Annotation error in public databases: misannotation of molecular function in enzyme superfamilies

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.

Genomic and Bioinformatic Resources for Perennial Fruit Species

In the post-genomic era, data management and development of bioinformatic tools are critical for the adequate exploitation of genomics data. In this review, we address the actual situation for the subset of crops represented by the perennial fruit species. The agronomical singularity of these species compared to plant and crop model species provides significant challenges on the implementation of good practices generally not addressed in other species. Studies are usually performed over several years in non-controlled environments, usage of rootstock is common, and breeders heavily rely on vegetative propagation. A reference genome is now available for all the major species as well as many members of the economically important genera for breeding purposes. Development of pangenome for these species is beginning to gain momentum which will require a substantial effort in term of bioinformatic tool development. The available tools for genome annotation and functional analysis will also be presented.

Functional characterization of prokaryotic dark matter: the road so far and what lies ahead

Eight-hundred thousand to one trillion prokaryotic species may inhabit our planet. Yet, fewer than two-hundred thousand prokaryotic species have been described. This uncharted fraction of microbial diversity, and its undisclosed coding potential, is known as the "microbial dark matter" (MDM). Next-generation sequencing has allowed to collect a massive amount of genome sequence data, leading to unprecedented advances in the field of genomics. Still, harnessing new functional information from the genomes of uncultured prokaryotes is often limited by standard classification methods. These methods often rely on sequence similarity searches against reference genomes from cultured species. This hinders the discovery of unique genetic elements that are missing from the cultivated realm. It also contributes to the accumulation of prokaryotic gene products of unknown function among public sequence data repositories, highlighting the need for new approaches for sequencing data analysis and classification. Increasing evidence indicates that these proteins of unknown function might be a treasure trove of biotechnological potential. Here, we outline the challenges, opportunities, and the potential hidden within the functional dark matter (FDM) of prokaryotes. We also discuss the pitfalls surrounding molecular and computational approaches currently used to probe these uncharted waters, and discuss future opportunities for research and applications.

Metascan: METabolic Analysis, SCreening and ANnotation of Metagenomes

Large scale next generation metagenomic sequencing of complex environmental samples paves the way for detailed analysis of nutrient cycles in ecosystems. For such an analysis, large scale unequivocal annotation is a prerequisite, which however is increasingly hampered by growing databases and analysis time. Hereto, we created a hidden Markov model (HMM) database by clustering proteins according to their KEGG indexing. HMM profiles for key genes of specific metabolic pathways and nutrient cycles were organized in subsets to be able to analyze each important elemental cycle separately. An important motivation behind the clustered database was to enable a high degree of resolution for annotation, while decreasing database size and analysis time. Here, we present Metascan, a new tool that can fully annotate and analyze deeply sequenced samples with an average analysis time of 11 min per genome for a publicly available dataset containing 2,537 genomes, and 1.1 min per genome for nutrient cycle analysis of the same sample. Metascan easily detected general proteins like cytochromes and ferredoxins, and additional pmoCAB operons were identified that were overlooked in previous analyses. For a mock community, the BEACON (F1) score was 0.72–0.93 compared to the information in NCBI GenBank. In combination with the accompanying database, Metascan provides a fast and useful annotation and analysis tool, as demonstrated by our proof-of-principle analysis of a complex mock community metagenome.

Comparative Genomics Provides Insights Into Genetic Diversity of Clostridium tyrobutyricum and Potential Implications for Late Blowing Defects in Cheese

Clostridium tyrobutyricum has been recognized as the main cause of late blowing defects (LBD) in cheese leading to considerable economic losses for the dairy industry. Although differences in spoilage ability among strains of this species have been acknowledged, potential links to the genetic diversity and functional traits remain unknown. In the present study, we aimed to investigate and characterize genomic variation, pan-genomic diversity and key traits of C. tyrobutyricum by comparing the genomes of 28 strains. A comparative genomics analysis revealed an “open” pangenome comprising 9,748 genes and a core genome of 1,179 genes shared by all test strains. Among those core genes, the majority of genes encode proteins related to translation, ribosomal structure and biogenesis, energy production and conversion, and amino acid metabolism. A large part of the accessory genome is composed of sets of unique, strain-specific genes ranging from about 5 to more than 980 genes. Furthermore, functional analysis revealed several strain-specific genes related to replication, recombination and repair, cell wall, membrane and envelope biogenesis, and defense mechanisms that might facilitate survival under stressful environmental conditions. Phylogenomic analysis divided strains into two clades: clade I contained human, mud, and silage isolates, whereas clade II comprised cheese and milk isolates. Notably, these two groups of isolates showed differences in certain hypothetical proteins, transcriptional regulators and ABC transporters involved in resistance to oxidative stress. To the best of our knowledge, this is the first study to provide comparative genomics of C. tyrobutyricum strains related to LBD. Importantly, the findings presented in this study highlight the broad genetic diversity of C. tyrobutyricum, which might help us understand the diversity in spoilage potential of C. tyrobutyricum in cheese and provide some clues for further exploring the gene modules responsible for the spoilage ability of this species.

Evaluating Plant Gene Models Using Machine Learning

Gene models are regions of the genome that can be transcribed into RNA and translated to proteins, or belong to a class of non-coding RNA genes. The prediction of gene models is a complex process that can be unreliable, leading to false positive annotations. To help support the calling of confident conserved gene models and minimize false positives arising during gene model prediction we have developed Truegene, a machine learning approach to classify potential low confidence gene models using 14 gene and 41 protein-based characteristics. Amino acid and nucleotide sequence-based features were calculated for conserved (high confidence) and non-conserved (low confidence) annotated genes from the published Pisum sativum Cameor genome. These features were used to train eXtreme Gradient Boost (XGBoost) classifier models to predict whether a gene model is likely to be real. The optimized models demonstrated a prediction accuracy ranging from 87% to 90% and an F-1 score of 0.91–0.94. We used SHapley Additive exPlanations (SHAP) and feature importance plots to identify the features that contribute to the model predictions, and we show that protein and gene-based features can be used to build accurate models for gene prediction that have applications in supporting future gene annotation processes.

Opportunities and challenges of using metagenomic data to bring uncultured microbes into cultivation

Although there is now an extensive understanding of the diversity of microbial life on earth through culture-independent metagenomic DNA sequence analyses, the isolation and cultivation of microbes remains critical to directly study them and confirm their metabolic and physiological functions, and their ecological roles. The majority of environmental microbes are as yet uncultured however; therefore, bringing these rare or poorly characterized groups into culture is a priority to further understand microbiome functions. Moreover, cultivated isolates may find utility in a range of applications, such as new probiotics, biocontrol agents, and agents for industrial processes. The growing abundance of metagenomic and meta-transcriptomic sequence information from a wide range of environments provides more opportunities to guide the isolation and cultivation of microbes of interest. In this paper, we discuss a range of successful methodologies and applications that have underpinned recent metagenome-guided isolation and cultivation of microbe efforts. These approaches include determining specific culture conditions to enrich for taxa of interest, to more complex strategies that specifically target the capture of microbial species through antibody engineering and genome editing strategies. With the greater degree of genomic information now available from uncultivated members, such as via metagenome-assembled genomes, the theoretical understanding of their cultivation requirements will enable greater possibilities to capture these and ultimately gain a more comprehensive understanding of the microbiomes. Video Abstract.

Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics

To discover novel catabolic enzymes and transporters, we combined high-throughput genetic data from 29 bacteria with an automated tool to find gaps in their catabolic pathways. GapMind for carbon sources automatically annotates the uptake and catabolism of 62 compounds in bacterial and archaeal genomes. For the compounds that are utilized by the 29 bacteria, we systematically examined the gaps in GapMind's predicted pathways, and we used the mutant fitness data to find additional genes that were involved in their utilization. We identified novel pathways or enzymes for the utilization of glucosamine, citrulline, myo-inositol, lactose, and phenylacetate, and we annotated 299 diverged enzymes and transporters. We also curated 125 proteins from published reports. For the 29 bacteria with genetic data, GapMind finds high-confidence paths for 85% of utilized carbon sources. In diverse bacteria and archaea, 38% of utilized carbon sources have high-confidence paths, which was improved from 27% by incorporating the fitness-based annotations and our curation. GapMind for carbon sources is available as a web server (http://papers.genomics.lbl.gov/carbon) and takes just 30 seconds for the typical genome.

Multiple profile models extract features from protein sequence data and resolve functional diversity of very different protein families

Functional classification of proteins from sequences alone has become a critical bottleneck in understanding the myriad of protein sequences that accumulate in our databases. The great diversity of homologous sequences hides, in many cases, a variety of functional activities that cannot be anticipated. Their identification appears critical for a fundamental understanding of the evolution of living organisms and for biotechnological applications. ProfileView is a sequence-based computational method, designed to functionally classify sets of homologous sequences. It relies on two main ideas: the use of multiple profile models whose construction explores evolutionary information in available databases, and a novel definition of a representation space in which to analyse sequences with multiple profile models combined together. ProfileView classifies protein families by enriching known functional groups with new sequences and discovering new groups and subgroups. We validate ProfileView on seven classes of widespread proteins involved in the interaction with nucleic acids, amino acids and small molecules, and in a large variety of functions and enzymatic reactions. Profile-View agrees with the large set of functional data collected for these proteins from the literature regarding the organisation into functional subgroups and residues that characterise the functions. In addition, ProfileView resolves undefined functional classifications and extracts the molecular determinants underlying protein functional diversity, showing its potential to select sequences towards accurate experimental design and discovery of novel biological functions. On protein families with complex domain architecture, ProfileView functional classification reconciles domain combinations, unlike phylogenetic reconstruction. ProfileView proves to outperform the functional classification approach PANTHER, the two k-mer based methods CUPP and eCAMI and a neural network approach based on Restricted Boltzmann Machines. It overcomes time complexity limitations of the latter.

From Tryptophan to Toxin: Nature's Convergent Biosynthetic Strategy to Aetokthonotoxin

Aetokthonotoxin (AETX) is a cyanobacterial neurotoxin that causes vacuolar myelinopathy, a neurological disease that is particularly deadly to bald eagles in the United States. The recently characterized AETX is structurally unique among cyanotoxins and is composed of a pentabrominated biindole nitrile. Herein we report the discovery of an efficient, five-enzyme biosynthetic pathway that the freshwater cyanobacterium Aetokthonos hydrillicola uses to convert two molecules of tryptophan to AETX. We demonstrate that the biosynthetic pathway follows a convergent route in which two functionalized indole monomers are assembled and then reunited by biaryl coupling catalyzed by the cytochrome P450 AetB. Our results revealed enzymes with novel biochemical functions, including the single-component flavin-dependent tryptophan halogenase AetF and the iron-dependent nitrile synthase AetD.

Investigation and Alteration of Organic Acid Synthesis Pathways in the Mammalian Gut Symbiont Bacteroides thetaiotaomicron

Members of the gut-dwelling Bacteroides genus have remarkable abilities in degrading a diverse set of fiber polysaccharide structures, most of which are found in the mammalian diet. As part of their metabolism, they convert these fibers to organic acids that can in turn provide energy to their host. While many studies have identified and characterized the genes and corresponding proteins involved in polysaccharide degradation, relatively little is known about Bacteroides genes involved in downstream metabolic pathways. Bacteroides thetaiotaomicron is one of the most studied species from the genus and is representative of this group in producing multiple organic acids as part of its metabolism. We focused here on several organic acid synthesis pathways in B. thetaiotaomicron, including those involved in formate, lactate, propionate, and acetate production. We identified potential genes involved in each pathway and characterized these through gene deletions coupled to growth assays and organic acid quantification. In addition, we developed and employed a Golden Gate-compatible plasmid system to simplify alteration of native gene expression levels. Our work both validates and contradicts previous bioinformatic gene annotations, and we develop a model on which to base future efforts. A clearer understanding of Bacteroides metabolic pathways can inform and facilitate efforts to employ these bacteria for improved human health or other utilization strategies.

IMPORTANCE Both humans and animals host a large community of bacteria and other microorganisms in their gastrointestinal tracts. This community breaks down dietary fiber and produces organic acids that are used as an energy source by the body and can also help the host resist infection by various pathogens. While the Bacteroides genus is one of the most common in the gut microbiota, it is only distantly related to bacteria with well-characterized metabolic pathways and it is therefore unclear whether research insights on organic acid production in those species can also be directly applied to the Bacteroides. By investigating multiple genetic pathways for organic acid production in Bacteroides thetaiotaomicron, we provide a basis for deeper understanding of these pathways. The work further enables greater understanding of Bacteroides–host relationships, as well as inter-species relationships in the microbiota, which are of importance for both human and animal gut health.

Functional Characterization of Structural Genomics Proteins in the Crotonase Superfamily

Members of the Crotonase superfamily, a mechanistically diverse family of proteins that share a conserved quaternary structure, can often catalyze more than one reaction. However, the spectrum of activity for its members has not been well studied. We report on measured crotonase and hydrolase activity for eight structural genomics (SG) proteins from the Crotonase superfamily plus two previously characterized proteins, intended as controls: human enoyl CoA hydratase (ECH) and Anabaena β-diketone hydrolase. Like most of the 15,000+ SG protein structures deposited in the Protein Data Bank (PDB), the eight SG proteins are of unknown or uncertain biochemical function. The functional characterization of the eight SG proteins is guided by the Structurally Aligned Local Sites of Activity (SALSA), a local-structure-based computational approach to functional annotation. For human ECH, the turnover number for hydrolase activity is threefold higher than that for ECH activity, although the catalytic efficiency is 160-fold higher for ECH. Three SG proteins originally annotated as ECHs were predicted by SALSA to be hydrolases and are observed to have higher catalytic efficiencies for hydrolase activity than for ECH activity, on par with the previously characterized hydrolase. Among the five SG proteins predicted by SALSA to be ECHs, all but one also show some hydrolase activity; all five exhibit lower ECH activity than the human ECH with respect to the crotonyl-CoA substrate. Here, we show examples demonstrating that SALSA can correct functional misannotations even within enzyme families that display promiscuous activity.

In silico prediction of HIV-1-host molecular interactions and their directionality

Human immunodeficiency virus type 1 (HIV-1) continues to be a major cause of disease and premature death. As with all viruses, HIV-1 exploits a host cell to replicate. Improving our understanding of the molecular interactions between virus and human host proteins is crucial for a mechanistic understanding of virus biology, infection and host antiviral activities. This knowledge will potentially permit the identification of host molecules for targeting by drugs with antiviral properties. Here, we propose a data-driven approach for the analysis and prediction of the HIV-1 interacting proteins (VIPs) with a focus on the directionality of the interaction: host-dependency versus antiviral factors. Using support vector machine learning models and features encompassing genetic, proteomic and network properties, our results reveal some significant differences between the VIPs and non-HIV-1 interacting human proteins (non-VIPs). As assessed by comparison with the HIV-1 infection pathway data in the Reactome database (sensitivity > 90%, threshold = 0.5), we demonstrate these models have good generalization properties. We find that the ‘direction’ of the HIV-1-host molecular interactions is also predictable due to different characteristics of ‘forward’/pro-viral versus ‘backward’/pro-host proteins. Additionally, we infer the previously unknown direction of the interactions between HIV-1 and 1351 human host proteins. A web server for performing predictions is available at http://hivpre.cvr.gla.ac.uk/.

TransDiscovery: Discovering Biotransformation from Human Microbiota by Integrating Metagenomic and Metabolomic Data

The human microbiome is a complex community of microorganisms, their enzymes, and the molecules they produce or modify. Recent studies show that imbalances in human microbial ecosystems can cause disease. Our microbiome affects our health through the products of biochemical reactions catalyzed by microbial enzymes (microbial biotransformations). Despite their significance, currently, there are no systematic strategies for identifying these chemical reactions, their substrates and molecular products, and their effects on health and disease. We present TransDiscovery, a computational algorithm that integrates molecular networks (connecting related molecules with similar mass spectra), association networks (connecting co-occurring molecules and microbes) and knowledge bases of microbial enzymes to discover microbial biotransformations, their substrates, and their products. After searching the metabolomics and metagenomics data from the American Gut Project and the Global Foodomic Project, TranDiscovery identified 17 potentially novel biotransformations from the human gut microbiome, along with the corresponding microbial species, substrates, and products.

The emerging potential of microbiome transplantation on human health interventions

The human microbiome has been the subject of intense research over the past few decades, in particular as a promising area for new clinical interventions. The microbiota colonizing the different body surfaces are of benefit for multiple physiological and metabolic processes of the human host and increasing evidence suggests an association between disturbances in the composition and functionality of the microbiota and several pathological conditions. This has provided a rationale for beneficial modulation of the microbiome. One approach being explored for modulating the microbiota in diseased individuals is transferring microbiota or microbiota constituents from healthy donors via microbiome transplantation. The great success of fecal microbiome transplantation for the treatment of Clostridioides difficile infections has encouraged the application of this procedure for the treatment of other diseases such as vaginal disorders via transplantation of vaginal microbiota, or of skin pathologies via the transplantation of skin microbiota. Microbiome modulation could even become a novel strategy for improving the efficacy of cancer therapies. This review discusses the principle, advantages and limitations of microbiome transplantation as well as different clinical contexts where microbiome transplantation has been applied.

Identification of putative essential protein domains from high-density transposon insertion sequencing

A first clue to gene function can be obtained by examining whether a gene is required for life in certain standard conditions, that is, whether a gene is essential. In bacteria, essential genes are usually identified by high-density transposon mutagenesis followed by sequencing of insertion sites (Tn-seq). These studies assign the term "essential" to whole genes rather than the protein domain sequences that encode the essential functions. However, genes can code for multiple protein domains that evolve their functions independently. Therefore, when essential genes code for more than one protein domain, only one of them could be essential. In this study, we defined this subset of genes as "essential domain-containing" (EDC) genes. Using a Tn-seq data set built-in Burkholderia cenocepacia K56-2, we developed an in silico pipeline to identify EDC genes and the essential protein domains they encode. We found forty candidate EDC genes and demonstrated growth defect phenotypes using CRISPR interference (CRISPRi). This analysis included two knockdowns of genes encoding the protein domains of unknown function DUF2213 and DUF4148. These putative essential domains are conserved in more than two hundred bacterial species, including human and plant pathogens. Together, our study suggests that essentiality should be assigned to individual protein domains rather than genes, contributing to a first functional characterization of protein domains of unknown function.

PANNZER-A practical tool for protein function prediction

The facility of next-generation sequencing has led to an explosion of gene catalogs for novel genomes, transcriptomes and metagenomes, which are functionally uncharacterized. Computational inference has emerged as a necessary substitute for first-hand experimental evidence. PANNZER (Protein ANNotation with Z-scoRE) is a high-throughput functional annotation web server that stands out among similar publically accessible web servers in supporting submission of up to 100,000 protein sequences at once and providing both Gene Ontology (GO) annotations and free text description predictions. Here, we demonstrate the use of PANNZER and discuss future plans and challenges. We present two case studies to illustrate problems related to data quality and method evaluation. Some commonly used evaluation metrics and evaluation datasets promote methods that favor unspecific and broad functional classes over more informative and specific classes. We argue that this can bias the development of automated function prediction methods. The PANNZER web server and source code are available at http://ekhidna2.biocenter.helsinki.fi/sanspanz/.

Introduction and adaptation of an emerging pathogen to olive trees in Italy

The invasive plant pathogen Xylella fastidiosa currently threatens European flora through the loss of economically and culturally important host plants. This emerging vector-borne bacterium, native to the Americas, causes several important diseases in a wide range of plants including crops, ornamentals, and trees. Previously absent from Europe, and considered a quarantine pathogen, X. fastidiosa was first detected in Apulia, Italy in 2013 associated with a devastating disease of olive trees (Olive Quick Decline Syndrome, OQDS). OQDS has led to significant economic, environmental, cultural, as well as political crises. Although the biology of X. fastidiosa diseases have been studied for over a century, there is still no information on the determinants of specificity between bacterial genotypes and host plant species, which is particularly relevant today as X. fastidiosa is expanding in the naive European landscape. We analysed the genomes of 79 X. fastidiosa samples from diseased olive trees across the affected area in Italy as well as genomes of the most genetically closely related strains from Central America. We provided insights into the ecological and evolutionary emergence of this pathogen in Italy. We first showed that the outbreak in Apulia is due to a single introduction from Central America that we estimated to have occurred in 2008 [95 % HPD: 1930–2016]. By using a combination of population genomic approaches and evolutionary genomics methods, we further identified a short list of genes that could play a major role in the adaptation of X. fastidiosa to this new environment. We finally provided experimental evidence for the adaptation of the strain to this new environment.

Accelerated discovery of novel glycoside hydrolases using targeted functional profiling and selective pressure on the rumen microbiome

BACKGROUND

Carbohydrate-active enzymes (CAZymes) form the most widespread and structurally diverse set of enzymes involved in the breakdown, biosynthesis, or modification of lignocellulose that can be found in living organisms. However, the structural diversity of CAZymes has rendered the targeted discovery of novel enzymes extremely challenging, as these proteins catalyze many different chemical reactions and are sourced by a vast array of microbes. Consequently, many uncharacterized members of CAZyme families of interest have been overlooked by current methodologies (e.g., metagenomic screening) used to discover lignocellulolytic enzymes.

RESULTS

In the present study, we combined phenotype-based selective pressure on the rumen microbiota with targeted functional profiling to guide the discovery of unknown CAZymes. In this study, we found 61 families of glycoside hydrolases (GH) (out of 182 CAZymes) from protein sequences deposited in the CAZy database-currently associated with more than 20,324 microbial genomes. Phenotype-based selective pressure on the rumen microbiome showed that lignocellulolytic bacteria (e.g., Fibrobacter succinogenes, Butyrivibrio proteoclasticus) and three GH families (e.g., GH11, GH13, GH45) exhibited an increased relative abundance in the rumen of feed efficient cattle when compared to their inefficient counterparts. These results paved the way for the application of targeted functional profiling to screen members of the GH11 and GH45 families against a de novo protein reference database comprised of 1184 uncharacterized enzymes, which led to the identification of 18 putative xylanases (GH11) and three putative endoglucanases (GH45). The biochemical proof of the xylanolytic activity of the newly discovered enzyme validated the computational simulations and demonstrated the stability of the most abundant xylanase.

CONCLUSIONS

These findings contribute to the discovery of novel enzymes for the breakdown, biosynthesis, or modification of lignocellulose and demonstrate that the rumen microbiome is a source of promising enzyme candidates for the biotechnology industry. The combined approaches conceptualized in this study can be adapted to any microbial environment, provided that the targeted microbiome is easy to manipulate and facilitates enrichment for the microbes of interest. Video Abstract.

Structure-Aware Mycobacterium tuberculosis Functional Annotation Uncloaks Resistance, Metabolic, and Virulence Genes

Accurate and timely functional genome annotation is essential for translating basic pathogen research into clinically impactful advances. Here, through literature curation and structure-function inference, we systematically update the functional genome annotation of Mycobacterium tuberculosis virulent type strain H37Rv. First, we systematically curated annotations for 589 genes from 662 publications, including 282 gene products absent from leading databases. Second, we modeled 1,711 underannotated proteins and developed a semiautomated pipeline that captured shared function between 400 protein models and structural matches of known function on Protein Data Bank, including drug efflux proteins, metabolic enzymes, and virulence factors. In aggregate, these structure- and literature-derived annotations update 940/1,725 underannotated H37Rv genes and generate hundreds of functional hypotheses. Retrospectively applying the annotation to a recent whole-genome transposon mutant screen provided missing function for 48% (13/27) of underannotated genes altering antibiotic efficacy and 33% (23/69) required for persistence during mouse tuberculosis (TB) infection. Prospective application of the protein models enabled us to functionally interpret novel laboratory generated pyrazinamide (PZA)-resistant mutants of unknown function, which implicated the emerging coenzyme A depletion model of PZA action in the mutants’ PZA resistance. Our findings demonstrate the functional insight gained by integrating structural modeling and systematic literature curation, even for widely studied microorganisms. Functional annotations and protein structure models are available at https://tuberculosis.sdsu.edu/H37Rv in human- and machine-readable formats.

IMPORTANCEMycobacterium tuberculosis, the primary causative agent of tuberculosis, kills more humans than any other infectious bacterium. Yet 40% of its genome is functionally uncharacterized, leaving much about the genetic basis of its resistance to antibiotics, capacity to withstand host immunity, and basic metabolism yet undiscovered. Irregular literature curation for functional annotation contributes to this gap. We systematically curated functions from literature and structural similarity for over half of poorly characterized genes, expanding the functionally annotated Mycobacterium tuberculosis proteome. Applying this updated annotation to recent in vivo functional screens added functional information to dozens of clinically pertinent proteins described as having unknown function. Integrating the annotations with a prospective functional screen identified new mutants resistant to a first-line TB drug, supporting an emerging hypothesis for its mode of action. These improvements in functional interpretation of clinically informative studies underscore the translational value of this functional knowledge. Structure-derived annotations identify hundreds of high-confidence candidates for mechanisms of antibiotic resistance, virulence factors, and basic metabolism and other functions key in clinical and basic tuberculosis research. More broadly, they provide a systematic framework for improving prokaryotic reference annotations.

Insights into the functional expansion of the astacin peptidase family in parasitic helminths

Helminths secrete a plethora of proteins involved in parasitism-related processes such as tissue penetration, migration, feeding and immunoregulation. Astacins, a family of zinc metalloproteases belonging to the peptidase family M12, are one of the most abundantly represented protein families in the secretomes of helminths. Despite their involvement in virulence, very few studies have addressed the role of this loosely defined protein group in parasitic helminths. Herein, we have analysed the predicted proteomes from 154 helminth species and confirmed the expansion of the astacin family in several nematode taxa. The astacin domain associated with up to 110 other domains into 145 unique domain architectures, where CUB and ShK constitute the principal and nearly independent bi-domain frameworks. The presence of co-existing domains suggests promiscuous adaptable functions to several roles. These activities could be related either to substrate specificity or to higher-order functions, such as anti-angiogenesis and immunomodulation, where the astacin domain would play an accessory role. Furthermore, some phylogenetically restricted mutations in the astacin domain affected residues located at the active cleft and binding sub-pockets, suggesting adaptation to different substrate specificities. Altogether, these findings suggest the astacin domain is a highly adaptable module that fulfils multiple proteolytic needs of the parasitic lifestyle. This study contributes to the understanding of helminth-secreted astacins and, ultimately, provides the foundation to guide future investigations about the role of this diverse family of proteins in host-parasite interactions.

Large-scale k-mer-based analysis of the informational properties of genomes, comparative genomics and taxonomy

Information theoretic approaches are ubiquitous and effective in a wide variety of bioinformatics applications. In comparative genomics, alignment-free methods, based on short DNA words, or k-mers, are particularly powerful. We evaluated the utility of varying k-mer lengths for genome comparisons by analyzing their sequence space coverage of 5805 genomes in the KEGG GENOME database. In subsequent analyses on four k-mer lengths spanning the relevant range (11, 21, 31, 41), hierarchical clustering of 1634 genus-level representative genomes using pairwise 21- and 31-mer Jaccard similarities best recapitulated a phylogenetic/taxonomic tree of life with clear boundaries for superkingdom domains and high subtree similarity for named taxons at lower levels (family through phylum). By analyzing ~14.2M prokaryotic genome comparisons by their lowest-common-ancestor taxon levels, we detected many potential misclassification errors in a curated database, further demonstrating the need for wide-scale adoption of quantitative taxonomic classifications based on whole-genome similarity.

Ultrahigh-Throughput Detection of Enzymatic Alcohol Dehydrogenase Activity in Microfluidic Droplets with a Direct Fluorogenic Assay

The exploration of large DNA libraries of metagenomic or synthetic origin is greatly facilitated by ultrahigh‐throughput assays that use monodisperse water‐in‐oil emulsion droplets as sequestered reaction compartments. Millions of samples can be generated and analysed in microfluidic devices at kHz speeds, requiring only micrograms of reagents. The scope of this powerful platform for the discovery of new sequence space is, however, hampered by the limited availability of assay substrates, restricting the functions and reaction types that can be investigated. Here, we broaden the scope of detectable biochemical transformations in droplet microfluidics by introducing the first fluorogenic assay for alcohol dehydrogenases (ADHs) in this format. We have synthesized substrates that release a pyranine fluorophore (8‐hydroxy‐1,3,6‐pyrenetrisulfonic acid, HPTS) when enzymatic turnover occurs. Pyranine is well retained in droplets for >6 weeks (i. e. 14‐times longer than fluorescein), avoiding product leakage and ensuring excellent assay sensitivity. Product concentrations as low as 100 nM were successfully detected, corresponding to less than one turnover per enzyme molecule on average. The potential of our substrate design was demonstrated by efficient recovery of a bona fide ADH with an >800‐fold enrichment. The repertoire of droplet screening is enlarged by this sensitive and direct fluorogenic assay to identify dehydrogenases for biocatalytic applications.

PASS: Protein Annotation Surveillance Site for Protein Annotation Using Homologous Clusters, NLP, and Sequence Similarity Networks

Advances in genome sequencing have accelerated the growth of sequenced genomes but at a cost in the quality of genome annotation. At the same time, computational analysis is widely used for protein annotation, but a dearth of experimental verification has contributed to inaccurate annotation as well as to annotation error propagation. Thus, a tool to help life scientists with accurate protein annotation would be useful. In this work we describe a website we have developed, the Protein Annotation Surveillance Site (PASS), which provides such a tool. This website consists of three major components: a database of homologous clusters of more than eight million protein sequences deduced from the representative genomes of bacteria, archaea, eukarya, and viruses, together with sequence information; a machine-learning software tool which periodically queries the UniprotKB database to determine whether protein function has been experimentally verified; and a query-able webpage where the FASTA headers of sequences from the cluster best matching an input sequence are returned. The user can choose from these sequences to create a sequence similarity network to assist in annotation or else use their expert knowledge to choose an annotation from the cluster sequences. Illustrations demonstrating use of this website are presented.

Experimental and computational investigation of enzyme functional annotations uncovers misannotation in the EC 1.1.3.15 enzyme class

Only a small fraction of genes deposited to databases have been experimentally characterised. The majority of proteins have their function assigned automatically, which can result in erroneous annotations. The reliability of current annotations in public databases is largely unknown; experimental attempts to validate the accuracy within individual enzyme classes are lacking. In this study we performed an overview of functional annotations to the BRENDA enzyme database. We first applied a high-throughput experimental platform to verify functional annotations to an enzyme class of S-2-hydroxyacid oxidases (EC 1.1.3.15). We chose 122 representative sequences of the class and screened them for their predicted function. Based on the experimental results, predicted domain architecture and similarity to previously characterised S-2-hydroxyacid oxidases, we inferred that at least 78% of sequences in the enzyme class are misannotated. We experimentally confirmed four alternative activities among the misannotated sequences and showed that misannotation in the enzyme class increased over time. Finally, we performed a computational analysis of annotations to all enzyme classes in the BRENDA database, and showed that nearly 18% of all sequences are annotated to an enzyme class while sharing no similarity or domain architecture to experimentally characterised representatives. We showed that even well-studied enzyme classes of industrial relevance are affected by the problem of functional misannotation.

Correct annotation of genomes is crucial for our understanding and utilization of functional gene diversity, yet the reliability of current protein annotations in public databases is largely unknown. In our work we validated annotations to an S-2-hydroxyacid oxidase enzyme class (EC 1.1.3.15) by assessing activity of 122 representative sequences in a high-throughput screening experiment. From this dataset we inferred that at least 78% of the sequences in the enzyme class are misannotated, and confirmed four alternative activities among the misannotated sequences. We showed that the misannotation is widespread throughout enzyme classes, affecting even well-studied classes of industrial relevance. Overall, our study highlights the value of experimental and computational validation of predicted functions within individual enzyme classes.

Thermostable lipases and their dynamics of improved enzymatic properties

Thermal stability is one of the most desirable characteristics in the search for novel lipases. The search for thermophilic microorganisms for synthesising functional enzyme biocatalysts with the ability to withstand high temperature, and capacity to maintain their native state in extreme conditions opens up new opportunities for their biotechnological applications. Thermophilic organisms are one of the most favoured organisms, whose distinctive characteristics are extremely related to their cellular constituent particularly biologically active proteins. Modifications on the enzyme structure are critical in optimizing the stability of enzyme to thermophilic conditions. Thermostable lipases are one of the most favourable enzymes used in food industries, pharmaceutical field, and actively been studied as potential biocatalyst in biodiesel production and other biotechnology application. Particularly, there is a trade-off between the use of enzymes in high concentration of organic solvents and product generation. Enhancement of the enzyme stability needs to be achieved for them to maintain their enzymatic activity regardless the environment. Various approaches on protein modification applied since decades ago conveyed a better understanding on how to improve the enzymatic properties in thermophilic bacteria. In fact, preliminary approach using advanced computational analysis is practically conducted before any modification is being performed experimentally. Apart from that, isolation of novel extremozymes from various microorganisms are offering great frontier in explaining the crucial native interaction within the molecules which could help in protein engineering. In this review, the thermostability prospect of lipases and the utility of protein engineering insights into achieving functional industrial usefulness at their high temperature habitat are highlighted. Similarly, the underlying thermodynamic and structural basis that defines the forces that stabilize these thermostable lipase is discussed. KEY POINTS: • The dynamics of lipases contributes to their non-covalent interactions and structural stability. • Thermostability can be enhanced by well-established genetic tools for improved kinetic efficiency. • Molecular dynamics greatly provides structure-function insights on thermodynamics of lipase.

Plant single-cell solutions for energy and the environment

Progress in sequencing, microfluidics, and analysis strategies has revolutionized the granularity at which multicellular organisms can be studied. In particular, single-cell transcriptomics has led to fundamental new insights into animal biology, such as the discovery of new cell types and cell type-specific disease processes. However, the application of single-cell approaches to plants, fungi, algae, or bacteria (environmental organisms) has been far more limited, largely due to the challenges posed by polysaccharide walls surrounding these species' cells. In this perspective, we discuss opportunities afforded by single-cell technologies for energy and environmental science and grand challenges that must be tackled to apply these approaches to plants, fungi and algae. We highlight the need to develop better and more comprehensive single-cell technologies, analysis and visualization tools, and tissue preparation methods. We advocate for the creation of a centralized, open-access database to house plant single-cell data. Finally, we consider how such efforts should balance the need for deep characterization of select model species while still capturing the diversity in the plant kingdom. Investments into the development of methods, their application to relevant species, and the creation of resources to support data dissemination will enable groundbreaking insights to propel energy and environmental science forward.

Characterization of an α-Glucosidase Enzyme Conserved in Gardnerella spp. Isolated from the Human Vaginal Microbiome

Gardnerella spp. in the vaginal microbiome are associated with bacterial vaginosis, in which a lactobacillus-dominated community is replaced with mixed bacteria, including Gardnerella species. Co-occurrence of multiple Gardnerella species in the vaginal environment is common, but different species are dominant in different women. Competition for nutrients, including glycogen, could play an important role in determining the microbial community structure. Digestion of glycogen into products that can be taken up and further processed by bacteria requires the combined activities of several enzymes collectively known as amylases, which belong to glycoside hydrolase family 13 (GH13) within the CAZy classification system. GH13 is a large and diverse family of proteins, making prediction of their activities challenging. SACCHARIS annotation of the GH13 family in Gardnerella resulted in identification of protein domains belonging to eight subfamilies. Phylogenetic analysis of predicted amylase sequences from 26 genomes demonstrated that a putative α-glucosidase-encoding sequence, CG400_06090, was conserved in all Gardnerella spp. The predicted α-glucosidase enzyme was expressed, purified, and functionally characterized. The enzyme was active on a variety of maltooligosaccharides with maximum activity at pH 7. K_m, k_cat, and k_cat/K_m values for the substrate 4-nitrophenyl α-d-glucopyranoside were 8.3 μM, 0.96 min^-1, and 0.11 μM^-1 min^-1, respectively. Glucose was released from maltose, maltotriose, maltotetraose, and maltopentaose, but no products were detected when the enzyme was incubated with glycogen. Our findings show that Gardnerella spp. produce an α-glucosidase enzyme that may contribute to the multistep process of glycogen metabolism by releasing glucose from maltooligosaccharides. IMPORTANCE Increased abundance of Gardnerella spp. is a diagnostic characteristic of bacterial vaginosis, an imbalance in the human vaginal microbiome associated with troubling symptoms, and negative reproductive health outcomes, including increased transmission of sexually transmitted infections and preterm birth. Competition for nutrients is likely an important factor in causing dramatic shifts in the vaginal microbial community but little is known about the contribution of bacterial enzymes to the metabolism of glycogen, a major carbon source available to vaginal bacteria. The significance of our research is characterizing the activity of an enzyme conserved in Gardnerella species that likely contributes to the ability of these bacteria to utilize glycogen.

Evolution of Toll, Spatzle and MyD88 in insects: the problem of the Diptera bias

BACKGROUND

Arthropoda, the most numerous and diverse metazoan phylum, has species in many habitats where they encounter various microorganisms and, as a result, mechanisms for pathogen recognition and elimination have evolved. The Toll pathway, involved in the innate immune system, was first described as part of the developmental pathway for dorsal-ventral differentiation in Drosophila. Its later discovery in vertebrates suggested that this system was extremely conserved. However, there is variation in presence/absence, copy number and sequence divergence in various genes along the pathway. As most studies have only focused on Diptera, for a comprehensive and accurate homology-based approach it is important to understand gene function in a number of different species and, in a group as diverse as insects, the use of species belonging to different taxonomic groups is essential.

RESULTS

We evaluated the diversity of Toll pathway gene families in 39 Arthropod genomes, encompassing 13 different Insect Orders. Through computational methods, we shed some light into the evolution and functional annotation of protein families involved in the Toll pathway innate immune response. Our data indicates that: 1) intracellular proteins of the Toll pathway show mostly species-specific expansions; 2) the different Toll subfamilies seem to have distinct evolutionary backgrounds; 3) patterns of gene expansion observed in the Toll phylogenetic tree indicate that homology based methods of functional inference might not be accurate for some subfamilies; 4) Spatzle subfamilies are highly divergent and also pose a problem for homology based inference; 5) Spatzle subfamilies should not be analyzed together in the same phylogenetic framework; 6) network analyses seem to be a good first step in inferring functional groups in these cases. We specifically show that understanding Drosophila's Toll functions might not indicate the same function in other species.

CONCLUSIONS

Our results show the importance of using species representing the different orders to better understand insect gene content, origin and evolution. More specifically, in intracellular Toll pathway gene families the presence of orthologues has important implications for homology based functional inference. Also, the different evolutionary backgrounds of Toll gene subfamilies should be taken into consideration when functional studies are performed, especially for TOLL9, TOLL, TOLL2_7, and the new TOLL10 clade. The presence of Diptera specific clades or the ones lacking Diptera species show the importance of overcoming the Diptera bias when performing functional characterization of Toll pathways.

Transcriptome-Wide Comparisons and Virulence Gene Polymorphisms of Host-Associated Genotypes of the Cnidarian Parasite Ceratonova shasta in Salmonids

Ceratonova shasta is an important myxozoan pathogen affecting the health of salmonid fishes in the Pacific Northwest of North America. Ceratonova shasta exists as a complex of host-specific genotypes, some with low to moderate virulence, and one that causes a profound, lethal infection in susceptible hosts. High throughput sequencing methods are powerful tools for discovering the genetic basis of these host/virulence differences, but deep sequencing of myxozoans has been challenging due to extremely fast molecular evolution of this group, yielding strongly divergent sequences that are difficult to identify, and unavoidable host contamination. We designed and optimized different bioinformatic pipelines to address these challenges. We obtained a unique set of comprehensive, host-free myxozoan RNA-seq data from C. shasta genotypes of varying virulence from different salmonid hosts. Analyses of transcriptome-wide genetic distances and maximum likelihood multigene phylogenies elucidated the evolutionary relationship between lineages and demonstrated the limited resolution of the established Internal Transcribed Spacer marker for C. shasta genotype identification, as this marker fails to differentiate between biologically distinct genotype II lineages from coho salmon and rainbow trout. We further analyzed the data sets based on polymorphisms in two gene groups related to virulence: cell migration and proteolytic enzymes including their inhibitors. The developed single-nucleotide polymorphism-calling pipeline identified polymorphisms between genotypes and demonstrated that variations in both motility and protease genes were associated with different levels of virulence of C. shasta in its salmonid hosts. The prospective use of proteolytic enzymes as promising candidates for targeted interventions against myxozoans in aquaculture is discussed. We developed host-free transcriptomes of a myxozoan model organism from strains that exhibited different degrees of virulence, as a unique source of data that will foster functional gene analyses and serve as a base for the development of potential therapeutics for efficient control of these parasites.

Methodological Advances to Study Contaminant Biotransformation: New Prospects for Understanding and Reducing Environmental Persistence?

Complex microbial communities in environmental systems play a key role in the detoxification of chemical contaminants by transforming them into less active metabolites or by complete mineralization. Biotransformation, i.e., transformation by microbes, is well understood for a number of priority pollutants, but a similar level of understanding is lacking for many emerging contaminants encountered at low concentrations and in complex mixtures across natural and engineered systems. Any advanced approaches aiming to reduce environmental exposure to such contaminants (e.g., novel engineered biological water treatment systems, design of readily degradable chemicals, or improved regulatory assessment strategies to determine contaminant persistence a priori) will depend on understanding the causal links among contaminant removal, the key driving agents of biotransformation at low concentrations (i.e., relevant microbes and their metabolic activities), and how their presence and activity depend on environmental conditions. In this Perspective, we present the current understanding and recent methodological advances that can help to identify such links, even in complex environmental microbiomes and for contaminants present at low concentrations in complex chemical mixtures. We discuss the ensuing insights into contaminant biotransformation across varying environments and conditions and ask how much closer we have come to designing improved approaches to reducing environmental exposure to contaminants.

Open Issues for Protein Function Assignment in Haloferax volcanii and Other Halophilic Archaea

Background: Annotation ambiguities and annotation errors are a general challenge in genomics. While a reliable protein function assignment can be obtained by experimental characterization, this is expensive and time-consuming, and the number of such Gold Standard Proteins (GSP) with experimental support remains very low compared to proteins annotated by sequence homology, usually through automated pipelines. Even a GSP may give a misleading assignment when used as a reference: the homolog may be close enough to support isofunctionality, but the substrate of the GSP is absent from the species being annotated. In such cases, the enzymes cannot be isofunctional. Here, we examined a variety of such issues in halophilic archaea (class Halobacteria), with a strong focus on the model haloarchaeon Haloferax volcanii. Results: Annotated proteins of Hfx. volcanii were identified for which public databases tend to assign a function that is probably incorrect. In some cases, an alternative, probably correct, function can be predicted or inferred from the available evidence, but this has not been adopted by public databases because experimental validation is lacking. In other cases, a probably invalid specific function is predicted by homology, and while there is evidence that this assigned function is unlikely, the true function remains elusive. We listed 50 of those cases, each with detailed background information, so that a conclusion about the most likely biological function can be drawn. For reasons of brevity and comprehension, only the key aspects are listed in the main text, with detailed information being provided in a corresponding section of the Supplementary Materials. Conclusions: Compiling, describing and summarizing these open annotation issues and functional predictions will benefit the scientific community in the general effort to improve the evaluation of protein function assignments and more thoroughly detail them. By highlighting the gaps and likely annotation errors currently in the databases, we hope this study will provide a framework for experimentalists to systematically confirm (or disprove) our function predictions or to uncover yet more unexpected functions.

Key amino acid residues in homoserine-acetyltransferase from M. tuberculosis give insight into the evolution of MetX family of enzymes - HAT, SAT and HST

Multiple sequence alignment of homoserine-acetyltransferases, serine-acetyltransferases and homoserine-succinyltransferases show they all belong to MetX family, having evolved from a common ancestor by conserving the catalytic site and substrate binding residues. The discrimination in the substrate selection arises due to the presence of substrate-specific residues lining the substrate-binding pocket. Mutation of Ala59 and Gly62 to Gly and Pro respectively in homoserine-acetyltransferase from M. tuberculosis resulted in a serine-acetyltransferase like enzyme as it acetylated both l-homoserine and l-serine. Homoserine-acetyltransferase from M. tuberculosis when mutated at positon 322 where Leu was converted to Arg, resulted in succinylation over acetylation of l-homoserine. Our studies establish the importance of the substrate binding residues in determining the type of activity possessed by MetX family, despite all of them having the same catalytic triad Ser-Asp-His. Hence key residues at the substrate binding pocket dictate whether the given enzyme shows predominant transferase or hydrolase activity.

Mantis: flexible and consensus-driven genome annotation

Background

The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation can be described as the identification of regions of interest (i.e., domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, challenges remain in terms of speed, flexibility, and reproducibility. In the big data era, it is also increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, and thus overcoming some limitations in overly relying on computationally generated data from single sources.

Results

We implemented a protein annotation tool, Mantis, which uses database identifiers intersection and text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for the customization of reference data and execution parameters, and is reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which significantly improved annotation performance compared to sequence-wide annotation. The parallelized implementation of Mantis results in short runtimes while also outputting high coverage and high-quality protein function annotations.

Conclusions

Mantis is a protein function annotation tool that produces high-quality consensus-driven protein annotations. It is easy to set up, customize, and use, scaling from single genomes to large metagenomes. Mantis is available under the MIT license at https://github.com/PedroMTQ/mantis.

Updates on defining and detecting diarrheagenic Escherichia coli pathotypes

PURPOSE OF REVIEW

Several types of Escherichia coli cause acute diarrhea in humans and are responsible for a large burden of disease globally. The purpose of this review is to summarize diarrheagenic Escherichia coli (DEC) pathotype definitions and discuss existing and emerging molecular, genomic, and gut microbiome methods to detect, define, and study DEC pathotypes.

RECENT FINDINGS

DEC pathotypes are currently diagnosed by molecular detection of unique virulence genes. However, some pathotypes have defied coherent molecular definitions because of imperfect gene targets, and pathotype categories are complicated by hybrid strains and isolation of pathotypes from asymptomatic individuals. Recent progress toward more efficient, sensitive, and multiplex DEC pathotype detection has been made using emerging PCR-based technologies. Genomics and gut microbiome detection methods continue to advance rapidly and are contributing to a better understanding of DEC pathotype diversity and functional potential.

SUMMARY

DEC pathotype categorizations and detection methods are useful but imperfect. The implementation of molecular and sequence-based methods and well designed epidemiological studies will continue to advance understanding of DEC pathotypes. Additional emphasis is needed on sequencing DEC genomes from regions of the world where they cause the most disease and from the pathotypes that cause the greatest burden of disease globally.

Comparative proteomic analysis to annotate the structural and functional association of the hypothetical proteins of S. maltophilia k279a and predict potential T and B cell targets for vaccination

Stenotrophomonas maltophilia is a multidrug-resistant bacterium with no precise clinical treatment. This bacterium can be a vital cause for death and different organ failures in immune-compromised, immune-competent, and long-time hospitalized patients. Extensive quorum sensing capability has become a challenge to develop new drugs against this pathogen. Moreover, the organism possesses about 789 proteins which function, structure, and pathogenesis remain obscured. In this piece of work, we tried to enlighten the aforementioned sectors using highly reliable bioinformatics tools validated by the scientific community. At first, the whole proteome sequence of the organism was retrieved and stored. Then we separated the hypothetical proteins and searched for the conserved domain with a high confidence level and multi-server validation, which resulted in 24 such proteins. Furthermore, all of their physical and chemical characterizations were performed, such as theoretical isoelectric point, molecular weight, GRAVY value, and many more. Besides, the subcellular localization, protein-protein interactions, functional motifs, 3D structures, antigenicity, and virulence factors were also evaluated. As an extension of this work, 'RTFAMSSER' and 'PAAPQPSAS' were predicted as potential T and B cell epitopes, respectively. We hope our findings will help in better understating the pathogenesis and smoothen the way to the cure.

Mining Synergistic Microbial Interactions: A Roadmap on How to Integrate Multi-Omics Data

Mining interspecies interactions remain a challenge due to the complex nature of microbial communities and the need for computational power to handle big data. Our meta-analysis indicates that genetic potential alone does not resolve all issues involving mining of microbial interactions. Nevertheless, it can be used as the starting point to infer synergistic interspecies interactions and to limit the search space (i.e., number of species and metabolic reactions) to a manageable size. A reduced search space decreases the number of additional experiments necessary to validate the inferred putative interactions. As validation experiments, we examine how multi-omics and state of the art imaging techniques may further improve our understanding of species interactions’ role in ecosystem processes. Finally, we analyze pros and cons from the current methods to infer microbial interactions from genetic potential and propose a new theoretical framework based on: (i) genomic information of key members of a community; (ii) information of ecosystem processes involved with a specific hypothesis or research question; (iii) the ability to identify putative species’ contributions to ecosystem processes of interest; and, (iv) validation of putative microbial interactions through integration of other data sources.

Questing functions and structures of hypothetical proteins from Campylobacter jejuni: a computer-aided approach

Campylobacter jejuni (C. jejuni) is considered to be one of the most frequent causes of bacterial gastroenteritis globally, especially in young children. The genome of C. jejuni contains many proteins with unknown functions termed as hypothetical proteins (HPs). These proteins might have essential biological role to show the full spectrum of this bacterium. Hence, our study aimed to determine the functions of HPs, pertaining to the genome of C. jejuni. An in-silico work flow integrating various tools were performed for functional assignment, three-dimensional structure determination, domain architecture predictors, subcellular localization, physicochemical characterization, and protein-protein interactions (PPIs). Sequences of 267 HPs of C. jejuni were analyzed and successfully attributed the function of 49 HPs with higher confidence. Here, we found proteins with enzymatic activity, transporters, binding and regulatory proteins as well as proteins with biotechnological interest. Assessment of the performance of various tools used in this analysis revealed an accuracy of 95% using receiver operating characteristic (ROC) curve analysis. Functional and structural predictions and the results from ROC analyses provided the validity of in-silico tools used in the present study. The approach used for this analysis leads us to assign the function of unknown proteins and relate them with the functions that have already been described in previous literature.

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

Detecting Gene Ontology misannotations using taxon-specific rate ratio comparisons

MOTIVATION

Many protein function databases are built on automated or semi-automated curations and can contain various annotation errors. The correction of such misannotations is critical to improving the accuracy and reliability of the databases.

RESULTS

We proposed a new approach to detect potentially incorrect Gene Ontology (GO) annotations by comparing the ratio of annotation rates (RAR) for the same GO term across different taxonomic groups, where those with a relatively low RAR usually correspond to incorrect annotations. As an illustration, we applied the approach to 20 commonly studied species in two recent UniProt-GOA releases and identified 250 potential misannotations in the 2018-11-6 release, where only 25% of them were corrected in the 2019-6-3 release. Importantly, 56% of the misannotations are 'Inferred from Biological aspect of Ancestor (IBA)' which is in contradiction with previous observations that attributed misannotations mainly to 'Inferred from Sequence or structural Similarity (ISS)', probably reflecting an error source shift due to the new developments of function annotation databases. The results demonstrated a simple but efficient misannotation detection approach that is useful for large-scale comparative protein function studies.

AVAILABILITY AND IMPLEMENTATION

https://zhanglab.ccmb.med.umich.edu/RAR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Structure-function relationships in NDP-sugar active SDR enzymes: Fingerprints for functional annotation and enzyme engineering

Short-chain Dehydrogenase/Reductase enzymes that are active on nucleotide sugars (abbreviated as NS-SDR) are of paramount importance in the biosynthesis of rare sugars and glycosides. Some family members have already been extensively characterized due to their direct implication in metabolic disorders or in the biosynthesis of virulence factors. In this review, we combine the knowledge gathered from studies that typically focused only on one NS-SDR activity with an in-depth analysis and overview of all of the different NS-SDR families (169,076 enzyme sequences). Through this structure-based multiple sequence alignment of NS-SDRs retrieved from public databases, we could identify clear patterns in conservation and correlation of crucial residues. Supported by this analysis, we suggest updating and extending the UDP-galactose 4-epimerase "hexagonal box model" to an "heptagonal box model" for all NS-SDR enzymes. This specificity model consists of seven conserved regions surrounding the NDP-sugar substrate that serve as fingerprint for each specificity. The specificity fingerprints highlighted in this review will be beneficial for functional annotation of the large group of NS-SDR enzymes and form a guide for future enzyme engineering efforts focused on the biosynthesis of rare and specialty carbohydrates.

Structural-Genetic Characterization Of Novel Butaryl co-A Dehydrogenase and Proposition of Butanol Biosynthesis Pathway in Pusillimonas ginsengisoli SBSA

Despite extensive use in the biofuel industry, only butyryl co-A dehydrogenase enzymes from the Clostridia group have undergone extensive structural and genetic characterization. The present study, portrays the characterization of structural, functional and phylogenetic properties of butyryl co-A dehydrogenase identified within the genome of Pusillimonas ginsengisoli SBSA. In silico characterization, homology modelling and docking data indicates that this protein is a homo-tetramer and 388 amino acid residue long, rich in alanine and leucine residue; having molecular weight of 42347.69 dalton. Its isoelectric point value is 5.78; indicate its neutral nature while 38.38 instability index value indicate its stable nature. Its thermostable nature evidenced by its high aliphatic index (93.14); makes its suitable for industry-based use. The secondary structure prediction analysis of butyryl co-A dehydrogenase unveiled that the proteins has secondary arrangements of 54% α-helix, 13% β-stand and 5% disordered conformation. However, phylogenetic analysis clearly indicates that probably horizontal gene transfer is the primary mechanism of spreading of this gene in this organism. Notably, multiple sequence alignment study of phylogenetically diverse butyryl co-A dehydrogenase sequence highlighted the presence of conserved amino acid residues i.e. YXV/LGXKXWXS/T. Physicochemical characterization of other relevant proteins involved in butanol metabolism of SBSA also has been carried out. However, metabolic construction of functional butanol biosynthesis pathway in SBSA, enlightened its cost-effective potential use in biofuel industry as an alternate to Clostridia system.

Structure, Function, and Thermal Adaptation of the Biotin Carboxylase Domain Dimer from Hydrogenobacter thermophilus 2-Oxoglutarate Carboxylase

2-Oxoglutarate carboxylase (OGC), a unique member of the biotin-dependent carboxylase family from the order Aquificales, captures dissolved CO₂ via the reductive tricarboxylic acid (rTCA) cycle. Structure and function studies of OGC may facilitate adaptation of the rTCA cycle to increase the level of carbon fixation for biofuel production. Here we compare the biotin carboxylase (BC) domain of Hydrogenobacter thermophilus OGC with the well-studied mesophilic homologues to identify features that may contribute to thermal stability and activity. We report three OGC BC X-ray structures, each bound to bicarbonate, ADP, or ADP-Mg²⁺, and propose that substrate binding at high temperatures is facilitated by interactions that stabilize the flexible subdomain B in a partially closed conformation. Kinetic measurements with varying ATP and biotin concentrations distinguish two temperature-dependent steps, consistent with biotin's rate-limiting role in organizing the active site. Transition state thermodynamic values derived from the Eyring equation indicate a larger positive ΔH^⧧ and a less negative ΔS^⧧ compared to those of a previously reported mesophilic homologue. These thermodynamic values are explained by partially rate limiting product release. Phylogenetic analysis of BC domains suggests that OGC diverged prior to Aquificales evolution. The phylogenetic tree identifies mis-annotations of the Aquificales BC sequences, including the Aquifex aeolicus pyruvate carboxylase structure. Notably, our structural data reveal that the OGC BC dimer comprises a "wet" dimerization interface that is dominated by hydrophilic interactions and structural water molecules common to all BC domains and likely facilitates the conformational changes associated with the catalytic cycle. Mutations in the dimerization domain demonstrate that dimerization contributes to thermal stability.

Visualizing the superfamily of metallo-β-lactamases through sequence similarity network neighborhood connectivity analysis

Protein sequence similarity networks (SSNs) constitute a convenient approach to analyze large polypeptide sequence datasets, and have been successfully applied to study a number of protein families over the past decade. SSN analysis is herein combined with traditional cladistic and phenetic phylogenetic analysis (respectively based on multiple sequence alignments and all-against-all three-dimensional protein structure comparisons) in order to assist the ancestral reconstruction and integrative revision of the superfamily of metallo-β-lactamases (MBLs). It is shown that only 198 out of 15,292 representative nodes contain at least one experimentally obtained protein structure in the Protein Data Bank or a manually annotated SwissProt entry, that is to say, only 1.3 % of the superfamily has been functionally and/or structurally characterized. Besides, neighborhood connectivity coloring, which measures local network interconnectivity, is introduced for detection of protein families within SSN clusters. This approach provides a clear picture of how many families remain unexplored in the superfamily, while most MBL research is heavily biased towards a few families. Further research is suggested in order to determine the SSN topological properties, which will be instrumental for the improvement of automated sequence annotation methods.

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

Biomedical institutions rely on data evaluation and are turning into data factories. Big-data storage centers, supercomputing systems, and increased algorithmic efficiency allow us to analyze the ever-increasing amount of data generated every day in biomedical research centers. In network science, the principal intrinsic problem is how to integrate the data and information from different experiments on genes or proteins. Data curation is an essential process in annotating new functional data to known genes or proteins, undertaken by a biobank curator, which is then reflected in the calculated networks. We provide an example of how protein-protein networks today have space-time limits. The next step is the integration of data and information from different biobanks. Omics data and networks are essential parts of this step but also have flawed protocols and errors. Consider data from patients with cancer: from biopsy procedures to experimental tests, to archiving methods and computational algorithms, these are continuously handled so require critical and continuous "updates" to obtain reproducible, reliable, and correct results. We show, as a second example, how all this distorts studies in cellular hepatocellular carcinoma. It is not unlikely that these flawed data have been polluting biobanks for some time before stringent conditions for the veracity of data were implemented in Big data. Therefore, all this could contribute to errors in future medical decisions.

Reversible phosphorylation of a protein from Trypanosoma equiperdum that exhibits homology with the regulatory subunits of mammalian cAMP-dependent protein kinases

Homologous proteins of the cAMP-dependent protein kinase (PKA) regulatory and catalytic subunits have been identified in Trypanosoma equiperdum (TeqR-like and TeqC-like, respectively). Partially purified TeqR-like from parasites isolated in the presence of glucose migrated as an apparent 55 kDa/57 kDa polypeptide doublet when separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. However, a single polypeptide of 57 kDa was obtained when parasites were deprived of glucose, a condition that has been shown to activate a TeqC-like enzyme. As revealed by immunoblots using anti-phospothreonine antibodies, the 57 kDa band corresponded to a form of TeqR-like that was phosphorylated in threonine residues. TeqR-like phosphorylation was reversible since the level of phospho-TeqR-like decreased once glucose was readded to glucose starved-parasites. Dephospho- and phospho-TeqR-like proteins are monomers with native molecular masses of 54.93-57.41 kDa, Stokes radii of 3.42-3.37 nm, and slightly asymmetric shapes (frictional ratio f/fo = 1.36-1.32). A protein kinase of ∼40 kDa was also partially purified from glucose deprived-trypanosomes, which corresponded to the TeqC-like enzyme by its ability to phosphorylate kemptide, its inhibition by PKA-specific inhibitors, and its immunorecognition by anti-PKA catalytic subunit antibodies. TeqR-like and TeqC-like did not coelute following anion-exchange chromatography, revealing that these proteins are not associated forming a complex in T. equiperdum. Yet, when TeqR-like was incubated in vitro with TeqC-like in the presence of Mg²⁺ and ATP, the 55 kDa dephospho form of the 55kDa/57 kDa polypeptide doublet of TeqR-like was converted into the 57 kDa phospho form, demonstrating that TeqR-like is a substrate for TeqC-like.

Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database

Advances in genome sequencing technology and computing power have brought about the explosive growth of sequenced genomes in public repositories with a concomitant increase in annotation errors. Many protein sequences are annotated using computational analysis rather than experimental verification, leading to inaccuracies in annotation. Confirmation of existing protein annotations is urgently needed before misannotation becomes even more prevalent due to error propagation. In this work we present a novel approach for automatically confirming the existence of manually curated information with experimental evidence of protein annotation. Our ensemble learning method uses a combination of recurrent convolutional neural network, logistic regression, and support vector machine models. Natural language processing in the form of word embeddings is used with journal publication titles retrieved from the UniProtKB database. Importantly, we use recall as our most significant metric to ensure the maximum number of verifications possible; results are reported to a human curator for confirmation. Our ensemble model achieves 91.25% recall, 71.26% accuracy, 65.19% precision, and an F1 score of 76.05% and outperforms the Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) model with fine-tuning using the same data.

Sequence analysis and specificity of distinct types of menaquinone methyltransferases indicate the widespread potential of methylmenaquinone production in bacteria and archaea

Menaquinone (MK) serves as an essential membranous redox mediator in various electron transport chains of aerobic and anaerobic respiration. In addition, the composition of the quinone/quinol pool has been widely used as a biomarker in microbial taxonomy. The HemN-like class C radical SAM methyltransferases (RSMTs) MqnK, MenK and MenK2 have recently been shown to facilitate specific menaquinone methylation reactions at position C-8 (MqnK/MenK) or C-7 (MenK2) to synthesize 8-methylmenaquinone, 7-methylmenaquinone and 7,8-dimethylmenaquinone. However, the vast majority of protein sequences from the MqnK/MenK/MenK2 family belong to organisms, whose capacity to produce methylated menaquinones has not been investigated biochemically. Here, representative putative menK and menK2 genes from Collinsella tanakaei and Ferrimonas marina were individually expressed in Escherichia coli (wild-type or ubiE deletion mutant) and the corresponding cells were found to produce methylated derivatives of the endogenous MK and 2-demethylmenaquinone. Cluster and phylogenetic analyses of 828 (methyl)menaquinone methyltransferase sequences revealed signature motifs that allowed to discriminate enzymes of the MqnK/MenK/MenK2 family from other radical SAM enzymes and to identify C-7-specific menaquinone methyltransferases of the MenK2 subfamily. This study will help to predict the methylation status of the quinone/quinol pool of a microbial species (or even a microbial community) from its (meta)genome and contribute to the future design of microbial quinone/quinol pools in a Synthetic Biology approach.

Leveraging computational genomics to understand the molecular basis of metal homeostasis

Genome-based data is helping to reveal the diverse strategies plants and algae use to maintain metal homeostasis. In addition to acquisition, distribution and storage of metals, acclimating to feast or famine can involve a wealth of genes that we are just now starting to understand. The fast-paced acquisition of genome-based data, however, is far outpacing our ability to experimentally characterize protein function. Computational genomic approaches are needed to fill the gap between what is known and unknown. To avoid misconstruing bioinformatically derived data, which is the root cause of the inaccurate functional annotations that plague databases, functional inferences from diverse sources and contextualization of that evidence with a robust understanding of protein family evolution is needed. Phylogenomic- and comparative-genomic-based studies can aid in the interpretation of experimental data or provide a spark for the discovery of a new function. These analyses not only lead to novel insight into a target protein's function but can generate thought-provoking insights across protein families.