Adventures in Computational
Enzymology
John Mitchell
MACiE Database
Mechanism, Annotation and Classification in Enzymes.
http://www.ebi.ac.uk/thornton-srv/databases/MACiE/
G.L. Holliday et al., Nucl. Acids Res., 35, D515-D520 (2007)
Enzyme Nomenclature and
Classification
EC Classification
Class
Subclass
Sub-subclass
Serial number
EC Classification
Chemical reaction
Enzyme Commission (EC) Nomenclature, 1992, Academic Press, San Diego, 6th Edition
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.
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.
MACiE Database
Mechanism, Annotation and Classification in Enzymes.
http://www.ebi.ac.uk/thornton-srv/databases/MACiE/
Coverage of MACiE
Representative – based on a non-homologous dataset,
and chosen to represent each available EC sub-subclass.
Coverage of MACiE
Structures exist for:
MACiE covers:
6 EC 1.-.-.-
6 EC 1.-.-.-
56 EC 1.2.-.-
53 EC 1.2.-.-
184 EC 1.2.3.-
156 EC 1.2.3.-
1312 EC 1.2.3.4
199 EC 1.2.3.4
Representative – based on a non-homologous dataset,
and chosen to represent each available EC sub-subclass.
Repertoire of Enzyme Catalysis
G.L. Holliday et al., J. Molec. Biol., 372, 1261-1277 (2007)
Number of steps in MACiE
Repertoire of Enzyme Catalysis
140
Intramolecular
120
Bimolecular
Unimolecular
Enzyme chemistry
is largely nucleophilic
100
80
60
40
20
0
Heterolytic
Elimination
Homolytic
Elimination
Electrophilic
Addition
Nucleophilic
Addition
Homolytic
Addition
Reaction Types
Electrophilic
Substitution
Nucleophilic
Substitution
Homolytic
Substitution
Repertoire of Enzyme Catalysis
450
400
Number of steps in MACiE
350
300
250
200
150
100
50
0
Proton
transfer
AdN2
E1
SN2
E2
Reaction Types
Radical
reaction
Tautom.
Others
Residue Catalytic Propensities
Evolution of Enzyme Function
D.E. Almonacid et al., to be published
Domains
Work with domains - evolutionary & structural units of proteins.
Map enzyme catalytic mechanisms to domains to quantify
convergent and divergent functional evolution of enzymes.
Functional Classification: EC
Chemical reaction
Enzyme Commission (EC) Nomenclature, 1992, Academic Press, San Diego, 6th Edition
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: CATH
Orengo, C. A., et al. Structure, 1997, 5, 1093
Dataset
To avoid the ambiguity of multi-domain structures
we use only single-domain proteins.
CATH
(single-domain)
Database entries
EC sub-subclasses
EC serial numbers
395
114
326
Enzymes in
PDB
>>799
184
1312
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
38.33
5.80
2.46
1.22
2.46 CATH/EC reaction
Convergent Evolution
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
38.33
5.80
2.46
1.22
2.46 CATH/EC reaction: Convergent Evolution
An average reaction has evolved independently in 2.46 superfamilies
Results: Divergent Evolution
EC reactions/CATH
c.-.-.-
C
4.75
A
3.14
T
1.36
H
1.20
c.s.-.-
19.50
7.00
1.79
1.36
c.s.ss.-
39.25
10.48
2.08
1.46
c.s.ss.sn
90.00
17.90
3.05
2.05
1.46 EC reactions/CATH
database entries/CATH
Divergent
Evolution
2.18
Results: Divergent Evolution
EC reactions/CATH
c.-.-.-
C
4.75
A
3.14
T
1.36
H
1.20
c.s.-.-
19.50
7.00
1.79
1.36
c.s.ss.-
39.25
10.48
2.08
1.46
c.s.ss.sn
90.00
17.90
3.05
2.05
1.46 EC reactions/CATH: Divergent Evolution
database
An average superfamily
hasentries/CATH
evolved 1.46 different reactions
2.18
Density Functional Theory
Calculations on
Dehydroquinase
Mattias Blomberg et al.,
to be published
DFT – System Size
• System sizes of ~100150 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.
ε=4
Q
M
MM
Q
M
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.
ε=4
Q
M
MM
Q
M
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 M0055
Mechanism:
trans-dehydration,
enol(ate) intermediate.
Proposed Mechanism of DHQase
Arg113
Arg113
Tyr28
NH+
NH
H2N
NH+
HO
O H
N H H
O
2HN
N Ala
82
O
Asn79
Pro15
-O2C
H
O
H O
O
N
H
N
H
NH
His106
Asn16
O
N H H
O
2HN
NH+
HO
HO
HO
OH
-O2C
His106
H2N
-O
HO
H
N
Tyr28
NH
NH
H2N
Arg113
Tyr28
OH
OH
-O2C
N Ala
82
O
H
O H
NHH O
O
Asn79
Pro15
N
H
Asn16
N
His106
O
N H H
HN O
2
N Ala
82
O
H
O
H O
O
Asn79
Pro15
N
H
H
NH
Asn16
Models of DHQase Active Site
Energetics of DHQase
Model A
Does Asn16 Protonate the DHQ Enolate?
Other 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)
Random Forest
Machine Learning Method
k-Nearest Neighbours
Machine Learning Method
Winnow (“Molecular Spam Filter”)
Machine Learning Method
Future 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
ACKNOWLEDGEMENTS
Dr Gemma Holliday
Dr Daniel Almonacid
Dr Noel O’Boyle
Dr Mattias Blomberg
Prof. Janet Thornton (EBI)
Dr Peter Murray-Rust
Dr Jochen Blumberger
ACKNOWLEDGEMENTS
Cambridge Overseas
Trust
All slides after here are for
information only
Similarity 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.
Repertoire of enzyme catalysis
Heterolytic
Unimolecular
Bimolecular
Intramolecular
Homolytic
Unimolecular
Bimolecular
Intramolecular
Elimination
Addition
Substitution
Electrophilic
Bimolecular
Intramolecular
Nucleophilic
Bimolecular
Intramolecular
Homolytic
Bimolecular
Intramolecular
Electrophilic
Unimolecular
Bimolecular
Intramolecular
Nucleophilic
Unimolecular
Bimolecular
Intramolecular
Homolytic
Unimolecular
Bimolecular
Intramolecular
Ingold, C. K. Cornell
University Press,
1969.
Repertoire 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
______________________________________________
Function of catalytic residues
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
________________________________________________
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
69/41
Mattias Blomberg
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
30 LGA Dockings
using AutoDock 4,
1.5 Å clustering.
10 structures from
the lowest energy
conformations. The
GSH thiol is placed in
the vicinity of FCN.
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
GSGSH
t (ps)
Distance GSH(S) – FCN (C) of the
different Protonation States of GSH
GS- Leaves the
Binding Pocket
GSGSH
t (ps)
MD snapshot of FosA active site
Residues Shown to Affect FosA Actvity
and Interactions with the Modelled GSH
Residue
Arg93
Lys90
Ser50
Tyr39
Gln36
Trp34
Gln91
His64
Tyr62
Cys48
Tyr128
Arg119
Trp46
Tyr65
Ser94
Glu95
Ser98
Tyr100
Asp103
His107
Glu110
Thr9
Interacting with GSH
Yes
Yes
No
Yes
Yes
Yes
No
No
Yes
No
Yes
Yes
No
No
No
Yes
No
No
No
No
No
No
Comments
FCN
Mn-ligand
Most of the observed changes in FosAFCN
activity can be identified with the interactions with
FCN or the modelled binding of GSHFCN
FCN
FCN
FCN
Mn-ligand
FCN
QM/MM-model of FosA
Restricted
Unrestricted
Preliminary Energetics for FosA