukaryotes. In Bacteria, they are confined in the nucleoid and in Viruses provide DNA binding function. Coils & Hairpins seem to be specialized in ribosome binding and methylation activities. Globules & Tadpoles bind antigens in Eukaryotes but are involved in killing other organisms and cytolysis in Bacteria. The Undefined class is used by Bacteria to bind toxic substances and mediate transport and movement between and within organisms in Viruses. Fully disordered proteins behave similarly, but are enriched for glycine residues and extracellular structures.

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

BackgroundAlthough structural domains in proteins (SDs) are important, half of the regions in the human proteome are currently left with no SD assignments. These unassigned regions consist not only of novel SDs, but also of intrinsically disordered (ID) regions since proteins, especially those in eukaryotes, generally contain a significant fraction of ID regions. As ID regions can be inferred from amino acid sequences, a method that combines SD and ID region assignments can determine the fractions of SDs and ID regions in any proteome.ResultsIn contrast to other available ID prediction programs that merely identify likely ID regions, the DICHOT system we previously developed classifies the entire protein sequence into SDs and ID regions. Application of DICHOT to the human proteome revealed that residue-wise ID regions constitute 35%, SDs with similarity to PDB structures comprise 52%, while SDs with no similarity to PDB structures account for the remaining 13%. The last group consists of novel structural domains, termed cryptic domains, which serve as good targets of structural genomics. The DICHOT method applied to the proteomes of other model organisms indicated that eukaryotes generally have high ID contents, while prokaryotes do not. In human proteins, ID contents differ among subcellular localizations: nuclear proteins had the highest residue-wise ID fraction (47%), while mitochondrial proteins exhibited the lowest (13%). Phosphorylation and O-linked glycosylation sites were found to be located preferentially in ID regions. As O-linked glycans are attached to residues in the extracellular regions of proteins, the modification is likely to protect the ID regions from proteolytic cleavage in the extracellular environment. Alternative splicing events tend to occur more frequently in ID regions. We interpret this as evidence that natural selection is operating at the protein level in alternative splicing.ConclusionsWe classified entire regions of proteins into the two categories, SDs and ID regions and thereby obtained various kinds of complete genome-wide statistics. The results of the present study are important basic information for understanding protein structural architectures and have been made publicly available at http://spock.genes.nig.ac.jp/~genome/DICHOT.

IDPs: Less Disordered and More Ordered than Expected

The longstanding structure-function paradigm, which states that a protein only serves a biological function in a structured state, had to be substantially revised with the description of intrinsic disorder in proteins. Intrinsically disordered regions that undergo a stimulus-dependent disorder-to-order transition are common to a large number of signaling proteins. However, little is known about the functionality of intrinsically disordered regions in plant proteins. Here we investigated intrinsic disorder in a plant-specific remorin protein that has been described as a signaling component in plant-microbe interactions. Using bioinformatic, biochemical, and biophysical approaches, we characterized the highly abundant remorin AtREM1.3, showing that its N-terminal region is intrinsically disordered. Although only the AtREM1.3 C-terminal domain is essential for stable homo-oligomerization, the N-terminal region facilitates this interaction. Furthermore, we confirmed the stable interaction between AtREM1.3 and four isoforms of the importin α protein family in a yeast two-hybrid system and by an in planta bimolecular fluorescent complementation assay. Phosphorylation of Ser-66 in the intrinsically disordered N-terminal region decreases the interaction strength with the importin α proteins. Hence, the N-terminal region may constitute a regulatory domain, stabilizing these interactions.

Proteomic and bioinformatic analysis of a nuclear intrinsically disordered proteome

Pathological unfoldomics of uncontrolled chaos: intrinsically disordered proteins and human diseases.

MetIoR: A meta predictor to predict intrinsic disorder in RNA binding proteins.

Evidence that many protein regions and even entire proteins lacking stable tertiary and/or secondary structure in solution (i.e., intrinsically disordered proteins) might be involved in protein-protein interactions, regulation, recognition, and signal transduction is rapidly accumulating. These signaling proteins play a crucial role in the development of several pathological conditions, including cancer. To test a hypothesis that intrinsic disorder is also abundant in cardiovascular disease (CVD), a data set of 487 CVD-related proteins was extracted from SWISS-PROT. CVD-related proteins are depleted in major order-promoting residues (Trp, Phe, Tyr, Ile, and Val) and enriched in some disorder-promoting residues (Arg, Gln, Ser, Pro, and Glu). The application of a neural network predictor of natural disordered regions (PONDR VL-XT) together with cumulative distribution function (CDF) analysis, charge-hydropathy plot (CH plot) analysis, and alpha-helical molecular recognition feature (alpha-MoRF) indicator revealed that CVD-related proteins are enriched in intrinsic disorder. In fact, the percentage of proteins with 30 or more consecutive residues predicted by PONDR VL-XT to be disordered was 57 +/- 4% for CVD-associated proteins. This value is close that described earlier for signaling proteins (66 +/- 6%) and is significantly larger than the content of intrinsic disorder in eukaryotic proteins from SWISS-PROT (47 +/- 4%) and in nonhomologous protein segments with a well-defined three-dimensional structure (13 +/- 4%). Furthermore, CDF and CH-plot analyses revealed that 120 and 36 CVD-related proteins, respectively, are wholly disordered. This high level of intrinsic disorder could be important for the function of CVD-related proteins and for the control and regulation of processes associated with cardiovascular disease. In agreement with this hypothesis, 198 alpha-MoRFs were predicted in 101 proteins from the CVD data set. A comparison of disorder predictions with the experimental structural and functional data for a subset of the CVD-associated proteins indicated good agreement between predictions and observations. Thus, our data suggest that intrinsically disordered proteins might play key roles in cardiovascular disease.

Biomedical research has a reproducibility problem since various crucial landmark papers could not be independently reproduced. While there are many causes related to statistical analysis, methodology or insufficient reporting of experimental details, this commentary argues that the complexity of biological material itself is, until now, a largely ignored source of irreproducibility. By discussing examples from evolutionary biology, intrinsically disordered proteins and current biomedical research, it contends that some results are irreproducible because we do not have the knowledge, the tools or the analytical ability to understand biological complexity and how it can give rise to different results. Instead of casting irreproducible research out as bad or sloppy science, they should serve as an inspiration for pioneering research not just to develop such tools but also to attempt to explore what lies beneath our current inability to deal with complexity.

PredIDR: Accurate prediction of protein intrinsic disorder regions using deep convolutional neural network

BackgroundIntrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) lack stable tertiary and/or secondary structure yet fulfills key biological functions. The recent recognition of IDPs and IDRs is leading to an entire field aimed at their systematic structural characterization and at determination of their mechanisms of action. Bioinformatics studies showed that IDPs and IDRs are highly abundant in different proteomes and carry out mostly regulatory functions related to molecular recognition and signal transduction. These activities complement the functions of structured proteins. IDPs and IDRs were shown to participate in both one-to-many and many-to-one signaling. Alternative splicing and posttranslational modifications are frequently used to tune the IDP functionality. Several individual IDPs were shown to be associated with human diseases, such as cancer, cardiovascular disease, amyloidoses, diabetes, neurodegenerative diseases, and others. This raises questions regarding the involvement of IDPs and IDRs in various diseases.ResultsIDPs and IDRs were shown to be highly abundant in proteins associated with various human maladies. As the number of IDPs related to various diseases was found to be very large, the concepts of the disease-related unfoldome and unfoldomics were introduced. Novel bioinformatics tools were proposed to populate and characterize the disease-associated unfoldome. Structural characterization of the members of the disease-related unfoldome requires specialized experimental approaches. IDPs possess a number of unique structural and functional features that determine their broad involvement into the pathogenesis of various diseases.ConclusionProteins associated with various human diseases are enriched in intrinsic disorder. These disease-associated IDPs and IDRs are real, abundant, diversified, vital, and dynamic. These proteins and regions comprise the disease-related unfoldome, which covers a significant part of the human proteome. Profound association between intrinsic disorder and various human diseases is determined by a set of unique structural and functional characteristics of IDPs and IDRs. Unfoldomics of human diseases utilizes unrivaled bioinformatics and experimental techniques, paves the road for better understanding of human diseases, their pathogenesis and molecular mechanisms, and helps develop new strategies for the analysis of disease-related proteins.