Adventures in Computational Adventures in Computational EnzymologyEnzymology

John Mitchell

MMechanism, AAnnotation and CClassification iin EEnzymes.http://www.ebi.ac.uk/thornton-srv/databases/MACiE/

MACiE DatabaseMACiE Database

G.L. Holliday et al., Nucl. Acids Res., 35, D515-D520 (2007)

Enzyme Nomenclature and Enzyme Nomenclature and ClassificationClassificationEC ClassificationEC Classification

Class

Subclass

Sub-subclass

Serial number

EC Classification

Enzyme Commission (EC) Nomenclature, 1992, Academic Press, San Diego, 6th Edition

Chemical reaction

The EC ClassificationThe EC Classification

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.

Only deals with overall reaction.

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.

MMechanism, AAnnotation and CClassification iin EEnzymes.http://www.ebi.ac.uk/thornton-srv/databases/MACiE/

MACiE DatabaseMACiE Database

Coverage of MACiE

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

Coverage of MACiE

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

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

Repertoire of Enzyme CatalysisRepertoire of Enzyme Catalysis

G.L. Holliday et al., J. Molec. Biol., 372, 1261-1277 (2007)

Repertoire of Enzyme Catalysis

0

20

40

60

80

100

120

140

HeterolyticElimination

HomolyticElimination

ElectrophilicAddition

NucleophilicAddition

HomolyticAddition

ElectrophilicSubstitution

NucleophilicSubstitution

HomolyticSubstitution

Reaction Types

Num

ber

of

step

s in

MA

CiE

Intramolecular

Bimolecular

Unimolecular

Enzyme chemistry is largely nucleophilic

0

50

100

150

200

250

300

350

400

450

Reaction Types

Num

ber

of

ste

ps in M

ACiE

ProtonProtontransfertransfer

AdAdNN22 E1E1 SSNN22 E2E2 RadicalRadicalreactionreaction

Tautom.Tautom. OthersOthers

Repertoire of Enzyme Catalysis

Residue Catalytic Propensities

Evolution of Enzyme FunctionEvolution of Enzyme Function

D.E. Almonacid et al., to be published

Work with domains - evolutionary & structural units of proteins.

Map enzyme catalytic mechanisms to domains to quantify convergent and divergent functional evolution of enzymes.

Domains

Functional Classification: EC

Enzyme Commission (EC) Nomenclature, 1992, Academic Press, San Diego, 6th Edition

Chemical reaction

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)

Coverage of MACiE

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

Coverage of SFLD

Based on a few evolutionarily related families

Coverage of EzCatDB

But without mechanisms.

Structural Classification: CATHOrengo, C. A., et al. Structure, 1997, 5, 1093

Dataset

CATH Enzymes in(single-domain) PDB

Database entries 395 >>799

EC sub-subclasses 114 184

EC serial numbers 326 1312

To avoid the ambiguity of multi-domain structures we use only single-domain proteins.

Numbers of CATH code occurrences per EC number

C

A

T

H

c.-.-.- c.s.-.- c.s.ss.- c.s.ss.sn

3.17

11.00

28.00

38.33

1.73

3.27

4.89

5.80

1.38

1.93

2.24

2.46

1.11

1.60

1.19

1.22

Results: Convergent Evolution

2.46 CATH/EC reaction

Convergent Evolution

Numbers of CATH code occurrences per EC number

C

A

T

H

c.-.-.- c.s.-.- c.s.ss.- c.s.ss.sn

3.17

11.00

28.00

38.33

1.73

3.27

4.89

5.80

1.38

1.93

2.24

2.46

1.11

1.60

1.19

1.22

Results: Convergent Evolution

2.46 CATH/EC reaction: Convergent EvolutionAn average reaction has evolved independently in 2.46 superfamilies

EC reactions/CATH

C4.75

19.50

39.25

90.00

c.-.-.-c.-.-.-

c.s.-.-c.s.-.-

c.s.ss.-

c.s.ss.sn

A3.14

7.00

10.48

17.90

T1.36

1.79

2.08

3.05

H1.20

1.36

1.462.05

database entries/CATH

2.18

Results: Divergent Evolution

1.46 EC reactions/CATH Divergent Evolution

EC reactions/CATH

C4.75

19.50

39.25

90.00

c.-.-.-c.-.-.-

c.s.-.-c.s.-.-

c.s.ss.-

c.s.ss.sn

A3.14

7.00

10.48

17.90

T1.36

1.79

2.08

3.05

H1.20

1.36

1.462.05

database entries/CATH

2.18

Results: Divergent Evolution

1.46 EC reactions/CATH: Divergent EvolutionAn average superfamily has evolved 1.46 different reactions

Density Functional Theory Calculations on

Dehydroquinase

Mattias Blomberg et al., to be published

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.

Dielectric Continuum or QM/MM?

• 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.

QM

ε=4

QM

MM

Dielectric Continuum or QM/MM?

• 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.

QM

ε=4

QM

MM

Dehydroquinase - Part of the

Shikimate Pathway

Shikimate & Chorismate Pathways

Dehydroquinase (Shikimate Pathway)

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.

Two Types of Dehydroquinases

• Type I: E. coli and S. typhi,

(EC 4.2.1.10) MACiE M0054

Mechanism: cis-dehydration,imine intermediate.

• Type II: S. coelicor, M. tuberculosis and H. pylori

(EC 4.2.1.10). MACiE M0055Mechanism:trans-dehydration,enol(ate) intermediate.

Proposed Mechanism of DHQase

Arg113

NH

NH+NH2 -O

Tyr28

NN

His106

H

N Ala82

HO

O

Pro15NH

O NHH

Asn16

H

OHOH

O

H

HO

-O2C

HO2HN

Asn79

Arg113

NH

NH+NH2 HO

Tyr28

N Ala82

HO

O

Pro15NH

O NH-

Asn16

HNN

His106

H

OHOH

O

-O2C

HO2HN

Asn79

OH

Arg113

NH

NHNH2 HO

Tyr28

NN

His106

N Ala82

HO

O

Pro15NH

O NHH

Asn16

H

OHOH

-O2C OO

HH

O2HN

Asn79

+

Models of DHQase Active Site

Energetics of DHQase

Model A

Does Asn16 Protonate the DHQ Enolate?

Other Things we doOther Things we do

Chemoinformatics for pharmaceutical design …

…using Machine Learning for prediction of solubility, bioavailability and bioactivity.

Machine Learning Methods

• Recognise patterns in data• Similar inputs Similar outputs• Make full use of all available information• One application is solubility

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

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.

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

Machine Learning Method

Random Forest

Machine Learning Method

k-Nearest Neighbours

Machine Learning Method

Winnow (“Molecular Spam Filter”)

Future DirectionsFuture Directions

Current coverage of MACiE

Representative – based on a non-homologous dataset

Future coverage of MACiE

Adding homologues – to facilitate study of divergent evolution

Divergent Evolution using MACiE

This will use our reaction similarity work to measure changes in chemistry

Using Machine Learning Methods to calculate and predict protein-ligand binding energies

Building on our previous work …

P.M. Marsden et al., Org. Biomol. Chem., 2, 3267 (2004)

Computational Toxicology

Predicting bioavailability problems, off-target activities and side effects of drug candidates

QM, QM/MM and MD Simulation Work

• Using computational chemistry to study enzyme mechanisms

Fosfomycin Resistance Protein A

ACKNOWLEDGEMENTSACKNOWLEDGEMENTS

Dr Gemma Holliday

Dr Daniel Almonacid

Dr Noel O’Boyle

Dr Mattias Blomberg

Prof. Janet Thornton (EBI)

Dr Peter Murray-Rust

Dr Jochen Blumberger

ACKNOWLEDGEMENTSACKNOWLEDGEMENTS

Cambridge Overseas

Trust

All slides after here are for information only

Similarity of Enzyme MechanismsSimilarity of Enzyme Mechanisms

N.M. O'Boyle, et al., J. Molec. Biol., 368, 1484-1499 (2007)

Measuring Similarity of Enzyme Mechanisms

Coverage of MACiE

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

UnimolecularHeterolytic Bimolecular

IntramolecularElimination

UnimolecularHomolytic Bimolecular

Intramolecular

Electrophilic BimolecularIntramolecular

Addition Nucleophilic BimolecularIntramolecular

Homolytic BimolecularIntramolecular

UnimolecularElectrophilic Bimolecular

Intramolecular

UnimolecularSubstitution Nucleophilic Bimolecular

Intramolecular

UnimolecularHomolytic Bimolecular

Intramolecular

Ingold, C. K. Cornell University Press,

1969.

Repertoire of enzyme catalysisRepertoire of enzyme catalysis

“Other reactions” and Named organic reactions currently supported in MACiE

______________________________________________

Aldol Condensation Hydride Transfer Amadori Rearrangement Isomerisation A-SN1 Michael Addition A-SN2 Nucleophilic Attack A-SNi Pericyclic Reaction Claisen Rearrangement Proton Transfer Condensation Radical Formation E1cb Radical Propagation Group Transfer Radical Termination Heterolysis Redox Homolysis Tautomerisation______________________________________________

Repertoire of enzyme catalysisRepertoire of enzyme catalysis

Functionality for amino acids currently supported in the MACiE

________________________________________________

Activating residue Proton acceptor Charge destabiliser Proton donor Charge stabiliser Proton relay Covalently attached Radical acceptor Electrophile Radical donor Hydride relay Radical relay Hydrogen bond acceptor Radical stabiliser Hydrogen bond donor Spectator Leaving group Steric hindrance Metal ligand Unknown function Nucleophile Unspecified steric role________________________________________________

Function of catalytic residuesFunction of catalytic residues

CMLReact

Customisable mark-up language

Allows validation

Uses dictionary technology

Separates content from presentation

Open Source

BUT still under development

An Overview of MACiE and CMLReact

Energetics of DHQase

Model A

TS1 - Proton Transfer

TS2 - Dehydration

Mattias Blomberg

69/41

Model C

Model A

Model B

Model C

Models A, B & C

MD and QM/MM Calculations on Fosfomycin Resistance Protein A

Fosfomycin Resistance Protein A

Fosfomycin Resistance Proteins

• Fosfomycin inhibits the first step in the bacterial cell-wall synthesis (MurA).

• Mn(II)-dependent soluble glutathione (GSH) transferase.

• FosA homologues in pathogenic bacteria: FosB and FosX.

Impact on Pathogens

• Low toxicity and broad-spectrum activity have resulted in an increased clinical use of fosfomycin

• Fosfomycin is most commonly used in treatments of lower urinary tract infections

• Fosfomycin alone or in combination with other drugs could also be useful against resistant Staphylococci and E. Coli, which can give serious infections for hospitalized patients (pneumonia, urinary tract infections, skin infections and bacteraemia).

Proposed Mechanism

• Lys90, Tyr100 and Arg119 mutants have a large effect on the turnover of the enzyme. They are all involved in the stabilization of the phosphonate group (Beharry et al, J Biol Chem, 2005, 17786.)

• Recent docking and mutation studies indicate that Trp34, Gln36, Tyr39, Ser50, Lys90 and Arg93 are involved in the binding of GSH (Rigsby et al, Arch. Biochem. Biophys, 2007, 277.)

• Tyr39 has been proposed to participate in the ionization of GSH (Rigsby et al, Arch. Biochem. Biophys, 2007, 277.)

Docking of GSH in FosA

10 structures from the lowest energy conformations. The GSH thiol is placed in the vicinity of FCN.

30 LGA Dockings using AutoDock 4, 1.5 Å clustering.

MD simulations

• Amber 9.

• FF03 force field, TIP3P water model.

• Truncated octahedron > 10 Å of water around the solute.

• 10 Å cutoff on non-bonding interactions

• Charges and Force constants for the Mn-centre (His, Glu, Mn, FCN) calculated using Gaussian 03.

Backbone RMSD residue 1-268

GS-

GSH

t (ps)

Distance GSH(S) – FCN (C) of the different Protonation States of GSH

GS-

GSH

t (ps)

GS- Leaves the Binding Pocket

MD snapshot of FosA active site

Residues Shown to Affect FosA Actvity and Interactions with the Modelled GSH

Residue Interacting with GSH CommentsArg93 Yes Lys90 YesSer50 No FCNTyr39 YesGln36 YesTrp34 YesGln91 No  His64 No Mn-ligandTyr62 Yes  Cys48 No FCN Tyr128 Yes  Arg119 YesTrp46 No FCNTyr65 NoSer94 No FCNGlu95 YesSer98 No FCNTyr100 No FCNAsp103 NoHis107 NoGlu110 No Mn-ligandThr9 No FCN

Most of the observed changes in FosAactivity can be identified with the interactions with

FCN or the modelled binding of GSH

QM/MM-model of FosA

Unrestricted

Restricted

Preliminary Energetics for FosA