DisProt: the Database of Disordered Proteins

Tick receptor for outer surface protein A from Ixodes ricinus - the first intrinsically disordered protein involved in vector-microbe recognition

The tick receptor for outer surface protein A (TROSPA) is the only identified factor involved in tick gut colonization by various Borrelia species. TROSPA is localized in the gut epithelium and can recognize and bind the outer surface bacterial protein OspA via an unknown mechanism. Based on earlier reports and our latest observations, we considered that TROSPA would be the first identified intrinsically disordered protein (IDP) involved in the interaction between a vector and a pathogenic microbe. To verify this hypothesis, we performed structural studies of a TROSPA mutant from Ixodes ricinus using both computational and experimental approaches. Irrespective of the method used, we observed that the secondary structure content of the TROSPA polypeptide chain is low. In addition, the collected SAXS data indicated that this protein is highly extended and exists in solution as a set of numerous conformers. These features are all commonly considered hallmarks of IDPs. Taking advantage of our SAXS data, we created structural models of TROSPA and proposed a putative mechanism for the TROSPA-OspA interaction. The disordered nature of TROSPA may explain the ability of a wide spectrum of Borrelia species to colonize the tick gut.

Are all regions of folded proteins that undergo ligand-dependent order-disorder transitions targets for allosteric peptide mimetics?

Although the classical view of how proteins function relied on well folded structures, it is now recognized that the functions of many proteins are dependent on being intrinsically disordered. The primary consideration in this work is the intermediate group of proteins that are overall well folded, but which contain small regions that undergo order-disorder transitions. In particular, the current focus is on those order-disorder transitions that are energetically coupled to ligand binding. As exemplified by the case of human liver pyruvate kinase (hL-PYK), peptides that mimic the sequence of the order-disorder region can be used as allosteric regulators of the enzyme. On the basis of this example and others reported in the literature, we propose that a similar use of peptides that mimic protein regions that experience ligand-dependent order-disorder transitions can be a generalized initiation point for the development of allosteric drugs.

Interplay between binding affinity and kinetics in protein-protein interactions

To clarify the interplay between the binding affinity and kinetics of protein-protein interactions, and the possible role of intrinsically disordered proteins in such interactions, molecular simulations were carried out on 20 protein complexes. With bias potential and reweighting techniques, the free energy profiles were obtained under physiological affinities, which showed that the bound-state valley is deep with a barrier height of 12 - 33 RT. From the dependence of the affinity on interface interactions, the entropic contribution to the binding affinity is approximated to be proportional to the interface area. The extracted dissociation rates based on the Arrhenius law correlate reasonably well with the experimental values (Pearson correlation coefficient R = 0.79). For each protein complex, a linear free energy relationship between binding affinity and the dissociation rate was confirmed, but the distribution of the slopes for intrinsically disordered proteins showed no essential difference with that observed for ordered proteins. A comparison with protein folding was also performed. Proteins 2016; 84:920-933. © 2016 Wiley Periodicals, Inc.

Single-residue posttranslational modification sites at the N-terminus, C-terminus or in-between: To be or not to be exposed for enzyme access

Many protein posttranslational modifications (PTMs) are the result of an enzymatic reaction. The modifying enzyme has to recognize the substrate protein's sequence motif containing the residue(s) to be modified; thus, the enzyme's catalytic cleft engulfs these residue(s) and the respective sequence environment. This residue accessibility condition principally limits the range where enzymatic PTMs can occur in the protein sequence. Non‐globular, flexible, intrinsically disordered segments or large loops/accessible long side chains should be preferred whereas residues buried in the core of structures should be void of what we call canonical, enzyme‐generated PTMs. We investigate whether PTM sites annotated in UniProtKB (with MOD_RES/LIPID keys) are situated within sequence ranges that can be mapped to known 3D structures. We find that N‐ or C‐termini harbor essentially exclusively canonical PTMs. We also find that the overwhelming majority of all other PTMs are also canonical though, later in the protein's life cycle, the PTM sites can become buried due to complex formation. Among the remaining cases, some can be explained (i) with autocatalysis, (ii) with modification before folding or after temporary unfolding, or (iii) as products of interaction with small, diffusible reactants. Others require further research how these PTMs are mechanistically generated in vivo.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

The contribution of protein intrinsic disorder to understand the role of genetic variants uncovered by autism spectrum disorders exome studies

Several autism spectrum disorders (ASD) exome studies suggest that coding single nucleotide variants (SNVs) play an important role on ASD etiology. Usually, the pathogenic effect of missense mutations is estimated through predictors that lose accuracy for those SNVs placed in intrinsically disordered regions of protein. Here, we used bioinformatics tools to investigate the effect of mutations described in ASD published exome studies (549 mutations) in protein disorder, considering post-translational modification, PEST and Molecular Recognition Features (MoRFs) motifs. Schizophrenia and type 2 diabetes (T2D) datasets were created for comparison purposes. The frequency of mutations predicted as disordered was comparable among the three datasets (38.1% in ASD, 35.7% in schizophrenia, 46.4% in T2D). However, the frequency of SNVs predicted to lead a gain or loss of functional sites or change intrinsic disorder tendencies was higher in ASD and schizophrenia than T2D (46.9%, 36.4%, and 23.1%, respectively). The results obtained by SIFT and PolyPhen-2 indicated that 38.9% and 34.4% of the mutations predicted, respectively, as tolerated and benign showed functional alterations in disorder properties. Given the frequency of mutations placed in IDRs and their functional impact, this study suggests that alterations in intrinsic disorder properties might play a role in ASD and schizophrenia etiologies. They should be taken into consideration when researching the pathogenicity of mutations in neurodevelopmental and psychiatric diseases. Finally, mutations with functional alterations in disorder properties must be potential targets for in vitro and in vivo functional studies.

Functional Diversification after Gene Duplication: Paralog Specific Regions of Structural Disorder and Phosphorylation in p53, p63, and p73

Conformational and functional flexibility promote protein evolvability. High evolvability allows related proteins to functionally diverge and perhaps to neostructuralize. p53 is a multifunctional protein frequently referred to as the Guardian of the Genome–a hub for e.g. incoming and outgoing signals in apoptosis and DNA repair. p53 has been found to be structurally disordered, an extreme form of conformational flexibility. Here, p53, and its paralogs p63 and p73, were studied for further insights into the evolutionary dynamics of structural disorder, secondary structure, and phosphorylation. This study is focused on the post gene duplication phase for the p53 family in vertebrates, but also visits the origin of the protein family and the early domain loss and gain events. Functional divergence, measured by rapid evolutionary dynamics of protein domains, structural properties, and phosphorylation propensity, is inferred across vertebrate p53 proteins, in p63 and p73 from fish, and between the three paralogs. In particular, structurally disordered regions are redistributed among paralogs, but within clades redistribution of structural disorder also appears to be an ongoing process. Despite its deemed importance as the Guardian of the Genome, p53 is indeed a protein with high evolvability as seen not only in rearranged structural disorder, but also in fluctuating domain sequence signatures among lineages.

PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach

The prediction of domain/linker residues in protein sequences is a crucial task in the functional classification of proteins, homology-based protein structure prediction, and high-throughput structural genomics. In this work, a novel consensus-based machine-learning technique was applied for residue-level prediction of the domain/linker annotations in protein sequences using ordered/disordered regions along protein chains and a set of physicochemical properties. Six different classifiers-decision tree, Gaussian naïve Bayes, linear discriminant analysis, support vector machine, random forest, and multilayer perceptron-were exhaustively explored for the residue-level prediction of domain/linker regions. The protein sequences from the curated CATH database were used for training and cross-validation experiments. Test results obtained by applying the developed PDP-CON tool to the mutually exclusive, independent proteins of the CASP-8, CASP-9, and CASP-10 databases are reported. An n-star quality consensus approach was used to combine the results yielded by different classifiers. The average PDP-CON accuracy and F-measure values for the CASP targets were found to be 0.86 and 0.91, respectively. The dataset, source code, and all supplementary materials for this work are available at https://cmaterju.org/cmaterbioinfo/ for noncommercial use.

A transducible nuclear/nucleolar protein, mLLP, regulates neuronal morphogenesis and synaptic transmission

Cell-permeable proteins are emerging as unconventional regulators of signal transduction and providing a potential for therapeutic applications. However, only a few of them are identified and studied in detail. We identify a novel cell-permeable protein, mouse LLP homolog (mLLP), and uncover its roles in regulating neural development. We found that mLLP is strongly expressed in developing nervous system and that mLLP knockdown or overexpression during maturation of cultured neurons affected the neuronal growth and synaptic transmission. Interestingly, extracellular addition of mLLP protein enhanced dendritic arborization, demonstrating the non-cell-autonomous effect of mLLP. Moreover, mLLP interacts with CCCTC-binding factor (CTCF) as well as transcriptional machineries and modulates gene expression involved in neuronal growth. Together, these results illustrate the characteristics and roles of previously unknown cell-permeable protein mLLP in modulating neural development.

Statistical prediction of protein structural, localization and functional properties by the analysis of its fragment mass distributions after proteolytic cleavage

Structural, localization and functional properties of unknown proteins are often being predicted from their primary polypeptide chains using sequence alignment with already characterized proteins and consequent molecular modeling. Here we suggest an approach to predict various structural and structure-associated properties of proteins directly from the mass distributions of their proteolytic cleavage fragments. For amino-acid-specific cleavages, the distributions of fragment masses are determined by the distributions of inter-amino-acid intervals in the protein, that in turn apparently reflect its structural and structure-related features. Large-scale computer simulations revealed that for transmembrane proteins, either α-helical or β -barrel secondary structure could be predicted with about 90% accuracy after thermolysin cleavage. Moreover, 3/4 intrinsically disordered proteins could be correctly distinguished from proteins with fixed three-dimensional structure belonging to all four SCOP structural classes by combining 3–4 different cleavages. Additionally, in some cases the protein cellular localization (cytosolic or membrane-associated) and its host organism (Firmicute or Proteobacteria) could be predicted with around 80% accuracy. In contrast to cytosolic proteins, for membrane-associated proteins exhibiting specific structural conformations, their monotopic or transmembrane localization and functional group (ATP-binding, transporters, sensors and so on) could be also predicted with high accuracy and particular robustness against missing cleavages.

Dynamic Protein Interaction Networks and New Structural Paradigms in Signaling

Understanding signaling and other complex biological processes requires elucidating the critical roles of intrinsically disordered proteins (IDPs) and regions (IDRs), which represent ∼30% of the proteome and enable unique regulatory mechanisms. In this review, we describe the structural heterogeneity of disordered proteins that underpins these mechanisms and the latest progress in obtaining structural descriptions of conformational ensembles of disordered proteins that are needed for linking structure and dynamics to function. We describe the diverse interactions of IDPs that can have unusual characteristics such as "ultrasensitivity" and "regulated folding and unfolding". We also summarize the mounting data showing that large-scale assembly and protein phase separation occurs within a variety of signaling complexes and cellular structures. In addition, we discuss efforts to therapeutically target disordered proteins with small molecules. Overall, we interpret the remodeling of disordered state ensembles due to binding and post-translational modifications within an expanded framework for allostery that provides significant insights into how disordered proteins transmit biological information.

Quarterly intrinsic disorder digest (January-February-March, 2014)

This is the 5^th issue of the Digested Disorder series that represents a reader's digest of the scientific literature on intrinsically disordered proteins. We continue to use only 2 criteria for inclusion of a paper to this digest: The publication date (a paper should be published within the covered time frame) and the topic (a paper should be dedicated to any aspect of protein intrinsic disorder). The current digest issue covers papers published during the first quarter of 2014; i.e., during the period of January, February, and March of 2014. Similar to previous issues, the papers are grouped hierarchically by topics they cover, and for each of the included papers a short description is given on its major findings.

There is Diversity in Disorder-"In all Chaos there is a Cosmos, in all Disorder a Secret Order"

The protein universe consists of a continuum of structures ranging from full order to complete disorder. As the structured part of the proteome has been intensively studied, stably folded proteins are increasingly well documented and understood. However, proteins that are fully, or in large part, disordered are much less well characterized. Here we collected NMR chemical shifts in a small database for 117 protein sequences that are known to contain disorder. We demonstrate that NMR chemical shift data can be brought to bear as an exquisite judge of protein disorder at the residue level, and help in validation. With the help of secondary chemical shift analysis we demonstrate that the proteins in the database span the full spectrum of disorder, but still, largely segregate into two classes; disordered with small segments of order scattered along the sequence, and structured with small segments of disorder inserted between the different structured regions. A detailed analysis reveals that the distribution of order/disorder along the sequence shows a complex and asymmetric distribution, that is highly protein-dependent. Access to ratified training data further suggests an avenue to improving prediction of disorder from sequence.

Protein unfolding as a switch from self-recognition to high-affinity client binding

Stress-specific activation of the chaperone Hsp33 requires the unfolding of a central linker region. This activation mechanism suggests an intriguing functional relationship between the chaperone's own partial unfolding and its ability to bind other partially folded client proteins. However, identifying where Hsp33 binds its clients has remained a major gap in our understanding of Hsp33's working mechanism. By using site-specific Fluorine-19 nuclear magnetic resonance experiments guided by in vivo crosslinking studies, we now reveal that the partial unfolding of Hsp33's linker region facilitates client binding to an amphipathic docking surface on Hsp33. Furthermore, our results provide experimental evidence for the direct involvement of conditionally disordered regions in unfolded protein binding. The observed structural similarities between Hsp33's own metastable linker region and client proteins present a possible model for how Hsp33 uses protein unfolding as a switch from self-recognition to high-affinity client binding.

Under stress conditions the molecular chaperone Hsp33 is activated to process unfolded proteins. Here, the authors use in vivo and in vitro crosslinking and ¹⁹F-NMR to elucidate the binding site for misfolded proteins and are able to propose a model for its mechanism of action.

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.

Paradoxes and wonders of intrinsic disorder: Complexity of simplicity

At first glance it may seem that intrinsically disordered proteins (IDPs) and IDP regions (IDPRs) are simpler than ordered proteins and domains on multiple levels. However, such multilevel simplicity equips these proteins with the ability to have very complex behavior.

A Period2 Phosphoswitch Regulates and Temperature Compensates Circadian Period

Period (PER) protein phosphorylation is a critical regulator of circadian period, yet an integrated understanding of the role and interaction between phosphorylation sites that can both increase and decrease PER2 stability remains elusive. Here, we propose a phosphoswitch model, where two competing phosphorylation sites determine whether PER2 has a fast or slow degradation rate. This mathematical model accurately reproduces the three-stage degradation kinetics of endogenous PER2. We predict and demonstrate that the phosphoswitch is intrinsically temperature sensitive, slowing down PER2 degradation as a result of faster reactions at higher temperatures. The phosphoswitch provides a biochemical mechanism for circadian temperature compensation of circadian period. This phosphoswitch additionally explains the phenotype of Familial Advanced Sleep Phase (FASP) and CK1ε(tau) genetic circadian rhythm disorders, metabolic control of PER2 stability, and how drugs that inhibit CK1 alter period. The phosphoswitch provides a general mechanism to integrate diverse stimuli to regulate circadian period.

Predicting Conformational Disorder

In the last two decades, it has become increasingly evident that a large number of proteins are either fully or partially disordered. Intrinsically disordered proteins are ubiquitous proteins that fulfill essential biological functions while lacking a stable 3D structure. Their conformational heterogeneity is encoded at the amino acid sequence level, thereby allowing intrinsically disordered proteins or regions to be recognized based on their sequence properties. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to crystallization. This chapter focuses on the methods currently employed for predicting disorder and identifying regions involved in induced folding.

Intrinsically disordered proteins in PubMed: what can the tip of the iceberg tell us about what lies below?

Papers that use IDP terminology represent only the tip of the iceberg of the larger body of literature on this subject.

Compartmentalization and Functionality of Nuclear Disorder: Intrinsic Disorder and Protein-Protein Interactions in Intra-Nuclear Compartments

The cell nucleus contains a number of membrane-less organelles or intra-nuclear compartments. These compartments are dynamic structures representing liquid-droplet phases which are only slightly denser than the bulk intra-nuclear fluid. They possess different functions, have diverse morphologies, and are typically composed of RNA (or, in some cases, DNA) and proteins. We analyzed 3005 mouse proteins localized in specific intra-nuclear organelles, such as nucleolus, chromatin, Cajal bodies, nuclear speckles, promyelocytic leukemia (PML) nuclear bodies, nuclear lamina, nuclear pores, and perinuclear compartment and compared them with ~29,863 non-nuclear proteins from mouse proteome. Our analysis revealed that intrinsic disorder is enriched in the majority of intra-nuclear compartments, except for the nuclear pore and lamina. These compartments are depleted in proteins that lack disordered domains and enriched in proteins that have multiple disordered domains. Moonlighting proteins found in multiple intra-nuclear compartments are more likely to have multiple disordered domains. Protein-protein interaction networks in the intra-nuclear compartments are denser and include more hubs compared to the non-nuclear proteins. Hubs in the intra-nuclear compartments (except for the nuclear pore) are enriched in disorder compared with non-nuclear hubs and non-nuclear proteins. Therefore, our work provides support to the idea of the functional importance of intrinsic disorder in the cell nucleus and shows that many proteins associated with sub-nuclear organelles in nuclei of mouse cells are enriched in disorder. This high level of disorder in the mouse nuclear proteins defines their ability to serve as very promiscuous binders, possessing both large quantities of potential disorder-based interaction sites and the ability of a single such site to be involved in a large number of interactions.

Discovery, Prevalence, and Persistence of Novel Circular Single-Stranded DNA Viruses in the Ctenophores Mnemiopsis leidyi and Beroe ovata

Gelatinous zooplankton, such as ctenophores and jellyfish, are important components of marine and brackish ecosystems and play critical roles in aquatic biogeochemistry. As voracious predators of plankton, ctenophores have key positions in aquatic food webs and are often successful invaders when introduced to new areas. Gelatinous zooplankton have strong impacts on ecosystem services, particularly in coastal environments. However, little is known about the factors responsible for regulating population dynamics of gelatinous organisms, including biological interactions that may contribute to bloom demise. Ctenophores are known to contain specific bacterial communities and a variety of invertebrate parasites and symbionts; however, no previous studies have examined the presence of viruses in these organisms. Building upon recent studies demonstrating a diversity of single-stranded DNA viruses that encode a replication initiator protein (Rep) in aquatic invertebrates, this study explored the presence of circular, Rep-encoding single-stranded DNA (CRESS-DNA) viruses in the ctenophores Mnemiopsis leidyi and Beroe ovata collected from the Skidaway River Estuary and Savannah River in Georgia, USA. Using rolling circle amplification followed by restriction enzyme digestion, this study provides the first evidence of viruses in ctenophores. Investigation of four CRESS-DNA viruses over an 8-month period using PCR demonstrated temporal trends in viral prevalence and indicated that some of the viruses may persist in ctenophore populations throughout the year. Although future work needs to examine the ecological roles of these ctenophore-associated viruses, this study indicates that viral infection may play a role in population dynamics of gelatinous zooplankton.

Bioinformatical parsing of folding-on-binding proteins reveals their compositional and evolutionary sequence design

Intrinsic disorder occurs when (part of) a protein remains unfolded during normal functioning. Intrinsically-disordered regions can contain segments that ‘fold on binding’ to another molecule. Here, we perform bioinformatical parsing of human ‘folding-on-binding’ (FB) proteins, into four subsets: Ordered regions, FB regions, Disordered regions that surround FB regions (‘Disordered-around-FB’), and Other-Disordered regions. We examined the composition and evolutionary behaviour (across vertebrate orthologs) of these subsets. From a convergence of three separate analyses, we find that for hydrophobicity, Ordered regions segregate from the other subsets, but the Ordered and FB regions group together as highly conserved, and the Disordered-around-FB and Other-Disordered regions as less conserved (with a lesser significant difference between Ordered and FB regions). FB regions are highly-conserved with net positive charge, whereas Disordered-around-FB have net negative charge and are relatively less hydrophobic than FB regions. Indeed, these Disordered-around-FB regions are excessively hydrophilic compared to other disordered regions generally. We describe how our results point towards a possible compositionally-based steering mechanism of folding-on-binding.

Transport Selectivity of Nuclear Pores, Phase Separation, and Membraneless Organelles

Nuclear pore complexes (NPCs) provide a selective passageway for receptor-mediated active transport between nucleus and cytoplasm, while maintaining the distinct molecular compositions of both compartments at large. In this review we discuss how NPCs gain a remarkable sorting selectivity from non-globular FG domains and their phase separation into dense polymer meshworks. The resulting sieve-like FG hydrogels are effective barriers to normal macromolecules but are at the same time highly permeable to shuttling nuclear transport receptors, which bind to FG motifs as well as to their designated cargoes. Phase separation driven by disordered protein domains was recently also recognized as being pivotal to the formation of membraneless organelles, making it an important emerging principle in cell biology.

Characterization of Ambra1 in asexual cycle of a non-vertebrate chordate, the colonial tunicate Botryllus schlosseri, and phylogenetic analysis of the protein group in Bilateria

Ambra1 is a positive regulator of autophagy, a lysosome-mediated degradative process involved both in physiological and pathological conditions. Nowadays, Ambra1 has been characterized only in mammals and zebrafish. Through bioinformatics searches and targeted cloning, we report the identification of the complete Ambra1 transcript in a non-vertebrate chordate, the tunicate Botryllus schlosseri. Tunicata is the sister group of Vertebrata and the only chordate group possessing species that reproduce also by blastogenesis (asexual reproduction). B. schlosseri Ambra1 deduced amino acid sequence is shorter than vertebrate homologues but still contains the typical WD40 domain. qPCR analyses revealed that the level of B. schlosseri Ambra1 transcription is temporally regulated along the colonial blastogenetic cycle. By means of similarity searches we identified Wdr5 and Katnb1 as proteins evolutionarily associated to Ambra1. Phylogenetic analyses on Bilateria indicate that: (i) Wdr5 is the most related to Ambra1, so that they may derive from an ancestral gene, (ii) Ambra1 forms a group of ancient genes evolved before the radiation of the taxon, (iii) these orthologous Ambra1 share the two conserved WD40/YVTN repeat-like-containing domains, and (iv) they are characterized by ancient duplications of WD40 repeats within the N-terminal domain.

Experimental detection of short regulatory motifs in eukaryotic proteins: tips for good practice as well as for bad

It has become clear in outline though not yet in detail how cellular regulatory and signalling systems are constructed. The essential machines are protein complexes that effect regulatory decisions by undergoing internal changes of state. Subcomponents of these cellular complexes are assembled into molecular switches. Many of these switches employ one or more short peptide motifs as toggles that can move between one or more sites within the switch system, the simplest being on-off switches. Paradoxically, these motif modules (termed short linear motifs or SLiMs) are both hugely abundant but difficult to research. So despite the many successes in identifying short regulatory protein motifs, it is thought that only the “tip of the iceberg” has been exposed. Experimental and bioinformatic motif discovery remain challenging and error prone. The advice presented in this article is aimed at helping researchers to uncover genuine protein motifs, whilst avoiding the pitfalls that lead to reports of false discovery.

KMAD: knowledge-based multiple sequence alignment for intrinsically disordered proteins

Summary: Intrinsically disordered proteins (IDPs) lack tertiary structure and thus differ from globular proteins in terms of their sequence–structure–function relations. IDPs have lower sequence conservation, different types of active sites and a different distribution of functionally important regions, which altogether make their multiple sequence alignment (MSA) difficult. The KMAD MSA software has been written specifically for the alignment and annotation of IDPs. It augments the substitution matrix with knowledge about post-translational modifications, functional domains and short linear motifs.

Results: MSAs produced with KMAD describe well-conserved features among IDPs, tend to agree well with biological intuition, and are a good basis for designing new experiments to shed light on this large, understudied class of proteins.

Availability and implementation: KMAD web server is accessible at http://www.cmbi.ru.nl/kmad/. A standalone version is freely available.

Contact: vriend@cmbi.ru.nl

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups). Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB) with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be considered as pre-training for various applications of deep learning in bioinformatics. The related data is available at Life Language Processing Website: http://llp.berkeley.edu and Harvard Dataverse: http://dx.doi.org/10.7910/DVN/JMFHTN.

Evolutionarily Conserved Sequence Features Regulate the Formation of the FG Network at the Center of the Nuclear Pore Complex

The nuclear pore complex (NPC) is the portal for bidirectional transportation of cargos between the nucleus and the cytoplasm. While most of the structural elements of the NPC, i.e. nucleoporins (Nups), are well characterized, the exact transport mechanism is still under much debate. Many of the functional Nups are rich in phenylalanine-glycine (FG) repeats and are believed to play the key role in nucleocytoplasmic transport. We present a bioinformatics study conducted on more than a thousand FG Nups across 252 species. Our results reveal the regulatory role of polar residues and specific sequences of charged residues, named 'like charge regions' (LCRs), in the formation of the FG network at the center of the NPC. Positively charged LCRs prepare the environment for negatively charged cargo complexes and regulate the size of the FG network. The low number density of charged residues in these regions prevents FG domains from forming a relaxed coil structure. Our results highlight the significant role of polar interactions in FG network formation at the center of the NPC and demonstrate that the specific localization of LCRs, FG motifs, charged, and polar residues regulate the formation of the FG network at the center of the NPC.

DisPredict: A Predictor of Disordered Protein Using Optimized RBF Kernel

Intrinsically disordered proteins or, regions perform important biological functions through their dynamic conformations during binding. Thus accurate identification of these disordered regions have significant implications in proper annotation of function, induced fold prediction and drug design to combat critical diseases. We introduce DisPredict, a disorder predictor that employs a single support vector machine with RBF kernel and novel features for reliable characterization of protein structure. DisPredict yields effective performance. In addition to 10-fold cross validation, training and testing of DisPredict was conducted with independent test datasets. The results were consistent with both the training and test error minimal. The use of multiple data sources, makes the predictor generic. The datasets used in developing the model include disordered regions of various length which are categorized as short and long having different compositions, different types of disorder, ranging from fully to partially disordered regions as well as completely ordered regions. Through comparison with other state of the art approaches and case studies, DisPredict is found to be a useful tool with competitive performance. DisPredict is available at https://github.com/tamjidul/DisPredict_v1.0.

High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder

Intrinsically disordered proteins and regions (IDPs and IDRs) lack stable 3D structure under physiological conditions in-vitro, are common in eukaryotes, and facilitate interactions with RNA, DNA and proteins. Current methods for prediction of IDPs and IDRs do not provide insights into their functions, except for a handful of methods that address predictions of protein-binding regions. We report first-of-its-kind computational method DisoRDPbind for high-throughput prediction of RNA, DNA and protein binding residues located in IDRs from protein sequences. DisoRDPbind is implemented using a runtime-efficient multi-layered design that utilizes information extracted from physiochemical properties of amino acids, sequence complexity, putative secondary structure and disorder and sequence alignment. Empirical tests demonstrate that it provides accurate predictions that are competitive with other predictors of disorder-mediated protein binding regions and complementary to the methods that predict RNA- and DNA-binding residues annotated based on crystal structures. Application in Homo sapiens, Mus musculus, Caenorhabditis elegans and Drosophila melanogaster proteomes reveals that RNA- and DNA-binding proteins predicted by DisoRDPbind complement and overlap with the corresponding known binding proteins collected from several sources. Also, the number of the putative protein-binding regions predicted with DisoRDPbind correlates with the promiscuity of proteins in the corresponding protein-protein interaction networks. Webserver: http://biomine.ece.ualberta.ca/DisoRDPbind/.

New genes drive the evolution of gene interaction networks in the human and mouse genomes

Background

The origin of new genes with novel functions creates genetic and phenotypic diversity in organisms. To acquire functional roles, new genes must integrate into ancestral gene-gene interaction (GGI) networks. The mechanisms by which new genes are integrated into ancestral networks, and their evolutionary significance, are yet to be characterized. Herein, we present a study investigating the rates and patterns of new gene-driven evolution of GGI networks in the human and mouse genomes.

Results

We examine the network topological and functional evolution of new genes that originated at various stages in the human and mouse lineages by constructing and analyzing three different GGI datasets. We find a large number of new genes integrated into GGI networks throughout vertebrate evolution. These genes experienced a gradual integration process into GGI networks, starting on the network periphery and gradually becoming highly connected hubs, and acquiring pleiotropic and essential functions. We identify a few human lineage-specific hub genes that have evolved brain development-related functions. Finally, we explore the possible underlying mechanisms driving the GGI network evolution and the observed patterns of new gene integration process.

Conclusions

Our results unveil a remarkable network topological integration process of new genes: over 5000 new genes were integrated into the ancestral GGI networks of human and mouse; new genes gradually acquire increasing number of gene partners; some human-specific genes evolved into hub structure with critical phenotypic effects. Our data cast new conceptual insights into the evolution of genetic networks.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0772-4) contains supplementary material, which is available to authorized users.

An Overview of Predictors for Intrinsically Disordered Proteins over 2010-2014

The sequence-structure-function paradigm of proteins has been changed by the occurrence of intrinsically disordered proteins (IDPs). Benefiting from the structural disorder, IDPs are of particular importance in biological processes like regulation and signaling. IDPs are associated with human diseases, including cancer, cardiovascular disease, neurodegenerative diseases, amyloidoses, and several other maladies. IDPs attract a high level of interest and a substantial effort has been made to develop experimental and computational methods. So far, more than 70 prediction tools have been developed since 1997, within which 17 predictors were created in the last five years. Here, we presented an overview of IDPs predictors developed during 2010-2014. We analyzed the algorithms used for IDPs prediction by these tools and we also discussed the basic concept of various prediction methods for IDPs. The comparison of prediction performance among these tools is discussed as well.

Tandem Repeats in Proteins: Prediction Algorithms and Biological Role

Tandem repetitions in protein sequence and structure is a fascinating subject of research which has been a focus of study since the late 1990s. In this survey, we give an overview on the multi-faceted aspects of research on protein tandem repeats (PTR for short), including prediction algorithms, databases, early classification efforts, mechanisms of PTR formation and evolution, and synthetic PTR design. We also touch on the rather open issue of the relationship between PTR and flexibility (or disorder) in proteins. Detection of PTR either from protein sequence or structure data is challenging due to inherent high (biological) signal-to-noise ratio that is a key feature of this problem. As early in silico analytic tools have been key enablers for starting this field of study, we expect that current and future algorithmic and statistical breakthroughs will have a high impact on the investigations of the biological role of PTR.

Elastin-like polypeptides as models of intrinsically disordered proteins

Elastin-like polypeptides (ELPs) are a class of stimuli-responsive biopolymers inspired by the intrinsically disordered domains of tropoelastin that are composed of repeats of the VPGXG pentapeptide motif, where X is a "guest residue". They undergo a reversible, thermally triggered lower critical solution temperature (LCST) phase transition, which has been utilized for a variety of applications including protein purification, affinity capture, immunoassays, and drug delivery. ELPs have been extensively studied as protein polymers and as biomaterials, but their relationship to other disordered proteins has heretofore not been established. The biophysical properties of ELPs that lend them their unique material behavior are similar to the properties of many intrinsically disordered proteins (IDP). Their low sequence complexity, phase behavior, and elastic properties make them an interesting "minimal" artificial IDP, and the study of ELPs can hence provide insights into the behavior of other more complex IDPs. Motivated by this emerging realization of the similarities between ELPs and IDPs, this review discusses the biophysical properties of ELPs, their biomedical utility, and their relationship to other disordered polypeptide sequences.

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/.

Unfoldome variation upon plant-pathogen interactions: strawberry infection by Colletotrichum acutatum

Intrinsically disordered proteins (IDPs) are proteins that lack secondary and/or tertiary structure under physiological conditions. These proteins are very abundant in eukaryotic proteomes and play crucial roles in all molecular mechanisms underlying the response to environmental challenges. In plants, different IDPs involved in stress response have been identified and characterized. Nevertheless, a comprehensive evaluation of protein disorder in plant proteomes under abiotic or biotic stresses is not available so far. In the present work the transcriptome dataset of strawberry (Fragaria X ananassa) fruits interacting with the fungal pathogen Colletotrichum acutatum was actualized onto the woodland strawberry (Fragaria vesca) genome. The obtained cDNA sequences were translated into protein sequences, which were subsequently subjected to disorder analysis. The results, providing the first estimation of disorder abundance associated to plant infection, showed that the proteome activated in the strawberry red fruit during the active fungal propagation is remarkably depleted in disorder. On the other hand, in the resistant white fruit, no significant disorder reduction is observed in the proteins expressed in response to fungal infection. Four representative proteins, FvSMP, FvPRKRIP, FvPCD-4 and FvFAM32A-like, predicted as mainly disordered and never experimentally characterized before, were isolated, and the absence of structure was validated at the secondary and tertiary level using circular dichroism and differential scanning fluorimetry. Their quaternary structure was also established using light scattering. The results are discussed considering the role of protein disorder in plant defense.

MERA: a webserver for evaluating backbone torsion angle distributions in dynamic and disordered proteins from NMR data

MERA (Maximum Entropy Ramachandran map Analysis from NMR data) is a new webserver that generates residue-by-residue Ramachandran map distributions for disordered proteins or disordered regions in proteins on the basis of experimental NMR parameters. As input data, the program currently utilizes up to 12 different parameters. These include three different types of short-range NOEs, three types of backbone chemical shifts ((15)N, (13)C(α), and (13)C'), six types of J couplings ((3)JHNHα, (3)JC'C', (3)JC'Hα, (1)JHαCα, (2)JCαN and (1)JCαN), as well as the (15)N-relaxation derived J(0) spectral density. The Ramachandran map distributions are reported in terms of populations of their 15° × 15° voxels, and an adjustable maximum entropy weight factor is available to ensure that the obtained distributions will not deviate more from a newly derived coil library distribution than required to account for the experimental data. MERA output includes the agreement between each input parameter and its distribution-derived value. As an application, we demonstrate performance of the program for several residues in the intrinsically disordered protein α-synuclein, as well as for several static and dynamic residues in the folded protein GB3.

Accurate Ab Initio and Template-Based Prediction of Short Intrinsically-Disordered Regions by Bidirectional Recurrent Neural Networks Trained on Large-Scale Datasets

Intrinsically-disordered regions lack a well-defined 3D structure, but play key roles in determining the function of many proteins. Although predictors of disorder have been shown to achieve relatively high rates of correct classification of these segments, improvements over the the years have been slow, and accurate methods are needed that are capable of accommodating the ever-increasing amount of structurally-determined protein sequences to try to boost predictive performances. In this paper, we propose a predictor for short disordered regions based on bidirectional recurrent neural networks and tested by rigorous five-fold cross-validation on a large, non-redundant dataset collected from MobiDB, a new comprehensive source of protein disorder annotations. The system exploits sequence and structural information in the forms of frequency profiles, predicted secondary structure and solvent accessibility and direct disorder annotations from homologous protein structures (templates) deposited in the Protein Data Bank. The contributions of sequence, structure and homology information result in large improvements in predictive accuracy. Additionally, the large scale of the training set leads to low false positive rates, making our systems a robust and efficient way to address high-throughput disorder prediction.

New Insights into the Roles of Nogo-A in CNS Biology and Diseases

Nogos have become a hot topic for its well-known number Nogo-A's big role in clinical matters. It has been recognized that the expression of Nogo-A and the receptor NgR1 inhibit the neuron's growth after CNS injuries or the onset of the MS. The piling evidence supports the notion that the Nogo-A is also involved in the synaptic plasticity, which was shown to negatively regulate the strength of synaptic transmission. The occurrence of significant schizophrenia-like behavioral phenotypes in Nogo-A KO rats also added strong proof to this conclusion. This review mainly focuses on the structure of Nogo-A and its corresponding receptor-NgR1, its intra- and extra-cellular signaling, together with its major physiological functions such as regulation of migration and distribution and its related diseases like stroke, AD, ALS and so on.

Rational design of antibodies targeting specific epitopes within intrinsically disordered proteins

Antibodies are powerful tools in life sciences research, as well as in diagnostic and therapeutic applications, because of their ability to bind given molecules with high affinity and specificity. Using current methods, however, it is laborious and sometimes difficult to generate antibodies to target specific epitopes within a protein, in particular if these epitopes are not effective antigens. Here we present a method to rationally design antibodies to enable them to bind virtually any chosen disordered epitope in a protein. The procedure consists in the sequence-based design of one or more complementary peptides targeting a selected disordered epitope and the subsequent grafting of such peptides on an antibody scaffold. We illustrate the method by designing six single-domain antibodies to bind different epitopes within three disease-related intrinsically disordered proteins and peptides (α-synuclein, Aβ42, and IAPP). Our results show that all these designed antibodies bind their targets with good affinity and specificity. As an example of an application, we show that one of these antibodies inhibits the aggregation of α-synuclein at substoichiometric concentrations and that binding occurs at the selected epitope. Taken together, these results indicate that the design strategy that we propose makes it possible to obtain antibodies targeting given epitopes in disordered proteins or protein regions.

Structural modeling and in silico analysis of non-synonymous single nucleotide polymorphisms of human 3β-hydroxysteroid dehydrogenase type 2

Single-nucleotide polymorphisms (SNPs), a most common type of genetic mutations, result from single base pair alterations. Non-synonymous SNPs (nsSNP) occur in the coding regions of a gene and result in single amino acid substitution which might have the potential to affect the function as well as structure of the corresponding protein. In human the 3β-hydroxysteroid dehydrogenases/Δ^4,5-isomerase type 2 (HSD3B2) is an important membrane-bound enzyme involved in the dehydrogenation and Δ^4,5-isomerization of the Δ⁵-steroid precursors into their respective Δ⁴-ketosteroids in the biosynthesis of steroid hormones such as glucocorticoids, mineralocorticoids, progesterone, androgens, and estrogens in tissues such as adrenal gland, ovary, and testis. Most of the nsSNPs of HSD3B2 are still uncharacterized in terms of their disease causing potential. So, this study has been undertaken to explore and extend the knowledge related to the effect of nsSNPs on the stability and function of the HSD3B2. In this study sixteen nsSNP of HSD3B2 were subjected to in silico analysis using nine different algorithms: SIFT, PROVEAN, PolyPhen, MutPred, SNPeffect, nsSNP Analyzer, PhD SNP, stSNP, and I Mutant 2.0. The results obtained from the analysis revealed that the prioritization of diseases associated amino acid substitution as evident from possible alteration in structure–function relationship. Structural phylogenetic analysis using ConSurf revealed that the functional residues are highly conserved in human HSD3B2; and most of the disease associated nsSNPs are within these conserved residues. Structural theoritical models of HSD3B2 were created using HHPred, Phyre2 and RaptorX server. The predicted models were evaluated to get the best one for structural understanding of amino acid substitutions in three dimensional spaces.

Computational approaches for inferring the functions of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are ubiquitously involved in cellular processes and often implicated in human pathological conditions. The critical biological roles of these proteins, despite not adopting a well-defined fold, encouraged structural biologists to revisit their views on the protein structure-function paradigm. Unfortunately, investigating the characteristics and describing the structural behavior of IDPs is far from trivial, and inferring the function(s) of a disordered protein region remains a major challenge. Computational methods have proven particularly relevant for studying IDPs: on the sequence level their dependence on distinct characteristics determined by the local amino acid context makes sequence-based prediction algorithms viable and reliable tools for large scale analyses, while on the structure level the in silico integration of fundamentally different experimental data types is essential to describe the behavior of a flexible protein chain. Here, we offer an overview of the latest developments and computational techniques that aim to uncover how protein function is connected to intrinsic disorder.

Protein Folding and Mechanisms of Proteostasis

Highly sophisticated mechanisms that modulate protein structure and function, which involve synthesis and degradation, have evolved to maintain cellular homeostasis. Perturbations in these mechanisms can lead to protein dysfunction as well as deleterious cell processes. Therefore in recent years the etiology of a great number of diseases has been attributed to failures in mechanisms that modulate protein structure. Interconnections among metabolic and cell signaling pathways are critical for homeostasis to converge on mechanisms associated with protein folding as well as for the preservation of the native structure of proteins. For instance, imbalances in secretory protein synthesis pathways lead to a condition known as endoplasmic reticulum (ER) stress which elicits the adaptive unfolded protein response (UPR). Therefore, taking this into consideration, a key part of this paper is developed around the protein folding phenomenon, and cellular mechanisms which support this pivotal condition. We provide an overview of chaperone protein function, UPR via, spatial compartmentalization of protein folding, proteasome role, autophagy, as well as the intertwining between these processes. Several diseases are known to have a molecular etiology in the malfunction of mechanisms responsible for protein folding and in the shielding of native structure, phenomena which ultimately lead to misfolded protein accumulation. This review centers on our current knowledge about pathways that modulate protein folding, and cell responses involved in protein homeostasis.