Adventures in Computational Adventures in Computational EnzymologyEnzymology

John Mitchell

University of St Andrews

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

The MACiE DatabaseThe MACiE Database

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

Gemma Holliday, Daniel Almonacid, Noel O’Boyle,

Janet Thornton, Peter Murray-Rust, Gail Bartlett,

James Torrance, John Mitchell

Enzyme Nomenclature and Enzyme Nomenclature and ClassificationClassificationEC ClassificationEC Classification

Class

Subclass

Sub-subclass

Serial number

The EC ClassificationThe EC Classification

Reaction direction arbitrary

Cofactors and active site residues ignored

Doesn’t deal with structural and sequence information

However, it was never intended to do so

Deals with overall reaction, not 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

Active Site residues

Cofactors Need a database of enzyme mechanisms

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

MACiE DatabaseMACiE Database

Global Usage of MACiE

MACiE Entries

MACiE Mechanisms are Sourced from the Literature

Coverage of MACiE

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

EC is not Everything

• Different mechanisms can occur with exactly the same EC number.

• MACiE has six beta-lactamases, all with different mechanisms but the same overall reaction.

EC 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.-.-.-

61 EC 1.2.-.-

204 EC 1.2.3.-

1776 EC 1.2.3.4

MACiE covers:

6 EC 1.-.-.-

57 EC 1.2.-.-

183 EC 1.2.3.-

321 EC 1.2.3.4

EC Coverage of MACiE

Repertoire of Enzyme CatalysisRepertoire of Enzyme Catalysis

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

G.L. Holliday et al., J. Molec. Biol., 390, 560-577 (2009)

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

Repertoire of Enzyme Catalysis

Enzyme chemistry is largely nucleophilic

Repertoire of Enzyme Catalysis

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

Repertoire of Enzyme Catalysis

Repertoire of Enzyme Catalysis

Repertoire of Enzyme Catalysis

Repertoire of Enzyme Catalysis

We do see a few steps corresponding to well-known organic reactions; but these are the exception.

Residue Catalytic Propensities

Residue Catalytic Functions

Phospholipidosis

Lowe et al., Molec. Pharmaceutics, 7, 1708 (2010)

• An adverse effect caused by drugs• Excess accumulation of phospholipids• Often by cationic amphiphilic drugs• Affects many cell types• Causes delay in the drug development

process

Phospholipidosis

Lowe et al., Molec. Pharmaceutics, 7, 1708 (2010)

• Causes delay in the drug development process

• May or may not be related to human pathologies such as Niemann-Pick disease

Hiraoka, M. et al. 2006. Mol. Cell. Biol. 26(16):6139-6148

Electron micrographs of alveolar macrophages (A and B) and peritoneal macrophages (C and D) obtained from 3-month-old Lpla2+/+ and Lpla2-/- mice

Tomizawa et al.,

Literature Mined Dataset

R. Lowe, R.C. Glen, J.B.O. Mitchell Mol. Pharm. 2010 VOL. 7, NO. 5, 1708–1714

• Produced our own dataset of 185 compounds (from literature survey)

• 102 PPL+ and 83PPL-• Each compound is an experimentally

confirmed positive or negative

Some PPL+ molecules, from Reasor et al., Exp Biol Med, 226, 825 (2001)

Represent molecules using descriptors (we used E-Dragon & Circular Fingerprints)

10001101010011001101 10110101000011101101

10111101010001001100 10000001110011100111

10100101011101001110 10011111110001001010

Split data into N folds, then train on (N-2) of them, keeping one for parameter optimisation and one for unseen testing. Average results over all runs (each molecule is predicted once per N-fold validation).

We also repeat the whole process several times with randomly different assignments of which molecules are in which folds.

Experimental Design

Models are built using machine learning techniques such as Random Forest …

… or Support Vector Machine

Average MCC Values:

RF SVM

0.619 0.650

Results

So we have built a good predictive model that can learn the features that predispose a molecule to being PPL+, and can make predictions from chemical structure.

This is useful – one could add it to a virtual screening protocol.

But can we understand anything new about how phospholipidosis occurs?

Read up on gene expression studies related to phospholipidosis …

Sawada et al. listed genes which they found to be up- or down- regulated in phospholipidosis

As with all gene expression experiments, some of these will be highly relevant, others will be noise. Can we help interpret these data?

Mechanism?

H. Sawada, K. Takami, S. Asahi Toxicological Sciences 2005 282-292

What expertise do we have available amongst our team, colleagues & collaborators?

•Multiple target prediction

•Maths

•Programming

Florian Nigsch

Hamse Mussa

Rob Lowe

• Multiple target prediction

Predicting off-target interactions of drugs. Not with the primary pharmaceutical target, but with other targets relevant to side effects.

CHEMBL

Filtered CHEMBL, 241145 compounds & 1923 targets

Data mining and filtering

Random 99:1 split of the whole dataset, 10 repeats

10 models

Phospholipidosis dataset: 100 PPL+, 82 PPL- compounds

Predicted target associations

Target PS scores

ChEMBL Mining

• Mined the ChEMBL (03) database for compounds and targets they interact with

• Target description included the word "enzyme", "cytosolic", "receptor", "agonist" or "ion channel"

• A high cut-off (weak binding) was used on Ki/Kd/IC50 values (< 500μM) to define activity

Method

• Number of Compounds : 241145• Number of Targets : 1923• Split the data into 10 different partitions

of training and validation• Used circular fingerprints with SYBYL atom

types to define similarities between molecules

Multi-class Classification

Algorithms:

• Parzen-Rosenblatt window• Naive Bayes

Parzen-Rosenblatt window

jx

jii KN

xp xx ,1

)|(

using a Gaussian kernel

K(xi, xj) =

22 2

)()(

)(

1

hexp

hji

Tji

d

xxxx

(xi - xj)T(xi - xj) corresponds to the number of features in which xi and xj disagree

• Rank likely targets using estimates of class-condition probabilities

Partition No. PRW Rank NB Rank

1 17.049 74.104

2 16.343 76.251

3 18.424 79.078

4 16.212 73.539

5 17.339 73.535

6 18.630 77.244

7 20.694 78.560

8 18.870 74.464

9 16.584 76.235

10 18.200 78.077

Average 17.835 76.109

When we test the two methods, PRW ranks known targets better than Naïve Bayes does. Hence we use PRW for our study.

Assemble List of Targets Relevant to Sawada’s Suggested Mechanisms

Mechanisms:

1. Inhibition of lysosomal phospholipase activity;

2. Inhibition of lysosomal enzyme transport;

3. Enhanced phospholipid biosynthesis;

4. Enhanced cholesterol biosynthesis.

Assemble List of Targets Relevant to Sawada’s Suggested Mechanisms

Inhibition of lysosomal phospholipase activity

Enhanced phospholipid biosynthesis

Enhanced cholesterol biosynthesis

Assigning Scores to Targets

N

iip xCPS

1

)()(

• Use these 10 models of target interactions• Predict targets for phospholipidosis dataset• Score targets according to the likelihood of

involvement in phospholipidosis• Use the top 100 predicted targets per

compound as we seek off-target interactions

N

iip xCPS

1

)()(

• Score measures tendency of target to interact with PPL+ rather than PPL- compounds.

M1 & M5 are involved in phospholipase C regulation & may be relevant; but not in Sawada’s list.

62

We consider a PS score significant if the target is predicted to interact with at least 50 more PPL+ compounds than PPL- compounds.

Our Scores for 8 of Sawada’s PPL-Relevant Targets

Mechanism Target Rank PS

1 Sphingomyelin phosphodiesterase (SMPD) (h) 225 55

Lysosomal Phospholipase A1 (LYPLA1) (r) 163= 90

Phospholipase A2 (PLA2) (h) 152= 97

3 Elongation of very long chain fatty acids protein 6 (ELOVL6) (h) 1203= -10

Acyl-CoA desaturase (SCD) (m) 610= 0

4 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) (h) 456= 10

Squalene monooxygenase (SQLE) (h) 437= 14

Lanosterol synthase (LSS) (h) 114= 134

Inhibition of lysosomal phospholipase activity

Enhanced phospholipid biosynthesis

Enhanced cholesterol biosynthesis

Our Scores for Sawada’s PPL-Relevant Targets

Mechanism Target Rank PS

1 Sphingomyelin phosphodiesterase (SMPD) (h) 225 55

Lysosomal Phospholipase A1 (LYPLA1) (r) 163= 90

Phospholipase A2 (PLA2) (h) 152= 97

3Elongation of very long chain fatty acids protein 6 (ELOVL6) (h) 1203= -10

Acyl-CoA desaturase (SCD) (m) 610= 0

4 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) (h) 456= 10

Squalene monooxygenase (SQLE) (h) 437= 14

Lanosterol synthase (LSS) (h) 114= 134

Inhibition of lysosomal phospholipase activity

Enhanced phospholipid biosynthesis

Enhanced cholesterol biosynthesis

Other Mechanisms• The mechanisms and targets suggested here

are insufficient to explain all the PPL+ compounds in our data set.

• We expect that other targets and possibly mechanisms are important.

• Our method can’t test direct compound – phospholipid binding.

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ACKNOWLEDGEMENTSACKNOWLEDGEMENTS

Dr Gemma Holliday

Dr Rob Lowe

Dr Daniel Almonacid

Prof. Janet Thornton

Dr Florian Nigsch

Dr Hamse Mussa

Prof. Bobby Glen

Dr Andreas Bender

Alexios Koutsoukas

ACKNOWLEDGEMENTSACKNOWLEDGEMENTS

Cambridge Overseas

Trust