Divergent Evolution in Enolase Superfamily: Strategies for Assigning Functions

Abstract

Nature's strategies for evolving catalytic functions can be deciphered from the information contained in the rapidly expanding protein sequence databases. However, the functions of many proteins in the protein sequence and structure databases are either uncertain (too divergent to assign function based on homology) or unknown (no homologs), thereby limiting the utility of the databases. The mechanistically diverse enolase superfamily is a paradigm for understanding the structural bases for evolution of enzymatic function. We describe strategies for assigning functions to members of the enolase superfamily that should be applicable to other superfamilies. Nature continues to evolve an extraordinarily diverse collection of enzymes.2 The reactions they catalyze and the metabolic pathways in which they participate provide organisms with the ability to thrive in different metabolic niches; they also provide potential for biomedical and chemical applications. To fully understand the importance and implications of this diversity, the challenges are both to provide descriptions of the full range of functional diversity, i.e. molecular and biological functions, and to use those descriptions to understand and exploit natural design principles. However, the number of protein sequences 2 is growing much faster than the number of experimentally based annotations (ExPASy UniProtKB/Swiss-Prot Database). Therefore, novel approaches for reliable functional annotation are required to maximize the utility of the data.In genome sequencing projects, automated methods identify the sequences and then typically annotate their functions by transfer from a homolog using simple pairwise comparisons (1), despite the availability of more sophisticated and orthogonal tools (2). A recent analysis of the annotation error rate revealed that it can be very high, sometimes exceeding 80% for particular monofunctional families (3). Even now, this error rate prevents reliable use of annotations to infer in vitro molecular and in vivo biological functions; the problem will become even more critical as the errors in functional annotation are propagated.Assignment of correct functions to homologous enzymes is confounded because they need not catalyze the same reaction. From a survey of structurally characterized superfamilies, almost 40% are functionally diverse, i.e. different members catalyze reactions with different EC numbers (4). Thus, trivial annotation transfer by sequence homology is often not sufficient to assign function. In mechanistically diverse enzyme superfamilies, all of the homologous enzymes catalyze different reactions using a conserved partial reaction; in functionally distinct enzyme suprafamilies, homologous enzymes catalyze different reactions that do not share any mechanistic attribute (5).This minireview focuses on approaches for functional assignment in the mechanistically diverse enolase superfamily, a paradigm for understanding relationships among sequence, structure, and function in homologous enzymes. The current challenge...

Homology models guide discovery of diverse enzyme specificities among dipeptide epimerases in the enolase superfamily

“…A recent survey by Babbitt and coworkers estimates that there are approximately 275 superfamilies encompassing two or more distinct biochemical functions, representing approximately one-third of the known enzyme universe (5). The functionally diverse enolase superfamily has provided a particularly challenging "model system" for developing methods for predicting and characterizing enzyme specificity (6). To date, more than 20 distinct substrates have been identified for members of the enolase superfamily [see the Structure-Function Linkage Database (SFLD; http://sfld.rbvi.ucsf.edu)].…”

Purification, crystallization and structural elucidation ofD-galactaro-1,4-lactone cycloisomerase fromAgrobacterium tumefaciensinvolved in pectin degradation

“…With the exception of the racemization of mandelate, all known members of the subgroup dehydrate acyclic acid sugars; in both cases the first step in the reaction is the abstraction of a proton to the carboxylate group of the substrate, followed by the stabilization of an enediolate intermediate by a divalent metal cation in the active site. During the subsequent processing of the intermediate, the C3 hydroxyl group departs as a water molecule (Andberg et al, 2012;Gerlt et al, 2011). In the case of A. tumefaciens d-galactaro-1,5-lactone isomerase (AtGCI) the substrate is a lactone; thus, instead of elimination of water from the carbon to the carboxylate the cleavage of the carbon-oxygen bond at this position yields the acyclic diacid ( Fig.…”

Quantitative assessment of protein function prediction programs

“…The methodologies for function prediction from experimental data are still not possible on the high-throughput scale, due to their high cost and slow speed (Godzik et al, 2007;Blaby-Haas and de Crécy-Lagard, 2011;Gerlt et al, 2012). Consequently, there is a gap for high-throughput protein characterization.…”

“…Many protein sequences are already known and are available in public databases (Godzik et al, 2007;Chitale and Kihara, 2011), but the newly discovered sequences require fast and reliable functional annotation to increase data utility in subsequent searches (Godzik et al, 2007;Gerlt et al, 2012). Experimental characterization is still the most reliable way to define the function associated with a protein sequence, but it is a slow, expensive, and time-consuming process to perform for newly discovered sequences.…”