Disease mutations in disordered regions--exception to the rule?

Accelerating the Conformational Sampling of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) are a class of proteins lacking a well-defined secondary structure. Instead, they are able to attain multiple conformations, bind to multiple targets, and respond to changes in their surroundings. Functionally, IDPs have been associated with molecular recognition, cell regulation, and signal transduction. The dynamic conformational ensemble of IDPs is highly environmental and binding partner dependent, rendering the characterization of IDPs extremely challenging. Here, we compare the sampling efficiencies of conventional molecular dynamics (MD), well-tempered metadynamics (WT-META), and bias-exchange metadynamics (BE-META). The total simulation time was over 10 μs, and a 20-mer peptide derived from the Neh2 domain of the Nuclear factor erythroid 2-related factor 2 (Nrf2) protein was simulated. BE-META, with a neutral replica and seven biased replicas employing a set of seven relevant collective variables (CVs), provided the most reliable and efficient sampling. Finally, we propose a free-energy reconstruction method based on the probability distribution of the secondary structure contents. This postprocessing analysis confirms the presence of not only the β-hairpin conformation of the free Neh2 peptide but also its rare bound-state-like conformation, both of that have been experimentally observed. In addition, our simulations also predict other possible conformations to be verified with future experiments.

Reads meet rotamers: structural biology in the age of deep sequencing

Structure has traditionally been interrelated with sequence, usually in the framework of comparing sequences across species sharing a common fold. However, the nature of information within the sequence and structure databases is evolving, changing the type of comparisons possible. In particular, we now have a vast amount of personal genome sequences from human populations and a greater fraction of new structures contain interacting proteins within large complexes. Consequently, we have to recast our conception of sequence conservation and its relation to structure-for example, focusing more on selection within the human population. Moreover, within structural biology there is less emphasis on the discovery of novel folds and more on relating structures to networks of protein interactions. We cover this changing mindset here.

Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment

Accurate assessment of genetic variation in human DNA sequencing studies remains a nontrivial challenge in clinical genomics and genome informatics. Ascribing functional roles and/or clinical significances to single nucleotide variants identified from a next-generation sequencing study is an important step in genome interpretation. Experimental characterization of all the observed functional variants is yet impractical; thus, the prediction of functional and/or regulatory impacts of the various mutations using in silico approaches is an important step toward the identification of functionally significant or clinically actionable variants. The relationships between genotypes and the expressed phenotypes are multilayered and biologically complex; such relationships present numerous challenges and at the same time offer various opportunities for the design of in silico variant assessment strategies. Over the past decade, many bioinformatics algorithms have been developed to predict functional consequences of single nucleotide variants in the protein coding regions. In this review, we provide an overview of the bioinformatics resources for the prediction, annotation and visualization of coding single nucleotide variants. We discuss the currently available approaches and major challenges from the perspective of protein sequence, structure, function and interactions that require consideration when interpreting the impact of putatively functional variants. We also discuss the relevance of incorporating integrated workflows for predicting the biomedical impact of the functionally important variations encoded in a genome, exome or transcriptome. Finally, we propose a framework to classify variant assessment approaches and strategies for incorporation of variant assessment within electronic health records.

Prediction of protein disorder on amino acid substitutions

Intrinsically disordered regions of proteins are known to have many functional roles in cell signaling and regulatory pathways. The altered expression of these proteins due to mutations is associated with various diseases. Currently, most of the available methods focus on predicting the disordered proteins or the disordered regions in a protein. On the other hand, methods developed for predicting protein disorder on mutation showed a poor performance with a maximum accuracy of 70%. Hence, in this work, we have developed a novel method to classify the disorder-related amino acid substitutions using amino acid properties, substitution matrices, and the effect of neighboring residues that showed an accuracy of 90.0% with a sensitivity and specificity of 94.9 and 80.6%, respectively, in 10-fold cross-validation. The method was evaluated with a test set of 20% data using 10 iterations, which showed an average accuracy of 88.9%. Furthermore, we systematically analyzed the features responsible for the better performance of our method and observed that neighboring residues play an important role in defining the disorder of a given residue in a protein sequence. We have developed a prediction server to identify disorder-related mutations, and it is available at http://www.iitm.ac.in/bioinfo/DIM_Pred/.

Anatomy of protein disorder, flexibility and disease-related mutations

Integration of protein structural information with human genetic variation and pathogenic mutations is essential to understand molecular mechanisms associated with the effects of polymorphisms on protein interactions and cellular processes. We investigate occurrences of non-synonymous SNPs in ordered and disordered protein regions by systematic mapping of common variants and disease-related SNPs onto these regions. We show that common variants accumulate in disordered regions; conversely pathogenic variants are significantly depleted in disordered regions. These different occurrences of pathogenic and common SNPs can be attributed to a negative selection on random mutations in structurally highly constrained regions. New approaches in the study of quantitative effects of pathogenic-related mutations should effectively account for all the possible contexts and relative functional constraints in which the sequence variation occurs.

An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

How do disordered regions achieve comparable functions to structured domains?

The traditional structure to function paradigm conceives of a protein's function as emerging from its structure. In recent years, it has been established that unstructured, intrinsically disordered regions (IDRs) in proteins are equally crucial elements for protein function, regulation and homeostasis. In this review, we provide a brief overview of how IDRs can perform similar functions to structured proteins, focusing especially on the formation of protein complexes and assemblies and the mediation of regulated conformational changes. In addition to highlighting instances of such functional equivalence, we explain how differences in the biological and physicochemical properties of IDRs allow them to expand the functional and regulatory repertoire of proteins. We also discuss studies that provide insights into how mutations within functional regions of IDRs can lead to human diseases.

Evolutionarily conserved network properties of intrinsically disordered proteins

BACKGROUND

Intrinsically disordered proteins (IDPs) lack a stable tertiary structure in isolation. Remarkably, however, a substantial portion of IDPs undergo disorder-to-order transitions upon binding to their cognate partners. Structural flexibility and binding plasticity enable IDPs to interact with a broad range of partners. However, the broader network properties that could provide additional insights into the functional role of IDPs are not known.

RESULTS

Here, we report the first comprehensive survey of network properties of IDP-induced sub-networks in multiple species from yeast to human. Our results show that IDPs exhibit greater-than-expected modularity and are connected to the rest of the protein interaction network (PIN) via proteins that exhibit the highest betweenness centrality and connect to fewer-than-expected IDP communities, suggesting that they form critical communication links from IDP modules to the rest of the PIN. Moreover, we found that IDPs are enriched at the top level of regulatory hierarchy.

CONCLUSION

Overall, our analyses reveal coherent and remarkably conserved IDP-centric network properties, namely, modularity in IDP-induced network and a layer of critical nodes connecting IDPs with the rest of the PIN.

Modulation of the disordered conformational ensembles of the p53 transactivation domain by cancer-associated mutations

Intrinsically disordered proteins (IDPs) are frequently associated with human diseases such as cancers, and about one-fourth of disease-associated missense mutations have been mapped into predicted disordered regions. Understanding how these mutations affect the structure-function relationship of IDPs is a formidable task that requires detailed characterization of the disordered conformational ensembles. Implicit solvent coupled with enhanced sampling has been proposed to provide a balance between accuracy and efficiency necessary for systematic and comparative assessments of the effects of mutations as well as post-translational modifications on IDP structure and interaction. Here, we utilize a recently developed replica exchange with guided annealing enhanced sampling technique to calculate well-converged atomistic conformational ensembles of the intrinsically disordered transactivation domain (TAD) of tumor suppressor p53 and several cancer-associated mutants in implicit solvent. The simulations are critically assessed by quantitative comparisons with several types of experimental data that provide structural information on both secondary and tertiary levels. The results show that the calculated ensembles reproduce local structural features of wild-type p53-TAD and the effects of K24N mutation quantitatively. On the tertiary level, the simulated ensembles are overly compact, even though they appear to recapitulate the overall features of transient long-range contacts qualitatively. A key finding is that, while p53-TAD and its cancer mutants sample a similar set of conformational states, cancer mutants could introduce both local and long-range structural modulations to potentially perturb the balance of p53 binding to various regulatory proteins and further alter how this balance is regulated by multisite phosphorylation of p53-TAD. The current study clearly demonstrates the promise of atomistic simulations for detailed characterization of IDP conformations, and at the same time reveals important limitations in the current implicit solvent protein force field that must be sufficiently addressed for reliable description of long-range structural features of the disordered ensembles.

Tumor suppressor p53 is the most frequently mutated protein in human cancers. Clinical studies have suggested that the type of p53 mutation can be linked to cancer prognosis, response to drug treatment, and patient survival. It is thus crucial to understand the molecular basis of p53 inactivation by various types of mutations, so as to understand the biological outcomes and assess potential cancer intervention strategies. Here, we utilize a recently developed replica exchange with guided annealing enhanced sampling technique to calculate well-converged atomistic conformational ensembles of the intrinsically disordered transactivation domain (TAD) of tumor suppressor p53 and several cancer-associated mutants in an implicit solvent protein force field. The calculated ensembles are in quantitative agreement with several types of existing NMR data on the wild-type protein and the K24N mutant. The results suggest that, while all sequences sample a similar set of conformational substates, cancer mutants could introduce both local and long-range structural modulations and in turn perturb the balance of p53 binding to various regulatory proteins and further alter how this balance is regulated by multisite phosphorylation of p53-TAD. The study also reveals important limitations in implicit solvent for simulations of disordered proteins like p53-TAD.

Targeted sequencing of the Paget's disease associated 14q32 locus identifies several missense coding variants in RIN3 that predispose to Paget's disease of bone

Paget's disease of bone (PDB) is a common disorder with a strong genetic component characterized by increased but disorganized bone remodelling. Previous genome-wide association studies identified a locus on chromosome 14q32 tagged by rs10498635 which was significantly associated with susceptibility to PDB in several European populations. Here we conducted fine-mapping and targeted sequencing of the candidate locus to identify possible functional variants. Imputation in 741 PDB patients and 2699 controls confirmed that the association was confined to a 60 kb region in the RIN3 gene and conditional analysis adjusting for rs10498635 identified no new independent signals. Sequencing of the RIN3 gene identified a common missense variant (p.R279C) that was strongly associated with the disease (OR = 0.64; P = 1.4 × 10⁻⁹), and was in strong linkage disequilibrium with rs10498635. A further 13 rare missense variants were identified, seven of which were novel and detected only in PDB cases. When combined, these rare variants were over-represented in cases compared with controls (OR = 3.72; P = 8.9 × 10⁻¹⁰). Most rare variants were located in a region that encodes a proline-rich, intrinsically disordered domain of the protein and many were predicted to be pathogenic. RIN3 was expressed in bone tissue and its expression level was ∼10-fold higher in osteoclasts compared with osteoblasts. We conclude that susceptibility to PDB at the 14q32 locus is mediated by a combination of common and rare coding variants in RIN3 and suggest that RIN3 may contribute to PDB susceptibility by affecting osteoclast function.

Proteome-wide analysis of human disease mutations in short linear motifs: neglected players in cancer?

Disease mutations are traditionally thought to impair protein functionality by disrupting the folded globular structure of proteins. However, 22% of human disease mutations occur in natively unstructured segments of proteins known as intrinsically disordered regions (IDRs). This therefore implicates defective IDR functionality in various human diseases including cancer. The functionality of IDRs is partly attributable to short linear motifs (SLiMs), but it remains an open question how much defects in SLiMs contribute to human diseases. A proteome-wide comparison of the distribution of missense mutations from disease and non-disease mutation datasets revealed that, in IDRs, disease mutations are more likely to occur within SLiMs than neutral missense mutations. Moreover, compared to neutral missense mutations, disease mutations more frequently impact functionally important residues of SLiMs, cause changes in the physicochemical properties of SLiMs, and disrupt more SLiM-mediated interactions. Analysis of these mutations resulted in a comprehensive list of experimentally validated or predicted SLiMs disrupted in disease. Furthermore, this in-depth analysis suggests that 'prostate cancer pathway' is particularly enriched for proteins with disease-related SLiMs. The contribution of mutations in SLiMs to disease may currently appear small when compared to mutations in globular domains. However, our analysis of mutations in predicted SLiMs suggests that this contribution might be more substantial. Therefore, when analysing the functional impact of mutations on proteins, SLiMs in proteins should not be neglected. Our results suggest that an increased focus on SLiMs in the coming decades will improve our understanding of human diseases and aid in the development of targeted treatments.

An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles

Rich data bearing on the structural and evolutionary principles of protein-protein interactions are paving the way to a better understanding of the regulation of function in the cell. This is particularly the case when these interactions are considered in the framework of key pathways. Knowledge of the interactions may provide insights into the mechanisms of crucial 'driver' mutations in oncogenesis. They also provide the foundation toward the design of protein-protein interfaces and inhibitors that can abrogate their formation or enhance them. The main features to learn from known 3-D structures of protein-protein complexes and the extensive literature which analyzes them computationally and experimentally include the interaction details which permit undertaking structure-based drug discovery, the evolution of complexes and their interactions, the consequences of alterations such as post-translational modifications, ligand binding, disease causing mutations, host pathogen interactions, oligomerization, aggregation and the roles of disorder, dynamics, allostery and more to the protein and the cell. This review highlights some of the recent advances in these areas, including design, inhibition and prediction of protein-protein complexes. The field is broad, and much work has been carried out in these areas, making it challenging to cover it in its entirety. Much of this is due to the fast increase in the number of molecules whose structures have been determined experimentally and the vast increase in computational power. Here we provide a concise overview.

Wrecked regulation of intrinsically disordered proteins in diseases: pathogenicity of deregulated regulators

Biologically active proteins without stable tertiary structure are common in all known proteomes. Functions of these intrinsically disordered proteins (IDPs) are typically related to regulation, signaling, and control. Cellular levels of these important regulators are tightly regulated by a variety mechanisms ranging from firmly controlled expression to precisely targeted degradation. Functions of IDPs are controlled by binding to specific partners, alternative splicing, and posttranslational modifications among other means. In the norm, right amounts of precisely activated IDPs have to be present in right time at right places. Wrecked regulation brings havoc to the ordered world of disordered proteins, leading to protein misfolding, misidentification, and missignaling that give rise to numerous human diseases, such as cancer, cardiovascular disease, neurodegenerative diseases, and diabetes. Among factors inducing pathogenic transformations of IDPs are various cellular mechanisms, such as chromosomal translocations, damaged splicing, altered expression, frustrated posttranslational modifications, aberrant proteolytic degradation, and defective trafficking. This review presents some of the aspects of deregulated regulation of IDPs leading to human diseases.

A disorder-to-order structural transition in the COOH-tail of Fz4 determines misfolding of the L501fsX533-Fz4 mutant

Frizzled 4 belongs to the superfamily of G protein coupled receptors. The unstructured cytosolic tail of the receptor is essential for its activity. The mutation L501fsX533 in the fz4 gene results in a new COOH-tail of the receptor and causes a form of Familial exudative vitreoretinopathy. Here we show that the mutated tail is structured. Two amphipathic helices, displaying affinity for membranes and resembling the structure of Influenza Hemagglutinin fusion peptide, constitute the new fold. This tail induces the aggregation of the receptor in the Endoplasmic Reticulum and it is sufficient to block the export to the Golgi of a chimeric VSVG protein containing the mutated tail. Affecting the tail's structure, net charge or amphipathicity relocates the mutated Fz4 receptor to the Plasma Membrane. Such disorder-to-order structural transition was never described in GPCRs and opens a new scenario on the possible effect of mutations on unstructured regions of proteins.

The intrinsically disordered C-terminal region of Arabidopsis thaliana TCP8 transcription factor acts both as a transactivation and self-assembly domain

TCPs are plant specific transcription factors with non-canonical basic helix-loop-helix domains. While Arabidopsis thaliana has 24 TCPs involved in cell proliferation and differentiation, their mode of action has not been fully elucidated. Using bioinformatic tools, we demonstrate that TCP transcription factors belong to the intrinsically disordered proteins (IDP) family and that disorder is higher in class I TCPs than in class II TCPs. In particular, using bioinformatic and biochemical approaches, we have characterized TCP8, a class I TCP. TCP8 exhibits three intrinsically disordered regions (IDR) made of more than 50 consecutive residues, in which phosphorylable Ser residues are mainly clustered. Phosphorylation of Ser-211 that belongs to the central IDR was confirmed by mass spectrometry. Yeast two-hybrid assays also showed that the C-terminal IDR corresponds to a transactivation domain. Moreover, biochemical experiments demonstrated that TCP8 tends to oligomerize in dimers, trimers and higher-order multimers. Bimolecular fluorescence complementation (BiFC) experiments carried out on a truncated form of TCP8 lacking the C-terminal IDR indicated that it is effectively required for the pronounced self-assembly of TCP8. These data were reinforced by the prediction of a coiled coil domain in this IDR. The C-terminal IDR acts thus as an oligomerization domain and also a transactivation domain. Moreover, many Molecular Recognition Features (MoRFs) were predicted, indicating that TCP8 could interact with several partners to fulfill a fine regulation of transcription in response to various stimuli.

Predicted disorder-to-order transition mutations in IκBα disrupt function

IκBα inhibits the transcription factor, NFκB, by forming a very tightly bound complex in which the ankyrin repeat domain (ARD) of IκBα interacts primarily with the dimerization domain of NFκB. The first four ankyrin repeats (ARs) of the IκBα ARD are well-folded, but the AR5-6 region is intrinsically disordered according to amide H/D exchange and protein folding/unfolding experiments. We previously showed that mutations towards the consensus sequence for stable ankyrin repeats resulted in a "prefolded" mutant. To investigate whether the consensus mutations were solely able to order the AR5-6 region, we used a predictor of protein disordered regions PONDR VL-XT to select mutations that would alter the intrinsic disorder towards a more ordered structure (D → O mutants). The algorithm predicted two mutations, E282W and P261F, neither of which correspond to the consensus sequence for ankyrin repeats. Amide exchange and CD were used to assess ordering. Although only the E282W was predicted to be more ordered by CD and amide exchange, stopped-flow fluorescence studies showed that both of the D → O mutants were less efficient at dissociating NFκB from DNA.

Intrinsically disordered proteins and intrinsically disordered protein regions

Intrinsically disordered proteins (IDPs) and IDP regions fail to form a stable structure, yet they exhibit biological activities. Their mobile flexibility and structural instability are encoded by their amino acid sequences. They recognize proteins, nucleic acids, and other types of partners; they accelerate interactions and chemical reactions between bound partners; and they help accommodate posttranslational modifications, alternative splicing, protein fusions, and insertions or deletions. Overall, IDP-associated biological activities complement those of structured proteins. Recently, there has been an explosion of studies on IDP regions and their functions, yet the discovery and investigation of these proteins have a long, mostly ignored history. Along with recent discoveries, we present several early examples and the mechanisms by which IDPs contribute to function, which we hope will encourage comprehensive discussion of IDPs and IDP regions in biochemistry textbooks. Finally, we propose future directions for IDP research.

DRD4 rare variants in Attention-Deficit/Hyperactivity Disorder (ADHD): further evidence from a birth cohort study

The dopamine receptor D4 (DRD4) is one of the most studied candidate genes for Attention-Deficit/Hyperactivity Disorder (ADHD). An excess of rare variants and non-synonymous mutations in the VNTR region of 7R allele in ADHD subjects was observed in previous studies with clinical samples. We hypothesize that genetic heterogeneity in the VNTR is an important factor in the pathophysiology of ADHD. The subjects included in the present study are members of the 1993 Pelotas Birth Cohort Study (N=5,249). We conducted an association study with the 4,101 subjects who had DNA samples collected. The hyperactivity-inattention scores were assessed through the parent version of the Strengths and Difficulties Questionnaire at 11 and 15 years of age. The contribution of allele’s length and rare variants to high hyperactivity/inattention scores predisposition was evaluated by multivariate logistic regression. No effect of allele length was observed on high scores of hyperactivity-inattention. By contrast, when resequencing/haplotyping was conducted in a subsample, all 7R rare variants as well as non-synonymous 7R rare variants were associated with high hyperactivity/inattention scores (OR=2.561; P=0.024 and OR=3.216; P=0.008 respectively). A trend for association was observed with 4R rare variants. New coding mutations covered 10 novel motifs and many of them are previously unreported deletions leading to different stop codons. Our findings suggest a contribution of DRD4 7R rare variants to high hyperactivity-inattention scores in a population-based sample from a large birth cohort. These findings provide further evidence for an effect of DRD4 7R rare variants and allelic heterogeneity in ADHD genetic susceptibility.

Electrostatically accelerated encounter and folding for facile recognition of intrinsically disordered proteins

Achieving facile specific recognition is essential for intrinsically disordered proteins (IDPs) that are involved in cellular signaling and regulation. Consideration of the physical time scales of protein folding and diffusion-limited protein-protein encounter has suggested that the frequent requirement of protein folding for specific IDP recognition could lead to kinetic bottlenecks. How IDPs overcome such potential kinetic bottlenecks to viably function in signaling and regulation in general is poorly understood. Our recent computational and experimental study of cell-cycle regulator p27 (Ganguly et al., J. Mol. Biol. (2012)) demonstrated that long-range electrostatic forces exerted on enriched charges of IDPs could accelerate protein-protein encounter via "electrostatic steering" and at the same time promote "folding-competent" encounter topologies to enhance the efficiency of IDP folding upon encounter. Here, we further investigated the coupled binding and folding mechanisms and the roles of electrostatic forces in the formation of three IDP complexes with more complex folded topologies. The surface electrostatic potentials of these complexes lack prominent features like those observed for the p27/Cdk2/cyclin A complex to directly suggest the ability of electrostatic forces to facilitate folding upon encounter. Nonetheless, similar electrostatically accelerated encounter and folding mechanisms were consistently predicted for all three complexes using topology-based coarse-grained simulations. Together with our previous analysis of charge distributions in known IDP complexes, our results support a prevalent role of electrostatic interactions in promoting efficient coupled binding and folding for facile specific recognition. These results also suggest that there is likely a co-evolution of IDP folded topology, charge characteristics, and coupled binding and folding mechanisms, driven at least partially by the need to achieve fast association kinetics for cellular signaling and regulation.

Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition

Characterizing changes in protein-protein interactions associated with sequence variants (e.g. disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies where cost and time are major considerations. To this end, we have coupled AP to data-independent mass spectrometric acquisition (SWATH), and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. Here, we use AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes, and propose a scalable pipeline for systems biology studies.

Insights into the Binding of Intrinsically Disordered Proteins from Molecular Dynamics Simulation

Intrinsically disordered proteins (IDPs) are a class of protein that, in the native state, possess no well-defined secondary or tertiary structure, existing instead as dynamic ensembles of conformations. They are biologically important, with approximately 20% of all eukaryotic proteins disordered, and found at the heart of many biochemical networks. To fulfil their biological roles, many IDPs need to bind to proteins and/or nucleic acids. And while unstructured in solution, IDPs typically fold into a well-defined three-dimensional structure upon interaction with a binding partner. The flexibility and structural diversity inherent to IDPs makes this coupled folding and binding difficult to study at atomic resolution by experiment alone, and computer simulation currently offers perhaps the best opportunity to understand this process. But simulation of coupled folding and binding is itself extremely challenging; these molecules are large and highly flexible, and their binding partners, such as DNA or cyclins, are also often large. Therefore, their study requires either or both simplified representations and advanced enhanced sampling schemes. It is not always clear that existing simulation techniques, optimized for studying folded proteins, are well-suited to IDPs. In this article, we examine the progress that has been made in the study of coupled folding and binding using molecular dynamics simulation. We summarise what has been learnt, and examine the state of the art in terms of both methodologies and models. We also consider the lessons to be learnt from advances in other areas of simulation and highlight the issues that remain of be addressed.

Molecular mechanisms of disease-causing missense mutations

Genetic variations resulting in a change of amino acid sequence can have a dramatic effect on stability, hydrogen bond network, conformational dynamics, activity and many other physiologically important properties of proteins. The substitutions of only one residue in a protein sequence, so-called missense mutations, can be related to many pathological conditions and may influence susceptibility to disease and drug treatment. The plausible effects of missense mutations range from affecting the macromolecular stability to perturbing macromolecular interactions and cellular localization. Here we review the individual cases and genome-wide studies that illustrate the association between missense mutations and diseases. In addition, we emphasize that the molecular mechanisms of effects of mutations should be revealed in order to understand the disease origin. Finally, we report the current state-of-the-art methodologies that predict the effects of mutations on protein stability, the hydrogen bond network, pH dependence, conformational dynamics and protein function.

Disease-associated mutations disrupt functionally important regions of intrinsic protein disorder

The effects of disease mutations on protein structure and function have been extensively investigated, and many predictors of the functional impact of single amino acid substitutions are publicly available. The majority of these predictors are based on protein structure and evolutionary conservation, following the assumption that disease mutations predominantly affect folded and conserved protein regions. However, the prevalence of the intrinsically disordered proteins (IDPs) and regions (IDRs) in the human proteome together with their lack of fixed structure and low sequence conservation raise a question about the impact of disease mutations in IDRs. Here, we investigate annotated missense disease mutations and show that 21.7% of them are located within such intrinsically disordered regions. We further demonstrate that 20% of disease mutations in IDRs cause local disorder-to-order transitions, which represents a 1.7–2.7 fold increase compared to annotated polymorphisms and neutral evolutionary substitutions, respectively. Secondary structure predictions show elevated rates of transition from helices and strands into loops and vice versa in the disease mutations dataset. Disease disorder-to-order mutations also influence predicted molecular recognition features (MoRFs) more often than the control mutations. The repertoire of disorder-to-order transition mutations is limited, with five most frequent mutations (R→W, R→C, E→K, R→H, R→Q) collectively accounting for 44% of all deleterious disorder-to-order transitions. As a proof of concept, we performed accelerated molecular dynamics simulations on a deleterious disorder-to-order transition mutation of tumor protein p63 and, in agreement with our predictions, observed an increased α-helical propensity of the region harboring the mutation. Our findings highlight the importance of mutations in IDRs and refine the traditional structure-centric view of disease mutations. The results of this study offer a new perspective on the role of mutations in disease, with implications for improving predictors of the functional impact of missense mutations.

Intrinsically unstructured or disordered proteins have been implicated in the etiology of a wide spectrum of diseases. However, the molecular mechanisms that relate mutations in intrinsically disordered regions (IDRs) to disease pathogenesis have not been investigated. Disordered proteins do not conform to the prevailing view of deleterious mutations which equates function, structure and evolutionary conservation – intrinsically disordered regions are functional, but lack a fixed three-dimensional structure and in general have low sequence conservation. Here we demonstrate that >20% of disease-associated missense mutations affect IDRs and interfere with their functions. We further show that 20% of deleterious mutations in IDRs induce predicted disorder-to-order transitions. Our predictions are supported by accelerated molecular dynamics simulations that show an increase in helical propensity of the region harboring a disease disorder-to-order transition mutation of tumor protein p63. Our results refine the traditional structure-centric view of disease mutations and offer a new perspective on the role of non-synonymous mutations in disease. Our findings have broad implications for improving predictors of the functional impact of missense mutations, and for interpretation of novel variants identified in large genome sequencing projects that aim to provide a better understanding of human genetic variation and its relevance to common diseases.

Towards the physical basis of how intrinsic disorder mediates protein function

Intrinsically disordered proteins (IDPs) are an important class of functional proteins that is highly prevalent in biology and has broad association with human diseases. In contrast to structured proteins, free IDPs exist as heterogeneous and dynamical conformational ensembles under physiological conditions. Many concepts have been discussed on how such intrinsic disorder may provide crucial functional advantages, particularly in cellular signaling and regulation. Establishing the physical basis of these proposed phenomena requires not only detailed characterization of the disordered conformational ensembles, but also mechanistic understanding of the roles of various ensemble properties in IDP interaction and regulation. Here, we review the experimental and computational approaches that may be integrated to address many important challenges of establishing a "structural" basis of IDP function, and discuss some of the key emerging ideas on how the conformational ensembles of IDPs may mediate function, especially in coupled binding and folding interactions.

Changes in predicted protein disorder tendency may contribute to disease risk

BACKGROUND

Recent studies suggest that many proteins or regions of proteins lack 3D structure. Defined as intrinsically disordered proteins, these proteins/peptides are functionally important. Recent advances in next generation sequencing technologies enable genome-wide identification of novel nucleotide variations in a specific population or cohort.

RESULTS

Using the exonic single nucleotide variations (SNVs) identified in the 1,000 Genomes Project and distributed by the Genetic Analysis Workshop 17, we systematically analysed the genetic and predicted disorder potential features of the non-synonymous variations. The result of experiments suggests that a significant change in the tendency of a protein region to be structured or disordered caused by SNVs may lead to malfunction of such a protein and contribute to disease risk.

CONCLUSIONS

After validation with functional SNVs on the traits distributed by GAW17, we conclude that it is valuable to consider structure/disorder tendencies while prioritizing and predicting mechanistic effects arising from novel genetic variations.

Uncertainty analysis in protein disorder prediction

A grand challenge in the proteomics and structural genomics era is the prediction of protein structure, including identification of those proteins that are partially or wholly unstructured. A number of predictors for identification of intrinsically disordered proteins (IDPs) have been developed over the last decade, but none can be taken as a fully reliable on its own. Using a single model for prediction is typically inadequate because prediction based on only the most accurate model ignores model uncertainty. In this paper, we present an empirical method to specify and measure uncertainty associated with disorder predictions. In particular, we analyze the uncertainty in the reference model itself and the uncertainty in data. This is achieved by training a set of models and developing several meta predictors on top of them. The best meta predictor achieved comparable or better results than any other single model, suggesting that incorporating different aspects of protein disorder prediction is important for the disorder prediction task. In addition, the best meta-predictor had more balanced sensitivity and specificity than any individual model. We also assessed the effects of changes in disorder prediction as a function of changes in the protein sequence. For collections of homologous sequences, we found that mutations caused many of the predicted disordered residues to be flipped to be predicted as ordered residues, while the reverse was observed much less frequently. These results suggest that disorder tendencies are more sensitive to allowed mutations than structure tendencies and the conservation of disorder is indeed less stable than conservation of structure.

AVAILABILITY

five meta-predictors and four single models developed for this study will be publicly freely accessible for non-commercial use.