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Adventures in Computational Enzymology

Adventures in Computational Enzymology. John Mitchell. MACiE Database. M echanism, A nnotation and C lassification i n E nzymes . http://www.ebi.ac.uk/thornton-srv/databases/MACiE/. G.L. Holliday et al ., Nucl. Acids Res ., 35 , D515-D520 (2007). EC Classification. Class. Subclass.

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Adventures in Computational Enzymology

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  1. Adventures in Computational Enzymology John Mitchell

  2. MACiE Database Mechanism,AnnotationandClassificationin Enzymes. http://www.ebi.ac.uk/thornton-srv/databases/MACiE/ G.L. Holliday et al., Nucl. Acids Res., 35, D515-D520 (2007)

  3. EC Classification Class Subclass Sub-subclass Serial number Enzyme Nomenclature and Classification

  4. EC Classification Chemical reaction Enzyme Commission (EC) Nomenclature, 1992, Academic Press, San Diego, 6th Edition

  5. The EC Classification • Only deals with overall reaction. • Reaction direction arbitrary. • Doesn’t deal with structural and sequence information. • Thus, cofactors and active site residues ignored. • However, it was never intended to describe mechanism.

  6. A New Representation of Enzyme Reactions? • Should be complementary to, but distinct from, the EC system. • Should take into account: • Reaction Mechanism; • Structure; • Sequence. • Need a database of enzyme mechanisms.

  7. MACiE Database Mechanism,AnnotationandClassificationin Enzymes. http://www.ebi.ac.uk/thornton-srv/databases/MACiE/

  8. Coverage of MACiE Representative – based on a non-homologous dataset, and chosen to represent each available EC sub-subclass.

  9. Coverage of MACiE Structures exist for: 6 EC 1.-.-.- 56 EC 1.2.-.- 184 EC 1.2.3.- 1312 EC 1.2.3.4 MACiE covers: 6 EC 1.-.-.- 53 EC 1.2.-.- 156 EC 1.2.3.- 199 EC 1.2.3.4 Representative – based on a non-homologous dataset, and chosen to represent each available EC sub-subclass.

  10. Repertoire of Enzyme Catalysis G.L. Holliday et al.,J. Molec. Biol., 372, 1261-1277 (2007)

  11. Repertoire of Enzyme Catalysis Enzyme chemistry is largely nucleophilic

  12. Proton transfer AdN2 E1 SN2 E2 Radical reaction Tautom. Others Repertoire of Enzyme Catalysis

  13. Residue Catalytic Propensities

  14. Evolution of Enzyme Function D.E. Almonacid et al., to be published

  15. Domains Work with domains - evolutionary & structural units of proteins. Map enzyme catalytic mechanisms to domains to quantify convergent and divergent functional evolution of enzymes.

  16. Functional Classification: EC Chemical reaction Enzyme Commission (EC) Nomenclature, 1992, Academic Press, San Diego, 6th Edition

  17. Enzyme Catalysis Databases G.L. Holliday et al., Nucleic Acids Res.,35, D515 (2007) S.C. Pegg et al., Biochemistry, 45, 2545 (2006) N. Nagano, Nucleic Acids Res., 33, D407 (2005)

  18. Coverage of MACiE Representative – based on a non-homologous dataset, and chosen to represent each available EC sub-subclass.

  19. Coverage of SFLD Based on a few evolutionarily related families

  20. Coverage of EzCatDB But without mechanisms.

  21. Structural Classification: CATH Orengo, C. A., et al. Structure, 1997, 5, 1093

  22. Dataset To avoid the ambiguity of multi-domain structures we use only single-domain proteins. • CATHEnzymes in(single-domain)PDB Database entries 395>>799 EC sub-subclasses 114 184 EC serial numbers 3261312

  23. Results:Convergent Evolution Numbers of CATH code occurrences per EC number c.s.-.- c.s.ss.- c.s.ss.sn c.-.-.- C 3.17 1.73 1.38 1.11 A 11.00 3.27 1.93 1.60 T 28.00 4.89 2.24 1.19 H 2.46 38.33 5.80 1.22 2.46CATH/EC reaction Convergent Evolution

  24. Results:Convergent Evolution Numbers of CATH code occurrences per EC number c.s.-.- c.s.ss.- c.s.ss.sn c.-.-.- C 3.17 1.73 1.38 1.11 A 11.00 3.27 1.93 1.60 T 28.00 4.89 2.24 1.19 H 2.46 38.33 5.80 1.22 2.46CATH/EC reaction: Convergent Evolution An average reaction has evolved independently in 2.46superfamilies

  25. Results: Divergent Evolution EC reactions/CATH H 1.20 1.36 T 1.36 1.79 2.08 3.05 A 3.14 7.00 10.48 17.90 C 4.75 19.50 39.25 90.00 c.-.-.- c.s.-.- c.s.ss.- c.s.ss.sn 1.46 2.05 1.46EC reactions/CATHDivergent Evolution databaseentries/CATH 2.18

  26. Results: Divergent Evolution EC reactions/CATH H 1.20 1.36 T 1.36 1.79 2.08 3.05 A 3.14 7.00 10.48 17.90 C 4.75 19.50 39.25 90.00 c.-.-.- c.s.-.- c.s.ss.- c.s.ss.sn 1.46 2.05 1.46EC reactions/CATH: Divergent Evolution An average superfamily has evolved 1.46 different reactions databaseentries/CATH 2.18

  27. Density Functional Theory Calculations onDehydroquinase Mattias Blomberg et al., to be published

  28. DFT – System Size System sizes of ~100-150 atoms can be treated using DFT That raises the question of how to treat the rest of the protein.

  29. Dielectric Continuum or QM/MM? ε=4 QM MM QM One approach is to cut out the active site residues and treat the rest of the protein as a dielectric continuum. Another approach is to treat the active site as QM and the rest of the protein using MM.

  30. Dielectric Continuum or QM/MM? ε=4 QM MM QM One approach is to cut out the active site residues and treat the rest of the protein as a dielectric continuum. Another approach is to treat the active site as QM and the rest of the protein using MM.

  31. Dehydroquinase - Part of the Shikimate Pathway

  32. Shikimate & Chorismate Pathways

  33. Dehydroquinase (Shikimate Pathway)

  34. Shikimate & Chorismate Pathways Biosynthetic pathway for phenylalanine, tyrosine and tryptophan. Present in plants, microorganisms and fungi but not in mammals. The target for Glyphosate, an important herbicide. Understanding the mechanisms and developing inhibitors is of great importance for the development of new herbicides, fungicides and antibiotics.

  35. Two Types of Dehydroquinases • Type II: S. coelicor, M. tuberculosis and H. pylori (EC 4.2.1.10). MACiE M0055 Mechanism: trans-dehydration, enol(ate) intermediate. Type I: E. coli and S. typhi, (EC 4.2.1.10) MACiE M0054 Mechanism: cis-dehydration, imine intermediate.

  36. Proposed Mechanism of DHQase

  37. Models of DHQase Active Site

  38. Energetics of DHQase Model A

  39. Does Asn16 Protonate the DHQ Enolate?

  40. Other Things we do Chemoinformatics for pharmaceutical design … …using Machine Learning for prediction of solubility, bioavailability and bioactivity.

  41. Machine Learning Methods Recognise patterns in data Similar inputs Similar outputs Make full use of all available information One application is solubility

  42. Machine Learning Methods • Can be used for Classification or for Regression • Can be used with chemoinformatics, physicochemical or experimental (e.g., assay) data as descriptors

  43. Solubility is an important issue in drug discovery and a major source of attrition This is expensive for the industry A good model for predicting the solubility of druglike molecules would be very valuable.

  44. Drug Disc.Today, 10 (4), 289 (2005)

  45. Random Forest Machine Learning Method

  46. k-Nearest Neighbours Machine Learning Method

  47. Winnow (“Molecular Spam Filter”) Machine Learning Method

  48. Future Directions

  49. Current coverage of MACiE Representative – based on a non-homologous dataset

  50. Future coverage of MACiE Adding homologues – to facilitate study of divergent evolution

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