AGeNNT: annotation of enzyme families by means of refined neighborhood networks

Abstract

BackgroundLarge enzyme families may contain functionally diverse members that give rise to clusters in a sequence similarity network (SSN). In prokaryotes, the genome neighborhood of a gene-product is indicative of its function and thus, a genome neighborhood network (GNN) deduced for an SSN provides strong clues to the specific function of enzymes constituting the different clusters. The Enzyme Function Initiative (http://enzymefunction.org/) offers services that compute SSNs and GNNs.ResultsWe have implemented AGeNNT that utilizes these services, albeit with datasets purged with respect to unspecific protein functions and overrepresented species. AGeNNT generates refined GNNs (rGNNs) that consist of cluster-nodes representing the sequences under study and Pfam-nodes representing enzyme functions encoded in the respective neighborhoods. For cluster-nodes, AGeNNT summarizes the phylogenetic relationships of the contributing species and a statistic indicates how unique nodes and GNs are within this rGNN. Pfam-nodes are annotated with additional features like GO terms describing protein function. For edges, the coverage is given, which is the relative number of neighborhoods containing the considered enzyme function (Pfam-node). AGeNNT is available at https://github.com/kandlinf/agennt.ConclusionsAn rGNN is easier to interpret than a conventional GNN, which commonly contains proteins without enzymatic function and overly specific neighborhoods due to phylogenetic bias. The implemented filter routines and the statistic allow the user to identify those neighborhoods that are most indicative of a specific metabolic capacity. Thus, AGeNNT facilitates to distinguish and annotate functionally different members of enzyme families.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1689-6) contains supplementary material, which is available to authorized users.

Genomic Enzymology: Web Tools for Leveraging Protein Family Sequence–Function Space and Genome Context to Discover Novel Functions

“…71 Clusters that contain orthologues, identified when they share the same genome neighbors, can be distinguished from clusters that have different genome contexts. An SSN is submitted to the EFI-GNT web tool.…”

Conserved genomic neighborhood is a strong but no perfect indicator for a direct interaction of microbial gene products

“…We chose a ± 10 neighborhood (for justification see Methods) and created for each SSN, i. e. rep_node file, a refined genome neighborhood network (rGNN) by utilizing a modified version of AGeNNT [21]. In an rGNN, the neighboring gene products are represented by the Pfam families ( pf_nodes ) they belong to and the pf_node -specific SeqCount values indicate the number of neighborhoods encoding this protein function.…”

“…The SSNs were uploaded to the EFI server for the generation of GNNs. “Raw” GNNs were converted to rGNNs by means of an updated version of AGeNNT [21] that was adapted to the current EFI interfaces. We oriented ourselves on the architecture of the E. coli genome and a systematic analysis performed during the design of AGeNNT [21] to determine the window size of the genomic neighborhood to be analyzed.…”

“…“Raw” GNNs were converted to rGNNs by means of an updated version of AGeNNT [21] that was adapted to the current EFI interfaces. We oriented ourselves on the architecture of the E. coli genome and a systematic analysis performed during the design of AGeNNT [21] to determine the window size of the genomic neighborhood to be analyzed. Approximately 80% of all E. coli transcription units have fewer than five genes and 80% of all directons, i. e., genes transcribed in the same direction with no intervening one transcribed in the opposite direction, have fewer than ten genes [31].…”

“…Approximately 80% of all E. coli transcription units have fewer than five genes and 80% of all directons, i. e., genes transcribed in the same direction with no intervening one transcribed in the opposite direction, have fewer than ten genes [31]. Moreover, a systematic screening indicated that ± 10 neighborhoods are best suited to identify gene clusters [21]. Thus, we selected a ± 10 neighborhood as default.…”