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Background: Transmembrane protein 95 ( TMEM95 ) plays a role in male fertility. Previous studies showed that genes with a significant impact on reproductive traits can also affect the growth traits of livestock. Thus, we speculated that the genetic variation of TMEM95 gene may have effects on growth traits of cattle. Results: Two SNPs were genotyped. The rs136174626 and rs41904693 were in the intron 4 and 3′-untranslated region, respectively. The linkage disequilibrium analysis illustrated that these two loci were not linked. The rs136174626 was associated with six growth traits of Nanyang cattle, four traits of Luxi cattle, and three traits of Ji’an cattle. For rs41904693 locus, the GG individuals had greater body height and abdominal girth in Ji’ an cattle than TT and TG individuals. In Jinnan cattle, GG and TT individuals had greater body height, height at hip cross, body length, and heart girth than TG individuals. The potential splice site prediction results suggest that the rs136174626 may influence the splicing efficiency of TMEM95, and the miRNA binding site prediction results showed that the rs41904693 may influence the expression of TMEM95 by affecting the binding efficiency of Bta-miR-1584 and TMEM95 3′-UTR. Conclusions: The findings of the study suggested that the two SNPs in TMEM95 could be a reliable basis for molecular breeding in cattle. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

Plant-specialized secondary metabolites have ecological functions in mediating interactions between plants and their entophytes. In this study, high-throughput gene sequencing was used to analyze the composition and abundance of bacteria from Ginkgo leaves at five different sampling times. The results indicated that the bacterial community structure varied during leaf developmental stage. Bacterial diversity was observed to be the highest at T2 stage and the lowest at T1 stage. Proteobacteria, Firmicutes, Actinobacteria, Chloroflexi, Cyanobacteria, and Bacteroidetes were found as the dominant phyla. The major genera also showed consistency across sampling times, but there was a significant variation in their abundance, such as Bacillus, Lysinibacillus, and Staphylococcus. Significant correlations were observed between endophytic bacteria and flavonoids. Especially, Staphylococcus showed a significant positive correlation with quercetin, and changes in the abundance of Staphylococcus also showed a strong correlation with flavonoid content. In order to determine the effect of flavonoids on endophytic bacteria of Ginkgo leaves, an extracorporeal culture of related strains (a strain of Staphylococcus and a strain of Deinococcus) was performed, and it was found that the effect of flavonoids on them remained consistent. The predicted result of Tax4Fun2 revealed that flavonoids might lead to a lower abundance of endophytic microorganisms, which further proved the correlation between bacterial communities and flavonoids. This study provided the first insight into the bacterial community composition during the development of Ginkgo leaves and the correlation between the endophytic bacteria and flavonoids.

Members of the DUF34 (domain of unknown function 34) family, also known as the NIF3 protein superfamily, are ubiquitous across superkingdoms. Proteins of this family have been widely annotated as “GTP cyclohydrolase I type 2” through electronic propagation based on one study. Here, the annotation status of this protein family was examined through comprehensive literature review and integrative bioinformatic analyses that revealed varied pleiotropic associations and phenotypes. This analysis combined with functional complementation studies strongly challenges the current annotation and suggests that DUF34 family members may serve as metal ion insertases, chaperones, or metallocofactor maturases. This general molecular function could explain how DUF34 subgroups participate in highly diversified pathways such as cell differentiation, metal ion homeostasis, pathogen virulence, redox and universal stress responses.

Rare variants in the cardiac potassium channel KV7.1 (KCNQ1) and sodium channel NaV1.5 (SCN5A) are implicated in genetic disorders of heart rhythm, including congenital long QT and Brugada syndromes (LQTS, BrS), but also occur in reference populations. We previously reported two sets of NaV1.5 (n = 356) and KV7.1 (n = 144) variants with in vitro characterized channel currents gathered from the literature. Here we investigated the ability to predict commonly reported NaV1.5 and KV7.1 variant functional perturbations by leveraging diverse features including variant classifiers PROVEAN, PolyPhen-2, and SIFT; evolutionary rate and BLAST position specific scoring matrices (PSSM); and structure-based features including “functional densities” which is a measure of the density of pathogenic variants near the residue of interest. Structure-based functional densities were the most significant features for predicting NaV1.5 peak current (adj. R2 = 0.27) and KV7.1 + KCNE1 half-maximal voltage of activation (adj. R2 = 0.29). Additionally, use of structure-based functional density values improves loss-of-function classification of SCN5A variants with an ROC-AUC of 0.78 compared with other predictive classifiers (AUC = 0.69; two-sided DeLong test p = .01). These results suggest structural data can inform predictions of the effect of uncharacterized SCN5A and KCNQ1 variants to provide a deeper understanding of their burden on carriers., Graphical abstract Unlabelled Image

Polyaluminium chloride (PAC) has been widely used as a chemical coagulant in water treatment. However, little is known about the impact of PAC performance on the microbial community in sediments. In this study, the archaeal, bacterial, and fungal communities in urban river sediments with and without PAC treatment were investigated. Prokaryotic diversity decreased at the PAC addition site (A2) and increased along with the river flow (from A3 to A4), while eukaryotic diversity was the opposite. The abundance of core microbiota showed a similar trend. For example, the dominant Proteobacteria presented the highest relative abundance in A1 (26.8%) and the lowest in A2 (15.3%), followed by A3 (17.5%) and A4 (23.0%). In contrast, Rozellomycota was more dominant in A2 (56.6%) and A3 (58.1%) than in A1 (6.2%) and A4 (16.3%). Salinity, total dissolved solids, and metal contents were identified as the key physicochemical factors affecting the assembly of core microorganisms. The predicted functions of archaea and fungi were mainly divided into methane cycling and saprotrophic nutrition, respectively, while bacterial function was more diversified. The above findings are helpful to enhance our understanding of microorganism response to PAC and have significance for water treatment within the framework of microecology.

Simple Summary Gut microbiota, such as Proteobacteria, Firmicutes, and Bacteroidetes, show chitin-degrading and/or antibiotic biosynthesis ability. To evaluate whether ecological factors (diet) and host conditions (body size, body mass, and body condition) affect the gut microbiota diversity, we analysed the diet composition and host condition in Fejervarya limnocharis among different seasons and/or sexes. The dietary difference was not significant among seasons and between males and females, but host condition seasonally changed. Using bioinformatics analysis, we observed that food variations and body mass were significantly correlated with gut microbial composition. Our findings suggest that gut microbiomes of F. limnocharis vary seasonally in response to diet variations when facing environmental changes. Abstract Organisms adapt to environmental fluctuations by varying their morphology and structural, physiological, and biochemical characteristics. Gut microbiome, varying rapidly in response to environmental shifts, has been proposed as a strategy for adapting to the fluctuating environment (e.g., new dietary niches). Here, we explored the adaptive mechanism of frog intestinal microbes in response to environmental changes. We collected 170 Fejervarya limnocharis during different seasons (spring, summer, autumn, and pre-hibernation) to study the compositional and functional divergence of gut microbiota and analysed the effects of seasonal feeding habits and body condition on intestinal microorganisms using 16S rRNA high-throughput sequencing, Tax4Fun function prediction analysis, and bioinformatics analysis. The results showed no significant dietary difference in various seasons and between males and females. However, a significantly positive correlation was detected between dietary diversity and food niche width. Host condition (body size, body mass, and body condition) also revealed seasonal changes. The frogs were colonised by 71 bacterial phyla and dominated by Proteobacteria, Firmicutes, and Bacteroidetes. Stenotrophomonas was the most abundant genus in the Proteobacteria. The composition, diversity, and function of intestinal microorganisms in different seasons were significantly different. Significant differences were observed in composition and function but not in the microbial diversity between sexes. Furthermore, seasonal foods and body mass were significantly correlated with gut microbial composition. Our results suggest that gut microbiomes of F. limnocharis vary seasonally in response to diet under fluctuating environments.

It was recently shown that the gut microbiota of both depression patients and depression model animals is significantly altered, suggesting that gut microbes are closely related to depression. Here, we investigated the effects of Sophora alopecuroides L.-derived alkaloids on the gut microbiota of mice with depression-like behaviors. We first established a mouse model of depression via chronic unpredictable mild stress (CUMS) and detected changes in depression-like behaviors and depression-related indicators. Simultaneously, 16S rRNA sequencing was performed to investigate gut microbiota changes. Sophora alopecuroides L.-derived alkaloids improved depression-like behaviors and depression-related indicators in mice. The alkaloids decreased the gut microbiota diversity of CUMS mice and depleted intestinal differentially abundant “harmful” microbiota genera. Spearman analysis showed that there is a certain correlation between the differential microbiota (Lactobacillus, Helicobacter, Oscillospira, Odoribacter, Mucispirillum, Ruminococcus), depression-like behaviors, and depression-related indicators. Combined with the predictive analysis of gut microbiota function, these results indicate that alkaloids improve depression in mice through modulating gut microbiota.

Cigar tobacco leaves (CTLs) contain abundant bacteria and fungi that are vital to leaf quality during fermentation. In this study, artificial fermentation was used for the fermentation of CTLs since it was more controllable and efficient than natural aging. The bacterial and fungal community structure and composition in unfermented and fermented CTLs were determined to understand the effects of microbes on the characteristics of CTLs during artificial fermentation. The relationship between the chemical contents and alterations in the microbial composition was evaluated, and the functions of bacteria and fungi in fermented CTLs were predicted to determine the possible metabolic pathways. After artificial fermentation, the bacterial and fungal community structure significantly changed in CTLs. The total nitrate and nicotine contents were most readily affected by the bacterial and fungal communities, respectively. FAPROTAX software predictions of the bacterial community revealed increases in functions related to compound transformation after fermentation. FUNGuild predictions of the fungal community revealed an increase in the content of saprotrophic fungi after fermentation. These data provide information regarding the artificial fermentation mechanism of CTLs and will inform safety and quality improvements., Tobacco‐associated bacteria and fungi affect the aroma and quality of cigars. The microbial community structure significantly changed in cigar tobacco leaves after artificial fermentation. The total nitrate and nicotine contents were altered by the bacterial and fungal communities, respectively.

Huntington’s disease is a rare neurodegenerative and autosomal dominant disorder. HD is caused by a mutation in the gene coding for huntingtin (Htt). The result is the production of a mutant Htt with an abnormally long polyglutamine repeat that leads to pathological Htt aggregates. Although the structure of human Htt has been determined, albeit at low resolution, its functions and how they are performed are largely unknown. Moreover, there is little information on the structure and function of Htt in other organisms. The comparison of Htt homologs can help to understand if there is a functional conservation of domains in the evolution of Htt in eukaryotes. In this work, through a computational approach, Htt homologs from lower eukaryotes have been analysed, identifying ordered domains and modelling their structure. Based on the structural models, a putative function for most of the domains has been predicted. A putative C. elegans Htt-like protein has also been analysed following the same approach. The results obtained support the notion that this protein is a orthologue of human Htt.

SummaryUnraveling gene functions is pivotal to understand the signaling cascades controlling plant development and stress responses. Given that experimental profiling is costly and labor intensive, the need for high-confidence computational annotations is evident. In contrast to detailed gene-specific functional information, transcriptomics data is widely available in both model and crop species. Here, we developed a novel automated function prediction (AFP) algorithm, leveraging complementary information present in multiple expression datasets through the analysis of study-specific gene co-expression networks. Benchmarking the prediction performance on recently characterized Arabidopsis thaliana genes, we showed that our method outperforms state-of-the-art expression-based approaches. Next, we predicted biological process annotations for known (n=15,790) and unknown (n=11,865) genes in A. thaliana and validated our predictions using experimental protein-DNA and protein-protein interaction data (covering >220 thousand interactions in total), obtaining a set of high-confidence functional annotations. 5,054 (42.6%) unknown genes were assigned at least one validated annotation, and 3,408 (53.0%) genes with only computational annotations gained at least one novel validated function. These omics-supported functional annotations shed light on a variety of developmental processes and molecular responses, such as flower and root development, defense responses to fungi and bacteria, and phytohormone signaling, and help alleviate the knowledge gap of biological process annotations in Arabidopsis. An in-depth analysis of two context-specific networks, modeling seed development and response to water deprivation, shows how previously uncharacterized genes function within the respective networks. Moreover, our AFP approach can be applied in future studies to facilitate gene discovery for crop improvement.Significance statementFor the majority of plant genes, it is unknown in which processes they are involved. Using a multi-omics approach, leveraging transcriptome, protein-DNA and protein-protein interaction data, we functionally annotated 42.6% of unknown Arabidopsis thaliana genes, providing insight into a variety of developmental processes and molecular responses, as well as a resource of annotations which can be explored by the community to facilitate future research.

In this study, the effect of aerobic denitrification on nitrogen removal was investigated using two zero-discharge biofloc-based recirculating aquaculture systems with representative carbon-to-nitrogen (C/N) ratios of 15 (CN15) and 20 (CN20). Aquaculture wastewater, residual feed, and fish feces were treated in an aerated suspended growth reactor (SGR, dissolved oxygen > 5.0 mg L–1). Low toxic NH3 (

Background Small open reading frame (smORF) is open reading frame with a length of less than 100 codons. Microproteins, translated from smORFs, have been found to participate in a variety of biological processes such as muscle formation and contraction, cell proliferation, and immune activation. Although previous studies have collected and annotated a large abundance of smORFs, functions of the vast majority of smORFs are still unknown. It is thus increasingly important to develop computational methods to annotate the functions of these smORFs. Results In this study, we collected 617,462 unique smORFs from three studies. The expression of smORF RNAs was estimated by reannotated microarray probes. Using a speed-optimized correlation algorism, the functions of smORFs were predicted by their correlated genes with known functional annotations. After applying our method to 5 known microproteins from literatures, our method successfully predicted their functions. Further validation from the UniProt database showed that at least one function of 202 out of 270 microproteins was predicted. Conclusions We developed a method, smORFunction, to provide function predictions of smORFs/microproteins in at most 265 models generated from 173 datasets, including 48 tissues/cells, 82 diseases (and normal). The tool can be available at https://www.cuilab.cn/smorfunction.

Background Karst caves are considered as extreme environments with nutrition deficiency, darkness, and oxygen deprivation, and they are also the sources of biodiversity and metabolic pathways. Microorganisms are usually involved in the formation and maintenance of the cave system through various metabolic activities, and are indicators of changes environment influenced by human. Zhijin cave is a typical Karst cave and attracts tourists in China. However, the bacterial diversity and composition of the Karst cave are still unclear. The present study aims to reveal the bacterial diversity and composition in the cave and the potential impact of tourism activities, and better understand the roles and co-occurrence pattern of the bacterial community in the extreme cave habitats. Results The bacterial community consisted of the major Proteobacteria, Actinobacteria, and Firmicutes, with Proteobacteria being the predominant phylum in the rock, soil, and stalactite samples. Compositions and specialized bacterial phyla of the bacterial communities were different among different sample types. The highest diversity index was found in the rock samples with a Shannon index of 4.71. Overall, Zhijin cave has relatively lower diversity than that in natural caves. The prediction of function showed that various enzymes, including ribulose-bisphosphate carboxylase, 4-hydroxybutyryl-CoA dehydratase, nitrogenase NifH, and Nitrite reductase, involved in carbon and nitrogen cycles were detected in Zhijin cave. Additionally, the modularity indices of all co-occurrence network were greater than 0.40 and the species interactions were complex across different sample types. Co-occurring positive interactions in the bacteria groups in different phyla were also observed. Conclusion These results uncovered that the oligotrophic Zhijin cave maintains the bacterial communities with the diverse metabolic pathways, interdependent and cooperative co-existence patterns. Moreover, as a hotspot for tourism, the composition and diversity of bacterial community are influenced by tourism activities. These afford new insights for further exploring the adaptation of bacteria to extreme environments and the conservation of cave ecosystem.

Background There are growing evidences showing that gut microbiota should play an important role in host appetite and feeding behavior. However, what kind of microbe(s) and how they affect porcine appetite remain unknown. Results In this study, 280 commercial Duroc pigs were raised in a testing station with the circadian feeding behavior records for a continuous period of 30–100 kg. We first analyzed the influences of host gender and genetics in porcine average daily feed intake (ADFI), but no significant effect was observed. We found that the Prevotella-predominant enterotype had a higher ADFI than the Treponema enterotype-like group. Furthermore, 12 out of the 18 OTUs positively associated with the ADFI were annotated to Prevotella, and Prevotella was the hub bacteria in the co-abundance network. These results suggested that Prevotella might be a keystone bacterial taxon for increasing host feed intake. However, some bacteria producing short-chain fatty acids (SCFAs) and lactic acid (e.g. Ruminococcaceae and Lactobacillus) showed negative associations with the ADFI. Predicted function capacity analysis showed that the genes for amino acid biosynthesis had significantly different enrichment between pigs with high and low ADFI. Conclusions The present study provided important information on the profound effect of gut microbiota on porcine appetite and feeding behavior. This will profit us to regulate porcine appetite through modulating the gut microbiome in the pig industry. Electronic supplementary material The online version of this article (10.1186/s12866-018-1364-8) contains supplementary material, which is available to authorized users.

One of the primary goals of bioinformatics is the identification of the function of genes. The most reliable way of doing this is through experimentation. However, this is a very slow and expensive process. While this is necessary in the beginning and will continue to be necessary for special cases, it becomes impractical when one considers the number of different genes encoded in the genomes of every living organism. A faster way is to instead identify the function of genes by comparing them to the smaller set of genes with known function. This comparison may be based on many different kinds of data, including sequence similarity and gene expression data. The goal of this dissertation is to provide tools to aid in the identification of the function of unknown genes. To that end, we first present a study in which gene expression data was used to annotate many unknown genes by clustering the expression data. We then present a tool for clustering gene expression data while also identifying short areas of high sequence similarity (motifs) among members of the clusters. Finally, we present a tool for identifying the functionally relevant sub-sections of protein sequences. These sub-sections can then be used to find proteins containing similar sub-sections, even though the rest of the protein may be quite different. This tool can thus find more distantly related proteins sharing functionally relevant features.

Cardiac hypertrophy (CH) is a common disease that originates from long‐term heart pressure overload and finally leads to heart failure. Recently, long non‐coding RNAs (lncRNAs) have attracted attention because they have broad and crucial functions in regulating complex biological processes. Some studies had found that lncRNAs play vital roles in complex cardiovascular diseases. However, the function and mechanism of lncRNAs in CH have not been elucidated. In our study, to investigate the potential roles of lncRNAs in CH, the Cardiac Hypertrophy‐associated LncRNAs‐Protein coding genes Network (CHLPN) was constructed by integrating gene microarray re‐annotation and subpathway enrichment analyses. After performing random walking with restart in CHLPN, we predicted 21 significant risk lncRNAs, of which 7 (Kis2, 1700110K17Rik, Gm17501, E330017L17Rik, C630043F03Rik, Gm9866 and Ube4bos1) formed a close module with their co‐expressed protein‐coding genes (PCGs). We found that the module might play crucial roles in the development of CH. In particular, 44 PCGs that were co‐expressed with six lncRNAs were enriched in CH‐related biological processes and pathways. We also found that some lncRNAs participated in the competitive endogenous RNA cross‐talk that might be involved in CH. These results indicate that the functional lncRNAs are related to post‐transcriptional regulation and could shed light on a new molecular diagnostic target of CH.

The R2R3-MYB genes make up one of the largest transcription factor families in plants, and play regulatory roles in various biological processes such as development, metabolism and defense response. Although genome-wide analyses of this gene family have been conducted in several species, R2R3-MYB genes have not been systematically analyzed in Medicago truncatula, a sequenced model legume plant. Here, we performed a comprehensive, genome-wide computational analysis of the structural characteristics, phylogeny, functions and expression patterns of M. truncatula R2R3-MYB genes. DNA binding domains are highly conserved among the 155 putative MtR2R3-MYB proteins that we identified. Chromosomal location analysis revealed that these genes were distributed across all eight chromosomes. Results showed that the expansion of the MtR2R3-MYB family was mainly attributable to segmental duplication and tandem duplication. A comprehensive classification was performed based on phylogenetic analysis of the R2R3-MYB gene families in M. truncatula, Arabidopsis thaliana and other plant species. Evolutionary relationships within clades were supported by clade-specific conserved motifs outside the MYB domain. Species-specific clades have been gained or lost during evolution, resulting in functional divergence. Also, tissue-specific expression patterns were investigated. The functions of stress response-related clades were further verified by the changes in transcript levels of representative R2R3-MYB genes upon treatment with abiotic and biotic stresses. This study is the first report on identification and characterization of R2R3-MYB gene family based on the genome of M. truncatula, and will facilitate functional analysis of this gene family in the future.

In a previous work, we in silico annotated protein sequences of Citrus genus plants as putative tryptophan decarboxylase (pTDC). Here, we investigated the structural properties of Citrus pTDCs by using the TDC sequence of Catharanthus roseus as an experimentally annotated reference to carry out comparative modeling and substrate docking analyses. The functional annotation as TDC was verified by combining 3D molecular modeling and docking simulations, evidencing the peculiarities and the structural similarities with C. roseus TDC. Docking with l-tryptophan as a ligand showed specificity of pTDC for this substrate. These combined results confirm our previous in silico annotation of the examined protein sequences of Citrus as TDC and provide support for TDC activity in this plant genus.

The SQUAMOSA promoter binding protein (SBP)-box genes encode a kind of plant-specific transcription factors (TFs) and play important roles in the regulation of plant development. In this study, a genome-wide characterization of this family was conducted in maize (Zea mays). Thirty-one SBP-box genes were identified to be distributed in nine chromosomes and 16 of them were complementary to the mature ZmmiR156 sequences. All the Z. mays SBP (ZmSBP) genes were classified into two clusters with eight subgroups according to the phylogenetic analysis of proteins, which were consistent with the pattern of exon-intron structures. The phylogenetic tree of the ZmSBP, Oryza sativa SBP-like (OsSPL) and Arabidopsis thaliana SBP-like (AtSPL) genes were constructed and all the SBP-box genes were divided into eight groups, which was the same as the classification of ZmSBP genes. The comparision of the expression profiles of all SBP-box genes in these three species indicated that most orthologous genes had similar expression patterns. The results from this study provided a basic understanding of the ZmSBP genes and might facilitate future researches for elucidating the SBP-box genes function in maize.

Protein function prediction has been the most worked upon and the most challenging problem for computational biologists. The vast majority of known proteins have yet not been characterised experimentally, and there is significant gap between their structures and functions. New un-annotated sequences are being added to the public protein databases (e.g. UniprotKB) at an enormous pace [1]. Such proteins with unknown functions might play key role in the metabolism, growth and development regulation. Thus, if functions of unknown proteins left undiscovered, researchers may skip important information(s). Based on their sequence, structure, evolutionary history, and their association with other proteins, tools of computational biology can provide insights into the function of proteins [2]. For proteins with well characterised close relatives, it is trivial to infer function. Orphan proteins without discernible sequence relatives present a greater challenge [3]. Here the task of experimental characterisation is blind and becomes unwieldy. It is highly unlikely that all known proteins will ever be completely experimentally characterised [4]. Thus, there is an emergent need to develop fast and accurate computational approaches to fulfil this requirement. Towards this end, we prepared a dataset for protein function prediction by extracting protein sequences and annotations of reviewed prokaryotic proteins (total count 323,719 as accessed on date March 10, 2019) belonging to 9 bacterial phyla Actinobacteria, Bacteroidetes, Chlamydiae, Cyanobacteria, Firmicutes, Fusobacteria, Proteobacteria, Spirochaetes and Tenericutes. Corresponding to the most frequent 1739 Gene Ontology (Molecular Function) terms, samples were filtered, and 171,212 proteins were retrieved for feature generation. The Dataset was generated by calculating the sequence, sub-sequence, physiochemical, annotation-based features for each 171,212 reviewed proteins using method in [10]. These features constitute a total of 9890 attributes for each sequence of protein along with 1739 Gene Ontology terms. Each protein sequence is assigned one or more of 1739 Gene Ontology (Molecular Function) term as its target label. The Dataset contains the Entry and Entry name of each sequence corresponding to UniprotKB Database. This dataset being huge in size (171,212 samples X 9890 features, 1739 classes with multiple values) and equipped with enough number of positive and negative samples of each 1739 class, is good for testing efficiency of any upcoming deep learning models [5]. We divided the full dataset of 171,212 reviewed proteins in the ratio 3:1 to form Train/Test dataset 1; train dataset with 128,409 samples and test dataset with 42,803 samples to facilitate training of a deep learning model. The train and test datasets are stratified to contain good proportion of each 1739 classes. We then prepared a dataset 2 of pathogenic unreviewed proteins of the 9 bacterial phyla each with 9890 features same as train/train dataset of reviewed proteins but without target labels in order to predict their functions using deep learning model proposed in [5].

WOS: 000445215600004, 30241466, Science Citation Index Expanded, Background: The automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these studies are limited to specific functional classes and levels of the Enzyme Commission (EC) number hierarchy. Besides, most of the previous methods incorporated only a single input feature type, which limits the applicability to the wide functional space. Here, we proposed a novel enzymatic function prediction tool, ECPred, based on ensemble of machine learning classifiers. Results: In ECPred, each EC number constituted an individual class and therefore, had an independent learning model. Enzyme vs. non-enzyme classification is incorporated into ECPred along with a hierarchical prediction approach exploiting the tree structure of the EC nomenclature. ECPred provides predictions for 858 EC numbers in total including 6 main classes, 55 subclass classes, 163 sub-subclass classes and 634 substrate classes. The proposed method is tested and compared with the state-of-the-art enzyme function prediction tools by using independent temporal hold-out and no-Pfam datasets constructed during this study. Conclusions: ECPred is presented both as a stand-alone and a web based tool to provide probabilistic enzymatic function predictions (at all five levels of EC) for uncharacterized protein sequences. Also, the datasets of this study will be a valuable resource for future benchmarking studies. ECPred is available for download, together with all of the datasets used in this study, at: https://github.com/cansyl/ECPred. ECPred webserver can be accessed through http://cansyl.metu.edu.tr/ECPred.html., YOK OYP scholarships, AD and ASR were supported by YOK OYP scholarships. The funding body did not play any role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Salinity tolerance has been extensively investigated in recent years due to its agricultural importance. Several features, such as the regulation of ionic transporters and metabolic adjustments, have been identified as salt tolerance hallmarks. Nevertheless, due to the complexity of the trait, the results achieved to date have met with limited success in improving the salt tolerance of rice plants when tested in the field, thus suggesting that a better understanding of the tolerance mechanisms is still required. In this work, differences between two varieties of rice with contrasting salt sensitivities were revealed by the imaging of photosynthetic parameters, ion content analysis and a transcriptomic approach. The transcriptomic analysis conducted on tolerant plants supported the setting up of an adaptive program consisting of sodium distribution preferentially limited to the roots and older leaves, and in the activation of regulatory mechanisms of photosynthesis in the new leaves. As a result, plants resumed grow even under prolonged saline stress. In contrast, in the sensitive variety, RNA-seq analysis revealed a misleading response, ending in senescence and cell death. The physiological response at the cellular level was investigated by measuring the intracellular profile of H2O2 in the roots, using a fluorescent probe. In the roots of tolerant plants, a quick response was observed with an increase in H2O2 production within 5 min after salt treatment. The expression analysis of some of the genes involved in perception, signal transduction and salt stress response confirmed their early induction in the roots of tolerant plants compared to sensitive ones. By inhibiting the synthesis of apoplastic H2O2, a reduction in the expression of these genes was detected. Our results indicate that quick H2O2 signaling in the roots is part of a coordinated response that leads to adaptation instead of senescence in salt-treated rice plants.

Bacteria play an important role in water purification in drinking water treatment systems. On one hand, bacteria present in the untreated water may help in its purification through biodegradation of the contaminants. On the other hand, some bacteria may be human pathogens and pose a threat to consumers. The present study investigated bacterial communities using Illumina MiSeq sequencing of 16S rRNA genes and their functions were predicted using PICRUSt in a treatment system, including the biofilms on sand filters and biological activated carbon (BAC) filters, in 4 months. In addition, quantitative analyses of specific bacterial populations were performed by real-time quantitative polymerase chain reaction (qPCR). The bacterial community composition of post-ozonation effluent, BAC effluent and disinfected water varied with sampling time. However, the bacterial community structures at other treatment steps were relatively stable, despite great variations of source water quality, resulting in stable treatment performance. Illumina MiSeq sequencing illustrated that Proteobacteria was dominant bacterial phylum. Chlorine disinfection significantly influenced the microbial community structure, while other treatment processes were synergetic. Bacterial communities in water and biofilms were distinct, and distinctions of bacterial communities also existed between different biofilms. By contrast, the functional composition of biofilms on different filters were similar. Some functional genes related to pollutant degradation were found widely distributed throughout the treatment processes. The distributions of Mycobacterium spp. and Legionella spp. in water and biofilms were revealed by real-time quantitative polymerase chain reaction (qPCR). Most bacteria, including potential pathogens, could be effectively removed by chlorine disinfection. However, some bacteria presented great resistance to chlorine. qPCRs showed that Mycobacterium spp. could not be effectively removed by chlorine. These resistant bacteria and, especially potential pathogens should receive more attention. Redundancy analysis (RDA) showed that turbidity, ammonia nitrogen and total organic carbon (TOC) exerted significant effects on community profiles. Overall, this study provides insight into variations of microbial communities in the treatment processes and aids the optimization of drinking water treatment plant design and operation for public health.

Plant tryptophan decarboxylase (TDC) converts tryptophan into tryptamine, precursor of indolealkylamine alkaloids. The recent finding of tryptamine metabolites in Citrus plants leads to hypothesize the existence of TDC activity in this genus. Here, we report for the first time that, in Citrus x limon seedlings, deuterium labeled tryptophan is decarboxylated into tryptamine, from which successively deuterated N,N,N-trimethyltryptamine is formed. These results give an evidence of the occurrence of the TDC activity and the successive methylation pathway of the tryptamine produced from the tryptophan decarboxylation. In addition, with the aim to identify the genetic basis for the presence of TDC, we carried out a sequence similarity search for TDC in the Citrus genomes using as a probe the TDC sequence reported for the plant Catharanthus roseus. We analyzed the genomes of both Citrus clementina and Citrus sinensis, available in public database, and identified putative protein sequences of aromatic l-amino acid decarboxylase. Similarly, 42 aromatic l-amino acid decarboxylase sequences from 23 plant species were extracted from public databases. Potential sequence signatures for functional TDC were then identified. With this research, we propose for the first time a putative protein sequence for TDC in the genus Citrus.

Genome scale metabolic model provides an overview of an organism's metabolic capability. These genome-specific metabolic reconstructions are based on identification of gene to protein to reaction (GPR) associations and, in turn, on homology with annotated genes from other organisms. Cyanobacteria are photosynthetic prokaryotes which have diverged appreciably from their nonphotosynthetic counterparts. They also show significant evolutionary divergence from plants, which are well studied for their photosynthetic apparatus. We argue that context-specific sequence and domain similarity can add to the repertoire of the GPR associations and significantly expand our view of the metabolic capability of cyanobacteria. We took an approach that combines the results of context-specific sequence-to-sequence similarity search with those of sequence-to-profile searches. We employ PSI-BLAST for the former, and CDD, Pfam, and COG for the latter. An optimization algorithm was devised to arrive at a weighting scheme to combine the different evidences with KEGG-annotated GPRs as training data. We present the algorithm in the form of software "Systematic, Homology-based Automated Re-annotation for Prokaryotes (SHARP)." We predicted 3,781 new GPR associations for the 10 prokaryotes considered of which eight are cyanobacteria species. These new GPR associations fall in several metabolic pathways and were used to annotate 7,718 gaps in the metabolic network. These new annotations led to discovery of several pathways that may be active and thereby providing new directions for metabolic engineering of these species for production of useful products. Metabolic model developed on such a reconstructed network is likely to give better phenotypic predictions.

Candidatus Liberibacter asiaticus (Ca. L. asiaticus) is a Gram-negative bacterium and the pathogen of Citrus Greening disease (Huanglongbing, HLB). As a parasitic bacterium, Ca. L. asiaticus harbors ABC transporters that play important roles in exchanging chemical compounds between Ca. L. asiaticus and its host. Here, we analyzed all the ABC transporter-related proteins in Ca. L. asiaticus. We identified 14 ABC transporter systems and predicted their structures and substrate specificities. In-depth sequence and structure analysis including multiple sequence alignment, phylogenetic tree reconstruction, and structure comparison further support their function predictions. Our study shows that this bacterium could use these ABC transporters to import metabolites (amino acids and phosphates) and enzyme cofactors (choline, thiamine, iron, manganese, and zinc), resist to organic solvent, heavy metal, and lipid-like drugs, maintain the composition of the outer membrane (OM), and secrete virulence factors. Although the features of most ABC systems could be deduced from the abundant experimental data on their orthologs, we reported several novel observations within ABC system proteins. Moreover, we identified seven nontransport ABC systems that are likely involved in virulence gene expression regulation, transposon excision regulation, and DNA repair. Our analysis reveals several candidates for further studies to understand and control the disease, including the type I virulence factor secretion system and its substrate that are likely related to Ca. L. asiaticus pathogenicity and the ABC transporter systems responsible for bacterial OM biosynthesis that are good drug targets.

The body of human genomic and proteomic evidence continues to grow at ever-increasing rates, while annotation efforts struggle to keep pace. A surprisingly small fraction of human genes have clear, documented associations with specific functions, and new functions continue to be found for characterized genes. Here we assembled an integrated collection of diverse genomic and proteomic data for 21,341 human genes and make quantitative associations of each to 4333 Gene Ontology terms. We combined guilt-by-profiling and guilt-by-association approaches to exploit features unique to the data types. Performance was evaluated by cross-validation, prospective validation, and by manual evaluation with the biological literature. Functional-linkage networks were also constructed, and their utility was demonstrated by identifying candidate genes related to a glioma FLN using a seed network from genome-wide association studies. Our annotations are presented—alongside existing validated annotations—in a publicly accessible and searchable web interface.

Ortholog identification is used in gene functional annotation, species phylogeny estimation, phylogenetic profile construction and many other analyses. Bioinformatics methods for ortholog identification are commonly based on pairwise protein sequence comparisons between whole genomes. Phylogenetic methods of ortholog identification have also been developed; these methods can be applied to protein data sets sharing a common domain architecture or which share a single functional domain but differ outside this region of homology. While promiscuous domains represent a challenge to all orthology prediction methods, overall structural similarity is highly correlated with proximity in a phylogenetic tree, conferring a degree of robustness to phylogenetic methods. In this article, we review the issues involved in orthology prediction when data sets include sequences with structurally heterogeneous domain architectures, with particular attention to automated methods designed for high-throughput application, and present a case study to illustrate the challenges in this area.

Recent advances in technologies for observing high-resolution genomic activities, such as whole-genome tiling arrays and high-throughput sequencers, provide detailed information for understanding genome functions. However, the functions of 50% of known Arabidopsis thaliana genes remain unknown or are annotated only on the basis of static analyses such as protein motifs or similarities. In this paper, we describe dynamic structure-based dynamic expression (DSDE) analysis, which sequentially predicts both structural and functional features of transcripts. We show that DSDE analysis inferred gene functions 12% more precisely than static structure-based dynamic expression (SSDE) analysis or conventional co-expression analysis based on previously determined gene structures of A. thaliana. This result suggests that more precise structural information than the fixed conventional annotated structures is crucial for co-expression analysis in systems biology of transcriptional regulation and dynamics. Our DSDE method, ARabidopsis Tiling-Array-based Detection of Exons version 2 and over-representation analysis (ARTADE2-ORA), precisely predicts each gene structure by combining two statistical analyses: a probe-wise co-expression analysis of multiple transcriptome measurements and a Markov model analysis of genome sequences. ARTADE2-ORA successfully identified the true functions of about 90% of functionally annotated genes, inferred the functions of 98% of functionally unknown genes and predicted 1,489 new gene structures and functions. We developed a database ARTADE2DB that integrates not only the information predicted by ARTADE2-ORA but also annotations and other functional information, such as phenotypes and literature citations, and is expected to contribute to the study of the functional genomics of A. thaliana. URL: http://artade.org.

IntroductionActivating mutations of the BRAF oncogene are frequently detected in papillary thyroid carcinoma (PTC) and have been associated with a worse prognosis. The amino acid substitution V600E accounts for 90% of all oncogenic BRAF mutations and is typically detected in classic PTCs, whereas other less frequent BRAF mutations seem to be associated with other PTC histotypes.CaseScreening for activating BRAF mutations in a series of 83 PTCs identified the most common V600E mutation in 39 cases (histologically, 38 classic PTCs and 1 sclerosing variant PTC) and a complex in-frame mutation involving amino acids V600–S605 in a stage III multicentric follicular variant PTC, occurring in a 50-year-old female patient, who was affected by hypothyroidism in autoimmune thyroiditis and had a family history of PTC and autoimmune thyroiditis. Since the identified BRAF mutation was novel in the literature, bioinformatic modeling was performed to predict its impact on BRAF activity. Although the mutation resulted in loss of a phosphorylation site in the activation loop of BRAF, it was predicted to increase BRAF kinase activity by mimicking an activating phosphorylation.ConclusionsThis study, which reports a new BRAF mutation, highlights the usefulness of bioinformatic modeling in the prediction of functional effects of new mutations and indicates that mutation-specific screening tests might miss some rare BRAF mutations. These facts should be taken into consideration in the molecular diagnosis of thyroid cancer and in the design of therapeutic protocols based on inhibitors of the BRAF pathway.

Several Thermotogales, previously reported as being strict anaerobes, have demonstrated the ability to grow and produce hydrogen in the presence of moderate amounts of molecular oxygen. Thermotoga neapolitana seems to be less sensitive to O 2 than other members of this order, including Thermotoga maritima, whose hydrogenase has been purified and characterized. Instead, the enzyme responsible for the hydrogen production by T. neapolitana has not yet been identified. After the recent sequencing of the T. neapolitana genome, it has been possible to search for the orthologous gene responsible for this unusal hydrogenase activity. By means of in silico analysis, we built a molecular model for both T. maritima and T. neapolitana proteins and analyzed conservation, focusing on the subtle structural differences responsible for the increased oxygen resistance in the latter and underscoring two mutations (E475S and T539L) which represent a specific adaption for more effective release of hydrogen in aerobic conditions.

The Auxin/indole-3-acetic acid (Aux/IAA) gene family plays key roles in the primary auxin-response process and controls a number of important traits in plants. However, the characteristics of the Aux/IAA gene family in hexaploid bread wheat (Triticum aestivum L.) have long been unknown. In this study, a comprehensive identification of the Aux/IAA gene family was performed using the latest draft genome sequence of the bread wheat ‘Chinese Spring’. Thirty-four Aux/IAA genes were identified, 30 of which have duplicated genes on the A, B or D sub-genome, with a total of 84 Aux/IAA sequences. These predicted Aux/IAA genes were non-randomly distributed in all the wheat chromosomes except for chromosome 2D. The information of wheat Aux/IAA proteins is also described. Based on an analysis of phylogeny, expression and adaptive evolution, we prove that the Aux/IAA family in wheat has been replicated twice in the two allopolyploidization events of bread wheat, when the tandem duplication also occurred. The duplicated genes have undergone an evolutionary process of purifying selection, resulting in the high conservation of copy genes among sub-genomes and functional redundancy among several members of the TaIAA family. However, functional divergence probably existed in most TaIAA members due to the diversity of the functional domain and expression pattern. Our research provides useful information for further research into the function of Aux/IAA genes in wheat.

Background High-throughput bio-techniques accumulate ever-increasing amount of genomic and proteomic data. These data are far from being functionally characterized, despite the advances in gene (or gene’s product proteins) functional annotations. Due to experimental techniques and to the research bias in biology, the regularly updated functional annotation databases, i.e., the Gene Ontology (GO), are far from being complete. Given the importance of protein functions for biological studies and drug design, proteins should be more comprehensively and precisely annotated. Results We proposed downward Random Walks (dRW) to predict missing (or new) functions of partially annotated proteins. Particularly, we apply downward random walks with restart on the GO directed acyclic graph, along with the available functions of a protein, to estimate the probability of missing functions. To further boost the prediction accuracy, we extend dRW to dRW-kNN. dRW-kNN computes the semantic similarity between proteins based on the functional annotations of proteins; it then predicts functions based on the functions estimated by dRW, together with the functions associated with the k nearest proteins. Our proposed models can predict two kinds of missing functions: (i) the ones that are missing for a protein but associated with other proteins of interest; (ii) the ones that are not available for any protein of interest, but exist in the GO hierarchy. Experimental results on the proteins of Yeast and Human show that dRW and dRW-kNN can replenish functions more accurately than other related approaches, especially for sparse functions associated with no more than 10 proteins. Conclusion The empirical study shows that the semantic similarity between GO terms and the ontology hierarchy play important roles in predicting protein function. The proposed dRW and dRW-kNN can serve as tools for replenishing functions of partially annotated proteins. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0713-y) contains supplementary material, which is available to authorized users.

N-Acetyl-β-D-glucosaminidase (O-GlcNAcase) is a key enzyme in the posttranslational modification of intracellular proteins by O-linked N-acetylglucosamine (O-GlcNAc). Here, we show that this protein contains two catalytic domains, one homologous to bacterial hyaluronidases and one belonging to the GCN5-related family of acetyltransferases (GNATs). Using sequence and structural information, we predict that the GNAT homologous region contains the O-GlcNAcase activity. Thus, O-GlcNAcase is the first member of the GNAT family not involved in transfer of acetyl groups, adding a new mode of evolution to this large protein family. Comparison with solved structures of different GNATs led to a reliable structure prediction and mapping of residues involved in binding of the GlcNAc-modified proteins and catalysis.

Haemophilus influenzae is a small pleomorphic Gram-negative bacteria which causes several chronic diseases, including bacteremia, meningitis, cellulitis, epiglottitis, septic arthritis, pneumonia, and empyema. Here we extensively analyzed the sequenced genome of H. influenzae strain Rd KW20 using protein family databases, protein structure prediction, pathways and genome context methods to assign a precise function to proteins whose functions are unknown. These proteins are termed as hypothetical proteins (HPs), for which no experimental information is available. Function prediction of these proteins would surely be supportive to precisely understand the biochemical pathways and mechanism of pathogenesis of Haemophilus influenzae. During the extensive analysis of H. influenzae genome, we found the presence of eight HPs showing lyase activity. Subsequently, we modeled and analyzed three-dimensional structure of all these HPs to determine their functions more precisely. We found these HPs possess cystathionine-β-synthase, cyclase, carboxymuconolactone decarboxylase, pseudouridine synthase A and C, D-tagatose-1,6-bisphosphate aldolase and aminodeoxychorismate lyase-like features, indicating their corresponding functions in the H. influenzae. Lyases are actively involved in the regulation of biosynthesis of various hormones, metabolic pathways, signal transduction, and DNA repair. Lyases are also considered as a key player for various biological processes. These enzymes are critically essential for the survival and pathogenesis of H. influenzae and, therefore, these enzymes may be considered as a potential target for structure-based rational drug design. Our structure–function relationship analysis will be useful to search and design potential lead molecules based on the structure of these lyases, for drug design and discovery. Electronic supplementary material The online version of this article (doi:10.1007/s13205-014-0231-z) contains supplementary material, which is available to authorized users.

Eicosapenta peptide repeats (EPRs) occur exclusively in flowering plant genomes and exhibit very high amino acid residue conservation across occurrence. DNA and amino acid sequence searches yielded no indications about the function due to absence of similarity to known sequences. Tertiary structure of an EPR protein coded by rice (Oryza sativa japonica) cDNA (GI: 32984786) was determined based on ab initio methodology in order to draw clues on functional significance of EPRs. The resultant structure comprised of seven α-helices and thirteen anti-parallel β-sheets. Surface-mapping of conserved residues onto the structure deduced that (i) regions equivalent to β α4- the primary function of EPR protein could be Ca(2+) binding, and (iii) the putative EPR Ca(2+) binding domain is structurally similar to calcium-binding domains of plant lectins. Additionally, the phylogenetic analysis showed an evolving taxa-specific distribution of EPR proteins observed in some GNA-like lectins.

Background: Uncharacterized proteins from newly sequenced genomes provide perfect targets for fold and function prediction. Results: For 38% of the entire genome of Mycoplasma genitalium , sequence similarity to a protein with a known structure can be recognized using a new sequence alignment algorithm. When comparing genomes of M. genitalium and Escherichia coli , > 80% of M. genitalium proteins have a significant sequence similarity to a protein in E. coli and there are > 40 examples that have not been recognized before. For all cases of proteins with significant profile similarities, there are strong analogies in their functions, if the functions of both proteins are known. The results presented here and other recent results strongly support the argument that such proteins are actually homologous. Assuming this homology allows one to make tentative functional assignments for > 50 previously uncharacterized proteins, including such intriguing cases as the putative β -lactam antibiotic resistance protein in M. genitalium. Conclusions: Using a new profile-to-profile alignment algorithm, the three-dimensional fold can be predicted for almost 40% of proteins from a genome of the small bacterium M. genitalium , and tentative function can be assigned to almost 80% of the entire genome. Some predictions lead to new insights about known functions or point to hitherto unexpected features of M.genitalium

Aquaporins (AQPs) are a family of channel proteins that transport water and/or small solutes across cell membranes. Aquaporins are present in Bacteria, Eukarya and Archaea. The classical AQP evolution 47 paradigm explains the inconsistent phylogenetic trees by multiple transfer events and emphasizes that the assignment of orthologous AQPs is not possible, making it difficult to integrate functional information. Recently, a novel phylogenetic framework of eukaryotic AQP evolution showed congruence between eukaryotic AQPs and organismal trees identifying 32 orthologous clusters in plants and animals (Gene 2012 503:165-176). In this article, we discuss in depth the methodological strength, the ability to predict functionality and the aquaporin community perception about the different paradigms of AQP evolution. Moreover, we show an updated review of AQPs transport functions in association with phylogenetic analyses. Finally, we discuss the possible effect of aquaporin data integration in the understanding of water and solute transport in eukaryotic cells. Fil: Pérez, Juliana Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular; Argentina Fil: Soto, Gabriela Cynthia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias. Centro de Investigación de Ciencias Veterinarias y Agronómicas. Instituto de Genética; Argentina Fil: Alleva, Karina Edith. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biologia Experimental y Aplicada; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Biodiversidad y Biología Experimental; Argentina Fil: Jozefkowicz, Cintia. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biologia Experimental y Aplicada; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Biodiversidad y Biología Experimental; Argentina Fil: Amodeo, Gabriela. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biologia Experimental y Aplicada; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Biodiversidad y Biología Experimental; Argentina Fil: Muschietti, Jorge Prometeo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Biodiversidad y Biología Experimental; Argentina Fil: Ayub, Nicolas Daniel. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias. Centro de Investigación de Ciencias Veterinarias y Agronómicas. Instituto de Genética; Argentina

Uncharacterized proteins pose a challenge not just to functional genomics, but also to biology in general. The knowledge of biochemical functions of such proteins is very critical for designing efficient therapeutic techniques. The bottleneck in hypothetical proteins annotation is the difficulty in collecting and aggregating enough biological information about the protein itself. In this paper, we propose and evaluate a protein annotation technique that aggregates different biological information conserved across many hypothetical proteins. To enhance the performance and to increase the prediction accuracy, we incorporate term specific relationships based on Gene Ontology (GO). Our method combines PPI (Protein Protein Interactions) data, protein motifs information, protein sequence similarity and protein homology data, with a context similarity measure based on Gene Ontology, to accurately infer functional information for unannotated proteins. We apply our method on Saccharomyces Cerevisiae species proteins. The aggregation of different sources of evidence with GO relationships increases the precision and accuracy of prediction compared to other methods reported in literature. We predicted with a precision and accuracy of 100% for more than half proteins of the input set and with an overall 81.35% precision and 80.04% accuracy.

High-throughput biology technologies have yielded complete genome sequences and functional genomics data for several organisms, including crucial microbial pathogens of humans, animals and plants. However, up to 50% of genes within a genome are often labeled "unknown", "uncharacterized" or "hypothetical", limiting our understanding of virulence and pathogenicity of these organisms. Even though biological functions of proteins encoded by these genes are not known, many of them have been predicted to be involved in key processes in these organisms. In particular, for Mycobacterium tuberculosis, some of these "hypothetical" proteins, for example those belonging to the Pro-Glu or Pro-Pro-Glu (PE/PPE) family, have been suspected to play a crucial role in the intracellular lifestyle of this pathogen, and may contribute to its survival in different environments. We have generated a functional interaction network for Mycobacterium tuberculosis proteins and used this to predict functions for many of its hypothetical proteins. Here we performed functional enrichment analysis of these proteins based on their predicted biological functions to identify annotations that are statistically relevant, and analysed and compared network properties of hypothetical proteins to the known proteins. From the statistically significant annotations and network information, we have tried to derive biologically meaningful annotations related to infection and disease. This quantitative analysis provides an overview of the functional contributions of Mycobacterium tuberculosis "hypothetical" proteins to many basic cellular functions, including its adaptability in the host system and its ability to evade the host immune response.

With high-throughput technologies providing vast amounts of data, it has become more important to provide systematic, quality annotations. The Gene Ontology (GO) project is the largest resource for cataloguing gene function. Nonetheless, its use is not yet ubiquitous and is still fraught with pitfalls. In this review, we provide a short primer to the GO for bioinformaticians. We summarize important aspects of the structure of the ontology, describe sources and types of functional annotations, survey measures of GO annotation similarity, review typical uses of GO and discuss other important considerations pertaining to the use of GO in bioinformatics applications., Briefings in Bioinformatics, 12 (6), ISSN:1467-5463, ISSN:1477-4054

Neisseria meningitidis is a human-specific pathogen known for its capability to cause sepsis and meningitis. Here we report the availability of 2 draft genome sequences obtained from patients infected during the same epidemic outbreak. Both bacterial isolates belong to serogroup C, but their genome sequences show local and remarkable differences compared with each other or with the reference genome of strain FAM18.

International audience; We report a high-quality draft genome sequence of the domesticated apple (Malus x domestica). We show that a relatively recent (> 50 million years ago) genome-wide duplication (GWD) has resulted in the transition from nine ancestral chromosomes to 17 chromosomes in the Pyreae. Traces of older GWDs partly support the monophyly of the ancestral paleohexaploidy of eudicots. Phylogenetic reconstruction of Pyreae and the genus Malus, relative to major Rosaceae taxa, identified the progenitor of the cultivated apple as M. sieversii. Expansion of gene families reported to be involved in fruit development may explain formation of the pome, a Pyreae-specific false fruit that develops by proliferation of the basal part of the sepals, the receptacle. In apple, a subclade of MADS-box genes, normally involved in flower and fruit development, is expanded to include 15 members, as are other gene families involved in Rosaceae-specific metabolism, such as transport and assimilation of sorbitol.

Background Large-scale sequencing projects have now become routine lab practice and this has led to the development of a new generation of tools involving function prediction methods, bringing the latter back to the fore. The advent of Gene Ontology, with its structured vocabulary and paradigm, has provided computational biologists with an appropriate means for this task. Methodology We present here a novel method called ARGOT (Annotation Retrieval of Gene Ontology Terms) that is able to process quickly thousands of sequences for functional inference. The tool exploits for the first time an integrated approach which combines clustering of GO terms, based on their semantic similarities, with a weighting scheme which assesses retrieved hits sharing a certain number of biological features with the sequence to be annotated. These hits may be obtained by different methods and in this work we have based ARGOT processing on BLAST results. Conclusions The extensive benchmark involved 10,000 protein sequences, the complete S. cerevisiae genome and a small subset of proteins for purposes of comparison with other available tools. The algorithm was proven to outperform existing methods and to be suitable for function prediction of single proteins due to its high degree of sensitivity, specificity and coverage.

Background Despite a plethora of functional genomic efforts, the function of many genes in sequenced genomes remains unknown. The increasing amount of microarray data for many species allows employing the guilt-by-association principle to predict function on a large scale: genes exhibiting similar expression patterns are more likely to participate in shared biological processes. Results We developed Prosecutor, an application that enables researchers to rapidly infer gene function based on available gene expression data and functional annotations. Our parameter-free functional prediction method uses a sensitive algorithm to achieve a high association rate of linking genes with unknown function to annotated genes. Furthermore, Prosecutor utilizes additional biological information such as genomic context and known regulatory mechanisms that are specific for prokaryotes. We analyzed publicly available transcriptome data sets and used literature sources to validate putative functions suggested by Prosecutor. We supply the complete results of our analysis for 11 prokaryotic organisms on a dedicated website. Conclusion The Prosecutor software and supplementary datasets available at http://www.prosecutor.nl allow researchers working on any of the analyzed organisms to quickly identify the putative functions of their genes of interest. A de novo analysis allows new organisms to be studied.

The identification of novel drug targets from genomic data involves the large-scale analysis of many protein sequences. Methods for automated structure and function prediction are an essential tool for this purpose. In this review we concentrate on the recent developments in the field of protein structure prediction and how these can be used to gain hints about the function of proteins. The current state-of-the-art is highlighted through recent community-wide experiments aimed at comparing different approaches. For structure prediction this allows the identification of key improvements to increase the crucial sequence to structure alignment needed for accurate models. Function prediction is a rapidly maturing field that is still being benchmarked. Definitions for protein function are presented and available methods, mostly concentrating on functional site descriptors and structural motifs, presented.

We predicted gene function using synthetic lethal genetic interactions between null alleles in Saccharomyces cerevisiae. Phenotypic and protein interaction data indicate that synthetic lethal gene pairs function in parallel or compensating pathways. Congruent gene pairs, defined as sharing synthetic lethal partners, are in single pathway branches. We predicted benomyl sensitivity and nuclear migration defects using congruence; these phenotypes were uncorrelated with direct synthetic lethality. We also predicted YLL049W as a new member of the dynein-dynactin pathway and provided new supporting experimental evidence. We performed synthetic lethal screens of the parallel mitotic exit network (MEN) and Cdc14 early anaphase release pathways required for late cell cycle. Synthetic lethal interactions bridged genes in these pathways, and high congruence linked genes within each pathway. Synthetic lethal interactions between MEN and all components of the Sin3/Rpd3 histone deacetylase revealed a novel function for Sin3/Rpd3 in promoting mitotic exit in parallel to MEN. These in silico methods can predict phenotypes and gene functions and are applicable to genomic synthetic lethality screens in yeast and analogous RNA interference screens in metazoans.

The PAAD/DAPIN/pyrin domain is the fourth member of the death domain superfamily, but unlike other members of this family, it is involved not only in apoptosis but also in innate immunity and several other processes. We have identified 40 PAAD domain-containing proteins by extensively searching the genomes of higher eukaryotes and viruses. Phylogenetic analyses suggest that there are five categories of PAAD domains that correlate with the domain architecture of the entire proteins. Homology models built on CARD and DD structures identified functionally important residues by studying conservation patterns on the surface of the models. Surface maps of each subfamily show different distributions of these residues, suggesting that domains from different subfamilies do not interact with each other, forming independent regulatory networks. Helix3 of PAAD is predicted to be critical for dimerization. Multiple alignment analysis and modeling suggest that it may be partly disordered, following a new paradigm for interaction proteins that are stabilized by protein–protein interactions., Research described here was supported by NIH grant GM60049.

Background The accumulation of protein structural data occurs more rapidly than it can be characterized by traditional laboratory means. This has motivated widespread efforts to predict enzyme function computationally. The most useful/accurate strategies employed to date are based on the detection of motifs in novel structures that correspond to a specific function. Functional residues are critical components of predictively useful motifs. We have implemented a novel method, to complement current approaches, which detects motifs solely on the basis of distance restraints between catalytic residues. Results ProMOL is a plugin for the PyMOL molecular graphics environment that can be used to create active site motifs for enzymes. A library of 181 active site motifs has been created with ProMOL, based on definitions published in the Catalytic Site Atlas (CSA). Searches with ProMOL produce better than 50% useful Enzyme Commission (EC) class suggestions for level 1 searches in EC classes 1, 4 and 5, and produce some useful results for other classes. 261 additional motifs automatically translated from Jonathan Barker’s JESS motif set [Bioinformatics 19:1644–1649, 2003] and a set of NMR motifs is under development. Alignments are evaluated by visual superposition, Levenshtein distance and root-mean-square deviation (RMSD) and are reasonably consistent with related search methods. Conclusion The ProMOL plugin for PyMOL provides ready access to template-based local alignments. Recent improvements to ProMOL, including the expanded motif library, RMSD calculations and output selection formatting, have greatly increased the program’s usability and speed, and have improved the way that the results are presented.

Background: Database-searching methods based on sequence similarity have become the most commonly used tools for characterizing newly sequenced proteins. Due to the often underestimated functional diversity in protein families and superfamilies, however, it is difficult to make the characterization specific and accurate. In this work, we have extended a method for active-site identification from predicted protein structures. Results: The structural conservation and variation of the active sites of the α / β hydrolases with known structures were studied. The similarities were incorporated into a three-dimensional motif that specifies essential requirements for the enzymatic functions. A threading algorithm was used to align 651 Escherichia coli open reading frames (ORFs) to one of the members of the α / β hydrolase fold family. These ORFs were then screened according to our three-dimensional motif and with an extra requirement that demands conservation of the key active-site residues among the proteins that bear significant sequence similarity to the ORFs. 17 ORFs from E. coli were predicted to have hydrolase activity and their putative active-site residues were identified. Most were in agreement with the experiments and results of other database-searching methods. The study further suggests that YHET_ECOLI, a hypothetical protein classified as a member of the UPF0017 family (an uncharacterized protein family), bears all the hallmarks of the α / β hydrolase family. Conclusions: The novel feature of our method is that it uses three-dimensional structural information for function prediction. The results demonstrate the importance and necessity of such a method to fill the gap between sequence alignment and function prediction; furthermore, the method provides a way to verify the structure predictions, which enables an expansion of the applicable scope of the threading algorithms.