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[This corrects the article DOI: 10.7717/peerj.5858.].

Zinc (Zn2+) homeostasis is critical for pathogen host colonization and invasion. Polyhistidine triad (Pht) proteins, located at the surface of various streptococci, have been proposed to be involved in Zn2+ homeostasis. The phtD gene, coding for a Zn2+-binding protein, is organized in an operon with adcAII coding for the extracellular part of a Zn2+ transporter. In the present work, we investigate the relationship between PhtD and AdcAII using biochemical and structural biology approaches. Immuno-precipitation experiments on purified membranes of Streptococcus pneumoniae (S. pneumoniae) demonstrate that native PhtD and AdcAII interact in vivo confirming our previous in vitro observations. NMR was used to demonstrate Zn2+ transfer from the Zn2+-bound form of a 137 amino acid N-terminal domain of PhtD (t-PhtD) to AdcAII. The high resolution NMR structure of t-PhtD shows that Zn2+ is bound in a tetrahedral site by histidines 83, 86, and 88 as well as by glutamate 63. Comparison of the NMR parameters measured for apo- and Zn2+-t-PhtD shows that the loss of Zn2+ leads to a diminished helical propensity at the C-terminus and increases the local dynamics and overall molecular volume. Structural comparison with the crystal structure of a 55-long fragment of PhtA suggests that Pht proteins are built from short repetitive units formed by three β-strands containing the conserved HxxHxH motif. Taken together, these results support a role for S. pneumoniae PhtD as a Zn2+ scavenger for later release to the surface transporter AdcAII, leading to Zn2+ uptake.

X‐ray crystallographic protein structures often contain disordered regions that are observed as missing electron density. Diffraction data may give little or no direct evidence as to the specific nature of disordered regions. We have developed a weighted window‐based disorder predictor optimized using crystallographic data. Performance of a predictor is strongly influenced by chain termini. Optimized score adjustment values for amino‐ and carboxy‐terminal positions demonstrate a simple, monotonic relationship between disorder and residue distance from termini. This optimized disorder predictor performs similarly to DISOPRED2 on crystallographically disordered regions. Data‐optimized residue disorder propensities show strong linear correlation with experimentally determined amino acid transfer energies between water and hydrogen‐bonding organic solvents, which primarily reflect residue hydrophobicity (exemplified by the Nozaki‐Tanford hydrophobicity scale). Disorder propensities do not correlate as well with transfer energies between water and apolar solvents, which primarily reflect a different hydropathic property: residue hydrophilicity (also reflected by the Kyte‐Doolittle hydropathy scale). Our results suggest that while hydrophobic side‐chain interactions are primarily involved in determining stability of the folded conformation, hydrogen bonding, and similar polar interactions are primarily involved in conformational and interaction specificity.

X-ray crystallography has shown that an antibody paratope typically binds 15-22 amino acids (aa) of an epitope, of which 2-5 randomly distributed aa contribute most of the binding energy. In contrast, researchers typically choose for B-cell epitope mapping short peptide antigens in antibody binding assays. Furthermore, short 6-11aa epitopes, and in particular non-epitopes, are overrepresented in published B-cell epitope datasets that are commonly used for development of B-cell epitope prediction approaches from protein antigen sequences. We hypothesized that such suboptimal-length peptides result in weak antibody binding and cause false-negative results. We tested the influence of peptide antigen length on antibody binding by analyzing data on more than 900 peptides used for B-cell epitope mapping of immunodominant proteins of Chlamydia spp. We demonstrate that short 7-12aa peptides of B-cell epitopes bind antibodies poorly, thus epitope mapping with short peptide antigens falsely classifies many B-cell epitopes as non-epitopes. We also show in published datasets of confirmed epitopes and non-epitopes a direct correlation between length of peptide antigens and antibody binding. Elimination of short, ≤11aa epitope/nonepitope sequences improved datasets for evaluation of in silico B-cell epitope prediction. Achieving up to 86% accuracy, protein disorder tendency is the best indicator of B-cell epitope regions for chlamydial and published datasets. For B-cell epitope prediction, the most effective approach is plotting disorder of protein sequences with the IUPred-L scale, followed by antibody reactivity testing of 16-30aa peptides from peak regions. This strategy overcomes the well-known inaccuracy of in silico B-cell epitope prediction from primary protein sequences.

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.

While very little genomic sequence is interpretable in terms of biological mechanism directly, the chances are much better for protein-coding genes that can be translated into protein sequences. This review considers the different concepts applicable to sequence analysis and function prediction of globular and non-globular protein segments. The publicly accessible ANNOTATOR software environment integrates most of the reliable protein sequence-based function prediction methods, protein domain databases and pathway, and protein–protein interaction collections developed in academia. As application example, the structural and functional domains of mel-28/ELYS, an important nuclear protein, are delineated and are proposed for experimental follow-up in structural biology and functional studies.

While functional roles of several long non-coding RNAs (lncRNAs) have been determined, the molecular mechanisms are not well understood. Here, we report the first experimentally derived secondary structure of a human lncRNA, the steroid receptor RNA activator (SRA), 0.87 kB in size. The SRA RNA is a non-coding RNA that coactivates several human sex hormone receptors and is strongly associated with breast cancer. Coding isoforms of SRA are also expressed to produce proteins, making the SRA gene a unique bifunctional system. Our experimental findings (SHAPE, in-line, DMS and RNase V1 probing) reveal that this lncRNA has a complex structural organization, consisting of four domains, with a variety of secondary structure elements. We examine the coevolution of the SRA gene at the RNA structure and protein structure levels using comparative sequence analysis across vertebrates. Rapid evolutionary stabilization of RNA structure, combined with frame-disrupting mutations in conserved regions, suggests that evolutionary pressure preserves the RNA structural core rather than its translational product. We perform similar experiments on alternatively spliced SRA isoforms to assess their structural features.

Whereas getting exact data about living systems and sophisticated experimental procedures have primarily absorbed the minds of researchers previously, the development of high-throughput technologies has caused the weight to increasingly shift to the problem of interpreting accumulated data in terms of biological function and biomolecular mechanisms. In "Data Mining Techniques for the Life Sciences", experts in the field contribute valuable information about the sources of information and the techniques used for "mining" new insights out of databases. Beginning with a section covering the concepts and structures of important groups of databases for biomolecular mechanism research, the book then continues with sections on formal methods for analyzing biomolecular data and reviews of concepts for analyzing biomolecular sequence data in context with other experimental results that can be mapped onto genomes. As a volume of the highly successful Methods in Molecular Biology series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results. Authoritative and easy to reference, "Data Mining Techniques for the Life Sciences" seeks to aid students and researchers in the life sciences who wish to get a condensed introduction into the vital world of biological databases and their many applications.

We provide first large scale analysis of the peculiarities of surface areas of 5658 dissimilar (below 50% sequence similarity) proteins with known 3D‐structures that bind to proteins, DNA or RNAs. We show here that area of the protein surface is highly correlated with the protein length. The size of the interface surface is only modestly correlated with the protein size, except for RNA‐binding proteins where larger proteins are characterized by larger interfaces. Disordered proteins with disordered interfaces are characterized by significantly larger per‐residue areas of their surfaces and interfaces when compared to the structured proteins. These result are applicable for proteins involved in interaction with DNA, RNA, and proteins and suggest that disordered proteins and binding regions are less compact and more likely to assume extended shape. We demonstrate that disordered protein binding residues in the interfaces of disordered proteins drive the increase in the per residue area of these interfaces. Our results can be used to predict in silico whether a given protomer from the DNA, RNA or protein complex is likely to be disordered in its unbound form.

We present a comprehensive bioinformatics analysis of the abundance and roles of intrinsic disorder in human proteins involved in the antiviral innate immune response. The commonness of intrinsic disorder and disorder-based binding sites is evaluated in 840 human antiviral proteins and proteins associated with innate immune response and defense response to virus. Among the mechanisms engaged in the innate immunity to viral infection are three receptor-based pathways activated by the specific recognition of various virus-associated patterns by several retinoic acid-inducible gene I-like receptors, toll-like receptors, and nucleotide oligomerization domain-like receptors. These modules are tightly regulated and intimately interconnected being jointly controlled via a complex set of protein-protein interactions. Focused analysis of the major players involved in these three pathways is performed to illustrate the roles of protein intrinsic disorder in controlling and regulating the innate antiviral immunity. We mapped the disorder into an integrated network of receptor-based pathways of human innate immunity to virus infection and demonstrate that proteins involved in regulation and execution of these innate immunity pathways possess substantial amount of intrinsic disorder. Disordered regions are engaged in a number of crucial functions, such as protein-protein interactions and interactions with other partners including nucleic acids and other ligands, and are enriched in posttranslational modification sites. Therefore, host cells use numerous advantages of intrinsically disordered proteins and regions to fight flexible invaders and viruses and to successfully overcome the viral invasion.

We perform a large-scale study of intrinsically disordered regions in proteins and protein complexes using a non-redundant set of hundreds of different protein complexes. In accordance with the conventional view that folding and binding are coupled, in many of our cases the disorder-to-order transition occurs upon complex formation and can be localized to binding interfaces. Moreover, analysis of disorder in protein complexes depicts a significant fraction of intrinsically disordered regions, with up to one third of all residues being disordered. We find that the disorder in homodimers, especially in symmetrical homodimers, is significantly higher than in heterodimers and offer an explanation for this interesting phenomenon. We argue that the mechanisms of regulation of binding specificity through disordered regions in complexes can be as common as for unbound monomeric proteins. The fascinating diversity of roles of disordered regions in various biological processes and protein oligomeric forms shown in our study may be a subject of future endeavors in this area.

We have developed EpDis and MassPred, extendable open source software tools that support bioinformatic research and enable parallel use of different methods for the prediction of T cell epitopes, disorder and disordered binding regions and hydropathy calculation. These tools offer a semi-automated installation of chosen sets of external predictors and an interface allowing for easy application of the prediction methods, which can be applied either to individual proteins or to datasets of a large number of proteins. In addition to access to prediction methods, the tools also provide visualization of the obtained results, calculation of consensus from results of different methods, as well as import of experimental data and their comparison with results obtained with different predictors. The tools also offer a graphical user interface and the possibility to store data and the results obtained using all of the integrated methods in the relational database or flat file for further analysis. The MassPred part enables a massive parallel application of all integrated predictors to the set of proteins. Both tools can be downloaded from http://bioinfo.matf.bg.ac.rs/home/downloads.wafl?cat=Software. Appendix A includes the technical description of the created tools and a list of supported predictors.

We employ the Rayleigh entropy maximization to develop a novel IDP scheme which requires computing only five features for each residue of a protein sequence, that is, the Shannon entropy, topological entropy, and the weighted average values of three propensities. Furthermore, our scheme is a linear classification method and hence requires computing simpler decision curves which are more robust as well as using fewer learning samples to compute. The simulation results of our scheme as well as some existing schemes demonstrate its effectiveness.

We describe a method to predict protein-protein interactions (PPIs) formed between structured domains and short peptide motifs. We take an integrative approach based on consensus patterns of known motifs in databases, structures of domain-motif complexes from the PDB and various sources of non-structural evidence. We combine this set of clues using a Bayesian classifier that reports the likelihood of an interaction and obtain significantly improved prediction performance when compared to individual sources of evidence and to previously reported algorithms. Our Bayesian approach was integrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to interactions formed between two structured domains. Around 80,000 new domain-motif mediated interactions were predicted, thus enhancing PrePPI’s coverage of the human protein interactome.

We analysed the structural properties of protein regions containing arrays of perfect and nearly perfect tandem repeats. Naturally occurring proteins with perfect repeats are practically absent among the proteins with known 3D structures. The great majority of such regions in the Protein Data Bank are found in the proteins designed de novo. The abundance of natural structured proteins with tandem repeats is inversely correlated with the repeat perfection: the chance of finding natural structured proteins in the Protein Data Bank increases with a decrease in the level of repeat perfection. Prediction of intrinsic disorder within the tandem repeats in the SwissProt proteins supports the conclusion that the level of repeat perfection correlates with their tendency to be unstructured. This correlation is valid across the various species and subcellular localizations, although the level of disordered tandem repeats varies significantly between these datasets. On average, in prokaryotes, tandem repeats of cytoplasmic proteins were predicted to be the most structured, whereas in eukaryotes, the most structured portion of the repeats was found in the membrane proteins. Our study supports the hypothesis that, in general, the repeat perfection is a sign of recent evolutionary events rather than of exceptional structural and (or) functional importance of the repeat residues.

We analysed the structural properties of protein regions containing arrays of perfect and nearly perfect tandem repeats. Naturally occurring proteins with perfect repeats are practically absent among the proteins with known 3D structures. The great majority of such regions in the Protein Data Bank are found in the proteins designed de novo. The abundance of natural structured proteins with tandem repeats is inversely correlated with the repeat perfection: the chance of finding natural structured proteins in the Protein Data Bank increases with a decrease in the level of repeat perfection. Prediction of intrinsic disorder within the tandem repeats in the SwissProt proteins supports the conclusion that the level of repeat perfection correlates with their tendency to be unstructured. This correlation is valid across the various species and subcellular localizations, although the level of disordered tandem repeats varies significantly between these datasets. On average, in prokaryotes, tandem repeats of cytoplasmic proteins were predicted to be the most structured, whereas in eukaryotes, the most structured portion of the repeats was found in the membrane proteins. Our study supports the hypothesis that, in general, the repeat perfection is a sign of recent evolutionary events rather than of exceptional structural and (or) functional importance of the repeat residues.

Viruses interact extensively with host proteins, but the mechanisms controlling these interactions are not well understood. We present a comprehensive analysis of eukaryotic linear motifs (ELMs) in 2,208 viral genomes and reveal that viruses exploit molecular mimicry of host-like ELMs to possibly assist in host-virus interactions. Using a statistical genomics approach, we identify a large number of potentially functional ELMs and observe that the occurrence of ELMs is often evolutionarily conserved but not uniform across virus families. Some viral proteins contain multiple types of ELMs, in striking similarity to complex regulatory modules in host proteins, suggesting that ELMs may act combinatorially to assist viral replication. Furthermore, a simple evolutionary model suggests that the inherent structural simplicity of ELMs often enables them to tolerate mutations and evolve quickly. Our findings suggest that ELMs may allow fast rewiring of host-virus interactions, which likely assists rapid viral evolution and adaptation to diverse environments.

Viral proteins bind to numerous cellular and viral proteins throughout the infection cycle. However, the mechanisms by which viral proteins interact with such large numbers of factors remain unknown. Cellular proteins that interact with multiple, distinct partners often do so through short sequences known as molecular recognition features (MoRFs) embedded within intrinsically disordered regions (IDRs). In this study, we report the first evidence that MoRFs in viral proteins play a similar role in targeting the host cell. Using a combination of evolutionary modeling, protein–protein interaction analyses and forward genetic screening, we systematically investigated two computationally predicted MoRFs within the N‐terminal IDR of the hepatitis C virus (HCV) Core protein. Sequence analysis of the MoRFs showed their conservation across all HCV genotypes and the canine and equine Hepaciviruses. Phylogenetic modeling indicated that the Core MoRFs are under stronger purifying selection than the surrounding sequence, suggesting that these modules have a biological function. Using the yeast two‐hybrid assay, we identified three cellular binding partners for each HCV Core MoRF, including two previously characterized cellular targets of HCV Core (DDX3X and NPM1). Random and site‐directed mutagenesis demonstrated that the predicted MoRF regions were required for binding to the cellular proteins, but that different residues within each MoRF were critical for binding to different partners. This study demonstrated that viruses may use intrinsic disorder to target multiple cellular proteins with the same amino acid sequence and provides a framework for characterizing the binding partners of other disordered regions in viral and cellular proteomes.

Vesicle trafficking systems play essential roles in the communication between the organelles of eukaryotic cells and also between cells and their environment. Endocytosis and the late secretory route are mediated by clathrin-coated vesicles, while the COat Protein I and II (COPI and COPII) routes stand for the bidirectional traffic between the ER and the Golgi apparatus. Despite similar fundamental organizations, the molecular machinery, functions, and evolutionary characteristics of the three systems are very different. In this work, we compiled the basic functional protein groups of the three main routes for human and yeast and analyzed them from the structural disorder perspective. We found similar overall disorder content in yeast and human proteins, confirming the well-conserved nature of these systems. Most functional groups contain highly disordered proteins, supporting the general importance of structural disorder in these routes, although some of them seem to heavily rely on disorder, while others do not. Interestingly, the clathrin system is significantly more disordered (∼23%) than the other two, COPI (∼9%) and COPII (∼8%). We show that this structural phenomenon enhances the inherent plasticity and increased evolutionary adaptability of the clathrin system, which distinguishes it from the other two routes. Since multi-functionality (moonlighting) is indicative of both plasticity and adaptability, we studied its prevalence in vesicle trafficking proteins and correlated it with structural disorder. Clathrin adaptors have the highest capability for moonlighting while also comprising the most highly disordered members. The ability to acquire tissue specific functions was also used to approach adaptability: clathrin route genes have the most tissue specific exons encoding for protein segments enriched in structural disorder and interaction sites. Overall, our results confirm the general importance of structural disorder in vesicle trafficking and suggest major roles for this structural property in shaping the differences of evolutionary adaptability in the three routes.