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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 Structure-Function Linkage Database (SFLD) is a web-accessible database designed to link enzyme sequence, structure and molecular function (Akiva et al., 2014; Pegg et al., 2006). Within the database, enzyme sequences are classified hierarchically into superfamilies, subgroups, and families (Figure 2.10.1). At the top of the hierarchy, distantly related enzymes within the same superfamily catalyze different overall reactions but are linked by a pattern of conserved amino acid residues in the active site that mediate a common chemical capability such as a partial reaction (Gerlt and Babbitt, 2001). At the bottom of the hierarchy, enzymes within the same family exhibit more sequence and structural similarities than proteins at the superfamily or subgroup level, and catalyze the same overall reaction via the same mechanism, mediated by a common set of catalytic residues. Figure 2.10.1 SFLD classification hierarchy. Sequences of unknown function are classified at a given level of the hierarchy only if sufficient evidence exists to support that classification. A sequence can be a member of a superfamily or subgroup even if its specific ... The SFLD also provides sequence similarity networks, useful for providing large-scale summaries of the relationships within large groups of related enzymes, and for making hypotheses regarding the function of uncharacterized proteins. Such hypotheses can be further investigated using SFLD data and tools, for example, the mapping of specific chemical capabilities to specific sequence and structural motifs. (See Basic Protocol.) Queries are currently limited to twelve superfamilies in the core SFLD (networks and detailed curation information available) and thirty-five additional superfamilies in the extended SFLD (networks and light curation information available). Additional superfamilies will be added as they can be curated.