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

Tiit Lukk, Ayano Sakai, Chakrapani Kalyanaraman, Shoshana D. Brown, Heidi J. Imker, Ling Song, Alexander A. Fedorov, Elena V. Fedorov, Rafael Toro, Brandan Hillerich, Ronald Seidel, Yury Patskovsky, Matthew W. Vetting, Satish K. Nair, Patricia C. Babbitt, Steven C. Almo, John A. Gerlt, Matthew P. Jacobson

Research output: Contribution to journalArticle

44 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)4122-4127
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume109
Issue number11
DOIs
StatePublished - Mar 13 2012

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Racemases and Epimerases
Phosphopyruvate Hydratase
Dipeptides
Enzymes
alanylglutamic acid
Cytophaga
Genome
Peptidoglycan
Recycling
Operon
Bacillus subtilis
Proteins

Keywords

  • Computational biology
  • Enzymology
  • Protein function

ASJC Scopus subject areas

  • General

Cite this

Homology models guide discovery of diverse enzyme specificities among dipeptide epimerases in the enolase superfamily. / Lukk, Tiit; Sakai, Ayano; Kalyanaraman, Chakrapani; Brown, Shoshana D.; Imker, Heidi J.; Song, Ling; Fedorov, Alexander A.; Fedorov, Elena V.; Toro, Rafael; Hillerich, Brandan; Seidel, Ronald; Patskovsky, Yury; Vetting, Matthew W.; Nair, Satish K.; Babbitt, Patricia C.; Almo, Steven C.; Gerlt, John A.; Jacobson, Matthew P.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 109, No. 11, 13.03.2012, p. 4122-4127.

Research output: Contribution to journalArticle

Lukk, T, Sakai, A, Kalyanaraman, C, Brown, SD, Imker, HJ, Song, L, Fedorov, AA, Fedorov, EV, Toro, R, Hillerich, B, Seidel, R, Patskovsky, Y, Vetting, MW, Nair, SK, Babbitt, PC, Almo, SC, Gerlt, JA & Jacobson, MP 2012, 'Homology models guide discovery of diverse enzyme specificities among dipeptide epimerases in the enolase superfamily', Proceedings of the National Academy of Sciences of the United States of America, vol. 109, no. 11, pp. 4122-4127. https://doi.org/10.1073/pnas.1112081109
Lukk, Tiit ; Sakai, Ayano ; Kalyanaraman, Chakrapani ; Brown, Shoshana D. ; Imker, Heidi J. ; Song, Ling ; Fedorov, Alexander A. ; Fedorov, Elena V. ; Toro, Rafael ; Hillerich, Brandan ; Seidel, Ronald ; Patskovsky, Yury ; Vetting, Matthew W. ; Nair, Satish K. ; Babbitt, Patricia C. ; Almo, Steven C. ; Gerlt, John A. ; Jacobson, Matthew P. / Homology models guide discovery of diverse enzyme specificities among dipeptide epimerases in the enolase superfamily. In: Proceedings of the National Academy of Sciences of the United States of America. 2012 ; Vol. 109, No. 11. pp. 4122-4127.
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AU - Fedorov, Alexander A.

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AU - Toro, Rafael

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AU - Seidel, Ronald

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N2 - 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.

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