Toward mechanistic classification of enzyme functions

Almonacid, Daniel E.; Babbitt, Patricia C.

doi:10.1016/j.cbpa.2011.03.008

Cited by 40 publications

(29 citation statements)

References 59 publications

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“…These proteins share 1) a (␤/␣) 7 ␤-barrel domain (a modified (␤/␣) 8 -barrel (TIM barrel)) containing the active site functional groups that support chemistry and 2) an ␣ϩ␤-capping domain formed from polypeptide segments at the N and C termini that provide many of the determinants for substrate recognition (6). Later, the structural similarity to enolase was recognized, 4 establishing the "enolase superfamily" (7). The substrates for MLE, MR, and enolase are carboxylate anions with a proton on the ␣-carbon.…”

Section: Sequence-structure-function Relationships In Enolase Superfamentioning

confidence: 99%

Divergent Evolution in Enolase Superfamily: Strategies for Assigning Functions

Gerlt

Babbitt

Jacobson

et al. 2012

Journal of Biological Chemistry

131

144

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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...

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Section: Sequence-structure-function Relationships In Enolase Superfamentioning

confidence: 99%

Divergent Evolution in Enolase Superfamily: Strategies for Assigning Functions

Gerlt

Babbitt

Jacobson

et al. 2012

Journal of Biological Chemistry

131

144

View full text Add to dashboard Cite

show abstract

“…Assigning biochemical function is challenging in functionally diverse enzyme superfamilies. 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).…”

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confidence: 99%

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

Lukk

Sakai

Kalyanaraman

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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The rapid advance in genome sequencing presents substantial challenges for protein functional assignment, with half or more of new protein sequences inferred from these genomes having uncertain assignments. The assignment of enzyme function in functionally diverse superfamilies represents a particular challenge, which we address through a combination of computational predictions, enzymology, and structural biology. Here we describe the results of a focused investigation of a group of enzymes in the enolase superfamily that are involved in epimerizing dipeptides. The first members of this group to be functionally characterized were Ala-Glu epimerases in Eschericiha coli and Bacillus subtilis, based on the operon context and enzymological studies; these enzymes are presumed to be involved in peptidoglycan recycling. We have subsequently studied more than 65 related enzymes by computational methods, including homology modeling and metabolite docking, which suggested that many would have divergent specificities;, i.e., they are likely to have different (unknown) biological roles. In addition to the Ala-Phe epimerase specificity reported previously, we describe the prediction and experimental verification of: (i) a new group of presumed Ala-Glu epimerases; (ii) several enzymes with specificity for hydrophobic dipeptides, including one from Cytophaga hutchinsonii that epimerizes D-Ala-D-Ala; and (iii) a small group of enzymes that epimerize cationic dipeptides. Crystal structures for certain of these enzymes further elucidate the structural basis of the specificities. The results highlight the potential of computational methods to guide experimental characterization of enzymes in an automated, large-scale fashion.computational biology | enzymology | protein function

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“…When predicted enzyme functions on our random control gene sets, TRACE could achieve a TOP precision of 36% and AUC value of 93.5%, in which TOP is defined as the proportion of highest-ranked positive control genes, and AUC defined as the value of area under a receiver operating characteristic curve (see Methods and Materials for further explanation). Enzymes were assigned with an EC number according to experimental characterization, rather than sequence similarity alone [27], [28]. Therefore, the high precision achieved by TRACE indicated that it can predict gene function not solely based on sequence similarity.…”

Section: Resultsmentioning

confidence: 99%

Tracing Evolutionary Footprints to Identify Novel Gene Functional Linkages

et al. 2013

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Systematic determination of gene function is an essential step in fully understanding the precise contribution of each gene for the proper execution of molecular functions in the cell. Gene functional linkage is defined as to describe the relationship of a group of genes with similar functions. With thousands of genomes sequenced, there arises a great opportunity to utilize gene evolutionary information to identify gene functional linkages. To this end, we established a computational method (called TRACE) to trace gene footprints through a gene functional network constructed from 341 prokaryotic genomes. TRACE performance was validated and successfully tested to predict enzyme functions as well as components of pathway. A so far undescribed chromosome partitioning-like protein ro03654 of an oleaginous bacteria Rhodococcus sp. RHA1 (RHA1) was predicted and verified experimentally with its deletion mutant showing growth inhibition compared to RHA1 wild type. In addition, four proteins were predicted to act as prokaryotic SNARE-like proteins, and two of them were shown to be localized at the plasma membrane. Thus, we believe that TRACE is an effective new method to infer prokaryotic gene functional linkages by tracing evolutionary events.

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Toward mechanistic classification of enzyme functions

Cited by 40 publications

References 59 publications

Divergent Evolution in Enolase Superfamily: Strategies for Assigning Functions

Divergent Evolution in Enolase Superfamily: Strategies for Assigning Functions

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

Tracing Evolutionary Footprints to Identify Novel Gene Functional Linkages

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