Revealing Unexplored Sequence-Function Space Using Sequence Similarity Networks

Synthetic Biology in Natural Product Biosynthesis

Synthetic biology has played an important role in the renaissance of natural products research during the post-genomics era. The development and integration of new tools have transformed the workflow of natural product discovery and engineering, generating multidisciplinary interest in the field. In this review, we summarize recent developments in natural product biosynthesis from three different aspects. First, advances in bioinformatics, experimental, and analytical tools to identify natural products associated with predicted biosynthetic gene clusters (BGCs) will be covered. This will be followed by an extensive review on the heterologous expression of natural products in bacterial, fungal and plant organisms. The native host-independent paradigm to natural product identification, pathway characterization, and enzyme discovery is where synthetic biology has played the most prominent role. Lastly, strategies to engineer biosynthetic pathways for structural diversification and complexity generation will be discussed, including recent advances in assembly-line megasynthase engineering, precursor-directed structural modification, and combinatorial biosynthesis.

Protein sequence modelling with Bayesian flow networks

Exploring the vast and largely uncharted territory of amino acid sequences is crucial for understanding complex protein functions and the engineering of novel therapeutic proteins. Whilst generative machine learning has advanced protein sequence modelling, no existing approach is proficient in both unconditional and conditional generation. In this work, we propose that Bayesian Flow Networks (BFNs), a recently introduced framework for generative modelling, can address these challenges. We present ProtBFN, a 650M parameter model trained on protein sequences curated from UniProtKB, which generates natural-like, diverse, structurally coherent, and novel protein sequences, significantly outperforming leading autoregressive and discrete diffusion models. Further, we fine-tune ProtBFN on heavy chains from the Observed Antibody Space to obtain an antibody-specific model, AbBFN, which we use to evaluate zero-shot conditional generation capabilities. AbBFN is found to be competitive with or better than antibody-specific BERT-style models when applied to predicting individual framework or complimentary determining regions.

Discovery, design, and engineering of enzymes based on molecular retrobiosynthesis

Biosynthesis-a process utilizing biological systems to synthesize chemical compounds-has emerged as a revolutionary solution to 21st-century challenges due to its environmental sustainability, scalability, and high stereoselectivity and regioselectivity. Recent advancements in artificial intelligence (AI) are accelerating biosynthesis by enabling intelligent design, construction, and optimization of enzymatic reactions and biological systems. We first introduce the molecular retrosynthesis route planning in biochemical pathway design, including single-step retrosynthesis algorithms and AI-based chemical retrosynthesis route design tools. We highlight the advantages and challenges of large language models in addressing the sparsity of chemical data. Furthermore, we review enzyme discovery methods based on sequence and structure alignment techniques. Breakthroughs in AI-based structural prediction methods are expected to significantly improve the accuracy of enzyme discovery. We also summarize methods for de novo enzyme generation for nonnatural or orphan reactions, focusing on AI-based enzyme functional annotation and enzyme discovery techniques based on reaction or small molecule similarity. Turning to enzyme engineering, we discuss strategies to improve enzyme thermostability, solubility, and activity, as well as the applications of AI in these fields. The shift from traditional experiment-driven models to data-driven and computationally driven intelligent models is already underway. Finally, we present potential challenges and provide a perspective on future research directions. We envision expanded applications of biocatalysis in drug development, green chemistry, and complex molecule synthesis.

Expansion of the Stereochemical Space of Triterpenes by Mining Noncanonical Oxidosqualene Cyclases Across the Diversity of Green Plants

Triterpenoids and steroids are structurally complex polycyclic natural products with potent biological functions, for example, as hormones. In all eukaryotes, the carbon skeletons of these compounds are generated by oxidosqualene cyclases, which carry out a polycyclization cascade to generate four or five rings with up to nine stereogenic centers in a targeted manner. The tight stereochemical control of this cascade reaction severely limits the stereochemical space accessible by known oxidosqualene cyclases. Considering that naturally occurring hormone stereoisomers have markedly different biological activities, finding ways to produce stereoisomers of triterpenes would be highly desirable to open new avenues for developing triterpenoid and steroid drugs. Here, we present a plant kingdom-wide sequence mining approach based on sequence similarity networks to search for noncanonical oxidosqualene cyclases that might produce triterpene stereoisomers. From 1,891 oxidosqualene cyclase sequences representing the diversity of green plants, six candidates were selected for functional evaluation by heterologous production in Nicotiana benthamiana. Of these six candidates, three produced rare or previously inaccessible triterpene stereoisomers, namely, (3S,13S)-malabarica-17,21-diene-3β,14-diol, 19-epi-lupeol, and a previously unknown hopanoid stereoisomer that we call protostahopenol. Site-directed mutagenesis revealed key residues important for catalytic activity. The sequence similarity network mining strategy employed here will facilitate the targeted discovery of enzymes with unusual activity in higher organisms, which are not amenable to common genome mining approaches. More importantly, our work expands the accessible stereochemical space of triterpenes and represents the first step to the development of new triterpenoid-derived drugs.

Metatranscriptomes-based sequence similarity networks uncover genetic signatures within parasitic freshwater microbial eukaryotes

BACKGROUND

Microbial eukaryotes play a crucial role in biochemical cycles and aquatic trophic food webs. Their taxonomic and functional diversity are increasingly well described due to recent advances in sequencing technologies. However, the vast amount of data produced by -omics approaches require data-driven methodologies to make predictions about these microorganisms' role within ecosystems. Using metatranscriptomics data, we employed a sequence similarity network-based approach to explore the metabolic specificities of microbial eukaryotes with different trophic modes in a freshwater ecosystem (Lake Pavin, France).

RESULTS

A total of 2,165,106 proteins were clustered in connected components enabling analysis of a great number of sequences without any references in public databases. This approach coupled with the use of an in-house trophic modes database improved the number of proteins considered by 42%. Our study confirmed the versatility of mixotrophic metabolisms with a large number of shared protein families among mixotrophic and phototrophic microorganisms as well as mixotrophic and heterotrophic microorganisms. Genetic similarities in proteins of saprotrophs and parasites also suggest that fungi-like organisms from Lake Pavin, such as Chytridiomycota and Oomycetes, exhibit a wide range of lifestyles, influenced by their degree of dependence on a host. This plasticity may occur at a fine taxonomic level (e.g., species level) and likely within a single organism in response to environmental parameters. While we observed a relative functional redundancy of primary metabolisms (e.g., amino acid and carbohydrate metabolism) nearly 130,000 protein families appeared to be trophic mode-specific. We found a particular specificity in obligate parasite-related Specific Protein Clusters, underscoring a high degree of specialization in these organisms.

CONCLUSIONS

Although no universal marker for parasitism was identified, candidate genes can be proposed at a fine taxonomic scale. We notably provide several protein families that could serve as keys to understanding host-parasite interactions representing pathogenicity factors (e.g., involved in hijacking host resources, or associated with immune evasion mechanisms). All these protein families could offer valuable insights for developing antiparasitic treatments in health and economic contexts. Video Abstract.

Bioinformatic and biochemical analysis uncovers novel activity in the 2-ER subfamily of OYEs

Members of the old yellow enzyme (OYE) family utilize a flavin mononucleotide cofactor to catalyze the asymmetric reduction of activated alkenes. The 2-enoate reductase (2-ER) subfamily are of particular industrial relevance as they can reduce α/β alkenes near electron-withdrawing groups. While the broader OYE family is being extensively explored for biocatalytic applications, oxygen sensitivity and poor expression yields associated with the presence of an Fe/S cluster in 2-ERs have hampered their characterization. Herein, we explore the use of pseudo-anaerobic preparation as a route to more widespread study of these enzymes and apply bioinformatics approaches to identify a subset of 2-ERs containing unusual mutations in both a key catalytic residue and the Fe/S cluster-binding motif. Biochemical analysis of a representative member from Burkholderia insecticola (OYEBi) reveals novel N-methyl-proline demethylation activity, which we hypothesize may play a role in osmotic stress regulation based on genomic neighborhood analysis.

Landscape profiling of PET depolymerases using a natural sequence cluster framework

Enzymes capable of breaking down polymers have been identified from natural sources and developed for industrial use in plastic recycling. However, there are many potential starting points for enzyme optimization that remain unexplored. We generated a landscape of 170 lineages of 1894 polyethylene terephthalate depolymerase (PETase) candidates and performed profiling using sampling approaches with features associated with PET-degrading capabilities. We identified three promising yet unexplored PETase lineages and two potent PETases, Mipa-P and Kubu-P. An engineered variant of Kubu-P outperformed benchmarks in terms of PET depolymerization in harsh environments, such as those with high substrate load and ethylene glycol as the solvent.

Proteome-wide bioinformatic annotation and functional validation of the monotopic phosphoglycosyl transferase superfamily

Phosphoglycosyl transferases (PGTs) are membrane proteins that initiate glycoconjugate biosynthesis by transferring a phospho-sugar moiety from a soluble nucleoside diphosphate sugar to a membrane-embedded polyprenol phosphate acceptor. The centrality of PGTs in complex glycan assembly and the current lack of functional information make these enzymes high-value targets for biochemical investigation. In particular, the small monotopic PGT family is exclusively bacterial and represents the minimal functional unit of the monotopic PGT superfamily. Here, we combine a sequence similarity network analysis with a generalizable, luminescence-based activity assay to probe the substrate specificity of this family of monoPGTs in the bacterial cell-membrane fraction. This strategy allows us to identify specificity on a far more significant scale than previously achievable and correlate preferred substrate specificities with predicted structural differences within the conserved monoPGT fold. Finally, we present the proof-of-concept for a small-scale inhibitor screen (eight nucleoside analogs) with four monoPGTs of diverse substrate specificity, thus building a foundation for future inhibitor discovery initiatives.

Enzymatic synthesis of azide by a promiscuous N-nitrosylase

Azides are energy-rich compounds with diverse representation in a broad range of scientific disciplines, including material science, synthetic chemistry, pharmaceutical science and chemical biology. Despite ubiquitous usage of the azido group, the underlying biosynthetic pathways for its formation remain largely unknown. Here we report the characterization of an enzymatic route for de novo azide construction. We demonstrate that Tri17, a promiscuous ATP- and nitrite-dependent enzyme, catalyses organic azide synthesis through sequential N-nitrosation and dehydration of aryl hydrazines. Through biochemical, structural and computational analyses, we further propose a plausible molecular mechanism for azide synthesis that sets the stage for future biocatalytic applications and biosynthetic pathway engineering.

Genomic Comparisons Revealed the Key Genotypes of Streptomyces sp. CB03234-GS26 to Optimize Its Growth and Relevant Production of Tiancimycins

Strain robustness and titer improvement are major challenges faced in the industrial development of natural products from Streptomyces. Tiancimycins (TNMs) produced by Streptomyces sp. CB03234 are promising anticancer payloads for antibody-drug conjugates, but further development is severely limited by the low titer of TNMs. Despite many efforts to generate various TNMs overproducers, the mechanisms underlying high TNMs production remain to be explored. Herein, genome resequencing and genomic comparisons of different TNMs overproducers were conducted to explore the unique genotypes in CB03234-GS26. Four target genes were selected for further bioinformatic analyses and genetic validations. The results indicated that the inactivation of histidine ammonia-lyase (HAL) showed the most significant effect by blocking the intracellular degradation of histidine to facilitate relevant enzymatic catalysis and thus improve the production of TNMs. Additionally, the potassium/proton antiporter (P/PA) was crucial for intracellular pH homeostasis, and its deficiency severely impaired the alkaline tolerance of the cells. Subsequent pan-genomic analysis suggested that HAL and P/PA are core enzymes that are highly conserved in Streptomyces. Therefore, HAL and P/PA represented novel targets to regulate secondary metabolism and enhance strain robustness and could become potential synthetic biological modules to facilitate development of natural products and strain improvement in Streptomyces.

Evolutionary-Scale Enzymology Enables Biochemical Constant Prediction Across a Multi-Peaked Catalytic Landscape

Quantitatively mapping enzyme sequence-catalysis landscapes remains a critical challenge in understanding enzyme function, evolution, and design. Here, we expand an emerging microfluidic platform to measure catalytic constants-k cat and K M-for hundreds of diverse naturally occurring sequences and mutants of the model enzyme Adenylate Kinase (ADK). This enables us to dissect the sequence-catalysis landscape's topology, navigability, and mechanistic underpinnings, revealing distinct catalytic peaks organized by structural motifs. These results challenge long-standing hypotheses in enzyme adaptation, demonstrating that thermophilic enzymes are not slower than their mesophilic counterparts. Combining the rich representations of protein sequences provided by deep-learning models with our custom high-throughput kinetic data yields semi-supervised models that significantly outperform existing models at predicting catalytic parameters of naturally occurring ADK sequences. Our work demonstrates a promising strategy for dissecting sequence-catalysis landscapes across enzymatic evolution and building family-specific models capable of accurately predicting catalytic constants, opening new avenues for enzyme engineering and functional prediction.

Investigating diversity and similarity between CBM13 modules and ricin-B lectin domains using sequence similarity networks

BACKGROUND

The CBM13 family comprises carbohydrate-binding modules that occur mainly in enzymes and in several ricin-B lectins. The ricin-B lectin domain resembles the CBM13 module to a large extent. Historically, ricin-B lectins and CBM13 proteins were considered completely distinct, despite their structural and functional similarities.

RESULTS

In this data mining study, we investigate structural and functional similarities of these intertwined protein groups. Because of the high structural and functional similarities, and differences in nomenclature usage in several databases, confusion can arise. First, we demonstrate how public protein databases use different nomenclature systems to describe CBM13 modules and putative ricin-B lectin domains. We suggest the introduction of a novel CBM13 domain identifier, as well as the extension of CAZy cross-references in UniProt to guard the distinction between CAZy and non-CAZy entries in public databases. Since similar problems may occur with other lectin families and CBM families, we suggest the introduction of novel CBM InterPro domain identifiers to all existing CBM families. Second, we investigated phylogenetic, nomenclatural and structural similarities between putative ricin-B lectin domains and CBM13 modules, making use of sequence similarity networks. We concluded that the ricin-B/CBM13 superfamily may be larger than initially thought and that several putative ricin-B lectin domains may display CAZyme functionalities, although biochemical proof remains to be delivered.

CONCLUSIONS

Ricin-B lectin domains and CBM13 modules are associated groups of proteins whose database semantics are currently biased towards ricin-B lectins. Revision of the CAZy cross-reference in UniProt and introduction of a dedicated CBM13 domain identifier in InterPro may resolve this issue. In addition, our analyses show that several proteins with putative ricin-B lectin domains show very strong structural similarity to CBM13 modules. Therefore ricin-B lectin domains and CBM13 modules could be considered distant members of a larger ricin-B/CBM13 superfamily.

Identification and Characterization of a Bacterial Periplasmic Solute Binding Protein That Binds l-Amino Acid Amides

Periplasmic solute-binding proteins (SBPs) are key ligand recognition components of bacterial ATP-binding cassette (ABC) transporters that allow bacteria to import nutrients and metabolic precursors from the environment. Periplasmic SBPs comprise a large and diverse family of proteins, of which only a small number have been empirically characterized. In this work, we identify a set of 610 unique uncharacterized proteins within the SBP_bac_5 family that are found in conserved operons comprising genes encoding (i) ABC transport systems and (ii) putative amidases from the FmdA_AmdA family. From these uncharacterized SBP_bac_5 proteins, we characterize a representative periplasmic SBP from Mesorhizobium sp. A09 (MeAmi_SBP) and show that MeAmi_SBP binds l-amino acid amides but not the corresponding l-amino acids. An X-ray crystal structure of MeAmi_SBP bound to l-serinamide highlights the residues that impart distinct specificity for l-amino acid amides and reveals a structural Ca2+ binding site within one of the lobes of the protein. We show that the residues involved in ligand and Ca2+ binding are conserved among the 610 SBPs from experimentally uncharacterized FmdA_AmdA amidase-associated ABC transporter systems, suggesting these homologous systems are also likely to be involved in the sensing, uptake, and metabolism of l-amino acid amides across many Gram-negative nitrogen-fixing soil bacteria. We propose that MeAmi_SBP is involved in the uptake of such solutes to supplement pathways such as the citric acid cycle and the glutamine synthetase-glutamate synthase pathway. This work expands our currently limited understanding of microbial interactions with l-amino acid amides and bacterial nitrogen utilization.

Community dynamics and metagenomic analyses reveal Bacteroidota's role in widespread enzymatic Fucus vesiculosus cell wall degradation

Enzymatic degradation of algae cell wall carbohydrates by microorganisms is under increasing investigation as marine organic matter gains more value as a sustainable resource. The fate of carbon in the marine ecosystem is in part driven by these degradation processes. In this study, we observe the microbiome dynamics of the macroalga Fucus vesiculosus in 25-day-enrichment cultures resulting in partial degradation of the brown algae. Microbial community analyses revealed the phylum Pseudomonadota as the main bacterial fraction dominated by the genera Marinomonas and Vibrio. More importantly, a metagenome-based Hidden Markov model for specific glycosyl hydrolyses and sulphatases identified Bacteroidota as the phylum with the highest potential for cell wall degradation, contrary to their low abundance. For experimental verification, we cloned, expressed, and biochemically characterised two α-L-fucosidases, FUJM18 and FUJM20. While protein structure predictions suggest the highest similarity to a Bacillota origin, protein-protein blasts solely showed weak similarities to defined Bacteroidota proteins. Both enzymes were remarkably active at elevated temperatures and are the basis for a potential synthetic enzyme cocktail for large-scale algal destruction.

Sequence similarity networks bear out hierarchical relationships of green cytochrome P450

Land plants have diversified enzyme families. One of the most prominent is the cytochrome P450 (CYP or CYP450) family. With over 443,000 CYP proteins sequenced across the tree of life, CYPs are ubiquitous in archaea, bacteria, and eukaryotes. Here, we focused on land plants and algae to study the role of CYP diversification. CYPs, acting as monooxygenases, catalyze hydroxylation reactions crucial for specialized plant metabolic pathways, including detoxification and phytohormone production; the CYPome consists of one enormous superfamily that is divided into clans and families. Their evolutionary history speaks of high substrate promiscuity; radiation and functional diversification have yielded numerous CYP families. To understand the evolutionary relationships within the CYPs, we employed sequence similarity network analyses. We recovered distinct clusters representing different CYP families, reflecting their diversified sequences that we link to the prediction of functionalities. Hierarchical clustering and phylogenetic analysis further elucidated relationships between CYP clans, uncovering their shared deep evolutionary history. We explored the distribution and diversification of CYP subfamilies across plant and algal lineages, uncovering novel candidates and providing insights into the evolution of these enzyme families. This identified unexpected relationships between CYP families, such as the link between CYP82 and CYP74, shedding light on their roles in plant defense signaling pathways. Our approach provides a methodology that brings insights into the emergence of new functions within the CYP450 family, contributing to the evolutionary history of plants and algae. These insights can be further validated and implemented via experimental setups under various external conditions.

Microbial sensor variation across biogeochemical conditions in the terrestrial deep subsurface

Microbes can be found in abundance many kilometers underground. While microbial metabolic capabilities have been examined across different geochemical settings, it remains unclear how changes in subsurface niches affect microbial needs to sense and respond to their environment. To address this question, we examined how microbial extracellular sensor systems vary with environmental conditions across metagenomes at different Deep Mine Microbial Observatory (DeMMO) subsurface sites. Because two-component systems (TCSs) directly sense extracellular conditions and convert this information into intracellular biochemical responses, we expected that this sensor family would vary across isolated oligotrophic subterranean environments that differ in abiotic and biotic conditions. TCSs were found at all six subsurface sites, the service water control, and the surface site, with an average of 0.88 sensor histidine kinases (HKs) per 100 genes across all sites. Abundance was greater in subsurface fracture fluids compared with surface-derived fluids, and candidate phyla radiation (CPR) bacteria presented the lowest HK frequencies. Measures of microbial diversity, such as the Shannon diversity index, revealed that HK abundance is inversely correlated with microbial diversity (r2 = 0.81). Among the geochemical parameters measured, HK frequency correlated most strongly with variance in dissolved organic carbon (r2 = 0.82). Taken together, these results implicate the abiotic and biotic properties of an ecological niche as drivers of sensor needs, and they suggest that microbes in environments with large fluctuations in organic nutrients (e.g., lacustrine, terrestrial, and coastal ecosystems) may require greater TCS diversity than ecosystems with low nutrients (e.g., open ocean).IMPORTANCEThe ability to detect extracellular environmental conditions is a fundamental property of all life forms. Because microbial two-component sensor systems convert information about extracellular conditions into biochemical information that controls their behaviors, we evaluated how two-component sensor systems evolved within the deep Earth across multiple sites where abiotic and biotic properties vary. We show that these sensor systems remain abundant in microbial consortia at all subterranean sampling sites and observe correlations between sensor system abundances and abiotic (dissolved organic carbon variation) and biotic (consortia diversity) properties. These results suggest that multiple environmental properties may drive sensor protein evolution and highlight the need for further studies of metagenomic and geochemical data in parallel to understand the drivers of microbial sensor evolution.

New dienelactone hydrolase from microalgae bacterial community-Antibiofilm activity against fish pathogens and potential applications for aquaculture

Biofilms are resistant to many traditional antibiotics, which has led to search for new antimicrobials from different and unique sources. To harness the potential of aquatic microbial resources, we analyzed the meta-omics datasets of microalgae-bacteria communities and mined them for potential antimicrobial and quorum quenching enzymes. One of the most interesting candidates (Dlh3), a dienelactone hydrolase, is a α/β-protein with predicted eight α-helices and eight β-sheets. When it was applied to one of the major fish pathogens, Edwardsiella anguillarum, the biofilm development was reproducibly inhibited by up to 54.5%. The transcriptome dataset in presence of Dlh3 showed an upregulation in functions related to self-defense like active genes for export mechanisms and transport systems. The most interesting point regarding the biotechnological potential for aquaculture applications of Dlh3 are clear evidence of biofilm inhibition and that health and division of a relevant fish cell model (CHSE-214) was not impaired by the enzyme.

Sequence similarity network and protein structure prediction offer insights into the evolution of microbial pathways for ferrous iron oxidation

IMPORTANCE

Microbial Fe(II) oxidation is a crucial process that harnesses and converts the energy available in Fe, contributing significantly to global element cycling. However, there are still many aspects of this process that remain unexplored. In this study, we utilized a combination of comparative genomics, sequence similarity network analysis, and artificial intelligence-driven structure modeling methods to address the lack of structural information on Fe(II) oxidation proteins and offer a comprehensive perspective on the evolution of Fe(II) oxidation pathways. Our findings suggest that several microbial Fe(II) oxidation pathways currently known may have originated within classes Gammaproteobacteria and Betaproteobacteria.

EFI-EST, EFI-GNT, and EFI-CGFP: Enzyme Function Initiative (EFI) Web Resource for Genomic Enzymology Tools

The Enzyme Function Initiative (EFI) provides a web resource with "genomic enzymology" web tools to leverage the protein (UniProt) and genome (European Nucleotide Archive; ENA; https://www.ebi.ac.uk/ena/) databases to assist the assignment of in vitro enzymatic activities and in vivo metabolic functions to uncharacterized enzymes (https://efi.igb.illinois.edu/). The tools enable (1) exploration of sequence-function space in enzyme families using sequence similarity networks (SSNs; EFI-EST), (2) easy access to genome context for bacterial, archaeal, and fungal proteins in the SSN clusters so that isofunctional families can be identified and their functions inferred from genome context (EFI-GNT); and (3) determination of the abundance of SSN clusters in NIH Human Metagenome Project metagenomes using chemically guided functional profiling (EFI-CGFP). We describe enhancements that enable SSNs to be generated from taxonomy categories, allowing higher resolution analyses of sequence-function space; we provide examples of the generation of taxonomy category-specific SSNs.

Deciphering the biosynthesis of a novel lipid in Mycobacterium tuberculosis expands the known roles of the nitroreductase superfamily

Mycobacterium tuberculosis's (Mtb) success as a pathogen is due in part to its sophisticated lipid metabolic programs, both catabolic and biosynthetic. Several of Mtb lipids have specific roles in pathogenesis, but the identity and roles of many are unknown. Here, we demonstrated that the tyz gene cluster in Mtb, previously implicated in resistance to oxidative stress and survival in macrophages, encodes the biosynthesis of acyl-oxazolones. Heterologous expression of tyzA (Rv2336), tyzB (Rv2338c) and tyzC (Rv2337c) resulted in the biosynthesis of C12:0-tyrazolone as the predominant compound, and the C12:0-tyrazolone was identified in Mtb lipid extracts. TyzA catalyzed the N-acylation of l-amino acids, with highest specificity for l-Tyr and l-Phe and lauroyl-CoA (kcat/KM = 5.9 ± 0.8 × 103 M-1s-1). In cell extracts, TyzC, a flavin-dependent oxidase (FDO) of the nitroreductase (NTR) superfamily, catalyzed the O2-dependent desaturation of the N-acyl-L-Tyr produced by TyzA, while TyzB, a ThiF homolog, catalyzed its ATP-dependent cyclization. The substrate preference of TyzB and TyzC appear to determine the identity of the acyl-oxazolone. Phylogenetic analyses revealed that the NTR superfamily includes a large number of broadly distributed FDOs, including five in Mtb that likely catalyze the desaturation of lipid species. Finally, TCA1, a molecule with activity against drug-resistant and persistent tuberculosis, failed to inhibit the cyclization activity of TyzB, the proposed secondary target of TCA1. Overall, this study identifies a novel class of Mtb lipids, clarifies the role of a potential drug target, and expands our understanding of the NTR superfamily.

Oxygen-insensitive nitroreductase E. coli NfsA, but not NfsB, is inhibited by fumarate

Escherichia coli NfsA and NfsB are founding members of two flavoprotein families that catalyze the oxygen-insensitive reduction of nitroaromatics and quinones by NAD(P)H. This reduction is required for the activity of nitrofuran antibiotics and the enzymes have also been proposed for use with nitroaromatic prodrugs in cancer gene therapy and biocatalysis, but the roles of the proteins in vivo in bacteria are not known. NfsA is NADPH-specific whereas NfsB can also use NADH. The crystal structures of E. coli NfsA and NfsB and several analogs have been determined previously. In our crystal trials, we unexpectedly observed NfsA bound to fumarate. We here present the X-ray structure of the E. coli NfsA-fumarate complex and show that fumarate acts as a weak inhibitor of NfsA but not of NfsB. The structural basis of this differential inhibition is conserved in the two protein families and occurs at fumarate concentrations found in vivo, so impacting the efficacy of these proteins.

Unleashing the potential of noncanonical amino acid biosynthesis to create cells with precision tyrosine sulfation

Despite the great promise of genetic code expansion technology to modulate structures and functions of proteins, external addition of ncAAs is required in most cases and it often limits the utility of genetic code expansion technology, especially to noncanonical amino acids (ncAAs) with poor membrane internalization. Here, we report the creation of autonomous cells, both prokaryotic and eukaryotic, with the ability to biosynthesize and genetically encode sulfotyrosine (sTyr), an important protein post-translational modification with low membrane permeability. These engineered cells can produce site-specifically sulfated proteins at a higher yield than cells fed exogenously with the highest level of sTyr reported in the literature. We use these autonomous cells to prepare highly potent thrombin inhibitors with site-specific sulfation. By enhancing ncAA incorporation efficiency, this added ability of cells to biosynthesize ncAAs and genetically incorporate them into proteins greatly extends the utility of genetic code expansion methods.

The 3D-structure, kinetics and dynamics of the E. coli nitroreductase NfsA with NADP+ provide glimpses of its catalytic mechanism

Nitroreductases activate nitroaromatic antibiotics and cancer prodrugs to cytotoxic hydroxylamines and reduce quinones to quinols. Using steady-state and stopped-flow kinetics, we show that the Escherichia coli nitroreductase NfsA is 20-50 fold more active with NADPH than with NADH and that product release may be rate-limiting. The crystal structure of NfsA with NADP+ shows that a mobile loop forms a phosphate-binding pocket. The nicotinamide ring and nicotinamide ribose are mobile, as confirmed in molecular dynamics (MD) simulations. We present a model of NADPH bound to NfsA. Only one NADP+ is seen bound to the NfsA dimers, and MD simulations show that binding of a second NADP(H) cofactor is unfavourable, suggesting that NfsA and other members of this protein superfamily may have a half-of-sites mechanism.

Investigating evolutionary relationships through cluster analysis: A teaching science with big data workshop session

Biochemistry is a data-heavy discipline, yet teaching students to work with large datasets is absent from many undergraduate Biochemistry programs. Ensuring that future generations of students arevbv confident in tackling problems using big data first requires that educators become comfortable teaching big data skills. The activity described herein introduces educators to working with big data and a framework for generating sequence similarity networks using JupyterLab and Python. This article reports a session from the virtual international 2021 IUBMB/ASBMB workshop, "Teaching Science with Big Data."

Structure-function characterization of an aldo-keto reductase involved in detoxification of the mycotoxin, deoxynivalenol

Deoxynivalenol (DON) is a mycotoxin, produced by filamentous fungi such as Fusarium graminearum, that causes significant yield losses of cereal grain crops worldwide. One of the most promising methods to detoxify this mycotoxin involves its enzymatic epimerization to 3-epi-DON. DepB plays a critical role in this process by reducing 3-keto-DON, an intermediate in the epimerization process, to 3-epi-DON. DepB_Rleg from Rhizobium leguminosarum is a member of the new aldo-keto reductase family, AKR18, and it has the unusual ability to utilize both NADH and NADPH as coenzymes, albeit with a 40-fold higher catalytic efficiency with NADPH compared to NADH. Structural analysis of DepB_Rleg revealed the putative roles of Lys-217, Arg-290, and Gln-294 in NADPH specificity. Replacement of these residues by site-specific mutagenesis to negatively charged amino acids compromised NADPH binding with minimal effects on NADH binding. The substrate-binding site of DepB_Rleg is larger than its closest structural homolog, AKR6A2, likely contributing to its ability to utilize a wide range of aldehydes and ketones, including the mycotoxin, patulin, as substrates. The structure of DepB_Rleg also suggests that 3-keto-DON can adopt two binding modes to facilitate 4-pro-R hydride transfer to either the re- or si-face of the C3 ketone providing a possible explanation for the enzyme's ability to convert 3-keto-DON to 3-epi-DON and DON in diastereomeric ratios of 67.2% and 32.8% respectively.

Sequence Similarity Network Analysis Provides Insight into the Temporal and Geographical Distribution of Mutations in SARS-CoV-2 Spike Protein

Severe acute respiratory syndrome-related coronavirus (SARS-CoV-2), which still infects hundreds of thousands of people globally each day despite various countermeasures, has been mutating rapidly. Mutations in the spike (S) protein seem to play a vital role in viral stability, transmission, and adaptability. Therefore, to control the spread of the virus, it is important to gain insight into the evolution and transmission of the S protein. This study deals with the temporal and geographical distribution of mutant S proteins from sequences gathered across the US over a period of 19 months in 2020 and 2021. The S protein sequences are studied using two approaches: (i) multiple sequence alignment is used to identify prominent mutations and highly mutable regions and (ii) sequence similarity networks are subsequently employed to gain further insight and study mutation profiles of concerning variants across the defined time periods and states. Additionally, we tracked the variants using visualizations on geographical maps. The visualizations produced using the Directed Weighted All Nearest Neighbors (DiWANN) networks and maps provided insights into the transmission of the virus that reflect well the statistics reported for the time periods studied. We found that the networks created using DiWANN are superior to commonly used approximate distance networks created using BLAST bitscores. The study offers a richer computational approach to analyze the transmission profile of the prominent S protein mutations in SARS-CoV-2 and can be extended to other proteins and viruses.

HelR is a helicase-like protein that protects RNA polymerase from rifamycin antibiotics

Rifamycin antibiotics such as rifampin are potent inhibitors of prokaryotic RNA polymerase (RNAP) used to treat tuberculosis and other bacterial infections. Although resistance arises in the clinic principally through mutations in RNAP, many bacteria possess highly specific enzyme-mediated resistance mechanisms that modify and inactivate rifamycins. The expression of these enzymes is controlled by a 19-bp cis-acting rifamycin-associated element (RAE). Guided by the presence of RAE sequences, we identify a helicase-like protein, HelR, in Streptomyces venezuelae that confers broad-spectrum rifamycin resistance. We show that HelR also promotes tolerance to rifamycins, enabling bacterial evasion of the toxic properties of these antibiotics. HelR forms a complex with RNAP and rescues transcription inhibition by displacing rifamycins from RNAP, thereby providing resistance by target protection . Furthermore, HelRs are broadly distributed in Actinobacteria, including several opportunistic Mycobacterial pathogens, offering yet another challenge for developing new rifamycin antibiotics.

Tools for computational design and high-throughput screening of therapeutic enzymes

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.

Sequence Conservation Does Not Always Signify a Functional Imperative as Observed in the Nitroreductase Superfamily

Consensus sequences have the potential to help classify the structure and function of proteins and highlight key regions that may contribute to their biological properties. Often, the level of significance will track with the extent of sequence conservation, but this should not be considered universal. Arg and Lys dominate a position adjacent to the N1 and C2 carbonyl of flavin mononucleotide (FMN) bound in the proteins of the nitroreductase superfamily. Although this placement satisfies expectations for stabilizing the reduced form of FMN, the substitution of these residues in three subfamilies promoting distinct reactions demonstrates their importance to catalysis as only modest. Replacing Arg34 with Lys, Gln, or Glu enhances FMN binding to a flavin destructase (BluB) by twofold and diminishes FMN turnover by no more than 25%. Similarly, replacing Lys14 with Arg, Gln, or Glu in a nitroreductase (NfsB) does not perturb the binding of the substrate nitrofurazone. The catalytic efficiency does decrease by 21-fold for the K14Q variant, but no change in the midpoint potential of FMN was observed with any of the variants. Equivalent substitution at Arg38 in iodotyrosine deiodinase (IYD) affects catalysis even more modestly (<10-fold). While the Arg/Lys to Glu substitution inactivates NfsB and IYD, this change also stabilizes one-electron transfer in IYD contrary to predictions based on other classes of flavoproteins. Accordingly, functional correlations developed in certain structural superfamilies may not necessarily translate well to other superfamilies.

Identifying protein function and functional links based on large-scale co-occurrence patterns

Objective

The vast majority of known proteins have not been experimentally tested even at the level of measuring their expression, and the function of many proteins remains unknown. In order to decipher protein function and examine functional associations, we developed "Cliquely", a software tool based on the exploration of co-occurrence patterns.

Computational model

Using a set of more than 23 million proteins divided into 404,947 orthologous clusters, we explored the co-occurrence graph of 4,742 fully sequenced genomes from the three domains of life. Edge weights in this graph represent co-occurrence probabilities. We use the Bron–Kerbosch algorithm to detect maximal cliques in this graph, fully-connected subgraphs that represent meaningful biological networks from different functional categories.

Main results

We demonstrate that Cliquely can successfully identify known networks from various pathways, including nitrogen fixation, glycolysis, methanogenesis, mevalonate and ribosome proteins. Identifying the virulence-associated type III secretion system (T3SS) network, Cliquely also added 13 previously uncharacterized novel proteins to the T3SS network, demonstrating the strength of this approach. Cliquely is freely available and open source. Users can employ the tool to explore co-occurrence networks using a protein of interest and a customizable level of stringency, either for the entire dataset or for a one of the three domains—Archaea, Bacteria, or Eukarya.

Redefining the coenzyme A transferase superfamily with a large set of manually-annotated proteins

The coenzyme A (CoA) transferases are a superfamily of proteins central to the metabolism of acetyl-CoA and other CoA thioesters. They are diverse group, catalyzing over a hundred biochemical reactions and spanning all three domains of life. A deeply rooted idea, proposed two decades ago, is these enzymes fall into three families (I, II, III). Here we find they fall into different families, which we achieve by analyzing all CoA transferases characterized to date. We manually annotated 94 CoA transferases with functional information (including rates of catalysis for 208 reactions) from 97 publications. This represents all enzymes we could find in the primary literature, and it is double the number annotated in four protein databases (BRENDA, KEGG, MetaCyc, UniProt). We found family I transferases are not closely related to each other in terms of sequence, structure, and reactions catalyzed. This family is not even monophyletic. These problems are solved by regrouping the three families into six, including one family with many non-CoA transferases. The problem (and solution) became apparent only by analyzing our large set of manually-annotated proteins. It would have been missed if we had used the small number of proteins annotated in UniProt and other databases. Our work is important to understanding the biology of CoA transferases. It also warns investigators doing phylogenetic analyses of proteins to go beyond information in databases. This article is protected by copyright. All rights reserved.

Biochemical and structural characterization of Haemophilus influenzae nitroreductase in metabolizing nitroimidazoles

Nitroheterocycle antibiotics, particularly 5-nitroimidazoles, are frequently used for treating anaerobic infections. The antimicrobial activities of these drugs heavily rely on the in vivo bioactivation, mainly mediated by widely distributed bacterial nitroreductases (NTRs). However, the bioactivation can also lead to severe toxicities and drug resistance. Mechanistic understanding of NTR-mediated 5-nitroimidazole metabolism can potentially aid addressing these issues. Here, we report the metabolism of structurally diverse nitroimidazole drug molecules by a NTR from a human pathogen Haemophilus influenzae (HiNfsB). Our detailed bioinformatic analysis uncovered that HiNfsB represents a group of unexplored oxygen-insensitive NTRs. Biochemical characterization of the recombinant enzyme revealed that HiNfsB effectively metabolizes ten clinically used nitroimidazoles. Furthermore, HiNfsB generated not only canonical nitroreduction metabolites but also stable, novel dimeric products from three nitroimidazoles, whose structures were proposed based on the results of high resolution MS and tandem MS analysis. X-ray structural analysis of the enzyme coupled with site-directed mutagenesis identified four active site residues important to its catalysis and broad substrate scope. Finally, transient expression of HiNfsB sensitized an E. coli mutant strain to 5-nitroimidazoles under anaerobic conditions. Together, these results advance our understanding of the metabolism of nitroimidazole antibiotics mediated by a new NTR group and reinforce the research on the natural antibiotic resistome for addressing the antibiotic resistance crisis.

The nitroreductase of Haemophilus influenzae metabolizes clinically used nitroimidazoles, generates dimeric metabolites and anaerobically sensitizes an E. coli mutant to antibiotics. We further uncover its biochemical and structural details.

Comparative Genomic Study of Vinyl Chloride Cluster and Description of Novel Species, Mycolicibacterium vinylchloridicum sp. nov

Advanced physicochemical and chemical absorption methods for chlorinated ethenes are feasible but incur high costs and leave traces of pollutants on the site. Biodegradation of such pollutants by anaerobic or aerobic bacteria is emerging as a potential alternative. Several mycobacteria including Mycolicibacterium aurum L1, Mycolicibacterium chubuense NBB4, Mycolicibacterium rhodesiae JS60, Mycolicibacterium rhodesiae NBB3 and Mycolicibacterium smegmatis JS623 have previously been described as assimilators of vinyl chloride (VC). In this study, we compared nucleotide sequence of VC cluster and performed a taxogenomic evaluation of these mycobacterial species. The results showed that the complete VC cluster was acquired by horizontal gene transfer and not intrinsic to the genus Mycobacterium sensu lato. These results also revealed the presence of an additional xcbF1 gene that seems to be involved in Coenzyme M biosynthesis, which is ultimately used in the VC degradation pathway. Furthermore, we suggest for the first time that S/N-Oxide reductase encoding gene was involved in the dissociation of the SsuABC transporters from the organosulfur, which play a crucial role in the Coenzyme M biosynthesis. Based on genomic data, M. aurum L1, M. chubuense NBB4, M. rhodesiae JS60, M. rhodesiae NBB3 and M. smegmatis JS623 were misclassified and form a novel species within the genus Mycobacterium sensu lato. Mycolicibacterium aurum L1^T (CECT 8761^T = DSM 6695^T) was the subject of polyphasic taxonomic studies and showed ANI and dDDH values of 84.7 and 28.5% with its close phylogenetic neighbour, M. sphagni ATCC 33027^T. Phenotypic, chemotaxonomic and genomic data considering strain L1^T (CECT 8761^T = DSM 6695^T) as a type strain of novel species with the proposed name, Mycolicibacterium vinylchloridicum sp. nov.

A shared mechanistic pathway for pyridoxal phosphate-dependent arginine oxidases

The mechanism by which molecular oxygen is activated by the organic cofactor pyridoxal phosphate (PLP) for oxidation reactions remains poorly understood. Recent work has identified arginine oxidases that catalyze desaturation or hydroxylation reactions. Here, we investigate a desaturase from the Pseudoalteromonas luteoviolacea indolmycin pathway. Our work, combining X-ray crystallographic, biochemical, spectroscopic, and computational studies, supports a shared mechanism with arginine hydroxylases, involving two rounds of single-electron transfer to oxygen and superoxide rebound at the 4' carbon of the PLP cofactor. The precise positioning of a water molecule in the active site is proposed to control the final reaction outcome. This proposed mechanism provides a unified framework to understand how oxygen can be activated by PLP-dependent enzymes for oxidation of arginine and elucidates a shared mechanistic pathway and intertwined evolutionary history for arginine desaturases and hydroxylases.

Navigating the amino acid sequence space between functional proteins using a deep learning framework

MOTIVATION

Shedding light on the relationships between protein sequences and functions is a challenging task with many implications in protein evolution, diseases understanding, and protein design. The protein sequence space mapping to specific functions is however hard to comprehend due to its complexity. Generative models help to decipher complex systems thanks to their abilities to learn and recreate data specificity. Applied to proteins, they can capture the sequence patterns associated with functions and point out important relationships between sequence positions. By learning these dependencies between sequences and functions, they can ultimately be used to generate new sequences and navigate through uncharted area of molecular evolution.

RESULTS

This study presents an Adversarial Auto-Encoder (AAE) approached, an unsupervised generative model, to generate new protein sequences. AAEs are tested on three protein families known for their multiple functions the sulfatase, the HUP and the TPP families. Clustering results on the encoded sequences from the latent space computed by AAEs display high level of homogeneity regarding the protein sequence functions. The study also reports and analyzes for the first time two sampling strategies based on latent space interpolation and latent space arithmetic to generate intermediate protein sequences sharing sequential properties of original sequences linked to known functional properties issued from different families and functions. Generated sequences by interpolation between latent space data points demonstrate the ability of the AAE to generalize and produce meaningful biological sequences from an evolutionary uncharted area of the biological sequence space. Finally, 3D structure models computed by comparative modelling using generated sequences and templates of different sub-families point out to the ability of the latent space arithmetic to successfully transfer protein sequence properties linked to function between different sub-families. All in all this study confirms the ability of deep learning frameworks to model biological complexity and bring new tools to explore amino acid sequence and functional spaces.

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.

The structures of E. coli NfsA bound to the antibiotic nitrofurantoin; to 1,4-benzoquinone and to FMN

NfsA is a dimeric flavoprotein that catalyses the reduction in nitroaromatics and quinones by NADPH. This reduction is required for the activity of nitrofuran antibiotics. The crystal structure of free Escherichia coli NfsA and several homologues have been determined previously, but there is no structure of the enzyme with ligands. We present here crystal structures of oxidised E. coli NfsA in the presence of several ligands, including the antibiotic nitrofurantoin. Nitrofurantoin binds with the furan ring, rather than the nitro group that is reduced, near the N5 of the FMN. Molecular dynamics simulations show that this orientation is only favourable in the oxidised enzyme, while potentiometry suggests that little semiquinone is formed in the free protein. This suggests that the reduction occurs by direct hydride transfer from FMNH⁻ to nitrofurantoin bound in the reverse orientation to that in the crystal structure. We present a model of nitrofurantoin bound to reduced NfsA in a viable hydride transfer orientation. The substrate 1,4-benzoquinone and the product hydroquinone are positioned close to the FMN N5 in the respective crystal structures with NfsA, suitable for reaction, but are mobile within the active site. The structure with a second FMN, bound as a ligand, shows that a mobile loop in the free protein forms a phosphate-binding pocket. NfsA is specific for NADPH and a similar conformational change, forming a phosphate-binding pocket, is likely to also occur with the natural cofactor.

Computational approaches to predict protein functional families and functional sites

Understanding the mechanisms of protein function is indispensable for many biological applications, such as protein engineering and drug design. However, experimental annotations are sparse, and therefore, theoretical strategies are needed to fill the gap. Here, we present the latest developments in building functional subclassifications of protein superfamilies and using evolutionary conservation to detect functional determinants, for example, catalytic-, binding- and specificity-determining residues important for delineating the functional families. We also briefly review other features exploited for functional site detection and new machine learning strategies for combining multiple features.

A Comprehensive Phylogenetic Analysis of the Serpin Superfamily

Serine protease inhibitors (serpins) are found in all kingdoms of life and play essential roles in multiple physiological processes. Owing to the diversity of the superfamily, phylogenetic analysis is challenging and prokaryotic serpins have been speculated to have been acquired from Metazoa through horizontal gene transfer due to their unexpectedly high homology. Here, we have leveraged a structural alignment of diverse serpins to generate a comprehensive 6,000-sequence phylogeny that encompasses serpins from all kingdoms of life. We show that in addition to a central “hub” of highly conserved serpins, there has been extensive diversification of the superfamily into many novel functional clades. Our analysis indicates that the hub proteins are ancient and are similar because of convergent evolution, rather than the alternative hypothesis of horizontal gene transfer. This work clarifies longstanding questions in the evolution of serpins and provides new directions for research in the field of serpin biology.

Genome analyses of 174 strains of Mycobacterium tuberculosis provide insight into the evolution of drug resistance and reveal potential drug targets

Mycobacterium tuberculosis is a known human pathogen that causes the airborne infectious disease tuberculosis (TB). Every year TB infects millions of people worldwide. The emergence of multi-drug resistant (MDR), extensively drug resistant (XDR) and totally drug resistant (TDR) M. tuberculosis strains against the first- and second-line anti-TB drugs has created an urgent need for the development and implementation of new drug strategies. In this study, the complete genomes of 174 strains of M. tuberculosis are analysed to understand the evolution of molecular drug target (MDT) genes. Phylogenomic placements of M. tuberculosis strains depicted close association and temporal clustering. Selection pressure analysis by deducing the ratio of non-synonymous to synonymous substitution rates (dN/dS) in 51 MDT genes of the 174 M. tuberculosis strains led to categorizing these genes into diversifying (D, dN/dS>0.70), moderately diversifying (MD, dN/dS=0.35-0.70) and stabilized (S, dN/dS<0.35) genes. The genes rpsL, gidB, pncA and ahpC were identified as diversifying, and Rv0488, kasA, ndh, ethR, ethA, embR and ddn were identified as stabilized genes. Furthermore, sequence similarity networks were drawn that supported these divisions. In the multiple sequence alignments of diversifying and stabilized proteins, previously reported resistance mutations were checked to predict sensitive and resistant strains of M. tuberculosis. Finally, to delineate the potential of stabilized or least diversified genes/proteins as anti-TB drug targets, protein-protein interactions of MDT proteins with human proteins were analysed. We predict that kasA (dN/dS=0.29), a stabilized gene that encodes the most host-interacting protein, KasA, should serve as a potential drug target for the treatment of TB.

Quantifying the taxonomic bias in enzymology

The ongoing biotechnological revolution is rooted in our knowledge of enzymes. However, metagenomics is showing how little we know about Earth's enzyme repertoire. Deep sequencing has revolutionized our view of the tree of life. The genomes of newly‐discovered organisms are replete with novel sequences, emphasizing the trove of enzyme structures and functions waiting to be explored by biochemists. Here, we sought to draw attention to the vastness of the “enzymatic dark matter” within the tree of life by placing enzymological knowledge in the context of phylogeny. We used kinetic parameters from the BRaunschweig ENzyme DAtabase (BRENDA) as our proxy for enzymological knowledge. Mapping 12,677 BRENDA entries onto the phylogenetic tree revealed that 55% of these data were from eukaryotes, even though they are the least diverse part of the tree. At the next taxonomic level, only four of 18 archaeal phyla and 24 of 111 bacterial phyla are represented in the BRENDA dataset. One phylum, the Proteobacteria, accounts for over half of all bacterial entries. Similarly, the supergroup Amorphea, which includes animals and fungi, contains over half the data on eukaryotes. Many major taxonomic groups are notable for their complete absence from BRENDA, including the ultra‐diverse bacterial Candidate Phyla Radiation. At the species level, five mammals (including human) contribute 15% of BRENDA entries. The taxonomic bias in enzymology is strong, but in the era of gene synthesis we now have the tools to address it. Doing so promises to enrich our biochemical understanding of life and uncover powerful new biocatalysts.

EnzymeMiner: automated mining of soluble enzymes with diverse structures, catalytic properties and stabilities

Millions of protein sequences are being discovered at an incredible pace, representing an inexhaustible source of biocatalysts. Despite genomic databases growing exponentially, classical biochemical characterization techniques are time-demanding, cost-ineffective and low-throughput. Therefore, computational methods are being developed to explore the unmapped sequence space efficiently. Selection of putative enzymes for biochemical characterization based on rational and robust analysis of all available sequences remains an unsolved problem. To address this challenge, we have developed EnzymeMiner—a web server for automated screening and annotation of diverse family members that enables selection of hits for wet-lab experiments. EnzymeMiner prioritizes sequences that are more likely to preserve the catalytic activity and are heterologously expressible in a soluble form in Escherichia coli. The solubility prediction employs the in-house SoluProt predictor developed using machine learning. EnzymeMiner reduces the time devoted to data gathering, multi-step analysis, sequence prioritization and selection from days to hours. The successful use case for the haloalkane dehalogenase family is described in a comprehensive tutorial available on the EnzymeMiner web page. EnzymeMiner is a universal tool applicable to any enzyme family that provides an interactive and easy-to-use web interface freely available at https://loschmidt.chemi.muni.cz/enzymeminer/.

Redox Coenzyme F420 Biosynthesis in Thermomicrobia Involves Reduction by Stand-Alone Nitroreductase Superfamily Enzymes

Coenzyme F₄₂₀ is a redox cofactor involved in hydride transfer reactions in archaea and bacteria. Since F₄₂₀-dependent enzymes are attracting increasing interest as tools in biocatalysis, F₄₂₀ biosynthesis is being revisited. While it was commonly accepted for a long time that the 2-phospho-l-lactate (2-PL) moiety of F₄₂₀ is formed from free 2-PL, it was recently shown that phosphoenolpyruvate is incorporated in Actinobacteria and that the C-terminal domain of the FbiB protein, a member of the nitroreductase (NTR) superfamily, converts dehydro-F₄₂₀ into saturated F₄₂₀ Outside the Actinobacteria, however, the situation is still unclear because FbiB is missing in these organisms and enzymes of the NTR family are highly diversified. Here, we show by heterologous expression and in vitro assays that stand-alone NTR enzymes from Thermomicrobia exhibit dehydro-F₄₂₀ reductase activity. Metabolome analysis and proteomics studies confirmed the proposed biosynthetic pathway in Thermomicrobium roseum These results clarify the biosynthetic route of coenzyme F₄₂₀ in a class of Gram-negative bacteria, redefine functional subgroups of the NTR superfamily, and offer an alternative for large-scale production of F₄₂₀ in Escherichia coli in the future.IMPORTANCE Coenzyme F₄₂₀ is a redox cofactor of Archaea and Actinobacteria, as well as some Gram-negative bacteria. Its involvement in processes such as the biosynthesis of antibiotics, the degradation of xenobiotics, and asymmetric enzymatic reductions renders F₄₂₀ of great relevance for biotechnology. Recently, a new biosynthetic step during the formation of F₄₂₀ in Actinobacteria was discovered, involving an enzyme domain belonging to the versatile nitroreductase (NTR) superfamily, while this process remained blurred in Gram-negative bacteria. Here, we show that a similar biosynthetic route exists in Thermomicrobia, although key biosynthetic enzymes show different domain architectures and are only distantly related. Our results shed light on the biosynthesis of F₄₂₀ in Gram-negative bacteria and refine the knowledge about sequence-function relationships within the NTR superfamily of enzymes. Appreciably, these results offer an alternative route to produce F₄₂₀ in Gram-negative model organisms and unveil yet another biochemical facet of this pathway to be explored by synthetic microbiologists.

The physical basis and practical consequences of biological promiscuity

Proteins interact with metabolites, nucleic acids, and other proteins to orchestrate the myriad catalytic, structural and regulatory functions that support life from the simplest microbes to the most complex multicellular organisms. These molecular interactions are often exquisitely specific, but never perfectly so. Adventitious "promiscuous" interactions are ubiquitous due to the thousands of macromolecules and small molecules crowded together in cells. Such interactions may perturb protein function at the molecular level, but as long as they do not compromise organismal fitness, they will not be removed by natural selection. Although promiscuous interactions are physiologically irrelevant, they are important because they can provide a vast reservoir of potential functions that can provide the starting point for evolution of new functions, both in nature and in the laboratory.

Bioinformatic Analysis of the Flavin-Dependent Amine Oxidase Superfamily: Adaptations for Substrate Specificity and Catalytic Diversity

The flavin-dependent amine oxidase (FAO) superfamily consists of over 9000 nonredundant sequences represented in all domains of life. Of the thousands of members identified, only 214 have been functionally annotated to date, and 40 unique structures are represented in the Protein Data Bank. The few functionally characterized members share a catalytic mechanism involving the oxidation of an amine substrate through transfer of a hydride to the FAD cofactor, with differences observed in substrate specificities. Previous studies have focused on comparing a subset of superfamily members. Here, we present a comprehensive analysis of the FAO superfamily based on reaction mechanism and substrate recognition. Using a dataset of 9192 sequences, a sequence similarity network, and subsequently, a genome neighborhood network were constructed, organizing the superfamily into eight subgroups that accord with substrate type. Likewise, through phylogenetic analysis, the evolutionary relationship of subgroups was determined, delineating the divergence between enzymes based on organism, substrate, and mechanism. In addition, using sequences and atomic coordinates of 22 structures from the Protein Data Bank to perform sequence and structural alignments, active-site elements were identified, showing divergence from the canonical aromatic-cage residues to accommodate large substrates. These specificity determinants are held in a structural framework comprising a core domain catalyzing the oxidation of amines with an auxiliary domain for substrate recognition. Overall, analysis of the FAO superfamily reveals a modular fold with cofactor and substrate-binding domains allowing for diversity of recognition via insertion/deletions. This flexibility allows facile evolution of new activities, as shown by reinvention of function between subfamilies.

The EFI Web Resource for Genomic Enzymology Tools: Leveraging Protein, Genome, and Metagenome Databases to Discover Novel Enzymes and Metabolic Pathways

The assignment of functions to uncharacterized proteins discovered in genome projects requires easily accessible tools and computational resources for large-scale, user-friendly leveraging of the protein, genome, and metagenome databases by experimentalists. This article describes the web resource developed by the Enzyme Function Initiative (EFI; accessed at https://efi.igb.illinois.edu/ ) that provides "genomic enzymology" tools ("web tools") for (1) generating sequence similarity networks (SSNs) for protein families (EFI-EST); (2) analyzing and visualizing genome context of the proteins in clusters in SSNs (in genome neighborhood networks, GNNs, and genome neighborhood diagrams, GNDs) (EFI-GNT); and (3) prioritizing uncharacterized SSN clusters for functional assignment based on metagenome abundance (chemically guided functional profiling, CGFP) (EFI-CGFP). The SSNs generated by EFI-EST are used as the input for EFI-GNT and EFI-CGFP, enabling easy transfer of information among the tools. The networks are visualized and analyzed using Cytoscape, a widely used desktop application; GNDs and CGFP heatmaps summarizing metagenome abundance are viewed within the tools. We provide a detailed example of the integrated use of the tools with an analysis of glycyl radical enzyme superfamily (IPR004184) found in the human gut microbiome. This analysis demonstrates that (1) SwissProt annotations are not always correct, (2) large-scale genome context analyses allow the prediction of novel metabolic pathways, and (3) metagenome abundance can be used to identify/prioritize uncharacterized proteins for functional investigation.

A subfamily roadmap of the evolutionarily diverse glycoside hydrolase family 16 (GH16)

Glycoside hydrolase family (GH) 16 comprises a large and taxonomically diverse family of glycosidases and transglycosidases that adopt a common β-jelly-roll fold and are active on a range of terrestrial and marine polysaccharides. Presently, broadly insightful sequence–function correlations in GH16 are hindered by a lack of a systematic subfamily structure. To fill this gap, we have used a highly scalable protein sequence similarity network analysis to delineate nearly 23,000 GH16 sequences into 23 robust subfamilies, which are strongly supported by hidden Markov model and maximum likelihood molecular phylogenetic analyses. Subsequent evaluation of over 40 experimental three-dimensional structures has highlighted key tertiary structural differences, predominantly manifested in active-site loops, that dictate substrate specificity across the GH16 evolutionary landscape. As for other large GH families (i.e. GH5, GH13, and GH43), this new subfamily classification provides a roadmap for functional glycogenomics that will guide future bioinformatics and experimental structure–function analyses. The GH16 subfamily classification is publicly available in the CAZy database. The sequence similarity network workflow used here, SSNpipe, is freely available from GitHub.