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Structure-based activity prediction for an enzyme of unknown function

Abstract

With many genomes sequenced, a pressing challenge in biology is predicting the function of the proteins that the genes encode. When proteins are unrelated to others of known activity, bioinformatics inference for function becomes problematic. It would thus be useful to interrogate protein structures for function directly. Here, we predict the function of an enzyme of unknown activity, Tm0936 from Thermotoga maritima, by docking high-energy intermediate forms of thousands of candidate metabolites. The docking hit list was dominated by adenine analogues, which appeared to undergo C6-deamination. Four of these, including 5-methylthioadenosine and S-adenosylhomocysteine (SAH), were tested as substrates, and three had substantial catalytic rate constants (105 M-1s-1). The X-ray crystal structure of the complex between Tm0936 and the product resulting from the deamination of SAH, S-inosylhomocysteine, was determined, and it corresponded closely to the predicted structure. The deaminated products can be further metabolized by T. maritima in a previously uncharacterized SAH degradation pathway. Structure-based docking with high-energy forms of potential substrates may be a useful tool to annotate enzymes for function.

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Figure 1: Sample transformations of metabolites from their ground state structure into the high-energy intermediate forms that were used for docking.
Figure 2: Binding and conversion of MTA by Tm0936.
Figure 3: Comparing the docking prediction and the crystallographic result.

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References

  1. Whisstock, J. C. & Lesk, A. M. Prediction of protein function from protein sequence and structure. Q. Rev. Biophys. 36, 307–340 (2003)

    Article  CAS  PubMed  Google Scholar 

  2. Gerlt, J. A. & Babbitt, P. C. Can sequence determine function? Genome. Biol. 1, REVIEWS0005 (2000)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Brenner, S. E. Errors in genome annotation. Trends Genet. 15, 132–133 (1999)

    Article  CAS  PubMed  Google Scholar 

  4. Devos, D. & Valencia, A. Intrinsic errors in genome annotation. Trends Genet. 17, 429–431 (2001)

    Article  CAS  PubMed  Google Scholar 

  5. Schapira, M., Abagyan, R. & Totrov, M. Nuclear hormone receptor targeted virtual screening. J. Med. Chem. 46, 3045–3059 (2003)

    Article  CAS  PubMed  Google Scholar 

  6. Rao, M. S. & Olson, A. J. Modelling of factor Xa-inhibitor complexes: a computational flexible docking approach. Proteins 34, 173–183 (1999)

    Article  CAS  PubMed  Google Scholar 

  7. Sukuru, S. C. et al. Discovering new classes of Brugia malayi asparaginyl-tRNA synthetase inhibitors and relating specificity to conformational change. J. Comput. Aided. Mol. Des. 20, 159–178 (2006)

    Article  ADS  CAS  PubMed  Google Scholar 

  8. Shoichet, B. K. Virtual screening of chemical libraries. Nature 432, 862–865 (2004)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Macchiarulo, A., Nobeli, I. & Thornton, J. M. Ligand selectivity and competition between enzymes in silico. Nature Biotechnol. 22, 1039–1045 (2004)

    Article  CAS  Google Scholar 

  10. Kalyanaraman, C., Bernacki, K. & Jacobson, M. P. Virtual screening against highly charged active Sites: identifying substrates of α–β barrel enzymes. Biochemistry 44, 2059–2071 (2005)

    Article  CAS  PubMed  Google Scholar 

  11. Irwin, J. J. & Shoichet, B. K. ZINC—a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 45, 177–182 (2005)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Schramm, V. L. Enzymatic transition states and transition state analogues. Curr. Opin. Struct. Biol. 15, 604–613 (2005)

    Article  CAS  PubMed  Google Scholar 

  13. Hermann, J. C., Ridder, L., Holtje, H. D. & Mulholland, A. J. Molecular mechanisms of antibiotic resistance: QM/MM modelling of deacylation in a class A β-lactamase. Org. Biomol. Chem. 4, 206–210 (2006)

    Article  CAS  PubMed  Google Scholar 

  14. Warshel, A. & Florian, J. Computer simulations of enzyme catalysis: finding out what has been optimized by evolution. Proc. Natl Acad. Sci. USA 95, 5950–5955 (1998)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. Holm, L. & Sander, C. An evolutionary treasure: unification of a broad set of amidohydrolases related to urease. Proteins 28, 72–82 (1997)

    Article  CAS  PubMed  Google Scholar 

  16. Seibert, C. M. & Raushel, F. M. Structural and catalytic diversity within the amidohydrolase superfamily. Biochemistry 44, 6383–6391 (2005)

    Article  CAS  PubMed  Google Scholar 

  17. Pegg, S. C. et al. Leveraging enzyme structure–function relationships for functional inference and experimental design: the structure–function linkage database. Biochemistry 45, 2545–2555 (2006)

    Article  CAS  PubMed  Google Scholar 

  18. Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Tantillo, D. J. & Houk, K. N. Transition state docking: a probe for noncovalent catalysis in biological systems. Application to antibody-catalyzed ester hydrolysis. J. Comput. Chem. 23, 84–95 (2002)

    Article  CAS  PubMed  Google Scholar 

  20. Hermann, J. C. et al. Predicting substrates by docking high-energy intermediates to enzyme structures. J. Am. Chem. Soc. 128, 15882–15891 (2006)

    Article  CAS  PubMed  Google Scholar 

  21. Nowlan, C. et al. Resolution of chiral phosphate, phosphonate, and phosphinate esters by an enantioselective enzyme library. J. Am. Chem. Soc. 128, 15892–15902 (2006)

    Article  CAS  PubMed  Google Scholar 

  22. Wei, B. Q., Baase, W. A., Weaver, L. H., Matthews, B. W. & Shoichet, B. K. A model binding site for testing scoring functions in molecular docking. J. Mol. Biol. 322, 339–355 (2002)

    Article  CAS  PubMed  Google Scholar 

  23. Lorber, D. M. & Shoichet, B. K. Hierarchical docking of databases of multiple ligand conformations. Curr. Top. Med. Chem. 5, 739–749 (2005)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Radzicka, A. & Wolfenden, R. A proficient enzyme. Science 267, 90–93 (1995)

    Article  ADS  CAS  PubMed  Google Scholar 

  25. Mohan, V., Gibbs, A. C., Cummings, M. D., Jaeger, E. P. & DesJarlais, R. L. Docking: successes and challenges. Curr. Pharm. Des. 11, 323–333 (2005)

    Article  CAS  PubMed  Google Scholar 

  26. Jorgensen, W. L. The many roles of computation in drug discovery. Science 303, 1813–1818 (2004)

    Article  ADS  CAS  PubMed  Google Scholar 

  27. Kairys, V., Fernandes, M. X. & Gilson, M. K. Screening drug-like compounds by docking to homology models: a systematic study. J. Chem. Inf. Model. 46, 365–379 (2006)

    Article  CAS  PubMed  Google Scholar 

  28. Klebe, G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov. Today 11, 580–594 (2006)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Speedie, M. K., Zulty, J. J. & Brothers, P. S-adenosylhomocysteine metabolism in Streptomyces flocculus. J. Bacteriol. 170, 4376–4378 (1988)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Tyler, P. C., Taylor, E. A., Fröhlich, R. F. G. & Schramm, V. L. Synthesis of 5′-methylthio coformycins: specific inhibitors for malarial adenosine deaminase. J. Am. Chem. Soc. 129, 6872–6879 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Meng, E. C., Shoichet, B. & Kuntz, I. D. Automated docking with grid-based energy evaluation. J. Comp. Chem. 13, 505–524 (1992)

    Article  CAS  Google Scholar 

  32. Gschwend, D. A. & Kuntz, I. D. Orientational sampling and rigid-body minimization in molecular docking revisited: on-the-fly optimization and degeneracy removal. J. Comput. Aided Mol. Des. 10, 123–132 (1996)

    Article  ADS  CAS  PubMed  Google Scholar 

  33. Irwin, J. J., Raushel, F. M. & Shoichet, B. K. Virtual screening against metalloenzymes for inhibitors and substrates. Biochemistry 44, 12316–12328 (2005)

    Article  CAS  PubMed  Google Scholar 

  34. Gilson, M. K. & Honig, B. H. Calculation of electrostatic potentials in an enzyme active site. Nature 330, 84–86 (1987)

    Article  ADS  CAS  PubMed  Google Scholar 

  35. Kuntz, I. D. et al. A Geometric approach to macromolecule–ligand interactions. J. Mol. Biol. 161, 269–288 (1982)

    Article  ADS  CAS  PubMed  Google Scholar 

  36. Muszbek, L., Polgar, J. & Fesus, L. Kinetic determination of blood coagulation Factor XIII in plasma. Clin. Chem. 31, 35–40 (1985)

    Article  CAS  PubMed  Google Scholar 

  37. Ellman, G. L. A colorimetric method for determining low concentrations of mercaptans. Arch. Biochem. Biophys. 74, 443–450 (1958)

    Article  CAS  PubMed  Google Scholar 

  38. Cleland, W. W. Statistical analysis of enzyme kinetic data. Methods Enzymol. 63, 103–138 (1979)

    Article  CAS  PubMed  Google Scholar 

  39. Cleland, W. W. Substrate inhibition. Methods Enzymol. 63, 500–513 (1979)

    Article  CAS  PubMed  Google Scholar 

  40. Otwinowski, Z. & Minor, W. in Methods in Enzymology Vol. 276 (eds Carter, C. W. & Sweet, R. M.) 307–326 (Academic Press, New York, 1997)

  41. Storoni, L. C., McCoy, A. J. & Read, R. J. Likelihood-enhanced fast rotation functions. Acta Crystallogr. D 60, 432–438 (2004)

    Article  PubMed  CAS  Google Scholar 

  42. Brunger, A. T. et al. Crystallography & NMR system: A new software suite for macromolecular structure determination. Acta Crystallogr. D 54, 905–921 (1998)

    Article  CAS  PubMed  Google Scholar 

  43. Jones, T. A. Diffraction methods for biological macromolecules. Interactive computer graphics: FRODO. Methods Enzymol. 115, 157–171 (1985)

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the National Institutes of Health, supporting docking analyses (to B.K.S.), large scale structural analysis (to S.C.A.), and function prediction (to F.M.R., B.K.S. and S.C.A.). F.M.R. thanks the Robert A. Welch Foundation for support. J.C.H. thanks the Deutsche Akademie der Naturforscher Leopoldina for a fellowship. We thank J. Irwin, V. Thomas and K. Babaoglu for reading this manuscript. The clone for Tm0172 was kindly supplied by the Joint Center for Structural Genomics.

Author Contributions J.C.H designed the docking database, performed the docking runs, and analysed the docking results. F.M.R. and R.M.-A. performed the enzymatic characterization of Tm0936 and Tm0172, including cloning and purification of the proteins. S.C.A., E.F. and A.A.F. determined the X-ray structure of Tm0936 with S-inosyl-homocysteine. J.C.H. and B.K.S. largely wrote the paper. All authors discussed the results and commented on the manuscript.

The complex structure of Tm0936 with SIH has been deposited in the PDB (accession code 2PLM).

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Correspondence to Brian K. Shoichet.

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This file contains Supplementary Tables S1-S4, Supplementary Figures S1-S3 with Legends and additional references. (PDF 719 kb)

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Hermann, J., Marti-Arbona, R., Fedorov, A. et al. Structure-based activity prediction for an enzyme of unknown function. Nature 448, 775–779 (2007). https://doi.org/10.1038/nature05981

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