The COMBREX project: design, methodology, and initial results

The Y-ome Conundrum: Insights into Uncharacterized Genes and Approaches for Functional Annotation

The ever-increasing availability of genome sequencing data has revealed a substantial number of uncharacterized genes without known functions across various organisms. The first comprehensive genome sequencing of E. coli K12 revealed that more than 50% of its open reading frames corresponded to transcripts with no known functions. The group of protein-coding genes without a functional description and/or a recognized pathway, beginning with the letter "Y", is classified as the "y-ome". Several efforts have been made to elucidate the functions of these genes and to recognize their role in biological processes. This review provides a brief update on various strategies employed when studying the y-ome, such as high-throughput experimental approaches, comparative omics, metabolic engineering, gene expression analysis, and data integration techniques. Additionally, we highlight recent advancements in functional annotation methods, including the use of machine learning, network analysis, and functional genomics approaches. Novel approaches are required to produce more precise functional annotations across the genome to reduce the number of genes with unknown functions.

A roadmap for the functional annotation of protein families: a community perspective

Over the last 25 years, biology has entered the genomic era and is becoming a science of 'big data'. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3-4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.

Identification of a transcription factor, PunR, that regulates the purine and purine nucleoside transporter punC in E. coli

Many genes in bacterial genomes are of unknown function, often referred to as y-genes. Recently, the analytic methods have divided bacterial transcriptomes into independently modulated sets of genes (iModulons). Functionally annotated iModulons that contain y-genes lead to testable hypotheses to elucidate y-gene function. The inversely correlated expression of a putative transporter gene, ydhC, relative to purine biosynthetic genes, has led to the hypothesis that it encodes a purine-related transporter and revealed a LysR-family regulator, YdhB, with a predicted 23-bp palindromic binding motif. RNA-Seq analysis of a ydhB knockout mutant confirmed the YdhB-dependent activation of ydhC in the presence of adenosine. The deletion of either the ydhC or the ydhB gene led to a substantially decreased growth rate for E. coli in minimal medium with adenosine, inosine, or guanosine as the nitrogen source. Taken together, we provide clear evidence that YdhB activates the expression of the ydhC gene that encodes a purine transporter in E. coli. We propose that the genes ydhB and ydhC be re-named as punR and punC, respectively.

Rodionova et al. find that the putative transporter gene, ydhC and its regulator ydhB are involved in purine transportation and that the expression of the ydhC gene is activated by the YdhB in E. coli. The authors suggest renaming the regulator PunR and the transporter PunC, respectively.

Metatranscriptomics for the Human Microbiome and Microbial Community Functional Profiling

Shotgun metatranscriptomics (MTX) is an increasingly practical way to survey microbial community gene function and regulation at scale. This review begins by summarizing the motivations for community transcriptomics and the history of the field. We then explore the principles, best practices, and challenges of contemporary MTX workflows: beginning with laboratory methods for isolation and sequencing of community RNA, followed by informatics methods for quantifying RNA features, and finally statistical methods for detecting differential expression in a community context. In thesecond half of the review, we survey important biological findings from the MTX literature, drawing examples from the human microbiome, other (nonhuman) host-associated microbiomes, and the environment. Across these examples, MTX methods prove invaluable for probing microbe-microbe and host-microbe interactions, the dynamics of energy harvest and chemical cycling, and responses to environmental stresses. We conclude with a review of open challenges in the MTX field, including making assays and analyses more robust, accessible, and adaptable to new technologies; deciphering roles for millions of uncharacterized microbial transcripts; and solving applied problems such as biomarker discovery and development of microbial therapeutics.

The Protein Interactome of Glycolysis in Escherichia coli

Glycolysis is regulated by numerous mechanisms including allosteric regulation, post-translational modification or protein-protein interactions (PPI). While glycolytic enzymes have been found to interact with hundreds of proteins, the impact of only some of these PPIs on glycolysis is well understood. Here we investigate which of these interactions may affect glycolysis in E. coli and possibly across numerous other bacteria, based on the stoichiometry of interacting protein pairs (from proteomic studies) and their conservation across bacteria. We present a list of 339 protein-protein interactions involving glycolytic enzymes but predict that ~70% of glycolytic interactors are not present in adequate amounts to have a significant impact on glycolysis. Finally, we identify a conserved but uncharacterized subset of interactions that are likely to affect glycolysis and deserve further study.

We need to keep a reproducible trace of facts, predictions, and hypotheses from gene to function in the era of big data

How do we scale biological science to the demand of next generation biology and medicine to keep track of the facts, predictions, and hypotheses? These days, enormous amounts of DNA sequence and other omics data are generated. Since these data contain the blueprint for life, it is imperative that we interpret it accurately. The abundance of DNA is only one part of the challenge. Artificial Intelligence (AI) and network methods routinely build on large screens, single cell technologies, proteomics, and other modalities to infer or predict biological functions and phenotypes associated with proteins, pathways, and organisms. As a first step, how do we systematically trace the provenance of knowledge from experimental ground truth to gene function predictions and annotations? Here, we review the main challenges in tracking the evolution of biological knowledge and propose several specific solutions to provenance and computational tracing of evidence in functional linkage networks.

The y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function

Experimental studies of Escherichia coli K-12 MG1655 often implicate poorly annotated genes in cellular phenotypes. However, we lack a systematic understanding of these genes. How many are there? What information is available for them? And what features do they share that could explain the gap in our understanding? Efforts to build predictive, whole-cell models of E. coli inevitably face this knowledge gap. We approached these questions systematically by assembling annotations from the knowledge bases EcoCyc, EcoGene, UniProt and RegulonDB. We identified the genes that lack experimental evidence of function (the ‘y-ome’) which include 1600 of 4623 unique genes (34.6%), of which 111 have absolutely no evidence of function. An additional 220 genes (4.7%) are pseudogenes or phantom genes. y-ome genes tend to have lower expression levels and are enriched in the termination region of the E. coli chromosome. Where evidence is available for y-ome genes, it most often points to them being membrane proteins and transporters. We resolve the misconception that a gene in E. coli whose primary name starts with ‘y’ is unannotated, and we discuss the value of the y-ome for systematic improvement of E. coli knowledge bases and its extension to other organisms.

Kinetic Investigation of a Presumed Nitronate Monooxygenase from Pseudomonas aeruginosa PAO1 Establishes a New Class of NAD(P)H:Quinone Reductases

PA0660 from Pseudomonas aeruginosa PAO1 is currently classified as a hypothetical nitronate monooxygenase (NMO), but no evidence at the transcript or protein level has been presented. In this study, PA0660 was purified and its biochemical and kinetic properties were characterized. Absorption spectroscopy and mass spectrometry demonstrated a tightly, noncovalently bound FMN in the active site of the enzyme. Analytical ultracentrifugation showed that the enzyme exists as a dimer in solution. Despite its annotation, PA0660 did not exhibit nitronate monooxygenase activity. The enzyme could be reduced with NADPH or NADH with a marked preference for NADPH, as indicated by ∼30-fold larger k_cat/ K_m and k_red/ K_d values. Turnover could be sustained with NAD(P)H and quinones, DCPIP, and to a lesser extent molecular oxygen. However, PA0660 did not turn over with methyl red, consistent with a lack of azoreductase activity. The enzyme turned over through a ping-pong bi-bi steady-state kinetic mechanism with NADPH and 1,4-benzoquinone showing a k_cat value of 90 s^-1. The rate constant for flavin reduction with saturating NADPH was 360 s^-1, whereas that for flavin oxidation with 1,4-benzoquinone was 270 s^-1, consistent with both hydride transfers from the pyridine nucleotide to the flavin and from the flavin to 1,4-benzoquinone being partially rate-limiting for enzyme turnover. A BlastP search and a multiple-sequence alignment analysis of PA0660 highlighted the presence of six conserved motifs in >1000 open reading frames currently annotated as hypothetical NMOs. Our results suggest that PA0660 should be classified as an NAD(P)H:quinone reductase and serve as a paradigm enzyme for a new class of enzymes.

Bioprospecting Reveals Class III ω-Transaminases Converting Bulky Ketones and Environmentally Relevant Polyamines

Amine transaminases of the class III ω-TAs are key enzymes for modification of chemical building blocks, but finding those capable of converting bulky ketones and (R) amines is still challenging. Here, by an extensive analysis of the substrate spectra of 10 class III ω-TAs, we identified a number of residues playing a role in determining the access and positioning of bulky ketones, bulky amines, and (R)- and (S) amines, as well as of environmentally relevant polyamines, particularly putrescine. The results presented can significantly expand future opportunities for designing (R)-specific class III ω-TAs to convert valuable bulky ketones and amines, as well as for deepening the knowledge into the polyamine catabolic pathways.

Amination of bulky ketones, particularly in (R) configuration, is an attractive chemical conversion; however, known ω-transaminases (ω-TAs) show insufficient levels of performance. By applying two screening methods, we discovered 10 amine transaminases from the class III ω-TA family that were 38% to 76% identical to homologues. We present examples of such enzymes preferring bulky ketones over keto acids and aldehydes with stringent (S) selectivity. We also report representatives from the class III ω-TAs capable of converting (R) and (S) amines and bulky ketones and one that can convert amines with longer alkyl substituents. The preference for bulky ketones was associated with the presence of a hairpin region proximal to the conserved Arg414 and residues conforming and close to it. The outward orientation of Arg414 additionally favored the conversion of (R) amines. This configuration was also found to favor the utilization of putrescine as an amine donor, so that class III ω-TAs with Arg414 in outward orientation may participate in vivo in the catabolism of putrescine. The positioning of the conserved Ser231 also contributes to the preference for amines with longer alkyl substituents. Optimal temperatures for activity ranged from 45 to 65°C, and a few enzymes retained ≥50% of their activity in water-soluble solvents (up to 50% [vol/vol]). Hence, our results will pave the way to design, in the future, new class III ω-TAs converting bulky ketones and (R) amines for the production of high-value products and to screen for those converting putrescine.

IMPORTANCE Amine transaminases of the class III ω-TAs are key enzymes for modification of chemical building blocks, but finding those capable of converting bulky ketones and (R) amines is still challenging. Here, by an extensive analysis of the substrate spectra of 10 class III ω-TAs, we identified a number of residues playing a role in determining the access and positioning of bulky ketones, bulky amines, and (R)- and (S) amines, as well as of environmentally relevant polyamines, particularly putrescine. The results presented can significantly expand future opportunities for designing (R)-specific class III ω-TAs to convert valuable bulky ketones and amines, as well as for deepening the knowledge into the polyamine catabolic pathways.

Kinetic Characterization of PA1225 from Pseudomonas aeruginosa PAO1 Reveals a New NADPH:Quinone Reductase

The pa1225 gene of Pseudomonas aeruginosa strain PAO1 was cloned, and the resulting enzyme (PA1225) was purified and revealed to be an NADPH:quinone reductase. By using kinetics, fluorescence, and mass spectrometric analyses, PA1225 was shown to utilize FAD to transfer a hydride ion from NADPH to quinones. The enzyme could also use NADH, but with an efficiency that was 40-fold lower than that of NADPH as suggested by the k_cat/ K_m values at pH 6.0. Similar initial rates of reaction were determined with 1,4-benzoquinone and 2,6-dimethoxy-1,4-benzoquinone in the range between 25 and 200 μM, suggesting a low K_m value for the quinone-oxidizing substrate. The lack of inhibition by NADP⁺ versus NADPH at saturating concentrations of 1,4-benzoquinone was consistent with a ping-pong bi-bi mechanism. The reductive half-reaction at pH 6.0 had K_d values of 0.07 mM with NADPH and 1.8 mM with NADH; the k_red for flavin reduction was independent of pH with values of ∼10 s^-1 with NADPH and ∼5 s^-1 with NADH. Thus, the enzyme specificity for the reducing substrate arises primarily from a tighter binding of NADPH than of NADH. At pH 6.0, the k_cat value with NADPH and 1,4-benzoquinone was 10.1 s^-1, consistent with the hydride transfer from NADPH to FAD being fully rate limiting for the overall turnover of the enzyme. The enzyme showed negligible NADPH oxidase and azoreductase activities. This study enables annotation of the pa1225 gene as NADPH:quinone reductase, elucidates the enzymatic function of PA1225 in P. aeruginosa PAO1, and establishes that PA1225 is not an azoreductase as previously proposed.

Enterococcus faecalis AHG0090 is a Genetically Tractable Bacterium and Produces a Secreted Peptidic Bioactive that Suppresses Nuclear Factor Kappa B Activation in Human Gut Epithelial Cells

Enterococcus faecalis is an early coloniser of the human infant gut and contributes to the development of intestinal immunity. To better understand the functional capacity of E. faecalis, we constructed a broad host range RP4 mobilizable vector, pEHR513112, that confers chloramphenicol resistance and used a metaparental mating approach to isolate E. faecalis AHG0090 from a fecal sample collected from a healthy human infant. We demonstrated that E. faecalis AHG0090 is genetically tractable and could be manipulated using traditional molecular microbiology approaches. E. faecalis AHG0090 was comparable to the gold-standard anti-inflammatory bacterium Faecalibacterium prausnitzii A2-165 in its ability to suppress cytokine-mediated nuclear factor kappa B (NF-κB) activation in human gut-derived LS174T goblet cell like and Caco-2 enterocyte-like cell lines. E. faecalis AHG0090 and F. prausnitzii A2-165 produced secreted low molecular weight NF-κB suppressive peptidic bioactives. Both bioactives were sensitive to heat and proteinase K treatments although the E. faecalis AHG0090 bioactive was more resilient to both forms of treatment. As expected, E. faecalis AHG0090 suppressed IL-1β-induced NF-κB-p65 subunit nuclear translocation and expression of the NF-κB regulated genes IL-6, IL-8 and CXCL-10. Finally, we determined that E. faecalis AHG0090 is distantly related to other commensal strains and likely encodes niche factors that support effective colonization of the infant gut.

Discovery of enzymes for toluene synthesis from anoxic microbial communities

Microbial toluene biosynthesis was reported in anoxic lake sediments more than three decades ago, but the enzyme catalyzing this biochemically challenging reaction has never been identified. Here we report the toluene-producing enzyme PhdB, a glycyl radical enzyme of bacterial origin that catalyzes phenylacetate decarboxylation, and its cognate activating enzyme PhdA, a radical S-adenosylmethionine enzyme, discovered in two distinct anoxic microbial communities that produce toluene. The unconventional process of enzyme discovery from a complex microbial community (>300,000 genes), rather than from a microbial isolate, involved metagenomics- and metaproteomics-enabled biochemistry, as well as in vitro confirmation of activity with recombinant enzymes. This work expands the known catalytic range of glycyl radical enzymes (only seven reaction types had been characterized previously) and aromatic-hydrocarbon-producing enzymes, and will enable first-time biochemical synthesis of an aromatic fuel hydrocarbon from renewable resources, such as lignocellulosic biomass, rather than from petroleum.

Determinants and Prediction of Esterase Substrate Promiscuity Patterns

Esterases receive special attention because of their wide distribution in biological systems and environments and their importance for physiology and chemical synthesis. The prediction of esterases' substrate promiscuity level from sequence data and the molecular reasons why certain such enzymes are more promiscuous than others remain to be elucidated. This limits the surveillance of the sequence space for esterases potentially leading to new versatile biocatalysts and new insights into their role in cellular function. Here, we performed an extensive analysis of the substrate spectra of 145 phylogenetically and environmentally diverse microbial esterases, when tested with 96 diverse esters. We determined the primary factors shaping their substrate range by analyzing substrate range patterns in combination with structural analysis and protein-ligand simulations. We found a structural parameter that helps rank (classify) the promiscuity level of esterases from sequence data at 94% accuracy. This parameter, the active site effective volume, exemplifies the topology of the catalytic environment by measuring the active site cavity volume corrected by the relative solvent accessible surface area (SASA) of the catalytic triad. Sequences encoding esterases with active site effective volumes (cavity volume/SASA) above a threshold show greater substrate spectra, which can be further extended in combination with phylogenetic data. This measure provides also a valuable tool for interrogating substrates capable of being converted. This measure, found to be transferred to phosphatases of the haloalkanoic acid dehalogenase superfamily and possibly other enzymatic systems, represents a powerful tool for low-cost bioprospecting for esterases with broad substrate ranges, in large scale sequence data sets.

Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction

BACKGROUND

The molecular function of a gene is most commonly inferred by sequence similarity. Therefore, genes that lack sufficient sequence similarity to characterized genes (such as certain classes of transcriptional regulators) are difficult to classify using most function prediction algorithms and have remained uncharacterized.

RESULTS

To identify novel transcriptional regulators systematically, we used a feature-based pipeline to screen protein families of unknown function. This method predicted 43 transcriptional regulator families in Arabidopsis thaliana, 7 families in Drosophila melanogaster, and 9 families in Homo sapiens. Literature curation validated 12 of the predicted families to be involved in transcriptional regulation. We tested 33 out of the 195 Arabidopsis putative transcriptional regulators for their ability to activate transcription of a reporter gene in planta and found twelve coactivators, five of which had no prior literature support. To investigate mechanisms of action in which the predicted regulators might work, we looked for interactors of an Arabidopsis candidate that did not show transactivation activity in planta and found that it might work with other members of its own family and a subunit of the Polycomb Repressive Complex 2 to regulate transcription.

CONCLUSIONS

Our results demonstrate the feasibility of assigning molecular function to proteins of unknown function without depending on sequence similarity. In particular, we identified novel transcriptional regulators using biological features enriched in transcription factors. The predictions reported here should accelerate the characterization of novel regulators.

Constraint-based metabolic modelling of marine microbes and communities

Constraint-based metabolic modelling (CBMM) consists in the use of computational methods and tools to perform genome-scale simulations and predict metabolic features at the whole cellular level. This approach is rapidly expanding in microbiology, as it combines reliable predictive abilities with conceptually and technically simple frameworks. Among the possible outcomes of CBMM, the capability to i) guide a focused planning of metabolic engineering experiments and ii) provide a system-level understanding of (single or community-level) microbial metabolic circuits also represent primary aims in present-day marine microbiology. In this work we briefly introduce the theoretical formulation behind CBMM and then review the most recent and effective case studies of CBMM of marine microbes and communities. Also, the emerging challenges and possibilities in the use of such methodologies in the context of marine microbiology/biotechnology are discussed. As the potential applications of CBMM have a very broad range, the topics presented in this review span over a large plethora of fields such as ecology, biotechnology and evolution.

In silico approach to designing rational metagenomic libraries for functional studies

Background

With the development of Next Generation Sequencing technologies, the number of predicted proteins from entire (meta-) genomes has risen exponentially. While for some of these sequences protein functions can be inferred from homology, an experimental characterization is still a requirement for the determination of protein function. However, functional characterization of proteins cannot keep pace with our capabilities to generate more and more sequence data.

Results

Here, we present an approach to reduce the number of proteins from entire (meta-) genomes to a reasonably small number for further experimental characterization without loss of important information. About 6.1 million predicted proteins from the Global Ocean Sampling Expedition Metagenome project were distributed into classes based either on homology to existing hidden markov models (HMMs) of known families, or de novo by assessment of pairwise similarity. 5.1 million of these proteins could be classified in this way, yielding 18,437 families. For 4,129 protein families, which did not match existing HMMs from databases, we could create novel HMMs. For each family, we then selected a representative protein, which showed the closest homology to all other proteins in this family. We then selected representatives of four families based on their homology to known and well-characterized lipases. From these four synthesized genes, we could obtain the novel esterase/lipase GOS54, validating our approach.

Conclusions

Using an in silico approach, we were able improve the success rate of functional screening and make entire (meta-) genomes amenable for biochemical characterization.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1668-y) contains supplementary material, which is available to authorized users.

Annotation of plasmid genes

Good annotation of plasmid genomes is essential to maximise the value of the rapidly increasing volume of plasmid sequences. This short review highlights some of the current issues and suggests some ways forward. Where a well-studied related plasmid system exists we recommend that new annotation adheres to the convention already established for that system, so long as it is based on sound principles and solid experimental evidence, even if some of the new genes are more similar to homologues in different systems. Where a well-established model does not exist we provide generic gene names that reflect likely biochemical activity rather than overall purpose particularly, for example, where genes clearly belong to a type IV secretion system but it is not known whether they function in conjugative transfer or virulence. We also recommend that annotators use a whole system naming approach to avoid ending up with an illogical mixture of names from other systems based on the highest scoring match from a BLAST search. In addition, where function has not been experimentally established we recommend using just the locus tag, rather than a function-related gene name, while recording possible functions as notes rather than in a provisional name.

Evaluating Computational Gene Ontology Annotations

Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computational annotations have broad coverage, but the quality of annotations may be variable, and therefore evaluating the quality of computational annotations is a critical concern.In this chapter, we provide an overview of strategies to evaluate the quality of computational annotations. First, we discuss why evaluating quality in this setting is not trivial. We highlight the various issues that threaten to bias the evaluation of computational annotations, most of which stem from the incompleteness of biological databases. Second, we discuss solutions that address these issues, for example, targeted selection of new experimental annotations and leveraging the existing experimental annotations.

Text Mining to Support Gene Ontology Curation and Vice Versa

In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

Next-Generation High-Throughput Functional Annotation of Microbial Genomes

Host infection by microbial pathogens cues global changes in microbial and host cell biology that facilitate microbial replication and disease. The complete maps of thousands of bacterial and viral genomes have recently been defined; however, the rate at which physiological or biochemical functions have been assigned to genes has greatly lagged. The National Institute of Allergy and Infectious Diseases (NIAID) addressed this gap by creating functional genomics centers dedicated to developing high-throughput approaches to assign gene function. These centers require broad-based and collaborative research programs to generate and integrate diverse data to achieve a comprehensive understanding of microbial pathogenesis. High-throughput functional genomics can lead to new therapeutics and better understanding of the next generation of emerging pathogens by rapidly defining new general mechanisms by which organisms cause disease and replicate in host tissues and by facilitating the rate at which functional data reach the scientific community.

Functional Annotation of a Presumed Nitronate Monoxygenase Reveals a New Class of NADH:Quinone Reductases

The protein PA1024 from Pseudomonas aeruginosa PAO1 is currently classified as 2-nitropropane dioxygenase, the previous name for nitronate monooxygenase in the GenBank^TM and PDB databases, but the enzyme was not kinetically characterized. In this study, PA1024 was purified to high levels, and the enzymatic activity was investigated by spectroscopic and polarographic techniques. Purified PA1024 did not exhibit nitronate monooxygenase activity; however, it displayed NADH:quinone reductase and a small NADH:oxidase activity. The enzyme preferred NADH to NADPH as a reducing substrate. PA1024 could reduce a broad spectrum of quinone substrates via a Ping Pong Bi Bi steady-state kinetic mechanism, generating the corresponding hydroquinones. The reductive half-reaction with NADH showed a k_red value of 24 s^-1 and an apparent K_d value estimated in the low micromolar range. The enzyme was not able to reduce the azo dye methyl red, routinely used in the kinetic characterization of azoreductases. Finally, we revisited and modified the existing six conserved motifs of PA1024, which define a new class of NADH:quinone reductases and are present in more than 490 hypothetical proteins in the GenBank^TM, the vast majority of which are currently misannotated as nitronate monooxygenase.

The healthy human microbiome

Humans are virtually identical in their genetic makeup, yet the small differences in our DNA give rise to tremendous phenotypic diversity across the human population. By contrast, the metagenome of the human microbiome—the total DNA content of microbes inhabiting our bodies—is quite a bit more variable, with only a third of its constituent genes found in a majority of healthy individuals. Understanding this variability in the “healthy microbiome” has thus been a major challenge in microbiome research, dating back at least to the 1960s, continuing through the Human Microbiome Project and beyond. Cataloguing the necessary and sufficient sets of microbiome features that support health, and the normal ranges of these features in healthy populations, is an essential first step to identifying and correcting microbial configurations that are implicated in disease. Toward this goal, several population-scale studies have documented the ranges and diversity of both taxonomic compositions and functional potentials normally observed in the microbiomes of healthy populations, along with possible driving factors such as geography, diet, and lifestyle. Here, we review several definitions of a ‘healthy microbiome’ that have emerged, the current understanding of the ranges of healthy microbial diversity, and gaps such as the characterization of molecular function and the development of ecological therapies to be addressed in the future.

Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

Characterising Complex Enzyme Reaction Data

The relationship between enzyme-catalysed reactions and the Enzyme Commission (EC) number, the widely accepted classification scheme used to characterise enzyme activity, is complex and with the rapid increase in our knowledge of the reactions catalysed by enzymes needs revisiting. We present a manual and computational analysis to investigate this complexity and found that almost one-third of all known EC numbers are linked to more than one reaction in the secondary reaction databases (e.g., KEGG). Although this complexity is often resolved by defining generic, alternative and partial reactions, we have also found individual EC numbers with more than one reaction catalysing different types of bond changes. This analysis adds a new dimension to our understanding of enzyme function and might be useful for the accurate annotation of the function of enzymes and to study the changes in enzyme function during evolution.

Estimating the success of enzyme bioprospecting through metagenomics: current status and future trends

Recent reports have suggested that the establishment of industrially relevant enzyme collections from environmental genomes has become a routine procedure. Across the studies assessed, a mean number of approximately 44 active clones were obtained in an average size of approximately 53,000 clones tested using naïve screening protocols. This number could be significantly increased in shorter times when novel metagenome enzyme sequences obtained by direct sequencing are selected and subjected to high-throughput expression for subsequent production and characterization. The pre-screening of clone libraries by naïve screens followed by the pyrosequencing of the inserts allowed for a 106-fold increase in the success rate of identifying genes encoding enzymes of interest. However, a much longer time, usually on the order of years, is needed from the time of enzyme identification to the establishment of an industrial process. If the hit frequency for the identification of enzymes performing at high turnover rates under real application conditions could be increased while still covering a high natural diversity, the very expensive and time-consuming enzyme optimization phase would likely be significantly shortened. At this point, it is important to review the current knowledge about the success of fine-tuned naïve- and sequence-based screening protocols for enzyme selection and to describe the environments worldwide that have already been subjected to enzyme screen programmes through metagenomic tools. Here, we provide such estimations and suggest the current challenges and future actions needed before environmental enzymes can be successfully introduced into the market.

COMBREX-DB: an experiment centered database of protein function: knowledge, predictions and knowledge gaps

The COMBREX database (COMBREX-DB; combrex.bu.edu) is an online repository of information related to (i) experimentally determined protein function, (ii) predicted protein function, (iii) relationships among proteins of unknown function and various types of experimental data, including molecular function, protein structure, and associated phenotypes. The database was created as part of the novel COMBREX (COMputational BRidges to EXperiments) effort aimed at accelerating the rate of gene function validation. It currently holds information on ∼3.3 million known and predicted proteins from over 1000 completely sequenced bacterial and archaeal genomes. The database also contains a prototype recommendation system for helping users identify those proteins whose experimental determination of function would be most informative for predicting function for other proteins within protein families. The emphasis on documenting experimental evidence for function predictions, and the prioritization of uncharacterized proteins for experimental testing distinguish COMBREX from other publicly available microbial genomics resources. This article describes updates to COMBREX-DB since an initial description in the 2011 NAR Database Issue.

Deep Question Answering for protein annotation

Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using state-of-the-art dictionary- and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a +100% improvement for both recall and precision. Database URL: http://eagl.unige.ch/DeepQA4PA/.

A close look at protein function prediction evaluation protocols

Background

The recently held Critical Assessment of Function Annotation challenge (CAFA2) required its participants to submit predictions for a large number of target proteins regardless of whether they have previous annotations or not. This is in contrast to the original CAFA challenge in which participants were asked to submit predictions for proteins with no existing annotations. The CAFA2 task is more realistic, in that it more closely mimics the accumulation of annotations over time. In this study we compare these tasks in terms of their difficulty, and determine whether cross-validation provides a good estimate of performance.

Results

The CAFA2 task is a combination of two subtasks: making predictions on annotated proteins and making predictions on previously unannotated proteins. In this study we analyze the performance of several function prediction methods in these two scenarios. Our results show that several methods (structured support vector machine, binary support vector machines and guilt-by-association methods) do not usually achieve the same level of accuracy on these two tasks as that achieved by cross-validation, and that predicting novel annotations for previously annotated proteins is a harder problem than predicting annotations for uncharacterized proteins. We also find that different methods have different performance characteristics in these tasks, and that cross-validation is not adequate at estimating performance and ranking methods.

Conclusions

These results have implications for the design of computational experiments in the area of automated function prediction and can provide useful insight for the understanding and design of future CAFA competitions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13742-015-0082-5) contains supplementary material, which is available to authorized users.

Functional Diversity of Haloacid Dehalogenase Superfamily Phosphatases from Saccharomyces cerevisiae: BIOCHEMICAL, STRUCTURAL, AND EVOLUTIONARY INSIGHTS

The haloacid dehalogenase (HAD)-like enzymes comprise a large superfamily of phosphohydrolases present in all organisms. The Saccharomyces cerevisiae genome encodes at least 19 soluble HADs, including 10 uncharacterized proteins. Here, we biochemically characterized 13 yeast phosphatases from the HAD superfamily, which includes both specific and promiscuous enzymes active against various phosphorylated metabolites and peptides with several HADs implicated in detoxification of phosphorylated compounds and pseudouridine. The crystal structures of four yeast HADs provided insight into their active sites, whereas the structure of the YKR070W dimer in complex with substrate revealed a composite substrate-binding site. Although the S. cerevisiae and Escherichia coli HADs share low sequence similarities, the comparison of their substrate profiles revealed seven phosphatases with common preferred substrates. The cluster of secondary substrates supporting significant activity of both S. cerevisiae and E. coli HADs includes 28 common metabolites that appear to represent the pool of potential activities for the evolution of novel HAD phosphatases. Evolution of novel substrate specificities of HAD phosphatases shows no strict correlation with sequence divergence. Thus, evolution of the HAD superfamily combines the conservation of the overall substrate pool and the substrate profiles of some enzymes with remarkable biochemical and structural flexibility of other superfamily members.

A manual curation strategy to improve genome annotation: application to a set of haloarchael genomes

Genome annotation errors are a persistent problem that impede research in the biosciences. A manual curation effort is described that attempts to produce high-quality genome annotations for a set of haloarchaeal genomes (Halobacterium salinarum and Hbt. hubeiense, Haloferax volcanii and Hfx. mediterranei, Natronomonas pharaonis and Nmn. moolapensis, Haloquadratum walsbyi strains HBSQ001 and C23, Natrialba magadii, Haloarcula marismortui and Har. hispanica, and Halohasta litchfieldiae). Genomes are checked for missing genes, start codon misassignments, and disrupted genes. Assignments of a specific function are preferably based on experimentally characterized homologs (Gold Standard Proteins). To avoid overannotation, which is a major source of database errors, we restrict annotation to only general function assignments when support for a specific substrate assignment is insufficient. This strategy results in annotations that are resistant to the plethora of errors that compromise public databases. Annotation consistency is rigorously validated for ortholog pairs from the genomes surveyed. The annotation is regularly crosschecked against the UniProt database to further improve annotations and increase the level of standardization. Enhanced genome annotations are submitted to public databases (EMBL/GenBank, UniProt), to the benefit of the scientific community. The enhanced annotations are also publically available via HaloLex.

Biochemical functional predictions for protein structures of unknown or uncertain function

With the exponential growth in the determination of protein sequences and structures via genome sequencing and structural genomics efforts, there is a growing need for reliable computational methods to determine the biochemical function of these proteins. This paper reviews the efforts to address the challenge of annotating the function at the molecular level of uncharacterized proteins. While sequence- and three-dimensional-structure-based methods for protein function prediction have been reviewed previously, the recent trends in local structure-based methods have received less attention. These local structure-based methods are the primary focus of this review. Computational methods have been developed to predict the residues important for catalysis and the local spatial arrangements of these residues can be used to identify protein function. In addition, the combination of different types of methods can help obtain more information and better predictions of function for proteins of unknown function. Global initiatives, including the Enzyme Function Initiative (EFI), COMputational BRidges to EXperiments (COMBREX), and the Critical Assessment of Function Annotation (CAFA), are evaluating and testing the different approaches to predicting the function of proteins of unknown function. These initiatives and global collaborations will increase the capability and reliability of methods to predict biochemical function computationally and will add substantial value to the current volume of structural genomics data by reducing the number of absent or inaccurate functional annotations.

EcoliNet: a database of cofunctional gene network for Escherichia coli

During the past several decades, Escherichia coli has been a treasure chest for molecular biology. The molecular mechanisms of many fundamental cellular processes have been discovered through research on this bacterium. Although much basic research now focuses on more complex model organisms, E. coli still remains important in metabolic engineering and synthetic biology. Despite its long history as a subject of molecular investigation, more than one-third of the E. coli genome has no pathway annotation supported by either experimental evidence or manual curation. Recently, a network-assisted genetics approach to the efficient identification of novel gene functions has increased in popularity. To accelerate the speed of pathway annotation for the remaining uncharacterized part of the E. coli genome, we have constructed a database of cofunctional gene network with near-complete genome coverage of the organism, dubbed EcoliNet. We find that EcoliNet is highly predictive for diverse bacterial phenotypes, including antibiotic response, indicating that it will be useful in prioritizing novel candidate genes for a wide spectrum of bacterial phenotypes. We have implemented a web server where biologists can easily run network algorithms over EcoliNet to predict novel genes involved in a pathway or novel functions for a gene. All integrated cofunctional associations can be downloaded, enabling orthology-based reconstruction of gene networks for other bacterial species as well. Database URL: http://www.inetbio.org/ecolinet.

Expanded microbial genome coverage and improved protein family annotation in the COG database

Microbial genome sequencing projects produce numerous sequences of deduced proteins, only a small fraction of which have been or will ever be studied experimentally. This leaves sequence analysis as the only feasible way to annotate these proteins and assign to them tentative functions. The Clusters of Orthologous Groups of proteins (COGs) database (http://www.ncbi.nlm.nih.gov/COG/), first created in 1997, has been a popular tool for functional annotation. Its success was largely based on (i) its reliance on complete microbial genomes, which allowed reliable assignment of orthologs and paralogs for most genes; (ii) orthology-based approach, which used the function(s) of the characterized member(s) of the protein family (COG) to assign function(s) to the entire set of carefully identified orthologs and describe the range of potential functions when there were more than one; and (iii) careful manual curation of the annotation of the COGs, aimed at detailed prediction of the biological function(s) for each COG while avoiding annotation errors and overprediction. Here we present an update of the COGs, the first since 2003, and a comprehensive revision of the COG annotations and expansion of the genome coverage to include representative complete genomes from all bacterial and archaeal lineages down to the genus level. This re-analysis of the COGs shows that the original COG assignments had an error rate below 0.5% and allows an assessment of the progress in functional genomics in the past 12 years. During this time, functions of many previously uncharacterized COGs have been elucidated and tentative functional assignments of many COGs have been validated, either by targeted experiments or through the use of high-throughput methods. A particularly important development is the assignment of functions to several widespread, conserved proteins many of which turned out to participate in translation, in particular rRNA maturation and tRNA modification. The new version of the COGs is expected to become an important tool for microbial genomics.

HAMAP in 2015: updates to the protein family classification and annotation system

HAMAP (High-quality Automated and Manual Annotation of Proteins—available at http://hamap.expasy.org/) is a system for the automatic classification and annotation of protein sequences. HAMAP provides annotation of the same quality and detail as UniProtKB/Swiss-Prot, using manually curated profiles for protein sequence family classification and expert curated rules for functional annotation of family members. HAMAP data and tools are made available through our website and as part of the UniRule pipeline of UniProt, providing annotation for millions of unreviewed sequences of UniProtKB/TrEMBL. Here we report on the growth of HAMAP and updates to the HAMAP system since our last report in the NAR Database Issue of 2013. We continue to augment HAMAP with new family profiles and annotation rules as new protein families are characterized and annotated in UniProtKB/Swiss-Prot; the latest version of HAMAP (as of 3 September 2014) contains 1983 family classification profiles and 1998 annotation rules (up from 1780 and 1720). We demonstrate how the complex logic of HAMAP rules allows for precise annotation of individual functional variants within large homologous protein families. We also describe improvements to our web-based tool HAMAP-Scan which simplify the classification and annotation of sequences, and the incorporation of an improved sequence-profile search algorithm.

The Chlamydomonas genome project: a decade on

The green alga Chlamydomonas reinhardtii is a popular unicellular organism for studying photosynthesis, cilia biogenesis, and micronutrient homeostasis. Ten years since its genome project was initiated an iterative process of improvements to the genome and gene predictions has propelled this organism to the forefront of the omics era. Housed at Phytozome, the plant genomics portal of the Joint Genome Institute (JGI), the most up-to-date genomic data include a genome arranged on chromosomes and high-quality gene models with alternative splice forms supported by an abundance of whole transcriptome sequencing (RNA-Seq) data. We present here the past, present, and future of Chlamydomonas genomics. Specifically, we detail progress on genome assembly and gene model refinement, discuss resources for gene annotations, functional predictions, and locus ID mapping between versions and, importantly, outline a standardized framework for naming genes.

Closing the loop: from paper to protein annotation using supervised Gene Ontology classification

Gene function curation of the literature with Gene Ontology (GO) concepts is one particularly time-consuming task in genomics, and the help from bioinformatics is highly requested to keep up with the flow of publications. In 2004, the first BioCreative challenge already designed a task of automatic GO concepts assignment from a full text. At this time, results were judged far from reaching the performances required by real curation workflows. In particular, supervised approaches produced the most disappointing results because of lack of training data. Ten years later, the available curation data have massively grown. In 2013, the BioCreative IV GO task revisited the automatic GO assignment task. For this issue, we investigated the power of our supervised classifier, GOCat. GOCat computes similarities between an input text and already curated instances contained in a knowledge base to infer GO concepts. The subtask A consisted in selecting GO evidence sentences for a relevant gene in a full text. For this, we designed a state-of-the-art supervised statistical approach, using a naïve Bayes classifier and the official training set, and obtained fair results. The subtask B consisted in predicting GO concepts from the previous output. For this, we applied GOCat and reached leading results, up to 65% for hierarchical recall in the top 20 outputted concepts. Contrary to previous competitions, machine learning has this time outperformed standard dictionary-based approaches. Thanks to BioCreative IV, we were able to design a complete workflow for curation: given a gene name and a full text, this system is able to select evidence sentences for curation and to deliver highly relevant GO concepts. Contrary to previous competitions, machine learning this time outperformed dictionary-based systems. Observed performances are sufficient for being used in a real semiautomatic curation workflow. GOCat is available at http://eagl.unige.ch/GOCat/.

DATABASE URL

http://eagl.unige.ch/GOCat4FT/.

Overview of the gene ontology task at BioCreative IV

Gene Ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation.

Database URL:http://www.biocreative.org/tasks/biocreative-iv/track-4-GO/.

The combined structural and kinetic characterization of a bacterial nitronate monooxygenase from Pseudomonas aeruginosa PAO1 establishes NMO class I and II

Nitronate monooxygenase (NMO) oxidizes the mitochondrial toxin propionate 3-nitronate (P3N) to malonate semialdehyde. The enzyme has been previously characterized biochemically in fungi, but no structural information is available. Based on amino acid similarity 4,985 genes are annotated in the GenBank(TM) as NMO. Of these, 4,424 (i.e. 89%) are bacterial genes, including several Pseudomonads that have been shown to use P3N as growth substrate. Here, we have cloned and expressed the gene pa4202 of Pseudomonas aeruginosa PAO1, purified the resulting protein, and characterized it. The enzyme is active on P3N and other alkyl nitronates, but cannot oxidize nitroalkanes. P3N is the best substrate at pH 7.5 and atmospheric oxygen with k(cat)(app)/K(m)(app) of 12 × 10(6) M(-1) s(-1), k(cat)(app) of 1300 s(-1), and K(m)(app) of 110 μm. Anerobic reduction of the enzyme with P3N yields a flavosemiquinone, which is formed within 7.5 ms, consistent with this species being a catalytic intermediate. Absorption spectroscopy, mass spectrometry, and x-ray crystallography demonstrate a tightly, non-covalently bound FMN in the active site of the enzyme. Thus, PA4202 is the first NMO identified and characterized in bacteria. The x-ray crystal structure of the enzyme was solved at 1.44 Å, showing a TIM barrel-fold. Four motifs in common with the biochemically characterized NMO from Cyberlindnera saturnus are identified in the structure of bacterial NMO, defining Class I NMO, which includes bacterial, fungal, and two animal NMOs. Notably, the only other NMO from Neurospora crassa for which biochemical evidence is available lacks the four motifs, defining Class II NMO.

Profiling the orphan enzymes

The emergence of Next Generation Sequencing generates an incredible amount of sequence and great potential for new enzyme discovery. Despite this huge amount of data and the profusion of bioinformatic methods for function prediction, a large part of known enzyme activities is still lacking an associated protein sequence. These particular activities are called "orphan enzymes". The present review proposes an update of previous surveys on orphan enzymes by mining the current content of public databases. While the percentage of orphan enzyme activities has decreased from 38% to 22% in ten years, there are still more than 1,000 orphans among the 5,000 entries of the Enzyme Commission (EC) classification. Taking into account all the reactions present in metabolic databases, this proportion dramatically increases to reach nearly 50% of orphans and many of them are not associated to a known pathway. We extended our survey to "local orphan enzymes" that are activities which have no representative sequence in a given clade, but have at least one in organisms belonging to other clades. We observe an important bias in Archaea and find that in general more than 30% of the EC activities have incomplete sequence information in at least one superkingdom. To estimate if candidate proteins for local orphans could be retrieved by homology search, we applied a simple strategy based on the PRIAM software and noticed that candidates may be proposed for an important fraction of local orphan enzymes. Finally, by studying relation between protein domains and catalyzed activities, it appears that newly discovered enzymes are mostly associated with already known enzyme domains. Thus, the exploration of the promiscuity and the multifunctional aspect of known enzyme families may solve part of the orphan enzyme issue. We conclude this review with a presentation of recent initiatives in finding proteins for orphan enzymes and in extending the enzyme world by the discovery of new activities.

Bioprospecting in the genomic age

The genomic revolution promises great advances in the search for useful biocatalysts. Function-based metagenomic approaches have identified several enzymes with properties that make them useful candidates for a variety of bioprocesses. As DNA sequencing costs continue to decline, the volume of genomic data, along with their corresponding predicted protein sequences, will continue to increase dramatically, necessitating new approaches to leverage this information for gene-based bioprospecting efforts. Additionally, as new functions are discovered and correlated with this sequence information, the knowledge of the often complex relationship between a protein's sequence and function will improve. This in turn will lead to better gene-based bioprospecting approaches and facilitate the tailoring of desired properties through protein engineering projects. In this chapter, we discuss a number of recent advances in bioprospecting within the context of the genomic age.