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.

RadicalSAM.org: A Resource to Interpret Sequence-Function Space and Discover New Radical SAM Enzyme Chemistry.

The radical SAM superfamily (RSS), arguably the most functionally diverse enzyme superfamily, is also one of the largest with ~700K members currently in the UniProt database. The vast majority of the members have uncharacterized enzymatic activities and metabolic functions. In this Perspective, we describe RadicalSAM.org, a new web-based resource that enables a user-friendly genomic enzymology strategy to explore sequence-function space in the RSS. The resource attempts to enable identification of isofunctional groups of radical SAM enzymes using sequence similarity networks (SSNs) and the genome context of the bacterial, archaeal, and fungal members provided by genome neighborhood diagrams (GNDs). Enzymatic activities and in vivo functions frequently can be inferred from genome context given the tendency for genes of related function to be clustered. We invite the scientific community to use RadicalSAM.org to (i) guide their experimental studies to discover new enzymatic activities and metabolic functions, (ii) contribute experimentally verified annotations to RadicalSAM.org to enhance the ability to predict novel activities and functions, and (iii) provide suggestions for improving this resource.

Discovery of new enzymatic functions and metabolic pathways using genomic enzymology web tools.

The continuing expansion of protein and genome sequence databases is an opportunity to identify novel enzymes with biotechnological applications. Whether applied to enzymology, chemical biology, systems biology, and microbiology, database mining must be 'user-friendly' so that experimentalists can devise focused strategies to discover the in vitro activities and in vivo functions of uncharacterized enzymes. We developed a suite of genomic enzymology tools (https://efi.igb.illinois.edu/) to (1) generate sequence similarity networks (SSNs) for exploration of sequence-function space in protein families (EFI-EST) and (2) provide genome context for members of protein families (EFI-GNT). Integrated analysis of this complementary information allows to generate testable hypotheses about new functions. After a brief overview of EFI-EST and EFI-GNT, we describe applications that illustrate their use.

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.

'Democratized' genomic enzymology web tools for functional assignment.

The protein databases contain an exponentially growing number of sequences as a result of the recent increase in ease and decrease in cost of genome sequencing. The rate of data accumulation far exceeds the rate of functional studies, producing an increase in genomic 'dark matter', sequences for which no precise and validated function is defined. Publicly accessible, that is 'democratized,' genomic enzymology web tools are essential to leverage the protein and genome databases for discovery of the in vitro activities and in vivo functions of novel enzymes and proteins belonging to the dark matter. In this review, we discuss the use of web tools that have proven successful for functional assignment. We also describe a mechanism for ensuring the capture of published functional data so that the quality of both curated and automated annotations transfer can be improved.