Superfamily and family analyses provide an effective tool for the functional classification of proteins, but must be automated for use on large datasets. We describe a 'gold standard' set of enzyme superfamilies, clustered according to specific sequence, structure, and functional criteria, for use in the validation of family and superfamily clustering methods. The gold standard set represents four fold classes and differing clustering difficulties, and includes five superfamilies, 91 families, 4,887 sequences and 282 structures.

Although the universe of protein structures is vast, these innumerable structures can be categorized into a finite number of folds. New functions commonly evolve by elaboration of existing scaffolds, for example, via domain insertions. Thus, understanding structural diversity of a protein fold evolving via domain insertions is a fundamental challenge. The haloalkanoic dehalogenase superfamily serves as an excellent model system wherein a variable cap domain accessorizes the ubiquitous Rossmann-fold core domain. Here, we determine the impact of the cap-domain insertion on the sequence and structure divergence of the core domain. Through quantitative analysis on a unique dataset of 154 core-domain-only and cap-domain-only structures, basic principles of their evolution have been uncovered. The relationship between sequence and structure divergence of the core domain is shown to be monotonic and independent of the corresponding type of domain insert, reflecting the robustness of the Rossmann fold to mutation. However, core domains with the same cap type share greater similarity at the sequence and structure levels, suggesting interplay between the cap and core domains. Notably, results reveal that the variance in structure maps to α-helices flanking the central β-sheet and not to the domain-domain interface. Collectively, these results hint at intramolecular coevolution where the fold diverges differentially in the context of an accessory domain, a feature that might also apply to other multidomain superfamilies.

The radical SAM superfamily contains over 100,000 homologous enzymes that catalyze a remarkably broad range of reactions required for life, including metabolism, nucleic acid modification, and biogenesis of cofactors. While the highly conserved SAM-binding motif responsible for formation of the key 5-deoxyadenosyl radical intermediate is a key structural feature that simplifies identification of superfamily members, our understanding of their structure-function relationships is complicated by the modular nature of their structures, which exhibit varied and complex domain architectures. To gain new insight about these relationships, we classified the entire set of sequences into similarity-based subgroups that could be visualized using sequence similarity networks. This superfamily-wide analysis reveals important features that had not previously been appreciated from studies focused on one or a few members. Functional information mapped to the networks indicates which members have been experimentally or structurally characterized, their known reaction types, and their phylogenetic distribution. Despite the biological importance of radical SAM chemistry, the vast majority of superfamily members have never been experimentally characterized in any way, suggesting that many new reactions remain to be discovered. In addition to 20 subgroups with at least one known function, we identified additional subgroups made up entirely of sequences of unknown function. Importantly, our results indicate that even general reaction types fail to track well with our sequence similarity-based subgroupings, raising major challenges for function prediction for currently identified and new members that continue to be discovered. Interactive similarity networks and other data from this analysis are available from the Structure-Function Linkage Database.

The radical SAM superfamily contains over 100,000 homologous enzymes that catalyze a remarkably broad range of reactions required for life, including metabolism, nucleic acid modification, and biogenesis of cofactors. While the highly conserved SAM-binding motif responsible for formation of the key 5'-deoxyadenosyl radical intermediate is a key structural feature that simplifies identification of superfamily members, our understanding of their structure-function relationships is complicated by the modular nature of their structures, which exhibit varied and complex domain architectures. To gain new insight about these relationships, we classified the entire set of sequences into similarity-based subgroups that could be visualized using sequence similarity networks. This superfamily-wide analysis reveals important features that had not previously been appreciated from studies focused on one or a few members. Functional information mapped to the networks indicates which members have been experimentally or structurally characterized, their known reaction types, and their phylogenetic distribution. Despite the biological importance of radical SAM chemistry, the vast majority of superfamily members have never been experimentally characterized in any way, suggesting that many new reactions remain to be discovered. In addition to 20 subgroups with at least one known function, we identified additional subgroups made up entirely of sequences of unknown function. Importantly, our results indicate that even general reaction types fail to track well with our sequence similarity-based subgroupings, raising major challenges for function prediction for currently identified and new members that continue to be discovered. Interactive similarity networks and other data from this analysis are available from the Structure-Function Linkage Database.

The identification of novel metabolites and the characterization of their biological functions are major challenges in biology. X-ray crystallography can reveal unanticipated ligands that persist through purification and crystallization. These adventitious protein-ligand complexes provide insights into new activities, pathways and regulatory mechanisms. We describe a new metabolite, carboxy-S-adenosyl-l-methionine (Cx-SAM), its biosynthetic pathway and its role in transfer RNA modification. The structure of CmoA, a member of the SAM-dependent methyltransferase superfamily, revealed a ligand consistent with Cx-SAM in the catalytic site. Mechanistic analyses showed an unprecedented role for prephenate as the carboxyl donor and the involvement of a unique ylide intermediate as the carboxyl acceptor in the CmoA-mediated conversion of SAM to Cx-SAM. A second member of the SAM-dependent methyltransferase superfamily, CmoB, recognizes Cx-SAM and acts as a carboxymethyltransferase to convert 5-hydroxyuridine into 5-oxyacetyl uridine at the wobble position of multiple tRNAs in Gram-negative bacteria, resulting in expanded codon-recognition properties. CmoA and CmoB represent the first documented synthase and transferase for Cx-SAM. These findings reveal new functional diversity in the SAM-dependent methyltransferase superfamily and expand the metabolic and biological contributions of SAM-based biochemistry. These discoveries highlight the value of structural genomics approaches in identifying ligands within the context of their physiologically relevant macromolecular binding partners, and in revealing their functions.

Assigning valid functions to proteins identified in genome projects is challenging: overprediction and database annotation errors are the principal concerns. We and others are developing computation-guided strategies for functional discovery with metabolite docking to experimentally derived or homology-based three-dimensional structures. Bacterial metabolic pathways often are encoded by genome neighbourhoods (gene clusters and/or operons), which can provide important clues for functional assignment. We recently demonstrated the synergy of docking and pathway context by predicting the intermediates in the glycolytic pathway in Escherichia coli. Metabolite docking to multiple binding proteins and enzymes in the same pathway increases the reliability of in silico predictions of substrate specificities because the pathway intermediates are structurally similar. Here we report that structure-guided approaches for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster allowed the correct prediction of the in vitro activity of a structurally characterized enzyme of unknown function (PDB 2PMQ), 2-epimerization of trans-4-hydroxy-L-proline betaine (tHyp-B) and cis-4-hydroxy-D-proline betaine (cHyp-B), and also the correct identification of the catabolic pathway in which Hyp-B 2-epimerase participates. The substrate-liganded pose predicted by virtual library screening (docking) was confirmed experimentally. The enzymatic activities in the predicted pathway were confirmed by in vitro assays and genetic analyses; the intermediates were identified by metabolomics; and repression of the genes encoding the pathway by high salt concentrations was established by transcriptomics, confirming the osmolyte role of tHyp-B. This study establishes the utility of structure-guided functional predictions to enable the discovery of new metabolic pathways.

Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins in 12 families in the PRS that represent ∼85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.

Protein function identification remains a significant problem. Solving this problem at the molecular functional level would allow mechanistic determinant identification-amino acids that distinguish details between functional families within a superfamily. Active site profiling was developed to identify mechanistic determinants. DASP and DASP2 were developed as tools to search sequence databases using active site profiling. Here, TuLIP (Two-Level Iterative clustering Process) is introduced as an iterative, divisive clustering process that utilizes active site profiling to separate structurally characterized superfamily members into functionally relevant clusters. Underlying TuLIP is the observation that functionally relevant families (curated by Structure-Function Linkage Database, SFLD) self-identify in DASP2 searches; clusters containing multiple functional families do not. Each TuLIP iteration produces candidate clusters, each evaluated to determine if it self-identifies using DASP2. If so, it is deemed a functionally relevant group. Divisive clustering continues until each structure is either a functionally relevant group member or a singlet. TuLIP is validated on enolase and glutathione transferase structures, superfamilies well-curated by SFLD. Correlation is strong; small numbers of structures prevent statistically significant analysis. TuLIP-identified enolase clusters are used in DASP2 GenBank searches to identify sequences sharing functional site features. Analysis shows a true positive rate of 96%, false negative rate of 4%, and maximum false positive rate of 4%. F-measure and performance analysis on the enolase search results and comparison to GEMMA and SCI-PHY demonstrate that TuLIP avoids the over-division problem of these methods. Mechanistic determinants for enolase families are evaluated and shown to correlate well with literature results.

The funding section was mistakenly not included in this paper. The article has now been updated to include this section. The publisher apologises for this error.