Genome wide survey and molecular modeling of hypothetical proteins containing 2Fe-2S and FMN binding domains suggests Rieske Dioxygenase Activity highlighting their potential roles in bioremediation

‘Conserved hypothetical’ proteins pose a challenge not just for functional genomics, but also to biology in general. As long as there are hundreds of conserved proteins with unknown function in model organisms such as Escherichia coli, Bacillus subtilis or Saccharomyces cerevisiae, any discussion towards a ‘complete’ understanding of these biological systems will remain a wishful thinking. Insilico approaches exhibit great promise towards attempts that enable appreciating the plausible roles of these hypothetical proteins. Among the majority of genomic proteins, two-thirds in unicellular organisms and more than 80% in metazoa, are multi-domain proteins, created as a result of gene duplication events. Aromatic ring-hydroxylating dioxygenases, also called Rieske dioxygenases (RDOs), are class of multi-domain proteins that catalyze the initial step in microbial aerobic degradation of many aromatic compounds. Investigations here address the computational characterization of hypothetical proteins containing Ferredoxin and Flavodoxin signatures. Consensus sequence of each class of oxidoreductase was obtained by a phylogenetic analysis, involving clustering methods based on evolutionary relationship. A synthetic sequence was developed by combining the consensus, which was used as the basis to search for their homologs via BLAST. The exercise yielded 129 multidomain hypothetical proteins containing both 2Fe-2S (Ferredoxin) and FNR (Flavodoxin) domains. In the current study, 17 proteins with N-terminus FNR domain and C-terminus 2Fe-2S domain are characterized, through homology modelling and docking exercises which suggest dioxygenase activity indicate their plausible roles in degradation of aromatic moieties.     

e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.     

CASP11 – An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline

In silico prediction of a protein’s tertiary structure remains an unsolved problem. The community-wide Critical Assessment of Protein Structure Prediction (CASP) experiment provides a double-blind study to evaluate improvements in protein structure prediction algorithms. We developed a protein structure prediction pipeline employing a three-stage approach, consisting of low-resolution topology search, high-resolution refinement, and molecular dynamics simulation to predict the tertiary structure of proteins from the primary structure alone or including distance restraints either from predicted residue-residue contacts, nuclear magnetic resonance (NMR) nuclear overhauser effect (NOE) experiments, or mass spectroscopy (MS) cross-linking (XL) data. The protein structure prediction pipeline was evaluated in the CASP11 experiment on twenty regular protein targets as well as thirty-three ‘assisted’ protein targets, which also had distance restraints available. Although the low-resolution topology search module was able to sample models with a global distance test total score (GDT_TS) value greater than 30% for twelve out of twenty proteins, frequently it was not possible to select the most accurate models for refinement, resulting in a general decay of model quality over the course of the prediction pipeline. In this study, we provide a detailed overall analysis, study one target protein in more detail as it travels through the protein structure prediction pipeline, and evaluate the impact of limited experimental data.     

Can Natural Proteins Designed with ‘Inverted’ Peptide Sequences Adopt Native-Like Protein Folds?

We have carried out a systematic computational analysis on a representative dataset of proteins of known three-dimensional structure, in order to evaluate whether it would possible to ‘swap’ certain short peptide sequences in naturally occurring proteins with their corresponding ‘inverted’ peptides and generate ‘artificial’ proteins that are predicted to retain native-like protein fold. The analysis of 3,967 representative proteins from the Protein Data Bank revealed 102,677 unique identical inverted peptide sequence pairs that vary in sequence length between 5–12 and 18 amino acid residues. Our analysis illustrates with examples that such ‘artificial’ proteins may be generated by identifying peptides with ‘similar structural environment’ and by using comparative protein modeling and validation studies. Our analysis suggests that natural proteins may be tolerant to accommodating such peptides.     

Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae

As we are moving into the post genome-sequencing era, various high-throughput experimental techniques have been developed to characterize biological systems on the genomic scale. Discovering new biological knowledge from the high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a Bayesian statistical method together with Boltzmann machine and simulated annealing for protein functional annotation in the yeast Saccharomyces cerevisiae through integrating various high-throughput biological data, including yeast two-hybrid data, protein complexes and microarray gene expression profiles. In our approach, we quantified the relationship between functional similarity and high-throughput data, and coded the relationship into ‘functional linkage graph’, where each node represents one protein and the weight of each edge is characterized by the Bayesian probability of function similarity between two proteins. We also integrated the evolution information and protein subcellular localization information into the prediction. Based on our method, 1802 out of 2280 unannotated proteins in yeast were assigned functions systematically.     

Reticulophagy and nucleophagy: New findings and unsolved issues

Autophagy targets various intracellular components ranging from proteins and nucleic acids to organelles for their degradation in lysosomes or vacuoles. In selective types of autophagy, receptor proteins play central roles in target selection. These proteins bind or localize to specific targets, and also interact with Atg8 family proteins on forming autophagosomal membranes, leading to the efficient sequestration of the targets by the membranes. Our recent study revealed that yeast cells actively degrade the endoplasmic reticulum (ER) and even part of the nucleus via selective autophagy under nitrogen-deprived conditions. We identified novel receptors, Atg39 and Atg40, specific to these pathways. Here, we summarize our findings on ‘reticulophagy’ (or ‘ER-phagy’) and ‘nucleophagy’, and discuss key issues that remain to be solved in future studies.     

Functional significance of protein assemblies predicted by the crystal structure of the restriction endonuclease BsaWI

Type II restriction endonuclease BsaWI recognizes a degenerated sequence 5′-W/CCGGW-3′ (W stands for A or T, ‘/’ denotes the cleavage site). It belongs to a large family of restriction enzymes that contain a conserved CCGG tetranucleotide in their target sites. These enzymes are arranged as dimers or tetramers, and require binding of one, two or three DNA targets for their optimal catalytic activity. Here, we present a crystal structure and biochemical characterization of the restriction endonuclease BsaWI. BsaWI is arranged as an ‘open’ configuration dimer and binds a single DNA copy through a minor groove contacts. In the crystal primary BsaWI dimers form an indefinite linear chain via the C-terminal domain contacts implying possible higher order aggregates. We show that in solution BsaWI protein exists in a dimer-tetramer-oligomer equilibrium, but in the presence of specific DNA forms a tetramer bound to two target sites. Site-directed mutagenesis and kinetic experiments show that BsaWI is active as a tetramer and requires two target sites for optimal activity. We propose BsaWI mechanism that shares common features both with dimeric Ecl18kI/SgrAI and bona fide tetrameric NgoMIV/SfiI enzymes.     

A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction

Background

Deep-sequencing has enabled the identification of large numbers of miRNAs and siRNAs, making the high-throughput target identification a main limiting factor in defining their function. In plants, several tools have been developed to predict targets, majority of them being trained on Arabidopsis datasets. An extensive and systematic evaluation has not been made for their suitability for predicting targets in species other than Arabidopsis. Nor, these have not been evaluated for their suitability for high-throughput target prediction at genome level.

Results

We evaluated the performance of 11 computational tools in identifying genome-wide targets in Arabidopsis and other plants with procedures that optimized score-cutoffs for estimating targets. Targetfinder was most efficient [89% ‘precision’ (accuracy of prediction), 97% ‘recall’ (sensitivity)] in predicting ‘true-positive’ targets in Arabidopsis miRNA-mRNA interactions. In contrast, only 46% of true positive interactions from non-Arabidopsis species were detected, indicating low ‘recall’ values. Score optimizations increased the ‘recall’ to only 70% (corresponding ‘precision’: 65%) for datasets of true miRNA-mRNA interactions in species other than Arabidopsis. Combining the results of Targetfinder and psRNATarget delivers high true positive coverage, whereas the intersection of psRNATarget and Tapirhybrid outputs deliver highly ‘precise’ predictions. The large number of ‘false negative’ predictions delivered from non-Arabidopsis datasets by all the available tools indicate the diversity in miRNAs-mRNA interaction features between Arabidopsis and other species. A subset of miRNA-mRNA interactions differed significantly for features in seed regions as well as the total number of matches/mismatches.

Conclusion

Although, many plant miRNA target prediction tools may be optimized to predict targets with high specificity in Arabidopsis, such optimized thresholds may not be suitable for many targets in non-Arabidopsis species. More importantly, non-conventional features of miRNA-mRNA interaction may exist in plants indicating alternate mode of miRNA target recognition. Incorporation of these divergent features would enable next-generation of algorithms to better identify target interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-348) contains supplementary material, which is available to authorized users.     

How do site-specific DNA-binding proteins find their targets?

Essentially all the biological functions of DNA depend on site-specific DNA-binding proteins finding their targets, and therefore ‘searching’ through megabases of non-target DNA. In this article, we review current understanding of how this sequence searching is done. We review how simple diffusion through solution may be unable to account for the rapid rates of association observed in experiments on some model systems, primarily the Lac repressor. We then present a simplified version of the ‘facilitated diffusion’ model of Berg, Winter and von Hippel, showing how non-specific DNA–protein interactions may account for accelerated targeting, by permitting the protein to sample many binding sites per DNA encounter. We discuss the 1-dimensional ‘sliding’ motion of protein along non-specific DNA, often proposed to be the mechanism of this multiple site sampling, and we discuss the role of short-range diffusive ‘hopping’ motions. We then derive the optimal range of sliding for a few physical situations, including simple models of chromosomes in vivo, showing that a sliding range of ~100 bp before dissociation optimizes targeting in vivo. Going beyond first-order binding kinetics, we discuss how processivity, the interaction of a protein with two or more targets on the same DNA, can reveal the extent of sliding and we review recent experiments studying processivity using the restriction enzyme EcoRV. Finally, we discuss how single molecule techniques might be used to study the dynamics of DNA site-specific targeting of proteins.     

RNA damage in biological conflicts and the diversity of responding RNA repair systems

RNA is targeted in biological conflicts by enzymatic toxins or effectors. A vast diversity of systems which repair or ‘heal’ this damage has only recently become apparent. Here, we summarize the known effectors, their modes of action, and RNA targets before surveying the diverse systems which counter this damage from a comparative genomics viewpoint. RNA-repair systems show a modular organization with extensive shuffling and displacement of the constituent domains; however, a general ‘syntax’ is strongly maintained whereby systems typically contain: a RNA ligase (either ATP-grasp or RtcB superfamilies), nucleotidyltransferases, enzymes modifying RNA-termini for ligation (phosphatases and kinases) or protection (methylases), and scaffold or cofactor proteins. We highlight poorly-understood or previously-uncharacterized repair systems and components, e.g. potential scaffolding cofactors (Rot/TROVE and SPFH/Band-7 modules) with their respective cognate non-coding RNAs (YRNAs and a novel tRNA-like molecule) and a novel nucleotidyltransferase associating with diverse ligases. These systems have been extensively disseminated by lateral transfer between distant prokaryotic and microbial eukaryotic lineages consistent with intense inter-organismal conflict. Components have also often been ‘institutionalized’ for non-conflict roles, e.g. in RNA-splicing and in RNAi systems (e.g. in kinetoplastids) which combine a distinct family of RNA-acting prim-pol domains with DICER-like proteins.     

Tandem affinity purification of miRNA target mRNAs (TAP-Tar)

MicroRNAs (miRNAs) bind to Argonaute proteins, and together they form the RISC complex and regulate target mRNA translation and/or stability. Identification of mRNA targets is key to deciphering the physiological functions and mode of action of miRNAs. In mammals, miRNAs are generally poorly homologous to their target sequence, and target identification cannot be based solely on bioinformatics. Here, we describe a biochemical approach, based on tandem affinity purification, in which mRNA/miRNA complexes are sequentially pulled down, first via the Argonaute moiety and then via the miRNA. Our ‘TAP-Tar’ procedure allows the specific pull down of mRNA targets of miRNA. It is useful for validation of targets predicted in silico, and, potentially, for discovery of previously uncharacterized targets.     

sPARTA: a parallelized pipeline for integrated analysis of plant miRNA and cleaved mRNA data sets,including new miRNA target-identification software

Parallel analysis of RNA ends (PARE) is a technique utilizing high-throughput sequencing to profile uncapped, mRNA cleavage or decay products on a genome-wide basis. Tools currently available to validate miRNA targets using PARE data employ only annotated genes, whereas important targets may be found in unannotated genomic regions. To handle such cases and to scale to the growing availability of PARE data and genomes, we developed a new tool, ‘sPARTA’ (small RNA-PARE target analyzer) that utilizes a built-in, plant-focused target prediction module (aka ‘miRferno’). sPARTA not only exhibits an unprecedented gain in speed but also it shows greater predictive power by validating more targets, compared to a popular alternative. In addition, the novel ‘seed-free’ mode, optimized to find targets irrespective of complementarity in the seed-region, identifies novel intergenic targets. To fully capitalize on the novelty and strengths of sPARTA, we developed a web resource, ‘comPARE’, for plant miRNA target analysis; this facilitates the systematic identification and analysis of miRNA-target interactions across multiple species, integrated with visualization tools. This collation of high-throughput small RNA and PARE datasets from different genomes further facilitates re-evaluation of existing miRNA annotations, resulting in a ‘cleaner’ set of microRNAs.     

Network-Based Data Integration for Selecting Candidate Virulence Associated Proteins in the Cereal Infecting Fungus Fusarium graminearum

The identification of virulence genes in plant pathogenic fungi is important for understanding the infection process, host range and for developing control strategies. The analysis of already verified virulence genes in phytopathogenic fungi in the context of integrated functional networks can give clues about the underlying mechanisms and pathways directly or indirectly linked to fungal pathogenicity and can suggest new candidates for further experimental investigation, using a ‘guilt by association’ approach. Here we study 133 genes in the globally important Ascomycete fungus Fusarium graminearum that have been experimentally tested for their involvement in virulence. An integrated network that combines information from gene co-expression, predicted protein-protein interactions and sequence similarity was employed and, using 100 genes known to be required for virulence, we found a total of 215 new proteins potentially associated with virulence of which 29 are annotated as hypothetical proteins. The majority of these potential virulence genes are located in chromosomal regions known to have a low recombination frequency. We have also explored the taxonomic diversity of these candidates and found 25 sequences, which are likely to be fungal specific. We discuss the biological relevance of a few of the potentially novel virulence associated genes in detail. The analysis of already verified virulence genes in phytopathogenic fungi in the context of integrated functional networks can give clues about the underlying mechanisms and pathways directly or indirectly linked to fungal pathogenicity and can suggest new candidates for further experimental investigation, using a ‘guilt by association’ approach.     

The 3' untranslated region of human vimentin mRNA interacts with protein complexes containing eEF-1gamma and HAX-1

Previously, we have shown that the vimentin 3′ untranslated region (3′UTR) contains a highly conserved region, which is sufficient for the perinuclear localization of a reporter mRNA. This region was shown to specifically bind protein(s) by band shift analyses. UV-cross-linking studies suggest these proteins are 46- and 35-kDa in mass. Here, we have used this sequence as ‘bait’ to isolate RNA binding proteins using the yeast three-hybrid method. This technique relies on a functional assay detecting bona fide RNA–protein interaction in vivo. Three cDNA isolates, HAX-1, eEF-1γ and hRIP, code for proteins of a size consistent with in vitro cross- linking studies. In all cases, recombinant proteins were capable of binding RNA in vitro. Although hRIP is thought to be a general mRNA binding protein, this represents an unreported activity for eEF-1γ and HAX-1. Moreover, HAX-1 binding appears to be specific to vimentin’s 3′UTR. Both in vivo synthesized eEF-1γ and HAX-1 proteins were ‘pulled out’ of HeLa whole cell extracts by binding to a RNA affinity column comprised of vimentin’s 3′UTR. Moreover, size-fractionation of extracts results in the separation of large complexes containing either eEF-1γ or HAX-1. Thus, in addition to their known functions, both eEF-1γ and HAX-1 are RNA binding proteins, which suggests new roles in mRNA translation and/or perinuclear localization.     

Identifying Areas of the Visual Field Important for Quality of Life in Patients with Glaucoma

PurposeThe purpose of this study was to create a vision-related quality of life (VRQoL) prediction system to identify visual field (VF) test points associated with decreased VRQoL in patients with glaucoma.MethodVRQoL score was surveyed in 164 patients with glaucoma using the ‘Sumi questionnaire’. A binocular VF was created from monocular VFs by using the integrated VF (IVF) method. VRQoL score was predicted using the ‘Random Forest’ method, based on visual acuity (VA) of better and worse eyes (better-eye and worse-eye VA) and total deviation (TD) values from the IVF. For comparison, VRQoL scores were regressed (linear regression) against: (i) mean of TD (IVF MD); (ii) better-eye VA; (iii) worse-eye VA; and (iv) IVF MD and better- and worse-eye VAs. The rank of importance of IVF test points was identified using the Random Forest method.ResultsThe root mean of squared prediction error associated with the Random Forest method (0.30 to 1.97) was significantly smaller than those with linear regression models (0.34 to 3.38, p<0.05, ten-fold cross validation test). Worse-eye VA was the most important variable in all VRQoL tasks. In general, important VF test points were concentrated along the horizontal meridian. Particular areas of the IVF were important for different tasks: peripheral superior and inferior areas in the left hemifield for the ‘letters and sentences’ task, peripheral, mid-peripheral and para-central inferior regions for the ‘walking’ task, the peripheral superior region for the ‘going out’ task, and a broad scattered area across the IVF for the ‘dining’ task.ConclusionThe VRQoL prediction model with the Random Forest method enables clinicians to better understand patients’ VRQoL based on standard clinical measurements of VA and VF.     

Engineered viruses to select genes encoding secreted and membrane-bound proteins in mammalian cells

We have developed a functional genomics tool to identify the subset of cDNAs encoding secreted and membrane-bound proteins within a library (the ‘secretome’). A Sindbis virus replicon was engineered such that the envelope protein precursor no longer enters the secretory pathway. cDNA fragments were fused to the mutant precursor and expression screened for their ability to restore membrane localization of envelope proteins. In this way, recombinant replicons were released within infectious viral particles only if the cDNA fragment they contain encodes a secretory signal. By using engineered viral replicons to selectively export cDNAs of interest in the culture medium, the methodology reported here efficiently filters genetic information in mammalian cells without the need to select individual clones. This adaptation of the ‘signal trap’ strategy is highly sensitive (1/200 000) and efficient. Indeed, of the 2546 inserts that were retrieved after screening various libraries, more than 97% contained a putative signal peptide. These 2473 clones encoded 419 unique cDNAs, of which 77% were previously annotated. Of the 94 cDNAs encoding proteins of unknown function, 24% either had no match in databases or contained a secretory signal that could not be predicted from electronic data.     

Identification of 113 conserved essential genes using a high-throughput gene disruption system in Streptococcus pneumoniae

The recent availability of bacterial genome sequence information permits the identification of conserved genes that are potential targets for novel antibiotic drug discovery. Using a coupled bioinformatic/experimental approach, a list of candidate conserved genes was generated using a Microbial Concordance bioinformatics tool followed by a targeted disruption campaign. Pneumococcal sequence data allowed for the design of precise PCR primers to clone the desired gene target fragments into the pEVP3 ‘suicide vector’. An insertion–duplication approach was employed that used the pEVP3 constructs and resulted in the introduction of a selectable chloramphenicol resistance marker into the chromosome. In the case of non-essential genes, cells can survive the disruption and form chloramphenicol-resistant colonies. A total of 347 candidate reading frames were subjected to disruption analysis, with 113 presumed to be essential due to lack of recovery of antibiotic-resistant colonies. In addition to essentiality determination, the same high-throughput methodology was used to overexpress gene products and to examine possible polarity effects for all essential genes.     

Predicting Agenesis of the Mandibular Second Premolar from Adjacent Teeth

Early diagnosis of agenesis of the mandibular second premolar (P2) enhances management of the dental arch in the growing child. The aim of this study was to explore the relationship in the development of the mandibular first molar (M1) and first premolar (P1) at early stages of P2 (second premolar). Specifically, we ask if the likelihood of P2 agenesis can be predicted from adjacent developing teeth. We selected archived dental panoramic radiographs with P2 at crown formation stages (N = 212) and calculated the likelihood of P2 at initial mineralisation stage ‘Ci’ given the tooth stage of adjacent teeth. Our results show that the probability of observing mandibular P2 at initial mineralisation stage ‘Ci’ decreased as both the adjacent P1 and M1 matured. The modal stage at P2 ‘Ci’ was P1 ‘Coc’ (cusp outline complete) and M1 ‘Crc’ (crown complete). Initial mineralisation of P2 was observed up to P1 ‘Crc’ and M1 stage ‘R½’ (root half). The chance of observing P2 at least ‘Coc’ (coalescence of cusps) was considerably greater prior to these threshold stages compared to later stages of P1 and M1. These findings suggest that P2 is highly unlikely to develop if P1 is beyond ‘Crc’ and M1 is beyond ‘R½’.