DisMeta: a meta server for construct design and optimization

Mieap forms membrane-less organelles involved in cardiolipin metabolism

Biomolecular condensates (BCs) are formed by proteins with intrinsically disordered regions (IDRs) via liquid-liquid phase separation. Mieap/Spata18, a p53-inducible protein, participates in suppression of colorectal tumors by promoting mitochondrial quality control. However, the regulatory mechanism involved remains unclear. Here, we report that Mieap is an IDR-containing protein that drives formation of BCs involved in cardiolipin metabolism. Mieap BCs specifically phase separate the mitochondrial phospholipid, cardiolipin. Mieap directly binds to cardiolipin in vitro. Lipidomic analysis of cardiolipin suggests that Mieap promotes enzymatic reactions in cardiolipin biosynthesis and remodeling. Accordingly, four cardiolipin biosynthetic enzymes, TAMM41, PGS1, PTPMT1, and CRLS1 and two remodeling enzymes, PLA2G6 and TAZ, are phase-separated by Mieap BCs. Mieap-deficient cells exhibit altered crista structure, leading to decreased respiration activity and ATP production in mitochondria. These results suggest that Mieap may form membrane-less organelles to compartmentalize and facilitate cardiolipin metabolism, thus potentially contributing to mitochondrial quality control.

Biomolecular Condensates: Structure, Functions, Methods of Research

The term "biomolecular condensates" is used to describe membraneless compartments in eukaryotic cells, accumulating proteins and nucleic acids. Biomolecular condensates are formed as a result of liquid-liquid phase separation (LLPS). Often, they demonstrate properties of liquid-like droplets or gel-like aggregates; however, some of them may appear to have a more complex structure and high-order organization. Membraneless microcompartments are involved in diverse processes both in cytoplasm and in nucleus, among them ribosome biogenesis, regulation of gene expression, cell signaling, and stress response. Condensates properties and structure could be highly dynamic and are affected by various internal and external factors, e.g., concentration and interactions of components, solution temperature, pH, osmolarity, etc. In this review, we discuss variety of biomolecular condensates and their functions in live cells, describe their structure variants, highlight domain and primary sequence organization of the constituent proteins and nucleic acids. Finally, we describe current advances in methods that characterize structure, properties, morphology, and dynamics of biomolecular condensates in vitro and in vivo.

Phase separations in oncogenesis, tumor progressions and metastasis: a glance from hallmarks of cancer

Liquid-liquid phase separation (LLPS) is a novel principle for interpreting precise spatiotemporal coordination in living cells through biomolecular condensate (BMC) formation via dynamic aggregation. LLPS changes individual molecules into membrane-free, droplet-like BMCs with specific functions, which coordinate various cellular activities. The formation and regulation of LLPS are closely associated with oncogenesis, tumor progressions and metastasis, the specific roles and mechanisms of LLPS in tumors still need to be further investigated at present. In this review, we comprehensively summarize the conditions of LLPS and identify mechanisms involved in abnormal LLPS in cancer processes, including tumor growth, metastasis, and angiogenesis from the perspective of cancer hallmarks. We have also reviewed the clinical applications of LLPS in oncologic areas. This systematic summary of dysregulated LLPS from the different dimensions of cancer hallmarks will build a bridge for determining its specific functions to further guide basic research, finding strategies to intervene in LLPS, and developing relevant therapeutic approaches.

Identification and Structural Modeling of the RNA Polymerase Omega Subunits in Chlamydiae and Other Obligate Intracellular Bacteria

Gene transcription in bacteria is carried out by the multisubunit RNA polymerase (RNAP), which is composed of a catalytic core enzyme and a promoter-recognizing σ factor. The core enzyme comprises two α subunits, one β subunit, one β' subunit, and one ω subunit. The ω subunit plays critical roles in the assembly of the core enzyme and other cellular functions, including the regulation of bacterial growth, the stress response, and biofilm formation. However, the identity of an ω subunit for the obligate intracellular bacterium Chlamydia has not previously been determined. Here, we report the identification of the hypothetical protein CTL0286 as the probable chlamydial ω subunit based on sequence, synteny, and AlphaFold and AlphaFold-Multimer three-dimensional-structure predictions. Our findings indicate that CTL0286 functions as the missing ω subunit of chlamydial RNAP. Our extended analysis also indicates that all obligate intracellular bacteria have ω orthologs. IMPORTANCE Chlamydiae are obligate intracellular bacteria that replicate only inside eukaryotic cells. Previously, it has not been possible to identify a candidate gene encoding the chlamydial RNA polymerase ω subunit, and it has been hypothesized that the chlamydial RNA polymerase ω subunit was lost in the evolutionary process through which Chlamydiae reduced their genome size and proteome sizes to adapt to an obligate intracellular lifestyle. Here, we report the identification of the chlamydial RNA polymerase ω subunit, based on conserved sequence, conserved synteny, AlphaFold-predicted conserved three-dimensional structure, and AlfaFold-Multimer-predicted conserved interactions. Our identification of the previously elusive chlamydial RNA polymerase ω subunit sets the stage for investigation of its roles in regulation of gene expression during chlamydial growth, development, and stress responses, and sets the stage for preparation and study of the intact chlamydial RNA polymerase and its interactions with inhibitors.

Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions

Intrinsically Disordered Proteins (IDPs) and Regions (IDRs) exist widely. Although without well-defined structures, they participate in many important biological processes. In addition, they are also widely related to human diseases and have become potential targets in drug discovery. However, there is a big gap between the experimental annotations related to IDPs/IDRs and their actual number. In recent decades, the computational methods related to IDPs/IDRs have been developed vigorously, including predicting IDPs/IDRs, the binding modes of IDPs/IDRs, the binding sites of IDPs/IDRs, and the molecular functions of IDPs/IDRs according to different tasks. In view of the correlation between these predictors, we have reviewed these prediction methods uniformly for the first time, summarized their computational methods and predictive performance, and discussed some problems and perspectives.

Novel insights into construct toxicity, strain optimization, and primary sequence design for producing recombinant silk fibroin and elastin-like peptide in E. coli

Spider silk proteins (spidroins) are a remarkable class of biomaterials that exhibit a unique combination of high-value attributes and can be processed into numerous morphologies for targeted applications in diverse fields. Recombinant production of spidroins represents the most promising route towards establishing the industrial production of the material, however, recombinant spider silk production suffers from fundamental difficulties that includes low titers, plasmid instability, and translational inefficiencies. In this work, we sought to gain a deeper understanding of upstream bottlenecks that exist in the field through the production of a panel of systematically varied spidroin sequences in multiple E. coli strains. A restriction on basal expression and specific genetic mutations related to stress responses were identified as primary factors that facilitated higher titers of the recombinant silk constructs. Using these findings, a novel strain of E. coli was created that produces recombinant silk constructs at levels 4-33 times higher than standard BL21(DE3). However, these findings did not extend to a similar recombinant protein, an elastin-like peptide. It was found that the recombinant silk proteins, but not the elastin-like peptide, exert toxicity on the E. coli host system, possibly through their high degree of intrinsic disorder. Along with strain engineering, a bioprocess design that utilizes longer culturing times and attenuated induction was found to raise recombinant silk titers by seven-fold and mitigate toxicity. Targeted alteration to the primary sequence of the recombinant silk constructs was also found to mitigate toxicity. These findings identify multiple points of focus for future work seeking to further optimize the recombinant production of silk proteins and is the first work to identify the intrinsic disorder and subsequent toxicity of certain spidroin constructs as a primary factor related to the difficulties of production.

Supramolecular assembly of GSK3α as a cellular response to amino acid starvation

The tolerance of amino acid starvation is fundamental to robust cellular fitness. Asparagine depletion is lethal to some cancer cells, a vulnerability that can be exploited clinically. We report that resistance to asparagine starvation is uniquely dependent on an N-terminal low-complexity domain of GSK3α, which its paralog GSK3β lacks. In response to depletion of specific amino acids, including asparagine, leucine, and valine, this domain mediates supramolecular assembly of GSK3α with ubiquitin-proteasome system components in spatially sequestered cytoplasmic bodies. This effect is independent of mTORC1 or GCN2. In normal cells, GSK3α promotes survival during essential amino acid starvation. In human leukemia, GSK3α body formation predicts asparaginase resistance, and sensitivity to asparaginase combined with a GSK3α inhibitor. We propose that GSK3α body formation provides a cellular mechanism to maximize the catalytic efficiency of proteasomal protein degradation in response to amino acid starvation, an adaptive response co-opted by cancer cells for asparaginase resistance.

Phase separation of FG-nucleoporins in nuclear pore complexes

The nuclear envelope (NE) is a bilayer membrane that separates and physically isolates the genetic material from the cytoplasm. Nuclear pore complexes (NPCs) are cylindrical structures embedded in the NE and remain the sole channel of communication between the nucleus and the cytoplasm. The interior of NPCs contains densely packed intrinsically disordered FG-nucleoporins (FG-Nups), consequently forming a permeability barrier. This barrier facilitates the selection and specificity of the cargoes that are imported, exported, or shuttled through the NPCs. Recent studies have revealed that FG-Nups undergo the process of liquid-liquid phase separation into liquid droplets. Moreover, these liquid droplets mimic the permeability barrier observed in the interior of NPCs. This review highlights the phase separation of FG-Nups occurring inside the NPCs rooted in the NE. We discuss the phase separation of FG-Nups and compare the different aspects contributing to their phase separation. Furthermore, several diseases caused by the aberrant phase separation of the proteins are examined with respect to NEs. By understanding the fundamental process of phase separation at the nuclear membrane, the review seeks to explore the parameters influencing this phenomenon as well as its importance, ultimately paving the way for better research on the structure-function relationship of biomolecular condensates.

Deep learning in prediction of intrinsic disorder in proteins

Intrinsic disorder prediction is an active area that has developed over 100 predictors. We identify and investigate a recent trend towards the development of deep neural network (DNN)-based methods. The first DNN-based method was released in 2013 and since 2019 deep learners account for majority of the new disorder predictors. We find that the 13 currently available DNN-based predictors are diverse in their topologies, sizes of their networks and the inputs that they utilize. We empirically show that the deep learners are statistically more accurate than other types of disorder predictors using the blind test dataset from the recent community assessment of intrinsic disorder predictions (CAID). We also identify several well-rounded DNN-based predictors that are accurate, fast and/or conveniently available. The popularity, favorable predictive performance and architectural flexibility suggest that deep networks are likely to fuel the development of future disordered predictors. Novel hybrid designs of deep networks could be used to adequately accommodate for diversity of types and flavors of intrinsic disorder. We also discuss scarcity of the DNN-based methods for the prediction of disordered binding regions and the need to develop more accurate methods for this prediction.

Surveying over 100 predictors of intrinsic disorder in proteins

INTRODUCTION

Intrinsic disorder prediction field develops, assesses, and deploys computational predictors of disorder in protein sequences and constructs and disseminates databases of these predictions. Over 40 years of research resulted in the release of numerous resources.

AREAS COVERED

We identify and briefly summarize the most comprehensive to date collection of over 100 disorder predictors. We focus on their predictive models, availability and predictive performance. We categorize and study them from a historical point of view to highlight informative trends.

EXPERT OPINION

We find a consistent trend of improvements in predictive quality as newer and more advanced predictors are developed. The original focus on machine learning methods has shifted to meta-predictors in early 2010s, followed by a recent transition to deep learning. The use of deep learners will continue in foreseeable future given recent and convincing success of these methods. Moreover, a broad range of resources that facilitate convenient collection of accurate disorder predictions is available to users. They include web servers and standalone programs for disorder prediction, servers that combine prediction of disorder and disorder functions, and large databases of pre-computed predictions. We also point to the need to address the shortage of accurate methods that predict disordered binding regions.

Intrinsically disordered features of carbonic anhydrase IX proteoglycan-like domain

hCA IX is a multi-domain protein belonging to the family of hCAs which are ubiquitous zinc enzymes that catalyze the reversible hydration of CO2 to HCO3- and H+. hCA IX is a tumor-associated enzyme with a limited distribution in normal tissues, but over-expressed in many tumors, and is a promising drug target. Although many studies concerning the CA IX catalytic domain were performed, little is known about the proteoglycan-like (PG-like) domain of hCA IX which has been poorly investigated so far. Here we attempt to fill this gap by providing an overview on the functional, structural and therapeutic studies of the PG-like domain of hCA IX which represents a unique feature within the CA family. The main studies and recent advances concerning PG role in modulating hCA IX catalytic activity as well as in tumor spreading and migration are here reported. Special attention has been paid to the newly discovered disordered features of the PG domain which open new perspectives about its molecular mechanisms of action under physiological and pathological conditions, since disorder is likely involved in mediating interactions with partner proteins. The emerged disordered features of PG domain will be explored for putative diagnostic and therapeutic applications involving CA IX targeting in tumors.

A common binding motif in the ET domain of BRD3 forms polymorphic structural interfaces with host and viral proteins

The extraterminal (ET) domain of BRD3 is conserved among BET proteins (BRD2, BRD3, BRD4), interacting with multiple host and viral protein-protein networks. Solution NMR structures of complexes formed between the BRD3 ET domain and either the 79-residue murine leukemia virus integrase (IN) C-terminal domain (IN_329-408) or its 22-residue IN tail peptide (IN_386-407) alone reveal similar intermolecular three-stranded β-sheet formations. ¹⁵N relaxation studies reveal a 10-residue linker region (IN_379-388) tethering the SH3 domain (IN_329-378) to the ET-binding motif (IN_389-405):ET complex. This linker has restricted flexibility, affecting its potential range of orientations in the IN:nucleosome complex. The complex of the ET-binding peptide of the host NSD3 protein (NSD3_148-184) and the BRD3 ET domain includes a similar three-stranded β-sheet interaction, but the orientation of the β hairpin is flipped compared with the two IN:ET complexes. These studies expand our understanding of molecular recognition polymorphism in complexes of ET-binding motifs with viral and host proteins.

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.

Computational Prediction of Intrinsic Disorder in Protein Sequences with the disCoP Meta-predictor

Intrinsically disordered proteins are either entirely disordered or contain disordered regions in their native state. These proteins and regions function without the prerequisite of a stable structure and were found to be abundant across all kingdoms of life. Experimental annotation of disorder lags behind the rapidly growing number of sequenced proteins, motivating the development of computational methods that predict disorder in protein sequences. DisCoP is a user-friendly webserver that provides accurate sequence-based prediction of protein disorder. It relies on meta-architecture in which the outputs generated by multiple disorder predictors are combined together to improve predictive performance. The architecture of disCoP is presented, and its accuracy relative to several other disorder predictors is briefly discussed. We describe usage of the web interface and explain how to access and read results generated by this computational tool. We also provide an example of prediction results and interpretation. The disCoP's webserver is publicly available at http://biomine.cs.vcu.edu/servers/disCoP/ .

DISOselect: Disorder predictor selection at the protein level

The intense interest in the intrinsically disordered proteins in the life science community, together with the remarkable advancements in predictive technologies, have given rise to the development of a large number of computational predictors of intrinsic disorder from protein sequence. While the growing number of predictors is a positive trend, we have observed a considerable difference in predictive quality among predictors for individual proteins. Furthermore, variable predictor performance is often inconsistent between predictors for different proteins, and the predictor that shows the best predictive performance depends on the unique properties of each protein sequence. We propose a computational approach, DISOselect, to estimate the predictive performance of 12 selected predictors for individual proteins based on their unique sequence-derived properties. This estimation informs the users about the expected predictive quality for a selected disorder predictor and can be used to recommend methods that are likely to provide the best quality predictions. Our solution does not depend on the results of any disorder predictor; the estimations are made based solely on the protein sequence. Our solution significantly improves predictive performance, as judged with a test set of 1,000 proteins, when compared to other alternatives. We have empirically shown that by using the recommended methods the overall predictive performance for a given set of proteins can be improved by a statistically significant margin. DISOselect is freely available for non-commercial users through the webserver at http://biomine.cs.vcu.edu/servers/DISOselect/.

Accuracy of protein-level disorder predictions

Experimental annotations of intrinsic disorder are available for 0.1% of 147 000 000 of currently sequenced proteins. Over 60 sequence-based disorder predictors were developed to help bridge this gap. Current benchmarks of these methods assess predictive performance on datasets of proteins; however, predictions are often interpreted for individual proteins. We demonstrate that the protein-level predictive performance varies substantially from the dataset-level benchmarks. Thus, we perform first-of-its-kind protein-level assessment for 13 popular disorder predictors using 6200 disorder-annotated proteins. We show that the protein-level distributions are substantially skewed toward high predictive quality while having long tails of poor predictions. Consequently, between 57% and 75% proteins secure higher predictive performance than the currently used dataset-level assessment suggests, but as many as 30% of proteins that are located in the long tails suffer low predictive performance. These proteins typically have relatively high amounts of disorder, in contrast to the mostly structured proteins that are predicted accurately by all 13 methods. Interestingly, each predictor provides the most accurate results for some number of proteins, while the best-performing at the dataset-level method is in fact the best for only about 30% of proteins. Moreover, the majority of proteins are predicted more accurately than the dataset-level performance of the most accurate tool by at least four disorder predictors. While these results suggests that disorder predictors outperform their current benchmark performance for the majority of proteins and that they complement each other, novel tools that accurately identify the hard-to-predict proteins and that make accurate predictions for these proteins are needed.

A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction

Intrinsically disordered proteins and regions are widely distributed in proteins, which are associated with many biological processes and diseases. Accurate prediction of intrinsically disordered proteins and regions is critical for both basic research (such as protein structure and function prediction) and practical applications (such as drug development). During the past decades, many computational approaches have been proposed, which have greatly facilitated the development of this important field. Therefore, a comprehensive and updated review is highly required. In this regard, we give a review on the computational methods for intrinsically disordered protein and region prediction, especially focusing on the recent development in this field. These computational approaches are divided into four categories based on their methodologies, including physicochemical-based method, machine-learning-based method, template-based method and meta method. Furthermore, their advantages and disadvantages are also discussed. The performance of 40 state-of-the-art predictors is directly compared on the target proteins in the task of disordered region prediction in the 10th Critical Assessment of protein Structure Prediction. A more comprehensive performance comparison of 45 different predictors is conducted based on seven widely used benchmark data sets. Finally, some open problems and perspectives are discussed.

TET2 missense variants in human neoplasia. A proposal of structural and functional classification

Background

The human TET2 gene plays a pivotal role in the epigenetic regulation of normal and malignant hematopoiesis. Somatic TET2 mutations have been repeatedly identified in age‐related clonal hematopoiesis and in myeloid neoplasms ranging from acute myeloid leukemia (AML) to myeloproliferative neoplasms. However, there have been no attempts to systematically explore the structural and functional consequences of the hundreds of TET2 missense variants reported to date.

Methods

We have sequenced the TET2 gene in 189 Spanish AML patients using Sanger sequencing and NGS protocols. Next, we performed a thorough bioinformatics analysis of TET2 protein and of the expected impact of all reported TET2 missense variants on protein structure and function, exploiting available structure‐and‐function information as well as 3D structure prediction tools.

Results

We have identified 38 TET2 allelic variants in the studied patients, including two frequent SNPs: p.G355D (10 cases) and p.I1762V (28 cases). Four of the detected mutations are reported here for the first time: c.122C>T (p.P41L), c.4535C>G (p.A1512G), c.4760A>G (p.D1587G), and c.5087A>T (p.Y1696F). We predict a complex multidomain architecture for the noncatalytic regions of TET2, and in particular the presence of well‐conserved α+β globular domains immediately preceding and following the actual catalytic unit. Further, we provide a rigorous interpretation of over 430 missense SNVs that affect the TET2 catalytic domain, and we hypothesize explanations for ~700 additional variants found within the regulatory regions of the protein. Finally, we propose a systematic classification of all missense mutants and SNPs reported to date into three major categories (severe, moderate, and mild), based on their predicted structural and functional impact.

Conclusions

The proposed classification of missense TET2 variants would help to assess their clinical impact on human neoplasia and may guide future structure‐and‐function investigations of TET family members.

Dynamic oligomerization of hRAGE's transmembrane and cytoplasmic domains within SDS micelles

The human Receptor for Advanced Glycation End Products (hRAGE) is a pattern recognition receptor implicated in inflammation and adhesion. It is involved in both innate and adaptive immunity. Its aberrant signaling is tied to the pathogenesis of diabetic complications, neurodegenerative disorders, and chronic inflammatory responses. Previous structural studies have focused on its extracellular domains with their canonical constant and variable Ig folds, and to a much lesser extent, the intrinsically disorder cytoplasmic domain. No experimental data are reported on the transmembrane domain, which is integral to signaling. We have constructed, expressed and purified the transmembrane domain attached to the cytoplasmic domain of hRAGE in E. coli. Multiple self-associated forms of these domains were observed in vitro. This pattern of mixed oligomers resembled previously reported in vivo forms of the complete receptor. The self-association of these two domains was further characterized using: SDS-PAGE, intrinsic tryptophan fluorescence and heteronuclear NMR spectroscopy. NMR conditions were assessed across time and temperature within micelles. Our data show that the transmembrane and cytoplasmic domains of hRAGE undergo dynamic oligomerizations that can occur in the absence of its extracellular domains or ligand binding. And, such associations are only partially disrupted even with prolonged incubation in strong detergents.

Modular type I polyketide synthase acyl carrier protein domains share a common N-terminally extended fold

Acyl carrier protein (ACP) domains act as interaction hubs within modular polyketide synthase (PKS) systems, employing specific protein-protein interactions to present acyl substrates to a series of enzyme active sites. Many domains from the multimodular PKS that generates the toxin mycolactone display an unusually high degree of sequence similarity, implying that the few sites which vary may do so for functional reasons. When domain boundaries based on prior studies were used to prepare two isolated ACP segments from this system for studies of their interaction properties, one fragment adopted the expected tertiary structure, but the other failed to fold, despite sharing a sequence identity of 49%. Secondary structure prediction uncovered a previously undetected helical region (H0) that precedes the canonical helix-bundle ACP topology in both cases. This article reports the NMR solution structures of two N-terminally extended mycolactone mACP constructs, mH0ACPa and mH0ACPb, both of which possess an additional α-helix that behaves like a rigid component of the domain. The interactions of these species with a phosphopantetheinyl transferase and a ketoreductase domain are unaffected by the presence of H0, but a shorter construct that lacks the H0 region is shown to be substantially less thermostable than mH0ACPb. Bioinformatics analysis suggests that the extended H0-ACP motif is present in 98% of type I cis-acyltransferase PKS chain-extension modules. The polypeptide linker that connects an H0-ACP motif to the preceding domain must therefore be ~12 residues shorter than previously thought, imposing strict limits on ACP-mediated substrate delivery within and between PKS modules.

Improvement of the Activity and Stability of Starch-Debranching Pullulanase from Bacillus naganoensis via Tailoring of the Active Sites Lining the Catalytic Pocket

Pullulanases are well-known debranching enzymes that hydrolyze α-1,6-glycosidic linkages in starch and oligosaccharides. However, most of the pullulanases exhibit limited activity for practical applications. Here, two sites (787 and 621) lining the catalytic pocket of Bacillus naganoensis pullulanase were identified as being critical for enzymatic activity by triple-code saturation mutagenesis. Subsequently, both sites were subjected to NNK-based saturation mutagenesis to obtain positive variants. Among the variants showing enhanced activity, the enzymatic activity and specific activity of D787C were 1.5-fold higher than those of the wild-type (WT). D787C also showed a 1.8-fold increase in k_cat and a 1.7-fold increase in k_cat/ K_m. In addition, D787C maintained higher activity compared with that of WT at temperatures over 60 °C. All the positive variants showed higher acid resistance, with D787C maintaining 90% residual activity at pH 4.0. Thus, enzymes with improved properties were obtained by saturation mutagenesis at the active site.

Methods for Physical Characterization of Phase-Separated Bodies and Membrane-less Organelles

Membrane-less organelles are cellular structures which arise through the phenomenon of phase separation. This process enables compartmentalization of specific sets of macromolecules (e.g., proteins, nucleic acids), thereby regulating cellular processes by increasing local concentration, and modulating the structure and dynamics of their constituents. Understanding the connection between structure, material properties and function of membrane-less organelles requires inter-disciplinary approaches, which address length and timescales that span several orders of magnitude (e.g., Ångstroms to micrometer, picoseconds to hours). In this review, we discuss the wide variety of methods that have been applied to characterize the morphology, rheology, structure and dynamics of membrane-less organelles and their components, in vitro and in live cells.

Decision-Tree Based Meta-Strategy Improved Accuracy of Disorder Prediction and Identified Novel Disordered Residues Inside Binding Motifs

Using computational techniques to identify intrinsically disordered residues is practical and effective in biological studies. Therefore, designing novel high-accuracy strategies is always preferable when existing strategies have a lot of room for improvement. Among many possibilities, a meta-strategy that integrates the results of multiple individual predictors has been broadly used to improve the overall performance of predictors. Nonetheless, a simple and direct integration of individual predictors may not effectively improve the performance. In this project, dual-threshold two-step significance voting and neural networks were used to integrate the predictive results of four individual predictors, including: DisEMBL, IUPred, VSL2, and ESpritz. The new meta-strategy has improved the prediction performance of intrinsically disordered residues significantly, compared to all four individual predictors and another four recently-designed predictors. The improvement was validated using five-fold cross-validation and in independent test datasets.

VPS13A and VPS13C are lipid transport proteins differentially localized at ER contact sites

Mutations in the human VPS13 genes are responsible for neurodevelopmental and neurodegenerative disorders including chorea acanthocytosis (VPS13A) and Parkinson's disease (VPS13C). The mechanisms of these diseases are unknown. Genetic studies in yeast hinted that Vps13 may have a role in lipid exchange between organelles. In this study, we show that the N-terminal portion of VPS13 is tubular, with a hydrophobic cavity that can solubilize and transport glycerolipids between membranes. We also show that human VPS13A and VPS13C bind to the ER, tethering it to mitochondria (VPS13A), to late endosome/lysosomes (VPS13C), and to lipid droplets (both VPS13A and VPS13C). These findings identify VPS13 as a lipid transporter between the ER and other organelles, implicating defects in membrane lipid homeostasis in neurological disorders resulting from their mutations. Sequence and secondary structure similarity between the N-terminal portions of Vps13 and other proteins such as the autophagy protein ATG2 suggest lipid transport roles for these proteins as well.

Biochemical, biophysical and molecular dynamics studies on the proteoglycan-like domain of carbonic anhydrase IX

Human carbonic anhydrase IX (hCA IX) is a tumour-associated enzyme present in a limited number of normal tissues, but overexpressed in several malignant human tumours. It is a transmembrane protein, where the extracellular region consists of a greatly investigated catalytic CA domain and a much less investigated proteoglycan-like (PG) domain. Considering its important role in tumour biology, here, we report for the first time the full characterization of the PG domain, providing insights into its structural and functional features. In particular, this domain has been produced at high yields in bacterial cells and characterized by means of biochemical, biophysical and molecular dynamics studies. Results show that it belongs to the family of intrinsically disordered proteins, being globally unfolded with only some local residual polyproline II secondary structure. The observed conformational flexibility may have several important roles in tumour progression, facilitating interactions of hCA IX with partner proteins assisting tumour spreading and progression.

Molecular heterogeneity and CXorf67 alterations in posterior fossa group A (PFA) ependymomas

Of nine ependymoma molecular groups detected by DNA methylation profiling, the posterior fossa type A (PFA) is most prevalent. We used DNA methylation profiling to look for further molecular heterogeneity among 675 PFA ependymomas. Two major subgroups, PFA-1 and PFA-2, and nine minor subtypes were discovered. Transcriptome profiling suggested a distinct histogenesis for PFA-1 and PFA-2, but their clinical parameters were similar. In contrast, PFA subtypes differed with respect to age at diagnosis, gender ratio, outcome, and frequencies of genetic alterations. One subtype, PFA-1c, was enriched for 1q gain and had a relatively poor outcome, while patients with PFA-2c ependymomas showed an overall survival at 5 years of > 90%. Unlike other ependymomas, PFA-2c tumors express high levels of OTX2, a potential biomarker for this ependymoma subtype with a good prognosis. We also discovered recurrent mutations among PFA ependymomas. H3 K27M mutations were present in 4.2%, occurring only in PFA-1 tumors, and missense mutations in an uncharacterized gene, CXorf67, were found in 9.4% of PFA ependymomas, but not in other groups. We detected high levels of wildtype or mutant CXorf67 expression in all PFA subtypes except PFA-1f, which is enriched for H3 K27M mutations. PFA ependymomas are characterized by lack of H3 K27 trimethylation (H3 K27-me3), and we tested the hypothesis that CXorf67 binds to PRC2 and can modulate levels of H3 K27-me3. Immunoprecipitation/mass spectrometry detected EZH2, SUZ12, and EED, core components of the PRC2 complex, bound to CXorf67 in the Daoy cell line, which shows high levels of CXorf67 and no expression of H3 K27-me3. Enforced reduction of CXorf67 in Daoy cells restored H3 K27-me3 levels, while enforced expression of CXorf67 in HEK293T and neural stem cells reduced H3 K27-me3 levels. Our data suggest that heterogeneity among PFA ependymomas could have clinicopathologic utility and that CXorf67 may have a functional role in these tumors.

Prosystemin, a prohormone that modulates plant defense barriers, is an intrinsically disordered protein

Prosystemin, originally isolated from Lycopersicon esculentum, is a tomato pro-hormone of 200 aminoacid residues which releases a bioactive peptide of 18 aminoacids called Systemin. This signaling peptide is involved in the activation of defense genes in solanaceous plants in response to herbivore feeding damage. Using biochemical, biophysical and bioinformatics approaches we characterized Prosystemin, showing that it is an intrinsically disordered protein possessing a few secondary structure elements within the sequence. Plant treatment with recombinant Prosystemin promotes early and late plant defense genes, which limit the development and survival of Spodoptera littoralis larvae fed with treated plants.

Prediction of protein disorder based on IUPred

Many proteins contain intrinsically disordered regions (IDRs), functional polypeptide segments that in isolation adopt a highly flexible conformational ensemble instead of a single, well-defined structure. Disorder prediction methods, which can discriminate ordered and disordered regions from the amino acid sequence, have contributed significantly to our current understanding of the distinct properties of intrinsically disordered proteins by enabling the characterization of individual examples as well as large-scale analyses of these protein regions. One popular method, IUPred provides a robust prediction of protein disorder based on an energy estimation approach that captures the fundamental difference between the biophysical properties of ordered and disordered regions. This paper reviews the energy estimation method underlying IUPred and the basic properties of the web server. Through an example, it also illustrates how the prediction output can be interpreted in a more complex case by taking into account the heterogeneous nature of IDRs. Various applications that benefited from IUPred to provide improved disorder predictions, complementing domain annotations and aiding the identification of functional short linear motifs are also described here. IUPred is freely available for noncommercial users through the web server (http://iupred.enzim.hu and http://iupred.elte.hu) . The program can also be downloaded and installed locally for large-scale analyses.

Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions

Computational prediction of intrinsic disorder in protein sequences dates back to late 1970 and has flourished in the last two decades. We provide a brief historical overview, and we review over 30 recent predictors of disorder. We are the first to also cover predictors of molecular functions of disorder, including 13 methods that focus on disordered linkers and disordered protein-protein, protein-RNA, and protein-DNA binding regions. We overview their predictive models, usability, and predictive performance. We highlight newest methods and predictors that offer strong predictive performance measured based on recent comparative assessments. We conclude that the modern predictors are relatively accurate, enjoy widespread use, and many of them are fast. Their predictions are conveniently accessible to the end users, via web servers and databases that store pre-computed predictions for millions of proteins. However, research into methods that predict many not yet addressed functions of intrinsic disorder remains an outstanding challenge.

Crystal Structure of the C-terminal Domain of Human eIF2D and Its Implications on Eukaryotic Translation Initiation

Protein synthesis is a key process in all living organisms. In eukaryotes, initiation factor 2 (eIF2) plays an important role in translation initiation as it selects and delivers the initiator tRNA to the small ribosomal subunit. Under stress conditions, phosphorylation of the α-subunit of eIF2 downregulates cellular protein synthesis. However, translation of certain mRNAs continues via the eIF2D-dependent non-canonical initiation pathway. The molecular mechanism of this process remains elusive. In addition, eIF2D plays a role in translation re-initiation and ribosome recycling. Currently, there has been no structural information of eIF2D. We have now determined the crystal structure of the C-terminal domains of eIF2D at 1.4-Å resolution. One domain has the fold similar to that of eIF1, which is crucial for the scanning and initiation codon selection. The second domain has a known SWIB/MDM2 fold, which was not observed before in other translation initiation factors. Our structure reveals atomic details of inter-domain interactions in the C-terminal part of eIF2D and sheds light on the possible role of these domains in eIF2D during translation.

Multitasking Immune Sp185/333 Protein, rSpTransformer-E1, and Its Recombinant Fragments Undergo Secondary Structural Transformation upon Binding Targets

The purple sea urchin, Strongylocentrotus purpuratus, expresses a diverse immune response protein family called Sp185/333. A recombinant Sp185/333 protein, previously called rSp0032, shows multitasking antipathogen binding ability, suggesting that the protein family mediates a flexible and effective immune response to multiple foreign cells. Bioinformatic analysis predicts that rSp0032 is intrinsically disordered, and its multiple binding characteristic suggests structural flexibility to adopt different conformations depending on the characteristics of the target. To address the flexibility and structural shifting hypothesis, circular dichroism analysis of rSp0032 suggests that it transforms from disordered (random coil) to α helical structure. This structural transformation may be the basis for the strong affinity between rSp0032 and several pathogen-associated molecular patterns. The N-terminal Gly-rich fragment of rSp0032 and the C-terminal His-rich fragment show unique transformations by either intensifying the α helical structure or changing from α helical to β strand depending on the solvents and molecules added to the buffer. Based on these results, we propose a name change from rSp0032 to rSpTransformer-E1 to represent its flexible structural conformations and its E1 element pattern. Given that rSpTransformer-E1 shifts its conformation in the presence of solvents and binding targets and that all Sp185/333 proteins are predicted to be disordered, many or all of these proteins may undergo structural transformation to enable multitasking binding activity toward a wide range of targets. Consequently, we also propose an overarching name change for the entire family from Sp185/333 proteins to SpTransformer proteins.

The "Sticky Patch" Model of Crystallization and Modification of Proteins for Enhanced Crystallizability

Crystallization of macromolecules has long been perceived as a stochastic process, which cannot be predicted or controlled. This is consistent with another popular notion that the interactions of molecules within the crystal, i.e., crystal contacts, are essentially random and devoid of specific physicochemical features. In contrast, functionally relevant surfaces, such as oligomerization interfaces and specific protein-protein interaction sites, are under evolutionary pressures so their amino acid composition, structure, and topology are distinct. However, current theoretical and experimental studies are significantly changing our understanding of the nature of crystallization. The increasingly popular "sticky patch" model, derived from soft matter physics, describes crystallization as a process driven by interactions between select, specific surface patches, with properties thermodynamically favorable for cohesive interactions. Independent support for this model comes from various sources including structural studies and bioinformatics. Proteins that are recalcitrant to crystallization can be modified for enhanced crystallizability through chemical or mutational modification of their surface to effectively engineer "sticky patches" which would drive crystallization. Here, we discuss the current state of knowledge of the relationship between the microscopic properties of the target macromolecule and its crystallizability, focusing on the "sticky patch" model. We discuss state-of-the-art in silico methods that evaluate the propensity of a given target protein to form crystals based on these relationships, with the objective to design variants with modified molecular surface properties and enhanced crystallization propensity. We illustrate this discussion with specific cases where these approaches allowed to generate crystals suitable for structural analysis.

A Second RNA-Binding Site in the NS1 Protein of Influenza B Virus

Influenza viruses cause a highly contagious respiratory disease in humans. The NS1 proteins of influenza A and B viruses (NS1A and NS1B proteins, respectively) are composed of two domains, a dimeric N-terminal domain and a C-terminal domain, connected by a flexible polypeptide linker. Here we report the 2.0-Å X-ray crystal structure and nuclear magnetic resonance studies of the NS1B C-terminal domain, which reveal a novel and unexpected basic RNA-binding site that is not present in the NS1A protein. We demonstrate that single-site alanine replacements of basic residues in this site lead to reduced RNA-binding activity, and that recombinant influenza B viruses expressing these mutant NS1B proteins are severely attenuated in replication. This novel RNA-binding site of NS1B is required for optimal influenza B virus replication. Most importantly, this study reveals an unexpected RNA-binding function in the C-terminal domain of NS1B, a novel function that distinguishes influenza B viruses from influenza A viruses.

PAT: predictor for structured units and its application for the optimization of target molecules for the generation of synthetic antibodies

Background

The identification of structured units in a protein sequence is an important first step for most biochemical studies. Importantly for this study, the identification of stable structured region is a crucial first step to generate novel synthetic antibodies. While many approaches to find domains or predict structured regions exist, important limitations remain, such as the optimization of domain boundaries and the lack of identification of non-domain structured units. Moreover, no integrated tool exists to find and optimize structural domains within protein sequences.

Results

Here, we describe a new tool, PAT (http://www.kimlab.org/software/pat) that can efficiently identify both domains (with optimized boundaries) and non-domain putative structured units. PAT automatically analyzes various structural properties, evaluates the folding stability, and reports possible structural domains in a given protein sequence. For reliability evaluation of PAT, we applied PAT to identify antibody target molecules based on the notion that soluble and well-defined protein secondary and tertiary structures are appropriate target molecules for synthetic antibodies.

Conclusion

PAT is an efficient and sensitive tool to identify structured units. A performance analysis shows that PAT can characterize structurally well-defined regions in a given sequence and outperforms other efforts to define reliable boundaries of domains. Specially, PAT successfully identifies experimentally confirmed target molecules for antibody generation. PAT also offers the pre-calculated results of 20,210 human proteins to accelerate common queries. PAT can therefore help to investigate large-scale structured domains and improve the success rate for synthetic antibody generation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1001-1) contains supplementary material, which is available to authorized users.

A recombinant Sp185/333 protein from the purple sea urchin has multitasking binding activities towards certain microbes and PAMPs

The purple sea urchin, Strongylocentrotus purpuratus, possesses a sophisticated innate immune system that responds to microbes effectively by swift expression of the highly diverse Sp185/333 gene family. The Sp185/333 proteins are predicted to have anti-pathogen functions based on inducible gene expression and their significant sequence diversity. Sp185/333 proteins are all predicted to be intrinsically disordered and do not exhibit sequence similarities to other known proteins. To test the anti-pathogen hypothesis, a recombinant Sp185/333 protein, rSp0032, was evaluated and found to exhibit specific binding to marine Vibrio diazotrophicus and to Saccharomyces cerevisiae, but not to two Bacillus species. rSp0032 also binds to LPS, β-1,3-glucan and flagellin but not to peptidoglycan. rSp0032 binding to LPS can be competed by LPS, β-1,3-glucan and flagellin but not by peptidoglycan. We speculate that the predicted intrinsically disordered structure of rSp0032 may adapt to different conformations in binding to a limited number of PAMPs and pathogens. Given that rSp0032 binds to a range of targets, and that up to 260 different Sp185/333 proteins can be expressed per individual sea urchin, this family of immune response proteins may facilitate effective host protection against a broad array of potential pathogens encountered in the marine environment.

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.

Protein stability: a crystallographer's perspective

Protein stability is a topic of major interest for the biotechnology, pharmaceutical and food industries, in addition to being a daily consideration for academic researchers studying proteins. An understanding of protein stability is essential for optimizing the expression, purification, formulation, storage and structural studies of proteins. In this review, discussion will focus on factors affecting protein stability, on a somewhat practical level, particularly from the view of a protein crystallographer. The differences between protein conformational stability and protein compositional stability will be discussed, along with a brief introduction to key methods useful for analyzing protein stability. Finally, tactics for addressing protein-stability issues during protein expression, purification and crystallization will be discussed.

Role of iso-receptors in receptor-receptor interactions with a focus on dopamine iso-receptor complexes

Intercellular and intracellular communication processes consist of signals and recognition/decoding apparatuses of these signals. In humans, the G protein-coupled receptor (GPCR) family represents the largest family of cell surface receptors. More than 30 years ago, it has been proposed that GPCR could form dimers or higher-order oligomers (receptor mosaics [RMs] at the plasma membrane level and receptor-receptor interactions [RRIs] have been proposed as a new integrative mechanism for chemical signals impinging on cell plasma membranes). The basic phenomena involved in RRIs are allostery and cooperativity of membrane receptors, and the present paper provides basic information concerning their relevance for the integrative functions of RMs. In this context, the possible role of iso-receptor RM is discussed (with a special focus on dopamine receptor subtypes and on some of the RMs they form with other dopamine iso-receptors), and it is proposed that two types of cooperativity, namely, homotropic and heterotropic cooperativity, could allow distinguishing two types of functionally different RMs. From a general point of view, the presence of iso-receptors and their topological organization within RMs allow the use of a reduced number of signals for the intercellular communication processes, since the target cells can recognize and decode the same signal in different ways. This theoretical aspect is further analyzed here by means of an analogy with artificial information systems. Thus, it is suggested that the ‘multiplexer’ and ‘demultiplexer’ concepts could, at least in part, model the role of RMs formed by iso-receptors in the information handling by the cell.

How to Study Intermediate Filaments in Atomic Detail

Studies of the intermediate filament (IF) structure are a prerequisite of understanding their function. In addition, the structural information is indispensable if one wishes to gain a mechanistic view on the disease-related mutations in the IFs. Over the years, considerable progress has been made on the atomic structure of the elementary building block of all IFs, the coiled-coil dimer. Here, we discuss the approaches, methods and practices that have contributed to this advance. With abundant genetic information on hand, bioinformatics approaches give important insights into the dimer structure, including the head and tail regions poorly assessable experimentally. At the same time, the most important contribution has been provided by X-ray crystallography. Following the "divide-and-conquer" approach, many fragments from several IF proteins could be crystallized and resolved to atomic resolution. We will systematically cover the main procedures of these crystallographic studies, suggest ways to maximize their efficiency, and also discuss the possible pitfalls and limitations. In addition, electron paramagnetic resonance with site-directed spin labeling was another method providing a major impact toward the understanding of the IF structure. Upon placing the spin labels into specific positions within the full-length protein, one can evaluate the proximity of the labels and their mobility. This makes it possible to make conclusions about the dimer structure in the coiled-coil region and beyond, as well as to explore the dimer-dimer contacts.

The mitochondrial fission receptor Mff selectively recruits oligomerized Drp1

The GTPase Drp1 is recruited to mitochondria by outer membrane receptors to drive mitochondrial fission. The receptor Mff selectively binds to oligomerized Drp1, in contrast to the alternative receptors MiD51 and MiD49. These results show that Drp1 receptors recruit different subpopulations of Drp1 and may explain their distinct biological effects.

Dynamin-related protein 1 (Drp1) is the GTP-hydrolyzing mechanoenzyme that catalyzes mitochondrial fission in the cell. Residing in the cytosol as dimers and tetramers, Drp1 is recruited by receptors on the mitochondrial outer membrane, where it further assembles into a helical ring that drives division via GTP-dependent constriction. The Drp1 receptor Mff is a major regulator of mitochondrial fission, and its overexpression results in increased fission. In contrast, the alternative Drp1 receptors MiD51 and MiD49 appear to recruit inactive forms of Drp1, because their overexpression inhibits fission. Using genetic and biochemical assays, we studied the interaction of Drp1 with Mff. We show that the insert B region of Drp1 inhibits Mff–Drp1 interactions, such that recombinant Drp1 mutants lacking insert B form a stable complex with Mff. Mff cannot bind to assembly-deficient mutants of Drp1, suggesting that Mff selectively interacts with higher-order complexes of Drp1. In contrast, the alternative Drp1 receptors MiD51 and MiD49 can recruit Drp1 dimers. Therefore Drp1 recruitment by Mff versus MiD51 and MiD49 may result in different outcomes because they recruit different subpopulations of Drp1 from the cytosol.

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.

Guiding automated NMR structure determination using a global optimization metric, the NMR DP score

ASDP is an automated NMR NOE assignment program. It uses a distinct bottom-up topology-constrained network anchoring approach for NOE interpretation, with 2D, 3D and/or 4D NOESY peak lists and resonance assignments as input, and generates unambiguous NOE constraints for iterative structure calculations. ASDP is designed to function interactively with various structure determination programs that use distance restraints to generate molecular models. In the CASD-NMR project, ASDP was tested and further developed using blinded NMR data, including resonance assignments, either raw or manually-curated (refined) NOESY peak list data, and in some cases (15)N-(1)H residual dipolar coupling data. In these blinded tests, in which the reference structure was not available until after structures were generated, the fully-automated ASDP program performed very well on all targets using both the raw and refined NOESY peak list data. Improvements of ASDP relative to its predecessor program for automated NOESY peak assignments, AutoStructure, were driven by challenges provided by these CASD-NMR data. These algorithmic improvements include (1) using a global metric of structural accuracy, the discriminating power score, for guiding model selection during the iterative NOE interpretation process, and (2) identifying incorrect NOESY cross peak assignments caused by errors in the NMR resonance assignment list. These improvements provide a more robust automated NOESY analysis program, ASDP, with the unique capability of being utilized with alternative structure generation and refinement programs including CYANA, CNS, and/or Rosetta.