DisProt: the Database of Disordered Proteins

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

Testing the length limit of loop grafting in a helical repeat protein

Alpha-helical repeat proteins such as consensus-designed tetratricopeptide repeats (CTPRs) are exceptionally stable molecules that are able to tolerate destabilizing sequence alterations and are therefore becoming increasingly valued as a modular platform for biotechnology and biotherapeutic applications. A simple approach to functionalize the CTPR scaffold that we are pioneering is the insertion of short linear motifs (SLiMs) into the loops between adjacent repeats. Here, we test the limits of the scaffold by inserting 17 highly diverse amino acid sequences of up to 58 amino acids in length into a two-repeat protein and examine the impact on protein folding, stability and solubility. The sequences include three SLiMs that bind oncoproteins and eleven naturally occurring linker sequences all predicted to be intrinsically disordered but with conformational preferences ranging from compact globules to expanded coils. We show that the loop-grafted proteins retain the native CTPR structure and are thermally stable with melting temperatures above 60 ​°C, despite the longest loop sequence being almost the same size as the CTPR scaffold itself (68 amino acids). Although the main determinant of the effect of stability was found to be loop length and was relatively insensitive to amino acid composition, the relationship between protein solubility and the loop sequences was more complex, with the presence of negatively charged amino acids enhancing the solubility. Our findings will help us to fully realize the potential of the repeat-protein scaffold, allowing a rational design approach to create artificial modular proteins with customized functional capabilities.

Comparative Assessment of Intrinsic Disorder Predictions with a Focus on Protein and Nucleic Acid-Binding Proteins

With over 60 disorder predictors, users need help navigating the predictor selection task. We review 28 surveys of disorder predictors, showing that only 11 include assessment of predictive performance. We identify and address a few drawbacks of these past surveys. To this end, we release a novel benchmark dataset with reduced similarity to the training sets of the considered predictors. We use this dataset to perform a first-of-its-kind comparative analysis that targets two large functional families of disordered proteins that interact with proteins and with nucleic acids. We show that limiting sequence similarity between the benchmark and the training datasets has a substantial impact on predictive performance. We also demonstrate that predictive quality is sensitive to the use of the well-annotated order and inclusion of the fully structured proteins in the benchmark datasets, both of which should be considered in future assessments. We identify three predictors that provide favorable results using the new benchmark set. While we find that VSL2B offers the most accurate and robust results overall, ESpritz-DisProt and SPOT-Disorder perform particularly well for disordered proteins. Moreover, we find that predictions for the disordered protein-binding proteins suffer low predictive quality compared to generic disordered proteins and the disordered nucleic acids-binding proteins. This can be explained by the high disorder content of the disordered protein-binding proteins, which makes it difficult for the current methods to accurately identify ordered regions in these proteins. This finding motivates the development of a new generation of methods that would target these difficult-to-predict disordered proteins. We also discuss resources that support users in collecting and identifying high-quality disorder predictions.

RELT stains prominently in B-cell lymphomas and binds the hematopoietic transcription factor MDFIC

Receptor Expressed in Lymphoid Tissues (RELT) is a human tumor necrosis factor receptor superfamily member (TNFRSF) that is expressed most prominently in cells and tissues of the hematopoietic system. RELL1 and RELL2 are two homologs that physically interact with RELT and co-localize with RELT at the plasma membrane. This study sought to further elucidate the function of RELT by identifying novel protein interactions with RELT family members. The transcription factor MyoD family inhibitor domain-containing (MDFIC) was identified in a yeast two-hybrid genetic screen using RELL1 as bait. MDFIC co-localizes with RELT family members at the plasma membrane; this co-localization was most prominently observed with RELL1 and RELL2. In vitro co-immunoprecipitation (Co-IP) was utilized to demonstrate that MDFIC physically interacts with RELT, RELL1, and RELL2. Co-IP using deletion mutants of MDFIC and RELT identified regions important for physical association between MDFIC and RELT family members and a computational analysis revealed that RELT family members are highly disordered proteins. Immunohistochemistry of normal human lymph nodes revealed RELT staining that was most prominent in macrophages. Interestingly, the level of RELT staining significantly increased progressively in low and high-grade B-cell lymphomas versus normal lymph nodes. RELT co-staining with CD20 was observed in B-cell lymphomas, indicating that RELT is expressed in malignant B cells. Collectively, these results further our understanding of RELT-associated signaling pathways, the protein structure of RELT family members, and provide preliminary evidence indicating an association of RELT with B-cell lymphomas.

Towards Decoding the Sequence-Based Grammar Governing the Functions of Intrinsically Disordered Protein Regions

A substantial portion of the proteome consists of intrinsically disordered regions (IDRs) that do not fold into well-defined 3D structures yet perform numerous biological functions and are associated with a broad range of diseases. It has been a long-standing enigma how different IDRs successfully execute their specific functions. Further putting a spotlight on IDRs are recent discoveries of functionally relevant biomolecular assemblies, which in some cases form through liquid-liquid phase separation. At the molecular level, the formation of biomolecular assemblies is largely driven by weak, multivalent, but selective IDR-IDR interactions. Emerging experimental and computational studies suggest that the primary amino acid sequences of IDRs encode a variety of their interaction behaviors. In this review, we focus on findings and insights that connect sequence-derived features of IDRs to their conformations, propensities to form biomolecular assemblies, selectivity of interaction partners, functions in the context of physiology and disease, and regulation of function. We also discuss directions of future research to facilitate establishing a comprehensive sequence-function paradigm that will eventually allow prediction of selective interactions and specificity of function mediated by IDRs.

Exploring the sequence fitness landscape of a bridge between protein folds

Most foldable protein sequences adopt only a single native fold. Recent protein design studies have, however, created protein sequences which fold into different structures apon changes of environment, or single point mutation, the best characterized example being the switch between the folds of the GA and GB binding domains of streptococcal protein G. To obtain further insight into the design of sequences which can switch folds, we have used a computational model for the fitness landscape of a single fold, built from the observed sequence variation of protein homologues. We have recently shown that such coevolutionary models can be used to design novel foldable sequences. By appropriately combining two of these models to describe the joint fitness landscape of GA and GB, we are able to describe the propensity of a given sequence for each of the two folds. We have successfully tested the combined model against the known series of designed GA/GB hybrids. Using Monte Carlo simulations on this landscape, we are able to identify pathways of mutations connecting the two folds. In the absence of a requirement for domain stability, the most frequent paths go via sequences in which neither domain is stably folded, reminiscent of the propensity for certain intrinsically disordered proteins to fold into different structures according to context. Even if the folded state is required to be stable, we find that there is nonetheless still a wide range of sequences which are close to the transition region and therefore likely fold switches, consistent with recent estimates that fold switching may be more widespread than had been thought.

While most proteins self-assemble (or “fold”) to a unique three-dimensional structure, a few have been identified that can fold into two distinct structures. These so-called “metamorphic” proteins that can switch folds have attracted a lot of recent interest, and it has been suggested that they may be much more widespread than currently appreciated. We have developed a computational model that captures the propensity of a given protein sequence to fold into either one of two specific structures (GA and GB), in order to investigate which sequences are able to fold to both GA and GB (“switch sequences”), versus just one of them. Our model predicts that there is a large number of switch sequences that could fold into both structures, but also that the most likely such sequences are those for which the folded structures have low stability, in agreement with available experimental data. This also suggests that intrinsically disordered proteins which can fold into different structures on binding may provide an evolutionary path in sequence space between protein folds.

Bioinformatic Analysis and Biophysical Characterization Reveal Structural Disorder in G0S2 Protein

G0S2 is a small protein of 103 residues in length that is involved in multiple cellular processes. To date, several reports have shown that G0S2 functions by making direct protein-protein interactions with key proteins. In lipolysis, G0S2 specifically interacts with adipose triglyceride lipase, inhibiting its activity and resulting in lipolysis being downregulated. In a similar way, G0S2 also participates in the regulation of apoptosis, cell proliferation, and oxidative phosphorylation; however, information regarding G0S2 structural and biophysical properties is limited. In this work, we conducted a comparative structural analysis of human and mouse G0S2 proteins. Bioinformatics suggests the presence of a disordered C-terminal region in human G0S2. Experimental characterization by size-exclusion chromatography and dynamic light scattering showed that human and mouse G0S2 have different hydrodynamic properties. In comparison to the mouse G0S2, which behaves similar to a globular protein, the human G0S2 shows an elongated conformation, most likely by displaying a disordered C-terminal region. Further analysis of hydrodynamic properties under denaturing conditions suggests the presence of a structural element in the mouse protein that undergoes an order to disorder transition at low urea concentration. Structural analysis by circular dichroism revealed that in native conditions, both proteins are mainly unstructured, showing the presence of beta sheet structures. Further analysis of CD data suggests that both proteins belong to the premolten globule family of intrinsically disordered proteins. We suggest that the intrinsic disorder observed in the G0S2 protein may facilitate its interaction with multiple partners in the regulation of cellular metabolism.

The dark side of Alzheimer's disease: unstructured biology of proteins from the amyloid cascade signaling pathway

Alzheimer's disease (AD) is a leading cause of age-related dementia worldwide. Despite more than a century of intensive research, we are not anywhere near the discovery of a cure for this disease or a way to prevent its progression. Among the various molecular mechanisms proposed for the description of the pathogenesis and progression of AD, the amyloid cascade hypothesis, according to which accumulation of a product of amyloid precursor protein (APP) cleavage, amyloid β (Aβ) peptide, induces pathological changes in the brain observed in AD, occupies a unique niche. Although multiple proteins have been implicated in this amyloid cascade signaling pathway, their structure-function relationships are mostly unexplored. However, it is known that two major proteins related to AD pathology, Aβ peptide, and microtubule-associated protein tau belong to the category of intrinsically disordered proteins (IDPs), which are the functionally important proteins characterized by a lack of fixed, ordered three-dimensional structure. IDPs and intrinsically disordered protein regions (IDPRs) play numerous vital roles in various cellular processes, such as signaling, cell cycle regulation, macromolecular recognition, and promiscuous binding. However, the deregulation and misfolding of IDPs may lead to disturbed signaling, interactions, and disease pathogenesis. Often, molecular recognition-related IDPs/IDPRs undergo disorder-to-order transition upon binding to their biological partners and contain specific disorder-based binding motifs, known as molecular recognition features (MoRFs). Knowing the intrinsic disorder status and disorder-based functionality of proteins associated with amyloid cascade signaling pathway may help to untangle the mechanisms of AD pathogenesis and help identify therapeutic targets. In this paper, we have used multiple computational tools to evaluate the presence of intrinsic disorder and MoRFs in 27 proteins potentially relevant to the amyloid cascade signaling pathway. Among these, BIN1, APP, APOE, PICALM, PSEN1 and CD33 were found to be highly disordered. Furthermore, their disorder-based binding regions and associated short linear motifs have also been identified. These findings represent important foundation for the future research, and experimental characterization of disordered regions in these proteins is required to better understand their roles in AD pathogenesis.

Structure, dynamics and functions of UBQLNs: at the crossroads of protein quality control machinery

Cells rely on protein homeostasis to maintain proper biological functions. Dysregulation of protein homeostasis contributes to the pathogenesis of many neurodegenerative diseases and cancers. Ubiquilins (UBQLNs) are versatile proteins that engage with many components of protein quality control (PQC) machinery in cells. Disease-linked mutations of UBQLNs are most commonly associated with amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and other neurodegenerative disorders. UBQLNs play well-established roles in PQC processes, including facilitating degradation of substrates through the ubiquitin-proteasome system (UPS), autophagy, and endoplasmic-reticulum-associated protein degradation (ERAD) pathways. In addition, UBQLNs engage with chaperones to sequester, degrade, or assist repair of misfolded client proteins. Furthermore, UBQLNs regulate DNA damage repair mechanisms, interact with RNA-binding proteins (RBPs), and engage with cytoskeletal elements to regulate cell differentiation and development. Important to the myriad functions of UBQLNs are its multidomain architecture and ability to self-associate. UBQLNs are linked to numerous types of cellular puncta, including stress-induced biomolecular condensates, autophagosomes, aggresomes, and aggregates. In this review, we focus on deciphering how UBQLNs function on a molecular level. We examine the properties of oligomerization-driven interactions among the structured and intrinsically disordered segments of UBQLNs. These interactions, together with the knowledge from studies of disease-linked mutations, provide significant insights to UBQLN structure, dynamics and function.

ShiftCrypt: a web server to understand and biophysically align proteins through their NMR chemical shift values

Nuclear magnetic resonance (NMR) spectroscopy data provides valuable information on the behaviour of proteins in solution. The primary data to determine when studying proteins are the per-atom NMR chemical shifts, which reflect the local environment of atoms and provide insights into amino acid residue dynamics and conformation. Within an amino acid residue, chemical shifts present multi-dimensional and complexly cross-correlated information, making them difficult to analyse. The ShiftCrypt method, based on neural network auto-encoder architecture, compresses the per-amino acid chemical shift information in a single, interpretable, amino acid-type independent value that reflects the biophysical state of a residue. We here present the ShiftCrypt web server, which makes the method readily available. The server accepts chemical shifts input files in the NMR Exchange Format (NEF) or NMR-STAR format, executes ShiftCrypt and visualises the results, which are also accessible via an API. It also enables the ”biophysically-based” pairwise alignment of two proteins based on their ShiftCrypt values. This approach uses Dynamic Time Warping and can optionally include their amino acid code information, and has applications in, for example, the alignment of disordered regions. The server uses a token-based system to ensure the anonymity of the users and results. The web server is available at www.bio2byte.be/shiftcrypt.

Design principles of gene evolution for niche adaptation through changes in protein-protein interaction networks

In contrast to fossorial and above-ground organisms, subterranean species have adapted to the extreme stresses of living underground. We analyzed the predicted protein–protein interactions (PPIs) of all gene products, including those of stress-response genes, among nine subterranean, ten fossorial, and 13 aboveground species. We considered 10,314 unique orthologous protein families and constructed 5,879,879 PPIs in all organisms using ChiPPI. We found strong association between PPI network modulation and adaptation to specific habitats, noting that mutations in genes and changes in protein sequences were not linked directly with niche adaptation in the organisms sampled. Thus, orthologous hypoxia, heat-shock, and circadian clock proteins were found to cluster according to habitat, based on PPIs rather than on sequence similarities. Curiously, "ordered" domains were preserved in aboveground species, while "disordered" domains were conserved in subterranean organisms, and confirmed for proteins in DistProt database. Furthermore, proteins with disordered regions were found to adopt significantly less optimal codon usage in subterranean species than in fossorial and above-ground species. These findings reveal design principles of protein networks by means of alterations in protein domains, thus providing insight into deep mechanisms of evolutionary adaptation, generally, and particularly of species to underground living and other confined habitats.

Intrinsic Disorder in Human Proteins Encoded by Core Duplicon Gene Families

Segmental duplications (i.e., highly homologous DNA fragments greater than 1 kb in length that are present within a genome at more than one site) are typically found in genome regions that are prone to rearrangements. A noticeable fraction of the human genome (∼5%) includes segmental duplications (or duplicons) that are assumed to play a number of vital roles in human evolution, human-specific adaptation, and genomic instability. Despite their importance for crucial events such as synaptogenesis, neuronal migration, and neocortical expansion, these segmental duplications continue to be rather poorly characterized. Of particular interest are the core duplicon gene (CDG) families, which are replicates sharing common "core" DNA among the randomly attached pieces and which expand along single chromosomes and might harbor newly acquired protein domains. Another important feature of proteins encoded by CDG families is their multifunctionality. Although it seems that these proteins might possess many characteristic features of intrinsically disordered proteins, to the best of our knowledge, a systematic investigation of the intrinsic disorder predisposition of the proteins encoded by core duplicon gene families has not been conducted yet. To fill this gap and to determine the degree to which these proteins might be affected by intrinsic disorder, we analyzed a set of human proteins encoded by the members of 10 core duplicon gene families, such as NBPF, RGPD, GUSBP, PMS2P, SPATA31, TRIM51, GOLGA8, NPIP, TBC1D3, and LRRC37. Our analysis revealed that the vast majority of these proteins are highly disordered, with their disordered regions often being utilized as means for the protein-protein interactions and/or targeted for numerous posttranslational modifications of different nature.

Relevance of Electrostatic Charges in Compactness, Aggregation, and Phase Separation of Intrinsically Disordered Proteins

The abundance of intrinsic disorder in the protein realm and its role in a variety of physiological and pathological cellular events have strengthened the interest of the scientific community in understanding the structural and dynamical properties of intrinsically disordered proteins (IDPs) and regions (IDRs). Attempts at rationalizing the general principles underlying both conformational properties and transitions of IDPs/IDRs must consider the abundance of charged residues (Asp, Glu, Lys, and Arg) that typifies these proteins, rendering them assimilable to polyampholytes or polyelectrolytes. Their conformation strongly depends on both the charge density and distribution along the sequence (i.e., charge decoration) as highlighted by recent experimental and theoretical studies that have introduced novel descriptors. Published experimental data are revisited herein in the frame of this formalism, in a new and possibly unitary perspective. The physicochemical properties most directly affected by charge density and distribution are compaction and solubility, which can be described in a relatively simplified way by tools of polymer physics. Dissecting factors controlling such properties could contribute to better understanding complex biological phenomena, such as fibrillation and phase separation. Furthermore, this knowledge is expected to have enormous practical implications for the design, synthesis, and exploitation of bio-derived materials and the control of natural biological processes.

De novo engineering of intracellular condensates using artificial disordered proteins

Phase separation of intrinsically disordered proteins (IDPs) is a remarkable feature of living cells to dynamically control intracellular partitioning. Despite the numerous new IDPs that have been identified, progress towards rational engineering in cells has been limited. To address this limitation, we systematically scanned the sequence space of native IDPs and designed artificial IDPs (A-IDPs) with different molecular weights and aromatic content, which exhibit variable condensate saturation concentrations and temperature cloud points in vitro and in cells. We created A-IDP puncta using these simple principles, which are capable of sequestering an enzyme and whose catalytic efficiency can be manipulated by the molecular weight of the A-IDP. These results provide a robust engineered platform for creating puncta with new, phase-separation-mediated control of biological function in living cells.

Modeling beta-sheet peptide-protein interactions: Rosetta FlexPepDock in CAPRI rounds 38-45

Peptide-protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38-45 included two peptide-protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using the Rosetta FlexPepDock peptide docking protocol we generated top-performing, high-accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L-MAG with DLC8. In addition, we were able to generate the only medium-accuracy models for a particularly challenging target, T121. In contrast to the classical peptide-mediated interaction, in which receptor side chains contact both peptide backbone and side chains, beta-sheet complementation involves a major contribution to binding by hydrogen bonds between main chain atoms. To establish how binding affinity and specificity are established in this special class of peptide-protein interactions, we extracted PeptiDBeta, a benchmark of solved structures of different protein domains that are bound by peptides via beta-sheet complementation, and tested our protocol for global peptide-docking PIPER-FlexPepDock on this dataset. We find that the beta-strand part of the peptide is sufficient to generate approximate and even high resolution models of many interactions, but inclusion of adjacent motif residues often provides additional information necessary to achieve high resolution model quality.

A convenient protein library for spectroscopic calibrations

While several Raman, CD or FTIR spectral libraries are available for well-characterized proteins of known structure, proteins themselves are usually very difficult to acquire, preventing a convenient calibration of new instruments and new recording methods. The problem is particularly critical in the field of FTIR spectroscopy where numerous new methods are becoming available on the market.

The present papers reports the construction of a protein library (cSP92) including commercially available products, that are well characterized experimentally for their purity and solubility in conditions compatible with the recording of FTIR spectra and whose high-resolution structure is available.

Overall, 92 proteins were selected. These proteins cover well the CATH space at the level of classes and architectures. In terms of secondary structure content, an analysis of their high-resolution structure by DSSP shows that the mean content in the different secondary structures present in cSP92 is very similar to the mean content found in the PDB.

The 92-protein set is analyzed in details for the distribution of helix length, number of strands in β- sheets, length of β-strands and amino acid content, all features that may be important for the interpretation of FTIR spectra.

Identification of Intrinsic Disorder in Complexes from the Protein Data Bank

Background: Intrinsically disordered proteins or regions (IDPs or IDRs) lack stable structures in solution, yet often fold upon binding with partners. IDPs or IDRs are highly abundant in all proteomes and represent a significant modification of sequence → structure → function paradigm. The Protein Data Bank (PDB) includes complexes containing disordered segments bound to globular proteins, but the molecular mechanisms of such binding interactions remain largely unknown. Results: In this study, we present the results of various disorder predictions on a nonredundant set of PDB complexes. In contrast to their structural appearances, many PDB proteins were predicted to be disordered when separated from their binding partners. These predicted-to-be-disordered proteins were observed to form structures depending upon various factors, including heterogroup binding, protein/DNA/RNA binding, disulfide bonds, and ion binding. Conclusions: This study collects many examples of disorder-to-order transition in IDP complex formation, thus revealing the unusual structure–function relationships of IDPs and providing an additional support for the newly proposed paradigm of the sequence → IDP/IDR ensemble → function.

Criticality in the conformational phase transition among self-similar groups in intrinsically disordered proteins: Probed by salt-bridge dynamics

Intrinsically disordered proteins (IDP) serve as one of the key components in the global proteome. In contrast to globular proteins, they harbor an enormous amount of physical flexibility enforcing them to be retained in conformational ensembles rather than stable folds. Previous studies in an aligned direction have revealed the importance of transient dynamical phenomena like that of salt-bridge formation in IDPs to support their physical flexibility and have further highlighted their functional relevance. For this characteristic flexibility, IDPs remain amenable and accessible to different ordered binding partners, supporting their potential multi-functionality. The current study further addresses this complex structure-functional interplay in IDPs using phase transition dynamics to conceptualize the underlying (avalanche type) mechanism of their being distributed across and hopping around degenerate structural states (conformational ensembles). For this purpose, extensive molecular dynamics simulations have been done and the data analyzed from a statistical physics perspective. Investigation of the plausible scope of 'self-organized criticality' (SOC) to fit into the complex dynamics of IDPs was found to be assertive, relating the conformational degeneracy of these proteins to their functional multiplicity. In accordance with the transient nature of 'salt-bridge dynamics', the study further uses it as a probe to explain the structural basis of the proposed criticality in the conformational phase transition among self-similar groups in IDPs. The analysis reveal scale-invariant self-similar fractal geometries in the structural conformations of different IDPs. The insights from the study has the potential to be extended further to benefit structural tinkering of IDPs in their functional characterization and drugging.

cnnAlpha: Protein disordered regions prediction by reduced amino acid alphabets and convolutional neural networks

Intrinsically disordered regions (IDR) play an important role in key biological processes and are closely related to human diseases. IDRs have great potential to serve as targets for drug discovery, most notably in disordered binding regions. Accurate prediction of IDRs is challenging because their genome wide occurrence and a low ratio of disordered residues make them difficult targets for traditional classification techniques. Existing computational methods mostly rely on sequence profiles to improve accuracy which is time consuming and computationally expensive. This article describes an ab initio sequence-only prediction method-which tries to overcome the challenge of accurate prediction posed by IDRs-based on reduced amino acid alphabets and convolutional neural networks (CNNs). We experiment with six different 3-letter reduced alphabets. We argue that the dimensional reduction in the input alphabet facilitates the detection of complex patterns within the sequence by the convolutional step. Experimental results show that our proposed IDR predictor performs at the same level or outperforms other state-of-the-art methods in the same class, achieving accuracy levels of 0.76 and AUC of 0.85 on the publicly available Critical Assessment of protein Structure Prediction dataset (CASP10). Therefore, our method is suitable for proteome-wide disorder prediction yielding similar or better accuracy than existing approaches at a faster speed.

Towards Computational Models of Identifying Protein Ubiquitination Sites

Ubiquitination is an important post-translational modification (PTM) process for the regulation of protein functions, which is associated with cancer, cardiovascular and other diseases. Recent initiatives have focused on the detection of potential ubiquitination sites with the aid of physicochemical test approaches in conjunction with the application of computational methods. The identification of ubiquitination sites using laboratory tests is especially susceptible to the temporality and reversibility of the ubiquitination processes, and is also costly and time-consuming. It has been demonstrated that computational methods are effective in extracting potential rules or inferences from biological sequence collections. Up to the present, the computational strategy has been one of the critical research approaches that have been applied for the identification of ubiquitination sites, and currently, there are numerous state-of-the-art computational methods that have been developed from machine learning and statistical analysis to undertake such work. In the present study, the construction of benchmark datasets is summarized, together with feature representation methods, feature selection approaches and the classifiers involved in several previous publications. In an attempt to explore pertinent development trends for the identification of ubiquitination sites, an independent test dataset was constructed and the predicting results obtained from five prediction tools are reported here, together with some related discussions.

Hydropathy Patterning Complements Charge Patterning to Describe Conformational Preferences of Disordered Proteins

Understanding the conformational ensemble of an intrinsically disordered protein (IDP) is of great interest due to its relevance to critical intracellular functions and diseases. It is now well established that the polymer scaling behavior can provide a great deal of information about the conformational properties as well as liquid-liquid phase separation of an IDP. It is, therefore, extremely desirable to be able to predict an IDP's scaling behavior from the protein sequence itself. The work in this direction so far has focused on highly charged proteins and how charge patterning can perturb their structural properties. As naturally occurring IDPs are composed of a significant fraction of uncharged amino acids, the rules based on charge content and patterning are only partially helpful in solving the problem. Here, we propose a new order parameter, sequence hydropathy decoration, which can provide a near-quantitative understanding of scaling and structural properties of IDPs devoid of charged residues. We combine this with a charge patterning parameter, sequence charge decoration, to obtain a general equation, parametrized from extensive coarse-grained simulation data, for predicting protein dimensions from the sequence. We finally test this equation against available experimental data and find a semiquantitative match in predicting the scaling behavior. We also provide guidance on how to extend this approach to experimental data, which should be feasible in the near future.

Mutually exclusive locales for N-linked glycans and disorder in human glycoproteins

Several post-translational protein modifications lie predominantly within regions of disorder: the biased localization has been proposed to expand the binding versatility of disordered regions. However, investigating a representative dataset of 500 human N-glycoproteins, we observed the sites of N-linked glycosylations or N-glycosites, to be predominantly present in the regions of predicted order. When compared with disordered stretches, ordered regions were not found to be enriched for asparagines, serines and threonines, residues that constitute the sequon signature for conjugation of N-glycans. We then investigated the basis of mutual exclusivity between disorder and N-glycosites on the basis of amino acid distribution: when compared with control ordered residue stretches without any N-glycosites, residue neighborhoods surrounding N-glycosites showed a depletion of bulky, hydrophobic and disorder-promoting amino acids and an enrichment for flexible and accessible residues that are frequently found in coiled structures. When compared with control disordered residue stretches without any N-glycosites, N-glycosite neighborhoods were depleted of charged, polar, hydrophobic and flexible residues and enriched for aromatic, accessible and order-promoting residues with a tendency to be part of coiled and β structures. N-glycosite neighborhoods also showed greater phylogenetic conservation among amniotes, compared with control ordered regions, which in turn were more conserved than disordered control regions. Our results lead us to propose that unique primary structural compositions and differential propensities for evolvability allowed for the mutual spatial exclusion of N-glycosite neighborhoods and disordered stretches.

Intrinsically disordered proteins of viruses: Involvement in the mechanism of cell regulation and pathogenesis

Intrinsically disordered proteins (IDPs) possess the property of inherent flexibility and can be distinguished from other proteins in terms of lack of any fixed structure. Such dynamic behavior of IDPs earned the name "Dancing Proteins." The exploration of these dancing proteins in viruses has just started and crucial details such as correlation of rapid evolution, high rate of mutation and accumulation of disordered contents in viral proteome at least understood partially. In order to gain a complete understanding of this correlation, there is a need to decipher the complexity of viral mediated cell hijacking and pathogenesis in the host organism. Further there is necessity to identify the specific patterns within viral and host IDPs such as aggregation; Molecular recognition features (MoRFs) and their association to virulence, host range and rate of evolution of viruses in order to tackle the viral-mediated diseases. The current book chapter summarizes the aforementioned details and suggests the novel opportunities for further research of IDPs senses in viruses.

Intrinsically disordered protein domains in flavivirus infection

Intrinsically disordered protein regions are at the core of biological processes and involved in key protein-ligand interactions. The Flavivirus proteins, of viruses of great biomedical importance such as Zika and dengue viruses, exemplify this. Several proteins of these viruses have disordered regions that are of the utmost importance for biological activity. Disordered proteins can adopt several conformations, each able to interact with and/or bind to different ligands. In fact, such interactions can help stabilize a particular fold. Moreover, by being promiscuous in the number of target molecules they can bind to, these protein regions increase the number of functions that their small proteome (10 proteins) can achieve. A folding energy waterfall better describes the protein folding landscape of these proteins. A disordered protein can be thought as rolling down the folding energy cascade, in order "to fall, fold and function". This is the case of many viral protein regions, as seen in the flaviviruses proteome. Given their small size, flaviviruses are a good model system for understanding the role of intrinsically disordered protein regions in viral function. Finally, studying these viruses disordered protein regions will certainly contribute to the development of therapeutic approaches against such promising (yet challenging) targets.

The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions within proteins (IDRs) serve an increasingly expansive list of biological functions, including regulation of transcription and translation, protein phosphorylation, cellular signal transduction, as well as mechanical roles. The strong link between protein function and disorder motivates a deeper fundamental characterization of IDPs and IDRs for discovering new functions and relevant mechanisms. We review recent advances in experimental techniques that have improved identification of disordered regions in proteins. Yet, experimentally curated disorder information still does not currently scale to the level of experimentally determined structural information in folded protein databases, and disorder predictors rely on several different binary definitions of disorder. To link secondary structure prediction algorithms developed for folded proteins and protein disorder predictors, we conduct molecular dynamics simulations on representative proteins from the Protein Data Bank, comparing secondary structure and disorder predictions with simulation results. We find that structure predictor performance from neural networks can be leveraged for the identification of highly dynamic regions within molecules, linked to disorder. Low accuracy structure predictions suggest a lack of static structure for regions that disorder predictors fail to identify. While disorder databases continue to expand, secondary structure predictors and molecular simulations can improve disorder predictor performance, which aids discovery of novel functions of IDPs and IDRs. These observations provide a platform for the development of new, integrated structural databases and fusion of prediction tools toward protein disorder characterization in health and disease.

Bioinformatics for Renal and Urinary Proteomics: Call for Aggrandization

The clinical sampling of urine is noninvasive and unrestricted, whereby huge volumes can be easily obtained. This makes urine a valuable resource for the diagnoses of diseases. Urinary and renal proteomics have resulted in considerable progress in kidney-based disease diagnosis through biomarker discovery and treatment. This review summarizes the bioinformatics tools available for this area of proteomics and the milestones reached using these tools in clinical research. The scant research publications and the even more limited bioinformatic tool options available for urinary and renal proteomics are highlighted in this review. The need for more attention and input from bioinformaticians is highlighted, so that progressive achievements and releases can be made. With just a handful of existing tools for renal and urinary proteomic research available, this review identifies a gap worth targeting by protein chemists and bioinformaticians. The probable causes for the lack of enthusiasm in this area are also speculated upon in this review. This is the first review that consolidates the bioinformatics applications specifically for renal and urinary proteomics.

Translation of the intrinsically disordered protein α-synuclein is inhibited by a small molecule targeting its structured mRNA

Many proteins are refractory to targeting because they lack small-molecule binding pockets. An alternative to drugging these proteins directly is to target the messenger (m)RNA that encodes them, thereby reducing protein levels. We describe such an approach for the difficult-to-target protein α-synuclein encoded by the SNCA gene. Multiplication of the SNCA gene locus causes dominantly inherited Parkinson's disease (PD), and α-synuclein protein aggregates in Lewy bodies and Lewy neurites in sporadic PD. Thus, reducing the expression of α-synuclein protein is expected to have therapeutic value. Fortuitously, the SNCA mRNA has a structured iron-responsive element (IRE) in its 5' untranslated region (5' UTR) that controls its translation. Using sequence-based design, we discovered small molecules that target the IRE structure and inhibit SNCA translation in cells, the most potent of which is named Synucleozid. Both in vitro and cellular profiling studies showed Synucleozid directly targets the α-synuclein mRNA 5' UTR at the designed site. Mechanistic studies revealed that Synucleozid reduces α-synuclein protein levels by decreasing the amount of SNCA mRNA loaded into polysomes, mechanistically providing a cytoprotective effect in cells. Proteome- and transcriptome-wide studies showed that the compound's selectivity makes Synucleozid suitable for further development. Importantly, transcriptome-wide analysis of mRNAs that encode intrinsically disordered proteins revealed that each has structured regions that could be targeted with small molecules. These findings demonstrate the potential for targeting undruggable proteins at the level of their coding mRNAs. This approach, as applied to SNCA, is a promising disease-modifying therapeutic strategy for PD and other α-synucleinopathies.

Analyzing IDPs in Interactomes

Intrinsically disordered proteins (IDPs) and regions (IDRs) are commonly found in all proteomes analyzed so far. These proteins/regions are subject to numerous posttranslational modifications (PTMs) and alternative splicing, are involved in a wide range of cellular functions, and often facilitate protein-protein interactions (PPIs). Some of these proteins contain molecular recognition features (MoRFs), which are IDRs that bind to partner proteins and undergo disorder-to-order transitions. Although many IDPs/IDRs can fold upon binding, a large fraction of these proteins are known to maintain significant amounts of disorder in their bound states. Being well-recognized interaction specialists, IDPs/IDRs can participate in one-to-many and many-to-one interactions, where one IDP/IDR binds to multiple partners potentially gaining very different structures in the bound state, or where multiple unrelated IDPs/IDRs bind to one partner. As a result, IDPs frequently serve as hubs (i.e., proteins with many links) in complex PPI networks. The goal of this chapter is to describe computational and bioinformatics tools that can be used to look at the disorder status of proteins within a given PPI network and also to gain some knowledge on the disorder-based functionality of the members of this network. To this end, description is provided for some of the use of UniProt and DisProt databases, several databases generating PPI networks (BioGRID, IntAct, DIP, MINT, HPRD, APID, KEGG, and STRING), Composition profiler, some tools for the per-residue disorder predictions (PONDR^® VLXT, PONDR^® VL3, PONDR^® VSL2, PONDR-FIT, and IUPred), binary disorder classifiers CH-plot and CDF-plot and their combined CH-CDF analysis, web-based tools for the visualization of disorder distribution in a query protein (D²P² and MobiDB), as well as some tools for evaluation disorder-based functionality of proteins (ANCHOR, MoRFpred, DEPP, and ModPred).

DEPICTER: Intrinsic Disorder and Disorder Function Prediction Server

Computational predictions of the intrinsic disorder and its functions are instrumental to facilitate annotation for the millions of unannotated proteins. However, access to these predictors is fragmented and requires substantial effort to find them and to collect and combine their results. The DEPICTER (DisorderEd PredictIon CenTER) server provides first-of-its-kind centralized access to 10 popular disorder and disorder function predictions that cover protein and nucleic acids binding, linkers, and moonlighting regions. It automates the prediction process, runs user-selected methods on the server side, visualizes the results, and outputs all predictions in a consistent and easy-to-parse format. DEPICTER also includes two accurate consensus predictors of disorder and disordered protein binding. Empirical tests on an independent (low similarity) benchmark dataset reveal that the computational tools included in DEPICTER generate accurate predictions that are significantly better than the results secured using sequence alignment. The DEPICTER server is freely available at http://biomine.cs.vcu.edu/servers/DEPICTER/.

An interplay of structure and intrinsic disorder in the functionality of peptidylarginine deiminases, a family of key autoimmunity-related enzymes

Citrullination is a post-translation modification of proteins, where the proteinaceous arginine residues are converted to non-coded citrulline residues. The immune tolerance to such citrullinated protein can be lost, leading to inflammatory and autoimmune diseases. Citrullination is a chemical reaction mediated by peptidylarginine deiminase enzymes (PADs), which are a family of calcium-dependent cysteine hydrolase enzymes that includes five isotypes: PAD1, PAD2, PAD3, PAD4, and PAD6. Each PAD has specific substrates and tissue distribution, where it modifies the arginine to produce a citrullinated protein with altered structure and function. All mammalian PADs have a sequence similarity of about 70-95%, whereas in humans, they are 50-55% homologous in their structure and amino acid sequences. Being calcium-dependent hydrolases, PADs are inactive under the physiological level of calcium, but could be activated due to distortions in calcium homeostasis, or when the cellular calcium levels are increased. In this article, we analyze some of the currently available data on the structural properties of human PADs, the mechanisms of their calcium-induced activation, and show that these proteins contain functionally important regions of intrinsic disorder. Citrullination represents an important trigger of multiple physiological and pathological processes, and as a result, PADs are recognized to play a number of important roles in autoimmune diseases, cancer, and neurodegeneration. Therefore, we also review the current state of the art in the development of PAD inhibitors with good potency and selectivity.

Integrating disorder in globular multidomain proteins: Fuzzy sensors and the role of SH3 domains

Intrinsically disordered proteins represent about one third of eukaryotic proteins. An additional third correspond to proteins containing folded domains as well as large intrinsically disordered regions (IDR). While IDRs may represent functionally autonomous domains, in some instances it has become clear that they provide a new layer of regulation for the activity displayed by the folded domains. The sensitivity of the conformational ensembles defining the properties of IDR to small changes in the cellular environment and the capacity to modulate this response through post-translational modifications makes IDR ideal sensors enabling continuous, integrative responses to complex cellular inputs. Folded domains (FD), on the other hand, are ideal effectors, e.g. by catalyzing enzymatic reactions or participating in binary on/off switches. In this perspective review we discuss the possible role of intramolecular fuzzy complexes to integrate the very different dynamic scales of IDR and FD, inspired on the recent observations of such dynamic complexes in Src family kinases, and we explore the possible general role of the SH3 domains connecting IDRs and FD.

Sequence specificity despite intrinsic disorder: How a disease-associated Val/Met polymorphism rearranges tertiary interactions in a long disordered protein

The role of electrostatic interactions and mutations that change charge states in intrinsically disordered proteins (IDPs) is well-established, but many disease-associated mutations in IDPs are charge-neutral. The Val66Met single nucleotide polymorphism (SNP) in precursor brain-derived neurotrophic factor (BDNF) is one of the earliest SNPs to be associated with neuropsychiatric disorders, and the underlying molecular mechanism is unknown. Here we report on over 250 μs of fully-atomistic, explicit solvent, temperature replica-exchange molecular dynamics (MD) simulations of the 91 residue BDNF prodomain, for both the V66 and M66 sequence. The simulations were able to correctly reproduce the location of both local and non-local secondary structure changes due to the Val66Met mutation, when compared with NMR spectroscopy. We find that the change in local structure is mediated via entropic and sequence specific effects. We developed a hierarchical sequence-based framework for analysis and conceptualization, which first identifies “blobs” of 4-15 residues representing local globular regions or linkers. We use this framework within a novel test for enrichment of higher-order (tertiary) structure in disordered proteins; the size and shape of each blob is extracted from MD simulation of the real protein (RP), and used to parameterize a self-avoiding heterogenous polymer (SAHP). The SAHP version of the BDNF prodomain suggested a protein segmented into three regions, with a central long, highly disordered polyampholyte linker separating two globular regions. This effective segmentation was also observed in full simulations of the RP, but the Val66Met substitution significantly increased interactions across the linker, as well as the number of participating residues. The Val66Met substitution replaces β-bridging between V66 and V94 (on either side of the linker) with specific side-chain interactions between M66 and M95. The protein backbone in the vicinity of M95 is then free to form β-bridges with residues 31-41 near the N-terminus, which condenses the protein. A significant role for Met/Met interactions is consistent with previously-observed non-local effects of the Val66Met SNP, as well as established interactions between the Met66 sequence and a Met-rich receptor that initiates neuronal growth cone retraction.

Intrinsically disordered proteins are proteins that have no well-defined structure in at least one functional form. Mutations in one amino acid may still affect their function significantly, especially in subtle ways with cumulative adverse effects on health. Here we report on molecular dynamics simulations of a protein that is critical for neuronal health throughout adulthood (brain-derived neurotrophic factor). We investigate the effects of a mutation carried by 30% of human population, which has been widely studied for its association with aging-related and stress-related disorders, reduced volume of the hippocampus, and variations in episodic memory. We identify a molecular mechanism in which the mutation may change the global conformations of the protein and its ability to bind to receptors.

An intrinsically disordered proteins community for ELIXIR

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are now recognised as major determinants in cellular regulation. This white paper presents a roadmap for future e-infrastructure developments in the field of IDP research within the ELIXIR framework. The goal of these developments is to drive the creation of high-quality tools and resources to support the identification, analysis and functional characterisation of IDPs. The roadmap is the result of a workshop titled “An intrinsically disordered protein user community proposal for ELIXIR” held at the University of Padua. The workshop, and further consultation with the members of the wider IDP community, identified the key priority areas for the roadmap including the development of standards for data annotation, storage and dissemination; integration of IDP data into the ELIXIR Core Data Resources; and the creation of benchmarking criteria for IDP-related software. Here, we discuss these areas of priority, how they can be implemented in cooperation with the ELIXIR platforms, and their connections to existing ELIXIR Communities and international consortia. The article provides a preliminary blueprint for an IDP Community in ELIXIR and is an appeal to identify and involve new stakeholders.

Entropy, Fluctuations, and Disordered Proteins

Entropy should directly reflect the extent of disorder in proteins. By clustering structurally related proteins and studying the multiple-sequence-alignment of the sequences of these clusters, we were able to link between sequence, structure, and disorder information. We introduced several parameters as measures of fluctuations at a given MSA site and used these as representative of the sequence and structure entropy at that site. In general, we found a tendency for negative correlations between disorder and structure, and significant positive correlations between disorder and the fluctuations in the system. We also found evidence for residue-type conservation for those residues proximate to potentially disordered sites. Mutation at the disorder site itself appear to be allowed. In addition, we found positive correlation for disorder and accessible surface area, validating that disordered residues occur in exposed regions of proteins. Finally, we also found that fluctuations in the dihedral angles at the original mutated residue and disorder are positively correlated while dihedral angle fluctuations in spatially proximal residues are negatively correlated with disorder. Our results seem to indicate permissible variability in the disordered site, but greater rigidity in the parts of the protein with which the disordered site interacts. This is another indication that disordered residues are involved in protein function.

Structural Analysis and Conformational Dynamics of STN1 Gene Mutations Involved in Coat Plus Syndrome

The human CST complex (CTC1-STN1-TEN1) is associated with telomere functions including genome stability. We have systemically analyzed the sequence of STN and performed structure analysis to establish its association with the Coat Plus (CP) syndrome. Many deleterious non-synonymous SNPs have been identified and subjected for structure analysis to find their pathogenic association and aggregation propensity. A 100-ns all-atom molecular dynamics simulation of WT, R135T, and D157Y structures revealed significant conformational changes in the case of mutants. Changes in hydrogen bonds, secondary structure, and principal component analysis further support the structural basis of STN1 dysfunction in such mutations. Free energy landscape analysis revealed the presence of multiple energy minima, suggesting that R135T and D157Y mutations destabilize and alter the conformational dynamics of STN1 and thus may be associated with the CP syndrome. Our study provides a valuable direction to understand the molecular basis of CP syndrome and offer a newer therapeutics approach to address CP syndrome.

Ferroptosis - An iron- and disorder-dependent programmed cell death

Programmed cell death (PCD) is an integral component of both developmental and pathological features of an organism. Recently, ferroptosis, a new form of PCD that is dependent on reactive oxygen species and iron, has been described. As with apoptosis, necroptosis, and autophagy, ferroptosis is associated with a large set of proteins assembled in protein-protein interaction (PPI) networks, interactability of which is driven by the presence of intrinsically disordered proteins (IDPs) and IDP regions (IDPRs). Previous investigations have identified the prevalence and functionality of IDPs/IDPRs in non-ferroptosis PCD. The intrinsic disorder in protein structures is used to increase the regulatory powers of these processes. As uncontrolled PCD is associated with the onset of various pathological traits, uncovering the association between intrinsic disorder and ferroptosis-related proteins is crucial. To understand this association, 31 human ferroptosis-related proteins were analyzed via a multi-dimensional array of bioinformatics and computational techniques. In addition, a disorder meta-predictor (PONDR® FIT) was implored to look at the evolutionary conservation of intrinsic disorder in these proteins. This study presents evidence that IDPs and IDPRs are prevalent in ferroptosis. The intrinsic disorder found in ferroptosis has far-reaching functional implications related to ferroptosis-related PPIs and molecular interactions.

Computational prediction of functions of intrinsically disordered regions

Intrinsically disorder regions (IDRs) are abundant in nature, particularly among Eukaryotes. While they facilitate a wide spectrum of cellular functions including signaling, molecular assembly and recognition, translation, transcription and regulation, only several hundred IDRs are annotated functionally. This annotation gap motivates the development of fast and accurate computational methods that predict IDR functions directly from protein sequences. We introduce and describe a comprehensive collection of 25 methods that provide accurate predictions of IDRs that interact with proteins and nucleic acids, that function as flexible linkers and that moonlight multiple functions. Virtually all of these predictors can be accessed online and many were developed in the last few years. They utilize a wide range of predictive architectures and take advantage of modern machine learning algorithms. Our empirical analysis shows that predictors that are available as webservers enjoy high rates of citations, attesting to their practical value and popularity. The most cited methods include DISOPRED3, ANCHOR, alpha-MoRFpred, MoRFpred, fMoRFpred and MoRFCHiBi. We present two case studies to demonstrate that predictions produced by these computational tools are relatively easy to interpret and that they deliver valuable functional clues. However, the current computational tools cover a relatively narrow range of disorder functions. Further development efforts that would cover a broader range of functions should be pursued. We demonstrate that a sufficient amount of functionally annotated IDRs that are associated with several other disorder functions is already available and can be used to design and validate novel predictors.

Structural and functional impact of non-synonymous SNPs in the CST complex subunit TEN1: structural genomics approach

TEN1 protein is a key component of CST complex, implicated in maintaining the telomere homeostasis, and provides stability to the eukaryotic genome. Mutations in TEN1 gene have higher chances of deleterious impact; thus, interpreting the number of mutations and their consequential impact on the structure, stability, and function is essentially important. Here, we have investigated the structural and functional consequences of nsSNPs in the TEN1 gene. A wide array of sequence- and structure-based computational prediction tools were employed to identify the effects of 78 nsSNPs on the structure and function of TEN1 protein and to identify the deleterious nsSNPs. These deleterious or destabilizing nsSNPs are scattered throughout the structure of TEN1. However, major mutations were observed in the α1-helix (12–16 residues) and β5-strand (88–96 residues). We further observed that mutations at the C-terminal region were having higher tendency to form aggregate. In-depth structural analysis of these mutations reveals that the pathogenicity of these mutations are driven mainly through larger structural changes because of alterations in non-covalent interactions. This work provides a blueprint to pinpoint the possible consequences of pathogenic mutations in the CST complex subunit TEN1.

Recent Advances in Computational Protocols Addressing Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDP) are abundant in the human genome and have recently emerged as major therapeutic targets for various diseases. Unlike traditional proteins that adopt a definitive structure, IDPs in free solution are disordered and exist as an ensemble of conformations. This enables the IDPs to signal through multiple signaling pathways and serve as scaffolds for multi-protein complexes. The challenge in studying IDPs experimentally stems from their disordered nature. Nuclear magnetic resonance (NMR), circular dichroism, small angle X-ray scattering, and single molecule Förster resonance energy transfer (FRET) can give the local structural information and overall dimension of IDPs, but seldom provide a unified picture of the whole protein. To understand the conformational dynamics of IDPs and how their structural ensembles recognize multiple binding partners and small molecule inhibitors, knowledge-based and physics-based sampling techniques are utilized in-silico, guided by experimental structural data. However, efficient sampling of the IDP conformational ensemble requires traversing the numerous degrees of freedom in the IDP energy landscape, as well as force-fields that accurately model the protein and solvent interactions. In this review, we have provided an overview of the current state of computational methods for studying IDP structure and dynamics and discussed the major challenges faced in this field.

Quality and bias of protein disorder predictors

Disorder in proteins is vital for biological function, yet it is challenging to characterize. Therefore, methods for predicting protein disorder from sequence are fundamental. Currently, predictors are trained and evaluated using data from X-ray structures or from various biochemical or spectroscopic data. However, the prediction accuracy of disordered predictors is not calibrated, nor is it established whether predictors are intrinsically biased towards one of the extremes of the order-disorder axis. We therefore generated and validated a comprehensive experimental benchmarking set of site-specific and continuous disorder, using deposited NMR chemical shift data. This novel experimental data collection is fully appropriate and represents the full spectrum of disorder. We subsequently analyzed the performance of 26 widely-used disorder prediction methods and found that these vary noticeably. At the same time, a distinct bias for over-predicting order was identified for some algorithms. Our analysis has important implications for the validity and the interpretation of protein disorder, as utilized, for example, in assessing the content of disorder in proteomes.

First Experimental Assessment of Protein Intrinsic Disorder Involvement in an RNA Virus Natural Adaptive Process

Intrinsic disorder (ID) in proteins is defined as a lack of stable structure in physiological conditions. Intrinsically disordered regions (IDRs) are highly abundant in some RNA virus proteomes. Low topological constraints exerted on IDRs are expected to buffer the effect of numerous deleterious mutations and could be related to the remarkable adaptive potential of RNA viruses to overcome resistance of their host. To experimentally test this hypothesis in a natural pathosystem, a set of four variants of Potato virus Y (PVY; Potyvirus genus) containing various ID degrees in the Viral genome-linked (VPg) protein, a key determinant of potyvirus adaptation, was designed. To estimate the ID contribution to the VPg-based PVY adaptation, the adaptive ability of the four PVY variants was monitored in the pepper host (Capsicum annuum) carrying a recessive resistance gene. Intriguingly, the two mutants with the highest ID content displayed a significantly higher ability to restore infection in the resistant host, whereas the less intrinsically disordered mutant was unable to restore infection. The role of ID on virus adaptation may be due either to a larger exploration of evolutionary pathways or the minimization of fitness penalty caused by resistance-breaking mutations. This pioneering study strongly suggests the positive impact of ID in an RNA virus adaptive capacity.

On the Need to Develop Guidelines for Characterizing and Reporting Intrinsic Disorder in Proteins

Since the early 2000s, numerous computational tools have been created and used to predict intrinsic disorder in proteins. At present, the output from these algorithms is difficult to interpret in the absence of standards or references for comparison. There are many reasons to establish a set of standard-based guidelines to evaluate computational protein disorder predictions. This viewpoint explores a handful of these reasons, including standardizing nomenclature to improve communication, rigor and reproducibility, and making it easier for newcomers to enter the field. An approach for reporting predicted disorder in single proteins with respect to whole proteomes is discussed. The suggestions are not intended to be formulaic; they should be viewed as a starting point to establish guidelines for interpreting and reporting computational protein disorder predictions.

c.259A>C in the fibrinogen gene of alpha chain (FGA) is a fibrinogen with thrombotic phenotype

Introduction

Dysfibrinogenemia is a rare inherited disease that results from mutation in one of the three fibrinogen genes. Diagnosis can be misleading since it may present as a bleeding tendency or thrombosis and a specific coagulation test for diagnosis is not routinely available

Aim

To search for a new candidate gene of thrombophilia in a family with three generations of arterial and venous thrombosis.

Methods

Whole exome sequencing followed by Sanger validation and segregation analysis was carried out. In addition, structural modeling was performed. Screening for thrombophilia along with blood counts, prothrombin time, activated partial thromboplastin, thrombin, reptilase time, and fibrinogen was done in each patient.

Results and discussion

A missense c.259A>C, p.K87Q (g.chr4: 155510050A-C) (rs764281241) in FGA gene was found in all three siblings without any other known thrombophilia marker to explain thrombosis in all three siblings. It is expected to be damaging by six out of seven prediction programs and is very rare in the entire population with Exac=0.000008.

Conclusion

The occurrence of the c.259A>C mutation in FGA may well explain the thrombosis phenotype of the affected family and is suggested as a new marker for thrombophilia phenotype.

Thermodynamically driven assemblies and liquid-liquid phase separations in biology

The sustenance of life depends on the high degree of organization that prevails through different levels of living organisms, from subcellular structures such as biomolecular complexes and organelles to tissues and organs. The physical origin of such organization is not fully understood, and even though it is clear that cells and organisms cannot maintain their integrity without consuming energy, there is growing evidence that individual assembly processes can be thermodynamically driven and occur spontaneously due to changes in thermodynamic variables such as intermolecular interactions and concentration. Understanding the phase separation in vivo requires a multidisciplinary approach, integrating the theory and physics of phase separation with experimental and computational techniques. This paper aims at providing a brief overview of the physics of phase separation and its biological implications, with a particular focus on the assembly of membraneless organelles. We discuss the underlying physical principles of phase separation from its thermodynamics to its kinetics. We also overview the wide range of methods utilized for experimental verification and characterization of phase separation of membraneless organelles, as well as the utility of molecular simulations rooted in thermodynamics and statistical physics in understanding the governing principles of thermodynamically driven biological self-assembly processes.

A new technique for predicting intrinsically disordered regions based on average distance map constructed with inter-residue average distance statistics

Background

It had long been thought that a protein exhibits its specific function through its own specific 3D-structure under physiological conditions. However, subsequent research has shown that there are many proteins without specific 3D-structures under physiological conditions, so-called intrinsically disordered proteins (IDPs). This study presents a new technique for predicting intrinsically disordered regions in a protein, based on our average distance map (ADM) technique. The ADM technique was developed to predict compact regions or structural domains in a protein. In a protein containing partially disordered regions, a domain region is likely to be ordered, thus it is unlikely that a disordered region would be part of any domain. Therefore, the ADM technique is expected to also predict a disordered region between domains.

Results

The results of our new technique are comparable to the top three performing techniques in the community-wide CASP10 experiment. We further discuss the case of p53, a tumor-suppressor protein, which is the most significant protein among cell cycle regulatory proteins. This protein exhibits a disordered character as a monomer but an ordered character when two p53s form a dimer.

Conclusion

Our technique can predict the location of an intrinsically disordered region in a protein with an accuracy comparable to the best techniques proposed so far. Furthermore, it can also predict a core region of IDPs forming definite 3D structures through interactions, such as dimerization. The technique in our study may also serve as a means of predicting a disordered region which would become an ordered structure when binding to another protein.

Electronic supplementary material

The online version of this article (10.1186/s12900-019-0101-3) contains supplementary material, which is available to authorized users.