Biology subjects

Knight, R.

Publications and source records attributed to Knight, R..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Pangenome comparison of Bacteroides fragilis genomospecies unveil genetic diversity and ecological insights

Bacteroides fragilis is a Gram-negative commensal bacterium commonly found in the human colon that differentiates into two genomospecies termed division I and II. We leverage a comprehensive collection of 694 B. fragilis whole genome sequences and report differential gene abundance to further support the recent proposal that divisions I and II represent separate species. In division I strains, we identify an increased abundance of genes related to complex carbohydrate degradation, colonization, and host niche occupancy, confirming the role of division I strains as gut commensals. In contrast, division II strains display an increased prevalence of plant cell wall degradation genes and exhibit a distinct geographic distribution, primarily originating from Asian countries, suggesting dietary influences. Notably, division II strains have an increased abundance of genes linked to virulence, survival in toxic conditions, and antimicrobial resistance, consistent with a higher incidence of these strains in bloodstream infections. This study provides new evidence supporting a recent proposal for classifying divisions I and II B. fragilis strains as distinct species, and our comparative genomic analysis reveals their niche-specific roles.

genomics↗

bioRxiv2023

Comparative metabolomics and microbiome analysis of Ethanol vs. OMNImet/gene GUT fecal stabilization

Metabolites from feces provide important insights into the functionality of the gut microbiome. As immediate freezing is not always feasible in gut microbiome studies, there is a need for sampling protocols that provide stability of the fecal metabolome and microbiome at room temperature (RT). For this purpose, we investigated the stability of various metabolites and the microbiome (16S ribosomal RNA) in feces collected in 95% ethanol (EtOH) or OMNImet(R)*GUT/ OMNIgene(R)*GUT. To simulate in field-collection scenarios, the samples were stored at different temperatures at varying durations (24h +4{degrees}C, 24h RT, 36h RT, 48h RT, and 7 days RT), and compared to aliquots immediately frozen at -80{degrees}C. We applied several targeted and untargeted metabolomics platforms to measure lipids, polar untargeted metabolites, endocannabinoids, short chain fatty acids (SCFAs), and bile acids (BAs). We found that SCFAs in the non-stabilized samples increased over time, while a stable profile was recorded in sample aliquots stored in 95% EtOH and OMNImet(R)*GUT. When comparing the metabolite levels between fecal aliquots stored at room temperature and at +4{degrees}C, we detected several changes in microbial metabolites, including multiple BAs and SCFAs. Taken together, we found that storing fecal samples at room temperature and stabilizing them in 95% EtOH yielded metabolomic results comparable to flash freezing. We also found that overall composition of the gut microbiome did not vary significantly between different storage types. However, there were notable differences observed in alpha diversity. Taken together, the stability of the metabolome and microbiome in 95 % EtOH provided similar results as the validated commercial collection kits OMNImet(R)*GUT and OMNIgene(R)*GUT, respectively. IMPORTANCEThe analysis of the gut metabolome and microbiome requires the separate collection of fecal specimens using conventional methods or commercial kits. However, these approaches can potentially introduce sampling errors and biases. In addition, the logistical requirements of studying large human cohorts have driven the need for home collection and transport of human fecal specimens at room temperature. By adopting a unified sampling approach at room temperature, we can enhance sampling convenience and practicality, leading to a more precise and comprehensive understanding of gut microbial function. However, the development and applications of such unified sampling systems still face limitations. The results presented in this study aim to address this knowledge gap by investigating the stability of metabolites and the microbiome (16S ribosomal RNA) from fecal samples collected using 95% EtOH, in comparison to well-established commercial collection kits for fecal metabolome (OMNImet(R)*GUT) and microbiome (OMNIgene(R) *GUT) profiling. Additionally, we perform a comparative analysis of various platforms and metabolomic coverage using matrices containing ethanol, evaluating aspects of sensitivity, robustness, and throughput.

biochemistry↗

bioRxiv2023

Time-Informed Dimensionality Reduction for Longitudinal Microbiome Studies

Longitudinal studies are crucial for understanding complex microbiome dynamics and their link to health. We introduce TEMPoral TEnsor Decomposition (TEMPTED), a time-informed dimensionality reduction method for high-dimensional longitudinal data that treats time as a continuous variable, effectively characterizing temporal information and handling varying temporal sampling. TEMPTED captures key microbial dynamics, facilitates beta-diversity analysis, and enhances reproducibility by transferring learned representations to new data. In simulations, it achieves 90% accuracy in phenotype classification, significantly outperforming existing methods. In real data, TEMPTED identifies vaginal microbial markers linked to term and preterm births, demonstrating robust performance across datasets and sequencing platforms.

bioinformatics↗

bioRxiv2023

Deep learning and host variable embedding augment microbiome-based simultaneous detection of multiple diseases

Microbiome has emerged as a promising indicator or predictor of human diseases. However, previous studies typically labeled each specimen as either healthy or with a specific disease, ignoring the prevalence of complications or comorbidities in actual cohorts, which may confound the microbial-disease associations. For instance, a patient may suffer from multiple diseases, making it challenging to detect their health status accurately. Furthermore, host phenotypes such as physiological characteristics and lifestyles can alter the microbiome structure, but this information has not yet been fully utilized in data models. To address these issues, we propose a highly explainable deep learning (DL) method called Meta-Spec. Using a deep neural network (DNN) based approach, it encodes and embeds the refined host variables with microbiome features, enabling the detection of multiple diseases and their correlations simultaneously. Our experiments showed that Meta-Spec outperforms regular machine learning (ML) strategies for multi-label disease screening in several cohorts. More importantly, Meta-Spec can successfully detect comorbidities that are often missed by regular ML approaches. In addition, due to its high interpretability, Meta-Spec captures key factors that shape disease patterns from host variables and microbial members. Hence, these efforts improve the feasibility and sensitivity of microbiome-based disease screening in practical scenarios, representing a significant step towards personalized medicine and better health outcomes.

bioinformatics↗

bioRxiv2023

Jumbo phages are active against extensively-drug-resistant eyedrop-associated Pseudomonas aeruginosa infections.

Antibiotic resistant bacteria present an emerging challenge to human health as the pressure instituted on the microbial world through the liberal use of antibiotics has resulted in their emergence across the globe. Those bacteria that acquire mobile genetic elements such as plasmids are especially concerning because those plasmids may be shared readily with other microbes that then can also become antibiotic resistant. Serious infections have recently been related to contamination of preservative-free eyedrops with extensively drug resistant (XDR) isolates of Pseudomonas aeruginosa, already resulting in three deaths. These drug-resistant isolates cannot be managed with most conventional antibiotics. We sought to identify alternatives to conventional antibiotics for lysis of these XDR isolates, and identified multiple bacteriophages (viruses that attack bacteria) that killed them efficiently. We found both jumbo phages (>200kb in genome size) and non-jumbo phages that were active against these isolates, the former killing more efficiently. Jumbo phages effectively killed the 3 separate XDR P. aeruginosa isolates both on solid and liquid medium. Given the ongoing nature of the XDR P. aeruginosa eyedrop outbreak, the identification of phages active against them provides physicians with several novel potential alternatives for treatment.

microbiology↗

bioRxiv2023

Variability and Bias in Microbiome Metagenomic Sequencing: an Interlaboratory Study Comparing Experimental Protocols

BackgroundSeveral studies have documented the significant impact of methodological choices in microbiome analyses. The myriad of methodological options available complicate the replication of results and generally limit the comparability of findings between independent studies that use differing techniques and measurement pipelines. Here we describe the Mosaic Standards Challenge (MSC), an international interlaboratory study designed to assess the impact of methodological variables on the results. The MSC did not prescribe methods but rather asked participating labs to analyze 7 shared reference samples (5x human stool samples and 2x mock communities) using their standard laboratory methods. To capture the array of methodological variables, each participating lab completed a metadata reporting sheet that included 100 different questions regarding the details of their protocol. The goal of this study was to survey the methodological landscape for microbiome metagenomic sequencing (MGS) analyses and the impact of methodological decisions on metagenomic sequencing results. ResultsA total of 44 labs participated in the MSC by submitting results (16S or WGS) along with accompanying metadata; thirty 16S rRNA gene amplicon datasets and 14 WGS datasets were collected. The inclusion of two types of reference materials (human stool and mock communities) enabled analysis of both MGS measurement variability between different protocols using the biologically-relevant stool samples, and MGS bias with respect to ground truth values using the DNA mixtures. Owing to the compositional nature of MGS measurements, analyses were conducted on the ratio of Firmicutes: Bacteroidetes allowing us to directly apply common statistical methods. The resulting analysis demonstrated that protocol choices have significant effects, including both bias of the MGS measurement associated with a particular methodological choices, as well as effects on measurement robustness as observed through the spread of results between labs making similar methodological choices. In the analysis of the DNA mock communities, MGS measurement bias was observed even when there was general consensus among the participating laboratories. ConclusionThis study was the result of a collaborative effort that included academic, commercial, and government labs. In addition to highlighting the impact of different methodological decisions on MGS result comparability, this work also provides insights for consideration in future microbiome measurement study design.

microbiology↗

bioRxiv2023

Evidence for microbially-mediated tradeoffs between growth and defense throughout coral evolution

Evolutionary tradeoffs between life-history strategies are central to animal evolution. However, because microbes can influence aspects of host physiology, behavior, and resistance to stress or disease, changes in animal-microbial symbioses have the potential to mediate life-history tradeoffs. Scleractinian corals provide a highly biodiverse and data-rich host system to test this idea, made more relevant by increases in coral disease outbreaks as a result of anthropogenic changes to climate and reef ecosystems. Identifying factors that determine coral disease susceptibility has therefore become a focus for reef conservation efforts. Using a comparative approach, we tested if coral microbiomes correlate with disease susceptibility across 425 million years of coral evolution by combining a cross-species coral microbiome survey (the "Global Coral Microbiome Project") with long-term disease prevalence data at multiple sites. Interpreting these data in their phylogenetic context, we show that microbial dominance and composition predict disease susceptibility. We trace this dominance-disease association to a single putatively beneficial bacterial symbiont, Endozoicomonas, whose relative abundance in coral tissue explained 30% of variation in disease susceptibility and 60% of variation in microbiome dominance across 40 coral genera. Conversely, Endozoicomonas abundances in coral tissue strongly correlated with high growth rates. These results demonstrate that the evolution of microbial symbiosis in corals correlates with both disease prevalence and growth rate. Exploration of the mechanistic basis for these findings will be important for our understanding of how microbial symbiosis influences animal life-history tradeoffs, and in efforts to use microbes to increase coral growth or disease resistance in-situ. Significance StatementThe evolution of tropical corals, like that of many organisms, involves tradeoffs in life-history strategy. We sought to test whether microbes influence coral life-history traits. Comparative data from a census of modern coral microbes, combined with long term disease surveys in three regions, provide evidence for a correlation between microbiome structure, growth rate, and disease susceptibility during coral evolution. These trends were driven primarily by changes in the relative abundance of Endozoicomonas in coral tissue microbiomes, suggesting the novel hypothesis that Endozoicomonas may allow corals to grow more quickly at the cost of greater vulnerability to disease. Thus, symbiosis with microbes may be an important aspect of animal life-history strategy.

microbiology↗

bioRxiv2023

Scaling deep phylogenetic embedding to ultra-large reference trees: a tree-aware ensemble approach

Phylogenetic placement of a query sequence on a backbone tree is increasingly used across biomedical sciences to identify the content of a sample from its DNA content. The accuracy of such analyses depends on the density of the backbone tree, making it crucial that placement methods scale to very large trees. Moreover, a new paradigm has been recently proposed to place sequences on the species tree using single-gene data. The goal is to better characterize the samples and to enable combined analyses of marker-gene (e.g., 16S rRNA gene amplicon) and genome-wide data. The recent method DEPP enables performing such analyses using metric learning. However, metric learning is hampered by a need to compute and save a quadratically growing matrix of pairwise distances during training. Thus, DEPP (or any distance-based method) does not scale to more than roughly ten thousand species, a problem that we faced when trying to use our recently released Greengenes2 (GG2) reference tree containing 331,270 species. Scalability problems can be addressed in phylogenetics using divide- and-conquer. However, applying divide- and-conquer to data-hungry machine learning methods needs nuance. This paper explores divide- and-conquer for training ensembles of DEPP models, culminating in a method called C-DEPP that uses carefully crafted techniques to enable quasi-linear scaling while maintaining accuracy. C-DEPP enables placing twenty million 16S fragments on the GG2 reference tree in 41 hours of computation.

bioinformatics↗

bioRxiv2023

Reply to: Caution Regarding the Specificities of Pan-Cancer Microbial Structure

The cancer microbiome field tremendously accelerated following the release of our manuscript nearly three years ago1, including direct validation of our cancer type-specific conclusions in independent, international cohorts2,3 and the tumor microbiomes adoption into the hallmarks of cancer4. Disentangling contamination signals from biological signals is an important consideration for this research field. Therefore, despite numerous, high-impact, peer-reviewed research papers that either validated our conclusions or extended them using data we released2,5-13, we carefully considered criticism raised by Gihawi et al. about potential mishandling of contaminants, batch effects, and machine learning approaches--all of which were central topics in our manuscript. Nonetheless, a close examination of each concern alongside the original manuscript and re-analyses of our published data strongly demonstrates the robustness of the original findings. To remove all doubt, however, we have reproduced all key conclusions from the original manuscript using only overlapping bacterial genera identified in a highly decontaminated, multi-cancer, international cohort (Weizmann Institute of Science, WIS)2, with or without batch correction, and with multiclass machine learning analyses to mitigate class imbalances. Our published pan-cancer mycobiome manuscript3 also affirms these findings using updated, state-of-the-art methods. We also note that every analysis shown here was possible using public data and code that we had already provided.

bioinformatics↗

bioRxiv2023

BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations

Quantifying the differential abundance (DA) of specific taxa among experimental groups in microbiome studies is challenging due to data characteristics (e.g., compositionality, sparsity) and specific study designs (e.g., repeated measures, meta-analysis, cross-over). Here we present BIRDMAn (Bayesian Inferential Regression for Differential Microbiome Analysis), a flexible DA method that can account for microbiome data characteristics and diverse experimental designs. Simulations show that BIRDMAn models are robust to uneven sequencing depth and provide a >20-fold improvement in statistical power over existing methods. We then use BIRDMAn to identify antibiotic-mediated perturbations undetected by other DA methods due to subject-level heterogeneity. Finally, we demonstrate how BIRDMAn can construct state-of-the-art cancer-type classifiers using The Cancer Genome Atlas (TCGA) dataset, with substantial accuracy improvements over random forests and existing DA tools across multiple sequencing centers. Collectively, BIRDMAn extracts more informative biological signals while accounting for study-specific experimental conditions than existing approaches.

bioinformatics↗

bioRxiv2022

Greengenes2 enables a shared data universe for microbiome studies

16S rRNA and shotgun metagenomics studies typically yield different results, usually attributed to biases in PCR amplification of 16S rRNA genes. Here, we introduce Greengenes2 and show that differences in reference phylogeny are more important. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy, and in phenotype effect size when analyzed with the same tree.

bioinformatics↗

bioRxiv2022

Time of Sample Collection Critical for Microbiome Replicability

Although many aspects of microbiome studies have been standardized to improve experimental replicability, none account for how the daily diurnal fluctuations in the gut lumen cause dynamic changes in 16S amplicon sequencing. Here we show that sample collection time affects the conclusions drawn from microbiome studies and are larger than the effect size of a daily experimental intervention or dietary changes. The timing of divergence of the microbiome composition between experimental and control groups are unique to each experiment. Sample collection times as short as only four hours apart lead to vastly different conclusions. Lack of consistency in the time of sample collection may explain poor cross-study replicability in microbiome research. Without looking at other data, the impact on other fields is unknown but potentially significant. One-Sentence SummaryIf we are not controlling for host circadian rhythm time in microbiome studies when performing experiments, it is like trying to measure sea level rise while not knowing that tides or waves exist.

systems biology↗

bioRxiv2022

Integrated multi-cohort analysis of the Parkinson's disease gut metagenome

BackgroundThe gut microbiome is altered in several neurologic disorders including Parkinsons disease (PD). ObjectivesProfile the fecal gut metagenome in PD for alterations in microbial composition, taxon abundance, metabolic pathways, and microbial gene products, and their relationship with disease progression. MethodsShotgun metagenomic sequencing was conducted on 244 stool donors from two independent cohorts in the United States, including individuals with PD (n=48, n=47, respectively), environmental Household Controls (HC, n=29, n=30), and community Population Controls (PC, n=41, n=49). Microbial features consistently altered in PD compared to HC and PC subjects were identified. Data were cross-referenced to public metagenomic datasets from two previous studies in Germany and China to determine generalizable microbiome features. ResultsThe gut microbiome in PD shows significant alterations in community composition. Robust taxonomic alterations include depletion of putative "beneficial" gut commensals Faecalibacterium prausnitzii and Eubacterium and Roseburia species, and increased abundance of Akkermansia muciniphila and Bifidobacterium species. Pathway enrichment analysis and metabolic potential, constructed from microbial gene abundance, revealed disruptions in microbial carbohydrate and lipid metabolism and increased amino acid and nucleotide metabolism. These global gene-level signatures indicate an increased response to oxidative stress, decreased cellular growth and microbial motility, and disrupted inter-community signaling. ConclusionsA metagenomic meta-analysis of PD shows consistent and novel alterations in taxonomic representation, functional metabolic potential, and microbial gene abundance across four independent studies from three continents. These data reveal stereotypic changes in the gut microbiome are a consistent feature of PD, highlighting potential diagnostic and therapeutic avenues for future research.

microbiology↗

bioRxiv2022

Effects of Variation in Urine Sample Storage Conditions on 16S Urogenital Microbiome Analyses

Replicability is a well-established challenge in microbiome research with a variety of contributing factors at all stages, from sample collection to code execution. Here, we focus on voided urine sample storage conditions for urogenital microbiome analysis. Using urine samples collected from 10 healthy adult women, we investigated the microbiome preservation efficacy of AssayAssure(R) Genelock (as opposed to no preservative) under different temperature conditions. We varied temperature over 48 hours in order to examine the impact of conditions samples may experience with home voided urine collection and shipping to a central biorepository. The following common lab and shipping conditions were investigated: -20C, ambient temperature, 4C, a freeze-thaw cycle, and a heat cycle. At 48 hours, all samples were stored at -80C until processing. After generating 16S rRNA gene amplicon sequencing data using the highly sensitive KatharoSeq protocol, we observed individual variation in both alpha and beta diversity metrics below interhuman differences, corroborating reports of individual microbiome variability in other specimen types. While there was no significant difference in beta diversity when comparing AssayAssure(R) Genelock vs. no preservative, we did observe a higher concordance with AssayAssure samples shipped at colder temperatures (-20C and 4C) when compared to the samples shipped at -20C without preservative. Our results indicate that AssayAssure does not introduce a significant amount of microbial bias when used on a range of temperatures but is most effective at colder temperatures. ImportanceThe urogenital microbiome is an understudied yet important human microbiome niche. Research has been stimulated by the relatively recent discovery that urine is not sterile: urinary tract microbes have been linked to health problems including urinary infections, incontinence, and cancer. The quality of life and economic impact of UTIs and urgency incontinence alone are enormous, with $3.5 billion and $82.6 billion respectively spent in the U.S. annually. Given the low biomass of urine, novelty of the field, and well-established replicability bias in microbiome studies, it is critical to study storage conditions on urine samples to minimize microbial biases. Efficient and reliable preservation methods permit home self-sample collection and shipping, increasing the accessibility of larger-scale studies. Here, we examined both buffer and temperature variation effects on 16S rRNA gene amplicon sequencing results from urogenital samples, providing data on the consequences of common storage methods on urogenital microbiome results.

microbiology↗

bioRxiv2022

Microbiome response in an urban river system is dominated by seasonality over wastewater treatment upgrades

Microorganisms such as coliform-forming bacteria are commonly used to assess freshwater quality for drinking and recreational use. However, such organisms do not exist in isolation; they exist within the context of dynamic, interactive microbial communities which vary through space and time. Elucidating spatiotemporal microbial dynamics is imperative for discriminating robust community changes from ephemeral ecological trends, and for improving our overall understanding of the relationship between microbial communities and ecosystem health. We conducted a seven-year (2013-2019) microbial time-series investigation in the Chicago Area Waterways (CAWS): an urban river system which, in 2016, experienced substantial upgrades to disinfection processes at two wastewater reclamation plants (WRPs) that discharge into the CAWS and improved stormwater capture, to improve river water quality and reduce flooding. Using culture-independent and culture-dependent approaches, we compared CAWS microbial ecology before and after the intervention. Examinations of time-resolved beta distances between WRP-adjacent sites showed that community similarity measures were often consistent with the spatial orientation of site locations to one another and to the WRP outfalls. Fecal coliform results suggested that upgrades reduced coliform-associated bacteria in the effluent and the downstream river community. However, examinations of whole community changes through time suggest that the upgrades did little to affect overall riverine community dynamics, which instead were overwhelmingly driven by yearly patterns consistent with seasonality. Such results emphasize the dynamic nature of microbiomes in open environmental systems such as the CAWS, but also suggest that the seasonal oscillations remain consistent even when perturbed. ImportanceThis study presents a systematic effort to combine 16S rRNA gene amplicon sequencing with traditional culture-based methods to evaluate the influence of treatment innovations and systems upgrades on the microbiome of the Chicago Area Waterway System, representing the longest and most comprehensive characterization of the microbiome of an urban waterway yet attempted. We found that the systems upgrades were successful in improving specific water quality measures immediately downstream of wastewater outflows. Additionally, we found that the implementation of the water quality improvement measures to the river system did not disrupt the overall dynamics of the downstream microbial community, which remained heavily influenced by seasonal trends.

microbiology↗

bioRxiv2022

A prebiotic diet modulates microglial states and motor deficits in α-synuclein overexpressing mice

Parkinsons disease (PD) is a movement disorder characterized by neuroinflammation, -synuclein pathology, and neurodegeneration. Most cases of PD are non-hereditary, suggesting a strong role for environmental factors, and it has been speculated that disease may originate in peripheral tissues such as the gastrointestinal (GI) tract before affecting the brain. The gut microbiome is altered in PD and may impact motor and GI symptoms as indicated by animal studies, though mechanisms of gut-brain interactions remain incompletely defined. Intestinal bacteria ferment dietary fibers into short-chain fatty acids, with fecal levels of these molecules differing between PD and healthy controls and in mouse models. Among other effects, dietary microbial metabolites can modulate activation of microglia, brain-resident immune cells implicated in PD. We therefore investigated whether a fiber-rich diet influences microglial function in -synuclein overexpressing (ASO) mice, a preclinical model with PD-like symptoms and pathology. Feeding a prebiotic high-fiber diet attenuates motor deficits and reduces -synuclein aggregation in the substantia nigra of mice. Concomitantly, the gut microbiome of ASO mice adopts a profile correlated with health upon prebiotic treatment, which also reduces microglial activation. Single-cell RNA-seq analysis of microglia from the substantia nigra and striatum uncovers increased pro-inflammatory signaling and reduced homeostatic responses in ASO mice compared to wild-type counterparts on standard diets. However, prebiotic feeding reverses pathogenic microglial states in ASO mice and promotes expansion of protective disease-associated macrophage (DAM) subsets of microglia. Notably, depletion of microglia using a CSF1R inhibitor eliminates the beneficial effects of prebiotics by restoring motor deficits to ASO mice despite feeding a prebiotic diet. These studies uncover a novel microglia-dependent interaction between diet and motor symptoms in mice, findings that may have implications for neuroinflammation and PD.

physiology↗

bioRxiv2022

Scalable power analysis and effect size exploration of microbiome community differences with Evident

Differentiating microbial communities among samples is a major objective in biomedicine. Quantifying the effect size of these differences allows researchers to understand the factors most associated with communities and to optimize the design and clinical resources required to address particular research questions. Here, we present Evident, a package for effect size calculations and power analysis on microbiome data and show that Evident scales to large datasets with numerous metadata covariates.

bioinformatics↗

bioRxiv2022

Sequence-structure-function relationships in the microbial protein universe

Abstract / SummaryFor the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that dont rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict [~]200,000 structures for diverse protein sequences from 1,003 representative genomes1 across the microbial tree of life, and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for shifting the focus from obtaining structures to putting them into context, to transform all branches of biology, including a shift from sequence-based to sequence-structure-function based meta-omics analyses.

bioinformatics↗