ISIS: interaction sites identified from sequence

Identification and visualization of protein binding regions with the ArDock server

ArDock (ardock.ibcp.fr) is a structural bioinformatics web server for the prediction and the visualization of potential interaction regions at protein surfaces. ArDock ranks the surface residues of a protein according to their tendency to form interfaces in a set of predefined docking experiments between the query protein and a set of arbitrary protein probes. The ArDock methodology is derived from large scale cross-docking studies where it was observed that randomly chosen proteins tend to dock in a non-random way at protein surfaces. The method predicts interaction site of the protein, or alternate interfaces in the case of proteins with multiple interaction modes. The server takes a protein structure as input and computes a score for each surface residue. Its output focuses on the interactive visualization of results and on interoperability with other services.

Discovery of receptor-ligand interfaces in the immunoglobulin superfamily

Cell-surface-anchored immunoglobulin superfamily (IgSF) proteins are widespread throughout the human proteome, forming crucial components of diverse biological processes including immunity, cell-cell adhesion, and carcinogenesis. IgSF proteins generally function through protein-protein interactions carried out between extracellular, membrane-bound proteins on adjacent cells, known as trans-binding interfaces. These protein-protein interactions constitute a class of pharmaceutical targets important in the treatment of autoimmune diseases, chronic infections, and cancer. A molecular-level understanding of IgSF protein-protein interactions would greatly benefit further drug development. A critical step toward this goal is the reliable identification of IgSF trans-binding interfaces. We propose a novel combination of structure and sequence information to identify trans-binding interfaces in IgSF proteins. We developed a structure-based binding interface prediction approach that can identify broad regions of the protein surface that encompass the binding interfaces and suggests that IgSF proteins possess binding supersites. These interfaces could theoretically be pinpointed using sequence-based conservation analysis, with performance approaching the theoretical upper limit of binding interface prediction accuracy, but achieving this in practice is limited by the current ability to identify an appropriate multiple sequence alignment for conservation analysis. However, an important contribution of combining the two orthogonal methods is that agreement between these approaches can estimate the reliability of the predictions. This approach was benchmarked on the set of 22 IgSF proteins with experimentally solved structures in complex with their ligands. Additionally, we provide structure-based predictions and reliability scores for the 62 IgSF proteins with known structure but yet uncharacterized binding interfaces.

Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique

MOTIVATION

The prediction of protein-protein interaction (PPI) sites is a key to mutation design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task considering the significant abundant sequences and the imbalance issue in samples.

RESULTS

A new ensemble learning-based method, Ensemble Learning of synthetic minority oversampling technique (SMOTE) for Unbalancing samples and RF algorithm (EL-SMURF), was proposed for PPI sites prediction in this study. The sequence profile feature and the residue evolution rates were combined for feature extraction of neighboring residues using a sliding window, and the SMOTE was applied to oversample interface residues in the feature space for the imbalance problem. The Multi-dimensional Scaling feature selection method was implemented to reduce feature redundancy and subset selection. Finally, the Random Forest classifiers were applied to build the ensemble learning model, and the optimal feature vectors were inserted into EL-SMURF to predict PPI sites. The performance validation of EL-SMURF on two independent validation datasets showed 77.1% and 77.7% accuracy, which were 6.2-15.7% and 6.1-18.9% higher than the other existing tools, respectively.

AVAILABILITY AND IMPLEMENTATION

The source codes and data used in this study are publicly available at http://github.com/QUST-AIBBDRC/EL-SMURF/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Decrypting protein surfaces by combining evolution, geometry, and molecular docking

The growing body of experimental and computational data describing how proteins interact with each other has emphasized the multiplicity of protein interactions and the complexity underlying protein surface usage and deformability. In this work, we propose new concepts and methods toward deciphering such complexity. We introduce the notion of interacting region to account for the multiple usage of a protein's surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility. We predict interacting patches by crossing evolutionary, physicochemical and geometrical properties of the protein surface with information coming from complete cross-docking (CC-D) simulations. We show that our predictions match well interacting regions and that the different sources of information are complementary. We further propose an indicator of whether a protein has a few or many partners. Our prediction strategies are implemented in the dynJET² algorithm and assessed on a new dataset of 262 protein on which we performed CC-D. The code and the data are available at: http://www.lcqb.upmc.fr/dynJET2/.

Formylated N-terminal methionine is absent from the Mycoplasma hyopneumoniae proteome: Implications for translation initiation

N-terminal methionine excision (NME) is a proteolytic pathway that cleaves the N-termini of proteins, a process that influences where proteins localise in the cell and their turnover rates. In bacteria, protein biosynthesis is initiated by formylated methionine start tRNA (fMet-tRNA_f^Met). The formyl group is attached by formyltransferase (FMT) and is subsequently removed by peptide deformylase (PDF) in most but not all proteins. Methionine aminopeptidase then cleaves deformylated methionine to complete the process. Components of NME, particularly PDF, are promising therapeutic targets for bacterial pathogens. In Mycoplasma hyopneumoniae, a genome-reduced, major respiratory pathogen of swine, pdf and fmt are absent from its genome. Our bioinformatic analysis uncovered additional enzymes involved in formylated N-terminal methionine (fnMet) processing missing in fourteen mycoplasma species, including M. hyopneumoniae but not in Mycoplasma pneumoniae, a major respiratory pathogen of humans. Consistent with our bioinformatic studies, an analysis of in-house tryptic peptide libraries confirmed the absence of fnMet in M. hyopneumoniae proteins but, as expected fnMet peptides were detected in the proteome of M. pneumoniae. Additionally, computational molecular modelling of M. hyopneumoniae translation initiation factors reveal structural and sequence differences in areas known to interact with fMet-tRNA_f^Met. Our data suggests that some mycoplasmas have evolved a translation process that does not require fnMet.

Rare Variants in BNC2 Are Implicated in Autosomal-Dominant Congenital Lower Urinary-Tract Obstruction

Congenital lower urinary-tract obstruction (LUTO) is caused by anatomical blockage of the bladder outflow tract or by functional impairment of urinary voiding. About three out of 10,000 pregnancies are affected. Although several monogenic causes of functional obstruction have been defined, it is unknown whether congenital LUTO caused by anatomical blockage has a monogenic cause. Exome sequencing in a family with four affected individuals with anatomical blockage of the urethra identified a rare nonsense variant (c.2557C>T [p.Arg853^∗]) in BNC2, encoding basonuclin 2, tracking with LUTO over three generations. Re-sequencing BNC2 in 697 individuals with LUTO revealed three further independent missense variants in three unrelated families. In human and mouse embryogenesis, basonuclin 2 was detected in lower urinary-tract rudiments. In zebrafish embryos, bnc2 was expressed in the pronephric duct and cloaca, analogs of the mammalian lower urinary tract. Experimental knockdown of Bnc2 in zebrafish caused pronephric-outlet obstruction and cloacal dilatation, phenocopying human congenital LUTO. Collectively, these results support the conclusion that variants in BNC2 are strongly implicated in LUTO etiology as a result of anatomical blockage.

Efficiently Predicting Hot Spots in PPIs by Combining Random Forest and Synthetic Minority Over-Sampling Technique

Hot spot residues bring into play the vital function in bioinformatics to find new medications such as drug design. However, current datasets are predominately composed of non-hot spots with merely a tiny percentage of hot spots. Conventional hot spots prediction methods may face great challenges towards the problem of imbalance training samples. This paper presents a classification method combining with random forest classification and oversampling strategy to improve the training performance. A strategy with an oversampling ability is used to generate hot spots data to balance the given training set. Random forest classification is then invoked to generate a set of forest trees for this oversampled training set. The final prediction performance can be computed recursively after the oversampling and training process. This proposed method is capable of randomly selecting features and constructing a robust random forest to avoid overfitting the training set. Experimental results from three data sets indicate that the performance of hot spots prediction has been significantly improved compared with existing classification methods.

Improved measures for evolutionary conservation that exploit taxonomy distances

Selective pressures on protein-coding regions that provide fitness advantages can lead to the regions' fixation and conservation in genome duplications and speciation events. Consequently, conservation analyses relying on sequence similarities are exploited by a myriad of applications across all biosciences to identify functionally important protein regions. While very potent, existing conservation measures based on multiple sequence alignments are so pervasive that improvements to solutions of many problems have become incremental. We introduce a new framework for evolutionary conservation with measures that exploit taxonomy distances across species. Results show that our taxonomy-based framework comfortably outperforms existing conservation measures in identifying deleterious variants observed in the human population, including variants located in non-abundant sequence domains such as intrinsically disordered regions. The predictive power of our approach emphasizes that the phenotypic effects of sequence variants can be taxonomy-level specific and thus, conservation needs to be interpreted accordingly.

Information on protein sequence variability and conservation can be leveraged to identify functionally important regions. Here, the authors develop new conservation measures that exploit taxonomy distances and LIST, a tool for predicting deleteriousness of human variants.

DNA-protein interaction: identification, prediction and data analysis

Life in living organisms is dependent on specific and purposeful interaction between other molecules. Such purposeful interactions make the various processes inside the cells and the bodies of living organisms possible. DNA-protein interactions, among all the types of interactions between different molecules, are of considerable importance. Currently, with the development of numerous experimental techniques, diverse methods are convenient for recognition and investigating such interactions. While the traditional experimental techniques to identify DNA-protein complexes are time-consuming and are unsuitable for genome-scale studies, the current high throughput approaches are more efficient in determining such interaction at a large-scale, but they are clearly too costly to be practice for daily applications. Hence, according to the availability of much information related to different biological sequences and clearing different dimensions of conditions in which such interactions are formed, with the developments related to the computer, mathematics, and statistics motivate scientists to develop bioinformatics tools for prediction the interaction site(s). Until now, there has been much progress in this field. In this review, the factors and conditions governing the interaction and the laboratory techniques for examining such interactions are addressed. In addition, developed bioinformatics tools are introduced and compared for this reason and, in the end, several suggestions are offered for the promotion of such tools in prediction with much more precision.

Missense variants in NOX1 and p22phox in a case of very-early-onset inflammatory bowel disease are functionally linked to NOD2

Whole-genome and whole-exome sequencing of individual patients allow the study of rare and potentially causative genetic variation. In this study, we sequenced DNA of a trio comprising a boy with very-early-onset inflammatory bowel disease (veoIBD) and his unaffected parents. We identified a rare, X-linked missense variant in the NAPDH oxidase NOX1 gene (c.C721T, p.R241C) in heterozygous state in the mother and in hemizygous state in the patient. We discovered that, in addition, the patient was homozygous for a common missense variant in the CYBA gene (c.T214C, p.Y72H). CYBA encodes the p22phox protein, a cofactor for NOX1. Functional assays revealed reduced cellular ROS generation and antibacterial capacity of NOX1 and p22phox variants in intestinal epithelial cells. Moreover, the identified NADPH oxidase complex variants affected NOD2-mediated immune responses, and p22phox was identified as a novel NOD2 interactor. In conclusion, we detected missense variants in a veoIBD patient that disrupt the host response to bacterial challenges and reduce protective innate immune signaling via NOD2. We assume that the patient's individual genetic makeup favored disturbed intestinal mucosal barrier function.

Residue-level determinants of angiopoietin-2 interactions with its receptor Tie2

We combined computational and experimental methods to interrogate the binding determinants of angiopoietin-2 (Ang2) to its receptor tyrosine kinase (RTK) Tie2-a central signaling system in angiogenesis, inflammation, and tumorigenesis. We used physics-based electrostatic and surface-area calculations to identify the subset of interfacial Ang2 and Tie2 residues that can affect binding directly. Using random and site-directed mutagenesis and yeast surface display (YSD), we validated these predictions and identified additional Ang2 positions that affected receptor binding. We then used burial-based calculations to classify the larger set of Ang2 residues that are buried in the Ang2 core, whose mutations can perturb the Ang2 structure and thereby affect interactions with Tie2 indirectly. Our analysis showed that the Ang2-Tie2 interface is dominated by nonpolar contributions, with only three Ang2 and two Tie2 residues that contribute electrostatically to intermolecular interactions. Individual interfacial residues contributed only moderately to binding, suggesting that engineering of this interface will require multiple mutations to reach major effects. Conversely, substitutions in substantially buried Ang2 residues were more prevalent in our experimental screen, reduced binding substantially, and are therefore more likely to have a deleterious effect that might contribute to oncogenesis. Computational analysis of additional RTK-ligand complexes, c-Kit-SCF and M-CSF-c-FMS, and comparison to previous YSD results, further show the utility of our combined methodology.

The novel EHEC gene asa overlaps the TEGT transporter gene in antisense and is regulated by NaCl and growth phase

Only a few overlapping gene pairs are known in the best-analyzed bacterial model organism Escherichia coli. Automatic annotation programs usually annotate only one out of six reading frames at a locus, allowing only small overlaps between protein-coding sequences. However, both RNAseq and RIBOseq show signals corresponding to non-trivially overlapping reading frames in antisense to annotated genes, which may constitute protein-coding genes. The transcription and translation of the novel 264 nt gene asa, which overlaps in antisense to a putative TEGT (Testis-Enhanced Gene Transfer) transporter gene is detected in pathogenic E. coli, but not in two apathogenic E. coli strains. The gene in E. coli O157:H7 (EHEC) was further analyzed. An overexpression phenotype was identified in two stress conditions, i.e. excess in salt or arginine. For this, EHEC overexpressing asa was grown competitively against EHEC with a translationally arrested asa mutant gene. RT-qPCR revealed conditional expression dependent on growth phase, sodium chloride, and arginine. Two potential promoters were computationally identified and experimentally verified by reporter gene expression and determination of the transcription start site. The protein Asa was verified by Western blot. Close homologues of asa have not been found in protein databases, but bioinformatic analyses showed that it may be membrane associated, having a largely disordered structure.

Compound heterozygous mutations in IL10RA combined with a complement factor properdin mutation in infantile-onset inflammatory bowel disease

OBJECTIVES

Inflammatory bowel diseases (IBDs) are chronic and multifactorial diseases resulting from a complex interaction of host genetic factors and environmental stimuli. Although many genome-wide association studies have identified host genetic factors associated with IBD, rare Mendelian forms of IBD have been reported in patients with very early onset forms. Therefore, this study aimed to identify genetic variants associated with infantile-onset IBD.

PARTICIPANTS AND METHODS

We obtained genomic DNA from whole blood samples of a male patient with infantile-onset IBD and nonconsanguineous Korean parents. Whole-exome sequencing was performed using trio samples. Then, we analyzed the data using susceptibility genes for monogenic forms of IBD and various immunodeficiencies and protein structural analysis.

RESULTS

The patient who presented with oral aphthous ulcers at the age of 14 days suffered from severe colitis and was refractory to medical treatment. Compound heterozygous mutations in IL10RA (p.R101W; p.T179T) were found in the patient. In addition, a hemizygous mutation in complement factor properdin (CFP) (p.L456V) located on the X-chromosome was detected, inherited from the patient's mother. Protein structural modeling suggested impaired properdin subunit interactions by p.L456V that may hamper protein oligomerization required for complement activation.

CONCLUSION

This study identified compound heterozygous mutations in IL10RA combined with a hemizygous CFP mutation in infantile-onset IBD by using whole-exome sequencing. CFP p.L456V may exacerbate symptoms of infantile-onset IBD by disturbing oligomerization of properdin.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Analysis of Hepatitis E virus (HEV) X-domain structural model

Hepatitis E viral infection is now emerging as a global health concern, which needs to be addressed. Mechanism of viral replication and release is attributed by the different genomic component of HEV. However, few proteins/domain like X and Y domain remain unexplored, so we aim to explore the physiochemical, structural and functional features of HEV ORF-1 X domain. Molecular modeling of the unknown X domain was carried out using Phyre2 and Swiss Model. Active ligand binding sites were predicted using Phyre2. The X-domain protein found to be stable and acidic in nature with high thermostability and better hydrophilic property. Twelve binding sites were predicted along with putative transferase and catalytic functional activity. Homology modeling showed 10 binding sites along with Mg2+ and Zn2+ as metallic heterogen ligands binding to predicted ligand-binding sites. This study may help to decipher the role of this unexplored X-domain of HEV, thereby improving our understanding of the pathogenesis of HEV infection.

Phylogenetic and Other Conservation-Based Approaches to Predict Protein Functional Sites

Proteins use their functional regions to exploit various activities, including binding to other proteins, nucleic acids, or drugs. Functional sites of the proteins have a tendency to be more conserved than the rest of the protein surface. Therefore, detection of the conserved residues using phylogenetic analysis is a general approach to predict functionally critical residues. In this chapter, we describe some of the available methods to predict functional sites and demonstrate a complete pipeline with tool alternatives at several steps. We explain the standard procedure and all intermediate stages including homology detection with BLAST search, multiple sequence alignment (MSA) and the construction of a phylogenetic tree for a given query sequence. Additionally, we demonstrate the prediction results of these methods on a case study. Finally, we discuss the possible challenges and bottlenecks throughout the pipeline. Our step-by-step description about the functional site prediction could be a helpful resource for the researchers interested in finding protein functional sites, to be used in drug discovery research.

Protein-Protein Docking Using Evolutionary Information

The structural modeling of protein complexes by docking simulations has been attracting increasing interest with the rise of proteomics and of the number of experimentally identified binary interactions. Structures of unbound partners, either modeled or experimentally determined, can be used as input to sample as extensively as possible all putative binding modes and single out the most plausible ones. At the scoring step, evolutionary information contained in the joint multiple sequence alignments of both partners can provide key insights to recognize correct interfaces. Here, we describe a computational protocol based on the InterEvDock web server to exploit coevolution constraints in protein-protein docking methods. We provide methodology guidelines to prepare the input protein structures and generate improved alignments. We also explain how to extract and use the information returned by the server through the analysis of two representative examples.

Elongation factor Tu is a multifunctional and processed moonlighting protein

Many bacterial moonlighting proteins were originally described in medically, agriculturally, and commercially important members of the low G + C Firmicutes. We show Elongation factor Tu (Ef-Tu) moonlights on the surface of the human pathogens Staphylococcus aureus (Sa_Ef-Tu) and Mycoplasma pneumoniae (Mpn_Ef-Tu), and the porcine pathogen Mycoplasma hyopneumoniae (Mhp_Ef-Tu). Ef-Tu is also a target of multiple processing events on the cell surface and these were characterised using an N-terminomics pipeline. Recombinant Mpn_Ef-Tu bound strongly to a diverse range of host molecules, and when bound to plasminogen, was able to convert plasminogen to plasmin in the presence of plasminogen activators. Fragments of Ef-Tu retain binding capabilities to host proteins. Bioinformatics and structural modelling studies indicate that the accumulation of positively charged amino acids in short linear motifs (SLiMs), and protein processing promote multifunctional behaviour. Codon bias engendered by an A + T rich genome may influence how positively-charged residues accumulate in SLiMs.

N-terminomics identifies widespread endoproteolysis and novel methionine excision in a genome-reduced bacterial pathogen

Proteolytic processing alters protein function. Here we present the first systems-wide analysis of endoproteolysis in the genome-reduced pathogen Mycoplasma hyopneumoniae. 669 N-terminal peptides from 164 proteins were identified, demonstrating that functionally diverse proteins are processed, more than half of which 75 (53%) were accessible on the cell surface. Multiple cleavage sites were characterised, but cleavage with arginine in P1 predominated. Putative functions for a subset of cleaved fragments were assigned by affinity chromatography using heparin, actin, plasminogen and fibronectin as bait. Binding affinity was correlated with the number of cleavages in a protein, indicating that novel binding motifs are exposed, and protein disorder increases, after a cleavage event. Glyceraldehyde 3-phosphate dehydrogenase was used as a model protein to demonstrate this. We define the rules governing methionine excision, show that several aminopeptidases are involved, and propose that through processing, genome-reduced organisms can expand protein function.

Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology

Cellular processes often depend on interactions between proteins and the formation of macromolecular complexes. The impairment of such interactions can lead to deregulation of pathways resulting in disease states, and it is hence crucial to gain insights into the nature of macromolecular assemblies. Detailed structural knowledge about complexes and protein-protein interactions is growing, but experimentally determined three-dimensional multimeric assemblies are outnumbered by complexes supported by non-structural experimental evidence. Here, we aim to fill this gap by modeling multimeric structures by homology, only using amino acid sequences to infer the stoichiometry and the overall structure of the assembly. We ask which properties of proteins within a family can assist in the prediction of correct quaternary structure. Specifically, we introduce a description of protein-protein interface conservation as a function of evolutionary distance to reduce the noise in deep multiple sequence alignments. We also define a distance measure to structurally compare homologous multimeric protein complexes. This allows us to hierarchically cluster protein structures and quantify the diversity of alternative biological assemblies known today. We find that a combination of conservation scores, structural clustering, and classical interface descriptors, can improve the selection of homologous protein templates leading to reliable models of protein complexes.

An Eigen-Binding Site Based Method for the Analysis of Anti-EGFR Drug Resistance in Lung Cancer Treatment

We explore the drug resistance mechanism in non-small cell lung cancer treatment by characterizing the drug-binding site of a protein mutant based on local surface and energy features. These features are transformed to an eigen-binding site space and used for drug resistance level prediction and analysis.

Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery

Protein-protein interactions (PPIs) are vital to maintaining cellular homeostasis. Several PPI dysregulations have been implicated in the etiology of various diseases and hence PPIs have emerged as promising targets for drug discovery. Surface residues and hotspot residues at the interface of PPIs form the core regions, which play a key role in modulating cellular processes such as signal transduction and are used as starting points for drug design. In this review, we briefly discuss how PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research. Next, we describe the principles of in silico PPI prediction and survey the existing PPI and PPI site prediction servers that are useful for drug discovery. Finally, we discuss the potential of in silico PPI prediction in drug discovery.

Integrating computational methods and experimental data for understanding the recognition mechanism and binding affinity of protein-protein complexes

Protein-protein interactions perform several functions inside the cell. Understanding the recognition mechanism and binding affinity of protein-protein complexes is a challenging problem in experimental and computational biology. In this review, we focus on two aspects (i) understanding the recognition mechanism and (ii) predicting the binding affinity. The first part deals with computational techniques for identifying the binding site residues and the contribution of important interactions for understanding the recognition mechanism of protein-protein complexes in comparison with experimental observations. The second part is devoted to the methods developed for discriminating high and low affinity complexes, and predicting the binding affinity of protein-protein complexes using three-dimensional structural information and just from the amino acid sequence. The overall view enhances our understanding of the integration of experimental data and computational methods, recognition mechanism of protein-protein complexes and the binding affinity.

Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes

Studying protein-protein interactions leads to a better understanding of the underlying principles of several biological pathways. Cost and labor-intensive experimental techniques suggest the need for computational methods to complement them. Several such state-of-the-art methods have been reported for analyzing diverse aspects such as predicting binding partners, interface residues, and binding affinity for protein-protein complexes with reliable performance. However, there are specific drawbacks for different methods that indicate the need for their improvement. This review highlights various available computational algorithms for analyzing diverse aspects of protein-protein interactions and endorses the necessity for developing new robust methods for gaining deep insights about protein-protein interactions.

Bph32, a novel gene encoding an unknown SCR domain-containing protein, confers resistance against the brown planthopper in rice

An urgent need exists to identify more brown planthopper (Nilaparvata lugens Stål, BPH) resistance genes, which will allow the development of rice varieties with resistance to BPH to counteract the increased incidence of this pest species. Here, using bioinformatics and DNA sequencing approaches, we identified a novel BPH resistance gene, LOC_Os06g03240 (MSU LOCUS ID), from the rice variety Ptb33 in the interval between the markers RM19291 and RM8072 on the short arm of chromosome 6, where a gene for resistance to BPH was mapped by Jirapong Jairin et al. and renamed as "Bph32". This gene encodes a unique short consensus repeat (SCR) domain protein. Sequence comparison revealed that the Bph32 gene shares 100% sequence identity with its allele in Oryza latifolia. The transgenic introgression of Bph32 into a susceptible rice variety significantly improved resistance to BPH. Expression analysis revealed that Bph32 was highly expressed in the leaf sheaths, where BPH primarily settles and feeds, at 2 and 24 h after BPH infestation, suggesting that Bph32 may inhibit feeding in BPH. Western blotting revealed the presence of Pph (Ptb33) and Tph (TN1) proteins using a Penta-His antibody, and both proteins were insoluble. This study provides information regarding a valuable gene for rice defence against insect pests.

Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids

Information about the interface sites of Protein–Protein Interactions (PPIs) is useful for many biological research works. However, despite the advancement of experimental techniques, the identification of PPI sites still remains as a challenging task. Using a statistical learning technique, we proposed a computational tool for predicting PPI interaction sites. As an alternative to similar approaches requiring structural information, the proposed method takes all of the input from protein sequences. In addition to typical sequence features, our method takes into consideration that interaction sites are not randomly distributed over the protein sequence. We characterized this positional preference using protein complexes with known structures, proposed a numerical index to estimate the propensity and then incorporated the index into a learning system. The resulting predictor, without using structural information, yields an area under the ROC curve (AUC) of 0.675, recall of 0.597, precision of 0.311 and accuracy of 0.583 on a ten-fold cross-validation experiment. This performance is comparable to the previous approach in which structural information was used. Upon introducing the B-factor data to our predictor, we demonstrated that the AUC can be further improved to 0.750. The tool is accessible at http://bsaltools.ym.edu.tw/predppis.

Progress and challenges in predicting protein interfaces

The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.

Evidence for the recent origin of a bacterial protein-coding, overlapping orphan gene by evolutionary overprinting

BACKGROUND

Gene duplication is believed to be the classical way to form novel genes, but overprinting may be an important alternative. Overprinting allows entirely novel proteins to evolve de novo, i.e., formerly non-coding open reading frames within functional genes become expressed. Only three cases have been described for Escherichia coli. Here, a fourth example is presented.

RESULTS

RNA sequencing revealed an open reading frame weakly transcribed in cow dung, coding for 101 residues and embedded completely in the -2 reading frame of citC in enterohemorrhagic E. coli. This gene is designated novel overlapping gene, nog1. The promoter region fused to gfp exhibits specific activities and 5' rapid amplification of cDNA ends indicated the transcriptional start 40-bp upstream of the start codon. nog1 was strand-specifically arrested in translation by a nonsense mutation silent in citC. This Nog1-mutant showed a phenotype in competitive growth against wild type in the presence of MgCl2. Small differences in metabolite concentrations were also found. Bioinformatic analyses propose Nog1 to be inner membrane-bound and to possess at least one membrane-spanning domain. A phylogenetic analysis suggests that the orphan gene nog1 arose by overprinting after Escherichia/Shigella separated from the other γ-proteobacteria.

CONCLUSIONS

Since nog1 is of recent origin, non-essential, short, weakly expressed and only marginally involved in E. coli's central metabolism, we propose that this gene is in an initial stage of evolution. While we present specific experimental evidence for the existence of a fourth overlapping gene in enterohemorrhagic E. coli, we believe that this may be an initial finding only and overlapping genes in bacteria may be more common than is currently assumed by microbiologists.

Prediction of Protein-Protein Interaction Sites Based on Naive Bayes Classifier

Protein functions through interactions with other proteins and biomolecules and these interactions occur on the so-called interface residues of the protein sequences. Identifying interface residues makes us better understand the biological mechanism of protein interaction. Meanwhile, information about the interface residues contributes to the understanding of metabolic, signal transduction networks and indicates directions in drug designing. In recent years, researchers have focused on developing new computational methods for predicting protein interface residues. Here we creatively used a 181-dimension protein sequence feature vector as input to the Naive Bayes Classifier- (NBC-) based method to predict interaction sites in protein-protein complexes interaction. The prediction of interaction sites in protein interactions is regarded as an amino acid residue binary classification problem by applying NBC with protein sequence features. Independent test results suggested that Naive Bayes Classifier-based method with the protein sequence features as input vectors performed well.

DynaFace: Discrimination between Obligatory and Non-obligatory Protein-Protein Interactions Based on the Complex's Dynamics

Protein-protein interfaces have been evolutionarily-designed to enable transduction between the interacting proteins. Thus, we hypothesize that analysis of the dynamics of the complex can reveal details about the nature of the interaction, and in particular whether it is obligatory, i.e., persists throughout the entire lifetime of the proteins, or not. Indeed, normal mode analysis, using the Gaussian network model, shows that for the most part obligatory and non-obligatory complexes differ in their decomposition into dynamic domains, i.e., the mobile elements of the protein complex. The dynamic domains of obligatory complexes often mix segments from the interacting chains, and the hinges between them do not overlap with the interface between the chains. In contrast, in non-obligatory complexes the interface often hinges between dynamic domains, held together through few anchor residues on one side of the interface that interact with their counterpart grooves in the other end. In automatic analysis, 117 of 139 obligatory (84.2%) and 203 of 246 non-obligatory (82.5%) complexes are correctly classified by our method: DynaFace. We further use DynaFace to predict obligatory and non-obligatory interactions among a set of 300 putative protein complexes. DynaFace is available at: http://safir.prc.boun.edu.tr/dynaface.

PETs: A Stable and Accurate Predictor of Protein-Protein Interacting Sites Based on Extremely-Randomized Trees

Protein-protein interaction (PPI) plays crucial roles in the performance of various biological processes. A variety of methods are dedicated to identify whether proteins have interaction residues, but it is often more crucial to recognize each amino acid. In practical applications, the stability of a prediction model is as important as its accuracy. However, random sampling, which is widely used in previous prediction models, often brings large difference between each training model. In this paper, a Predictor of protein-protein interaction sites based on Extremely-randomized Trees (PETs) is proposed to improve the prediction accuracy while maintaining the prediction stability. In PETs, a cluster-based sampling strategy is proposed to ensure the model stability: first, the training dataset is divided into subsets using specific features; second, the subsets are clustered using K-means; and finally the samples are selected from each cluster. Using the proposed sampling strategy, samples which have different types of significant features could be selected independently from different clusters. The evaluation shows that PETs is able to achieve better accuracy while maintaining a good stability. The source code and toolkit are available at https://github.com/BinXia/PETs.

Rubisco Activase Is Also a Multiple Responder to Abiotic Stresses in Rice

Ribulose-1,5-bisphosphate carboxylase/oxygenase activase (RCA) is a nuclear gene that encodes a chloroplast protein that plays an important role in photosynthesis. Some reports have indicated that it may play a role in acclimation to different abiotic stresses. In this paper, we analyzed the stress-responsive elements in the 2.0 kb 5’-upstream regions of the RCA gene promoter and the primary, secondary and tertiary structure of the protein. We identified some cis-elements of multiple stress-related components in the RCA promoter. Amino acid and evolution analyses showed that the RCA protein had conserved regions between different species; however, the size and type varied. The secondary structures, binding sites and tertiary structures of the RCA proteins were also different. This might reflect the differences in the transcription and translation levels of the two RCA isoforms during adaptation to different abiotic stresses. Although both the transcription and translation levels of RCA isoforms in the rice leaves increased under various stresses, the large isoform was increased more significantly in the chloroplast stroma and thylakoid. It can be concluded that RCA, especially RCA_L, is also a multiple responder to abiotic stresses in rice, which provides new insights into RCA functions.

Sequence specificity between interacting and non-interacting homologs identifies interface residues--a homodimer and monomer use case

Background

Protein families participating in protein-protein interactions may contain sub-families that have different binding characteristics, ranging from right binding to showing no interaction at all. Composition differences at the sequence level in these sub-families are often decisive to their differential functional interaction. Methods to predict interface sites from protein sequences typically exploit conservation as a signal. Here, instead, we provide proof of concept that the sequence specificity between interacting versus non-interacting groups can be exploited to recognise interaction sites.

Results

We collected homodimeric and monomeric proteins and formed homologous groups, each having an interacting (homodimer) subgroup and a non-interacting (monomer) subgroup. We then compiled multiple sequence alignments of the proteins in the homologous groups and identified compositional differences between the homodimeric and monomeric subgroups for each of the alignment positions. Our results show that this specificity signal distinguishes interface and other surface residues with 40.9 % recall and up to 25.1 % precision.

Conclusions

To our best knowledge, this is the first large scale study that exploits sequence specificity between interacting and non-interacting homologs to predict interaction sites from sequence information only. The performance obtained indicates that this signal contains valuable information to identify protein-protein interaction sites.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0758-y) contains supplementary material, which is available to authorized users.

A mutation in the POT1 gene is responsible for cardiac angiosarcoma in TP53-negative Li-Fraumeni-like families

Cardiac angiosarcoma (CAS) is a rare malignant tumour whose genetic basis is unknown. Here we show, by whole-exome sequencing of a TP53-negative Li-Fraumeni-like (LFL) family including CAS cases, that a missense variant (p.R117C) in POT1 (protection of telomeres 1) gene is responsible for CAS. The same gene alteration is found in two other LFL families with CAS, supporting the causal effect of the identified mutation. We extend the analysis to TP53-negative LFL families with no CAS and find the same mutation in a breast AS family. The mutation is recently found once in 121,324 studied alleles in ExAC server but it is not described in any other database or found in 1,520 Spanish controls. In silico structural analysis suggests how the mutation disrupts POT1 structure. Functional and in vitro studies demonstrate that carriers of the mutation show reduced telomere-bound POT1 levels, abnormally long telomeres and increased telomere fragility.

Mosaic composition of ribA and wspB genes flanking the virB8-D4 operon in the Wolbachia supergroup B-strain, wStr

The obligate intracellular bacterium, Wolbachia pipientis (Rickettsiales), is a widespread, vertically transmitted endosymbiont of filarial nematodes and arthropods. In insects, Wolbachia modifies reproduction, and in mosquitoes, infection interferes with replication of arboviruses, bacteria and plasmodia. Development of Wolbachia as a tool to control pest insects will be facilitated by an understanding of molecular events that underlie genetic exchange between Wolbachia strains. Here, we used nucleotide sequence, transcriptional and proteomic analyses to evaluate expression levels and establish the mosaic nature of genes flanking the T4SS virB8-D4 operon from wStr, a supergroup B-strain from a planthopper (Hemiptera) that maintains a robust, persistent infection in an Aedes albopictus mosquito cell line. Based on protein abundance, ribA, which contains promoter elements at the 5′-end of the operon, is weakly expressed. The 3′-end of the operon encodes an intact wspB, which encodes an outer membrane protein and is co-transcribed with the vir genes. WspB and vir proteins are expressed at similar, above average abundance levels. In wStr, both ribA and wspB are mosaics of conserved sequence motifs from Wolbachia supergroup A- and B-strains, and wspB is nearly identical to its homolog from wCobU4-2, an A-strain from weevils (Coleoptera). We describe conserved repeated sequence elements that map within or near pseudogene lesions and transitions between A- and B-strain motifs. These studies contribute to ongoing efforts to explore interactions between Wolbachia and its host cell in an in vitro system.

Identification and characterization of cis- and trans-acting elements involved in prophage induction in Streptococcus thermophilus J34

The genetic switch region of temperate Streptococcus thermophilus phage TP-J34 contains two divergently oriented promoters and several predicted operator sites. It separates lytic cycle-promoting genes from those promoting lysogeny. A polycistronic transcript comprises the genes coding for repressor Crh, metalloproteinase-motif protein Rir and superinfection exclusion lipoprotein Ltp. Weak promoters effecting monocistronic transcripts were localized for ltp and int (encoding integrase) by Northern blot and 5'-RACE-PCR. These transcripts appeared in lysogenic as well as lytic state. A polycistronic transcript comprising genes coh (encoding Cro homolog), ant (encoding putative antirepressor), orf7, orf8 and orf9 was only detected in the lytic state. Four operator sites, of which three were located in the intergenic regions between crh and coh, and one between coh and ant, were identified by competition electromobility shift assays. Cooperative binding of Crh to two operator sites immediately upstream of coh could be demonstrated. Coh was shown to bind to the operator closest to crh only. Oligomerization was proven by cross-linking Crh by glutaraldehyde. Knock-out of rir revealed a key role in prophage induction. Rir and Crh were shown to form a complex in solution and Rir prevented binding of Crh to its operator sites.

Biogeography of Nocardiopsis strains from hypersaline environments of Yunnan and Xinjiang Provinces, western China

The genus Nocardiopsis is a widespread group within the phylum Actinobacteria and has been isolated from various salty environments worldwide. However, little is known about whether biogeography affects Nocardiopsis distribution in various hypersaline environments. Such information is essential for understanding the ecology of Nocardiopsis. Here we analyzed 16S rRNA, gyrB, rpoB and sodA genes of 78 Nocardiopsis strains isolated from hypersaline environments in Yunnan and Xinjiang Provinces of western China. The obtained Nocardiopsis strains were classified into five operational taxonomic units, each comprising location-specific phylo- and genotypes. Statistical analyses showed that spatial distance and environmental factors substantially influenced Nocardiopsis distribution in hypersaline environments: the former had stronger influence at large spatial scales, whereas the latter was more influential at small spatial scales.

Assessing the function of STAS domain protein SypA in Vibrio fischeri using a comparative analysis

Colonization of the squid Euprymna scolopes by Vibrio fischeri requires biofilm formation dependent on the 18-gene symbiosis polysaccharide locus, syp. One key regulator, SypA, controls biofilm formation by an as-yet unknown mechanism; however, it is known that SypA itself is regulated by SypE. Biofilm-proficient strains form wrinkled colonies on solid media, while sypA mutants form biofilm-defective smooth colonies. To begin to understand the function of SypA, we used comparative analyses and mutagenesis approaches. sypA (and the syp locus) is conserved in other Vibrios, including two food-borne human pathogens, Vibrio vulnificus (rbdA) and Vibrio parahaemolyticus (sypA_VP). We found that both homologs could complement the biofilm defect of the V. fischeri sypA mutant, but their phenotypes varied depending on the biofilm-inducing conditions used. Furthermore, while SypA_VP retained an ability to be regulated by SypE, RbdA was resistant to this control. To better understand SypA function, we examined the biofilm-promoting ability of a number of mutant SypA proteins with substitutions in conserved residues, and found many that were biofilm-defective. The most severe biofilm-defective phenotypes occurred when changes were made to a conserved stretch of amino acids within a predicted α-helix of SypA; we hypothesize that this region of SypA may interact with another protein to promote biofilm formation. Finally, we identified a residue required for negative control by SypE. Together, our data provide insights into the function of this key biofilm regulator and suggest that the SypA orthologs may play similar roles in their native Vibrio species.

Better prediction of functional effects for sequence variants

Elucidating the effects of naturally occurring genetic variation is one of the major challenges for personalized health and personalized medicine. Here, we introduce SNAP2, a novel neural network based classifier that improves over the state-of-the-art in distinguishing between effect and neutral variants. Our method's improved performance results from screening many potentially relevant protein features and from refining our development data sets. Cross-validated on >100k experimentally annotated variants, SNAP2 significantly outperformed other methods, attaining a two-state accuracy (effect/neutral) of 83%. SNAP2 also outperformed combinations of other methods. Performance increased for human variants but much more so for other organisms. Our method's carefully calibrated reliability index informs selection of variants for experimental follow up, with the most strongly predicted half of all effect variants predicted at over 96% accuracy. As expected, the evolutionary information from automatically generated multiple sequence alignments gave the strongest signal for the prediction. However, we also optimized our new method to perform surprisingly well even without alignments. This feature reduces prediction runtime by over two orders of magnitude, enables cross-genome comparisons, and renders our new method as the best solution for the 10-20% of sequence orphans. SNAP2 is available at: https://rostlab.org/services/snap2web