Biology subjects

Robinson, S. L.

Publications and source records attributed to Robinson, S. L..

12 recordsLinked to original sources

bioRxiv2026

EnZight: A Structure-Guided Algorithm to Identify and Prioritize Substitution Hotspots for Enzyme Engineering

Homologous protein structures contain valuable information about tolerated sequence variation. However, translating this information into practical enzyme design strategies remains challenging. Here we present EnZight, a user-friendly web server that integrates homologous structural alignment with intuitive visualization to identify substitution hotspots in protein cores. EnZight exploits structurally aligned homologs to identify positions where the surrounding structural environment is conserved while the residue at the position varies across homologs. This enables prediction of substitutions that preserve fold integrity while modulating function and thermostability. The approach further provides interactive structural outputs that allow users to inspect and prioritize substitutions manually. To validate the use of EnZight, we used a polyurethane-degrading amidase as a proof of concept. We constructed 34 variants, and 97% were successfully expressed, indicating high foldability of the predicted substitutions. Several substitutions improved both catalytic turnover and thermostability, and, importantly, beneficial substitutions combined additively, enabling stepwise accumulation of improvements. The best triple mutant variant exhibited a six-fold increase in catalytic turnover and 2{degrees}C increase in apparent melting temperature. Enhanced activity toward the pharmaceutical micropollutant flutamide further demonstrates EnZight's broad applicability in identifying substitutions that enable enzyme optimization across diverse substrates.

bioengineering↗

bioRxiv2026

Laccase-mediated biotransformation potential for fluorinated compounds by geographically diverse human gut microbiota

The growing prevalence of synthetic organofluorine substances in agrochemicals, food packaging, and consumer products has led to increasing gastrointestinal exposure, with potential consequences for human health. Despite the extreme stability of fluorinated compounds, several microbial pathways for their transformation are known, including by laccases, a type of multicopper oxidase. However, the functionality of laccases in the gut microbiome, a natural contact point between food-associated chemicals and microbial biotransformation pathways, is poorly defined. Through a multi-study analysis of 1578 human gut metagenomes spanning a global gradient from hunter-gatherer societies to industrialized urban populations, we found that laccase-coding gene homologs are widely distributed in the human gut microbiome. We identified a significant association between both the abundance and phylogenetic diversity of laccase homologs with the degree of urbanization. As human gut microbial laccase activity has not been experimentally demonstrated, eight gut metagenome-derived laccases were heterologously expressed and screened for activity with a redox mediator system. Six of the eight laccases demonstrated activity. One of these gut microbial laccases and three previously characterized laccases were then tested for their capacity to deplete 11 different food-associated chemicals. All four effectively depleted the agrochemicals cyflumetofen and fluazinam, as well as the industrial chemical bisphenol AF to a lesser extent. By linking global gut metagenomes with activity assays, this work demonstrates the untapped potential of mining human gut metagenomes for laccases and other microbial enzymes that can actively modify various agricultural and industrial chemicals. ImportanceAs adverse effects of fluorinated compounds on human health are emerging, the responsible enzymes from the human gut microbiome of geographically diverse human cohorts mediating interactions with fluorinated compounds in the gastrointestinal tract remain poorly characterized. In a multi-study analysis of publicly available microbiome sequencing data, we linked the abundance, diversity, and phylogeny of laccases within the gut microbiome to the degree of urbanization of human cohorts, and experimentally demonstrated the ability of these laccases to deplete a range of food-associated fluorochemicals. Another significant contribution of our study is in the integration of rural catchment areas as a quantitative metric of urbanization in gut microbiome metagenomics and enzyme activity surveys.

microbiology↗

bioRxiv2026

An enzyme-level benchmark based on environmental bacterial laccases for predicting contaminant fate in water

Bacterial laccases are widespread multicopper oxidases whose roles in the fate of anthropogenic chemicals in aquatic environments remain poorly understood. Here, we integrate metagenomic analysis, a miniaturized high-throughput assay and machine learning to establish an enzyme-level benchmark for predicting biotransformation of wastewater-relevant trace organic contaminants by laccase-mediator systems. Using a laccase from an ammonia-oxidizing bacterium as a model enzyme, we screened 183 compounds and identified 38 that underwent significant removal. Following phylogenetic analysis of environmental homologs, we expressed and purified two additional laccases from the bacterial methanotrophic phylum Methylmirabilota and an archaeal phylum Thermoproteota, demonstrating the activity of this enzyme family across domains. Graph convolutional network models trained on the dataset achieved up to 78% accuracy in classifying degradable versus persistent chemicals, while quantum-chemical descriptors highlighted key electronic properties governing oxidation. This bottom-up approach to enzyme-chemical interactions establishes a trajectory towards predicting contaminant persistence in engineered and natural waters.

biochemistry↗

bioRxiv2025

PARAS: high-accuracy machine-learning of substrate specificities in nonribosomal peptide synthetases

Nonribosomal peptides are diverse natural products with important applications in medicine and agriculture. Bacterial and fungal genomes contain thousands of nonribosomal peptide biosynthetic gene clusters (BGCs) of unknown function, providing a promising resource for peptide discovery. Core structural features of such peptides can be inferred by predicting the substrate(s) of adenylation (A) domains in nonribosomal peptide synthetases (NRPSs). However, existing approaches to A domain prediction rely on limited datasets and often struggle with domains selecting large substrates or from less-studied taxa. Here, we systematically curate and computationally analyse 3,653 A domains and present two high-accuracy specificity predictors, PARAS and PARASECT. A type of A domain with unusually high L-tryptophan specificity was identified through the application of PARAS, and intact protein mass spectrometry to the corresponding NRPS showed it to direct the production of tryptopeptin-related metabolites in Streptomyces species. Together, these technologies will accelerate the characterisation of novel NRPSs and their metabolic products. PARAS and PARASECT are available at https://paras.bioinformatics.nl. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/631717v2_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@8e48aaorg.highwire.dtl.DTLVardef@144c78dorg.highwire.dtl.DTLVardef@890c11org.highwire.dtl.DTLVardef@1776de9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Variable inhibition of different Legionella species by antagonistic bacteria

The genus Legionella includes opportunistic pathogens inhabiting engineered aquatic ecosystems, where managing their presence and abundance is crucial for public health. In these environments, Legionella interact positively or negatively with multiple members of the microbial communities. Here, we identified bacteria and compounds with Legionella-antagonistic properties. We isolated 212 bacterial colonies from various water sources in Switzerland and screened them for their ability to inhibit one reference strain of L. pneumophila. Ten selected antagonistic isolates were subsequently tested with spot-on-lawn-assays for inhibition towards seven environmental and two clinical isolates of Legionella, representing different species and strains. The antagonists produced highly variable inhibition patterns, highlighting distinct differences in susceptibility among Legionella species, and even strains. Only three isolates, all identified as Pseudomonas lurida, inhibited all Legionella species. Furthermore, we analysed the genomes of the antagonistic bacteria, and identified genes for several probable inhibitory compounds. We specifically found the gene cluster for the biosurfactant viscosin to be uniquely encoded by three Pseudomonas lurida isolates. This compound was subsequently detected in the supernatant of co-cultures inoculated with the antagonists and Legionella. This study provides new insights on the ability of aquatic microorganisms to compete with Legionella in controlled laboratory settings. It also highlights the diversity across and within Legionella species in their resistance to external antagonistic stress, and confirms the anti-Legionella activity of selected biosurfactants. These results can contribute to the understanding of how different species inhabit separate niches in the environment, and expand the discussion around alternative Legionella mitigation strategies.

microbiology↗

bioRxiv2024

Machine learning reveals signatures of promiscuous microbial amidases for micropollutant biotransformations

Organic micropollutants - including pharmaceuticals, personal care products, pesticides and food additives - are prevalent in the environment and have unknown and potentially toxic effects. Humans are a direct source of micropollutants as the majority of pharmaceuticals are primarily excreted through urine. Urine contains its own microbiota with the potential to catalyze micropollutant biotransformations. Amidase signature (AS) enzymes are known for their promiscuous activity in micropollutant biotransformations, but the potential for AS enzymes from the urinary microbiota to transform micropollutants is not known. Moreover, characterization of AS enzymes to identify key chemical and enzymatic features predictive of biotransformation profiles is critical for developing benign-by-design chemicals and micropollutant removal strategies. In this study, we biochemically characterized a new AS enzyme with arylamidase activity from a urine isolate, Lacticaseibacillus rhamnosus, and demonstrated its capability to hydrolyze pharmaceuticals and other micropollutants. To uncover the signatures of AS enzyme-substrate specificity, we then designed a targeted enzyme library consisting of 40 arylamidase homologs from diverse urine isolates and tested it against 17 structurally diverse compounds. We found that 16 out of the 40 enzymes showed activity on at least one substrate and exhibited diverse substrate specificities, with the most promiscuous enzymes active on nine different substrates. Using an interpretable gradient boosting machine learning model, we identified chemical and amino acid features predictive of arylamidase biotransformations. Key chemical features from our substrates included the molecular weight of the amide carbonyl substituent and the number of charges in the molecule. Important amino acid features were found to be located on the protein surface and four predictive residues were located in close proximity of the substrate tunnel entrance. Overall, this work highlights the understudied role of urine-derived microbial arylamidases and contributes to enzyme sequence-structure-substrate-based predictions of micropollutant biotransformations.

biochemistry↗

bioRxiv2024

Enzymatic carbon-fluorine bond cleavage by human gut microbes

Fluorinated compounds are used for agrochemical, pharmaceutical, and numerous industrial applications, resulting in global contamination. In many molecules, fluorine is incorporated to enhance the half-life and improve bioavailability. Fluorinated compounds enter the human body through food, water, and xenobiotics including pharmaceuticals, exposing gut microbes to these substances. The human gut microbiota is known for its xenobiotic biotransformation capabilities, but it was not previously known whether gut microbial enzymes could break carbon-fluorine bonds, potentially altering the toxicity of these compounds. Here, through the development of a rapid, miniaturized fluoride detection assay for whole-cell screening, we discovered active gut microbial defluorinases. We biochemically characterized enzymes from diverse human gut microbial classes including Clostridia, Bacilli, and Coriobacteriia, with the capacity to hydrolyze (di)fluorinated organic acids and a fluorinated amino acid. Whole-protein alanine scanning, molecular dynamics simulations, and chimeric protein design enabled the identification of a disordered C-terminal protein segment involved in defluorination activity. Domain swapping exclusively of the C-terminus conferred defluorination activity to a non-defluorinating dehalogenase. To advance our understanding of the structural and sequence differences between defluorinating and non-defluorinating dehalogenases, we trained machine learning models which identified protein termini as important features. Models trained on 41-amino acid segments from protein C-termini alone predicted defluorination activity with 83% accuracy (compared to 95% accuracy based on full-length protein features). This work is relevant for therapeutic interventions and environmental and human health by uncovering specificity-determining signatures of fluorine biochemistry from the gut microbiome. SignificanceHumans have introduced carbon-fluorine bonds into numerous manufactured compounds, including pharmaceuticals, leading to the formation of toxic fluorinated byproducts. While the human gut microbiota is known for its ability to metabolize drugs, its encoded capacity to break the strong carbon-fluorine chemical bond was previously unknown. Here we discovered that human gut microbial enzymes are capable of cleaving carbon-fluorine bonds. We developed a 96-well colorimetric fluoride assay amenable to bacterial culture-based screening. We additionally conducted whole-protein alanine scanning mutagenesis and identified through machine learning that flexible C-terminal loop residues were predictive of defluorination. Taken in the context of flexible regions of other enzyme families known to perform fluorine chemistry, this work supports using convergent structural features to predict defluorination specificity.

biochemistry↗

bioRxiv2024

Zebrafish use conserved CLR and TLR signaling pathways to respond to fungal PAMPs in zymosan

Pattern recognition receptors (PRRs) such as C-type lectin receptors (CLRs) and Toll-like receptors (TLRs) are used by hosts to recognize pathogen-associated molecular patterns (PAMPs) in microorganisms and to initiate innate immune responses. While PRRs exist across invertebrate and vertebrate species, the functional homology of many of these receptors is still unclear. In this study, we investigate the innate immune response of zebrafish larvae to zymosan, a {beta}-glucan-containing particle derived from fungal cell walls. Macrophages and neutrophils robustly respond to zymosan and are required for zymosan-induced activation of the NF-{kappa}B transcription factor. Full activation of NF-{kappa}B in response to zymosan depends on Card9/Syk and Myd88, conserved CLR and TLR adaptor proteins, respectively. Two putative CLRs, Clec4c and Sclra, are both required for maximal sensing of zymosan and NF-{kappa}B activation. Altogether, we identify conserved PRRs and PRR signaling pathways in larval zebrafish that promote recognition of fungal PAMPs. These results inform modeling of human fungal infections in zebrafish and increase our knowledge of the evolution and conservation of PRR pathways in vertebrates.

immunology↗

bioRxiv2023

Disentangling abiotic and biotic effects of treated wastewater on stream biofilm resistomes enables the discovery of a new planctomycete beta-lactamase

BackgroundAntibiotic resistance, which is mediated by environmental reservoirs, poses a threat to human and animal health. Aquatic biofilms impacted by treated wastewater (WW) are known environmental reservoirs for antibiotic resistance, however the specific influence of biotic factors and abiotic factors from WW on the abundance of antibiotic resistance genes (ARGs) within aquatic biofilms remains unclear. Additionally, experimental evidence is limited as to whether genes with low sequence similarity to reference ARGs actually encode for functional ARGs, particularly within complex aquatic microbial communities. ResultsTo disentangle the effects of abiotic and biotic factors on ARG abundances, natural biofilms were previously grown in flume systems with different proportions of stream water and either ultrafiltered or nonultrafiltered WW. In this study, we conducted deep shotgun metagenomic sequencing of 75 biofilm, stream, and WW samples from these flume systems and compared the taxonomic and functional microbiome and resistome composition. Statistical analysis revealed an alignment of the resistome and microbiome composition and a significant association with experimental treatment. Several ARG classes exhibited an increase in metagenomic abundances in biofilms grown with increasing percentages of nonultrafiltered WW. In contrast, sulfonamide and BEL family beta-lactamase ARGs showed greater abundances in biofilms grown in ultrafiltered WW compared to nonultrafiltered WW. Overall, our results pointed toward the dominance of biotic factors over abiotic factors in determining ARG abundances in WW-impacted stream biofilms and suggested gene family-specific mechanisms for ARGs which exhibited divergent abundance patterns. To investigate one of these specific ARG families experimentally, we biochemically characterized a new beta-lactamase from the Planctomycetota (Phycisphaeraceae). This beta-lactamase displayed activity in the cleavage of cephalosporin analog despite sharing low sequence identity with known ARGs. ConclusionsThis discovery of a functional planctomycete beta-lactamase ARG is noteworthy, not only because it was the first beta-lactamase to be biochemically characterized from this phylum, but also because it was not detected by standard homology-based ARG tools. In summary, this study conducted metagenomic analysis on the relative importance of biotic and abiotic factors in the context of WW discharge and their impact on both known and new ARGs in aquatic biofilms.

microbiology↗

bioRxiv2023

Substrate promiscuity of xenobiotic-transforming hydrolases from stream biofilms impacted by treated wastewater

Organic contaminants enter aquatic ecosystems from various sources, including wastewater treatment plant effluent. Freshwater biofilms play a major role in the removal of organic contaminants from receiving water bodies, but knowledge of the molecular mechanisms driving contaminant biotransformations in complex stream biofilm (periphyton) communities remains limited. Previously, we demonstrated that biofilms in experimental flume systems grown at higher ratios of treated wastewater (WW) to stream water displayed an increased biotransformation potential for a number of organic contaminants. We identified a positive correlation between WW percentage and biofilm biotransformation rates for the widely-used insect repellent, N,N-diethyl-meta-toluamide (DEET). Here, we conducted deep shotgun sequencing of flume biofilms and identified a positive correlation between WW percentage and metagenomic read abundances of DEET hydrolase (DH) homologs. To test the causality of this association, we constructed a targeted metagenomic library of DH homologs from flume biofilms. We screened our complete metagenomic library for activity with four different substrates and a subset thereof with 183 WW-related organic compounds. The majority of active hydrolases in our library preferred aliphatic and aromatic ester substrates while, remarkably, only a single reference enzyme was capable of DEET hydrolysis. Of the 626 total enzyme-substrate combinations tested, approximately 5% were active enzyme-substrate pairs. Metagenomic DH family homologs revealed a broad substrate promiscuity spanning 22 different compounds when summed across all enzymes tested. We biochemically characterized the most promiscuous and active enzymes identified based on metagenomic analysis from uncultivated Rhodospirillaceae and Planctomycetaceae. In addition to characterizing new DH family enzymes, we exemplified a framework for linking metagenome-guided hypothesis generation with experimental validation. Overall, this study expands the scope of known enzymatic contaminant biotransformations for metagenomic hydrolases from WW-receiving stream biofilm communities. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/559296v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1b5cbaforg.highwire.dtl.DTLVardef@4f73f7org.highwire.dtl.DTLVardef@467d16org.highwire.dtl.DTLVardef@1be598d_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMetagenomic DEET hydrolase abundances higher in biofilms grown in treated wastewater. C_LIO_LIEleven out of 64 metagenomic hydrolases tested exhibited hydrolysis activity. C_LIO_LIRelated enzymes in a single family of DEET hydrolases biotransform 20+ contaminants. C_LIO_LIReference DEET hydrolase shows a substrate preference for benzamide moieties. C_LIO_LI True DEET hydrolases are in low abundance even in biofilms that degrade DEET. C_LI

biochemistry↗

bioRxiv2021

Uncharted biosynthetic potential of the ocean microbiome

Microbes are phylogenetically and metabolically diverse. Yet capturing this diversity, assigning functions to host organisms and exploring the biosynthetic potential in natural environments remains challenging. We reconstructed >25,000 draft genomes, including from >2,500 uncharacterized species, from globally-distributed ocean microbial communities, and combined them with [~]10,000 genomes from cultivated and single cells. Mining this resource revealed [~]40,000 putative biosynthetic gene clusters (BGCs), many from unknown phylogenetic groups. Among these, we discovered Candidatus Eudoremicrobiaceae as one of the most biosynthetically diverse microbes detected to date. Discrete transcriptional states structuring natural populations were associated with a potentially niche-partitioning role for BGC products. Together with the characterization of the first Eudoremicrobiaceae natural product, this study demonstrates how microbiomics enables prospecting for candidate bioactive compounds in underexplored microbes and environments.

microbiology↗

bioRxiv2019

Global analysis of adenylate-forming enzymes reveals β-lactone biosynthesis pathway in pathogenic Nocardia

Enzymes that cleave ATP to activate carboxylic acids play essential roles in primary and secondary metabolism in all domains of life. Class I adenylate-forming enzymes share a conserved structural fold but act on a wide range of substrates to catalyze reactions involved in bioluminescence, nonribosomal peptide biosynthesis, fatty acid activation, and {beta}-lactone formation. Despite their metabolic importance, the substrates and catalytic functions of the vast majority of adenylate-forming enzymes are unknown without tools available to accurately predict them. Given the crucial roles of adenylate-forming enzymes in biosynthesis, this also severely limits our ability to predict natural product structures from biosynthetic gene clusters. Here we used machine learning to predict adenylate-forming enzyme function and substrate specificity from protein sequence. We built a web-based predictive tool and used it to comprehensively map the biochemical diversity of adenylate-forming enzymes across >50,000 candidate biosynthetic gene clusters in bacterial, fungal, and plant genomes. Ancestral enzyme reconstruction and sequence similarity networking revealed a hub topology suggesting radial divergence of the adenylate-forming superfamily from a core enzyme scaffold most related to contemporary aryl-CoA ligases. Our classifier also predicted {beta}-lactone synthetases in novel biosynthetic gene clusters conserved across >90 different strains of Nocardia. To test our computational predictions, we purified a candidate {beta}-lactone synthetase from Nocardia brasiliensis and reconstituted the biosynthetic pathway in vitro to link the gene cluster to the {beta}-lactone natural product, nocardiolactone. We anticipate our machine learning approach will aid in functional classification of enzymes and advance natural product discovery.

biochemistry↗