WhichP450: Predicting which CYP450 isoforms are involved ...€¦ · which P450 from a list of 7 isoforms 2D6, 2C8, 2C9, 1A2, 2E1, 2C19, 3A4 − Not a modelling exercise that predicts

© 2009 Optibrium Ltd. Optibrium™, StarDrop™, Auto-Modeler™ and Glowing Molecule™ are trademarks of Optibrium Ltd.

Rasmus Leth*, Peter Hunt, Jonathan Tyzak, Matthew Segall UKQSAR/PCF Meeting,

Stevenage - 15th Mar 2016

WhichP450: Predicting which CYP450 isoforms are involved in the metabolism of a xenobiotic

© 2016 Optibrium Ltd.

Outline

• Why do we need this..?

• Methodology

• Descriptors

• Results

• Conclusions

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Why

• Various isoforms of CYPs

− Different active site requirements

− Orientations of molecules different

− Hence different oxidative metabolite profile possible

• Possibility of DDIs

• Possibility of effects from polymorphism

• All round toxicity profile may be different

• Different databases

− Expand the list of isoforms covered

− Use substrate data only, not inhibitor data

− Comparison with WhichCYP web-based tool.

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Pockets for PDB entries 4K9T (hCYP3A4 - purple) & 3E6I (hCYP2E1 - blue)


Methodology

• Utilised the literature data collected for our Regioselectivity models

• Annotated those molecules with not only where but by which CYP and a personal judgement of MAJOR vs MINOR metaboliser

• Total number of molecules used in this work is 484 with a 196 molecule/isoform test set

− A molecule, and site, can be a substrate (major or minor) for more than one isoform

• Assumes that a test compound is a substrate & predicts which P450 from a list of 7 isoforms 2D6, 2C8, 2C9, 1A2, 2E1, 2C19, 3A4

− Not a modelling exercise that predicts if a molecule is going to be a CYP substrate or not

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• Used SVM as the statistical method, producing a single multi-class model with probabilities for each isoform.

− Produces (n*(n-1))/2 binary classifiers & then votes for multi-class (n classes) determination

− SVM package is ‘e1071’ (LIBSVM) for use in R


SVM – Support Vector Machine

• Machine learning algorithm

• Applied to classification or regression

• Numerous applications in chemistry (discrimination between ligands, QSAR, text mining, etc.)

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Maximum separation hyperplane

Linear kernel polynomial kernel


Descriptor Fingerprints

Describing molecular features in binary or numerical language (01000100…01001000)

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Atom Pairs Morgan Radius Acetone atom pairs

CX – (2) – C.X3

CX – (2) – C.X3

C.X3 – (2) – O.X1

CX – (3) – CX

CX – (3) – O.X1

CX – (3) – O.X1 Considering atom 1 in benzoic acid amide

Radius 1 Radius 2 Radius 3

Topological Torsions

(NPI-TYPE-NBR)-(NPI-TYPE-NBR)- (NPI-TYPE-NBR)-(NPI-TYPE-NBR)


Descriptors

• RDKit code used to generate fingerprints

− via KNIME or Python protocol – binary fingerprints

− ((5 FPs * 3 lengths) + 7 other FPs) * 5 over sampling methods = 110 SVM models per run

• StarDrop fingerprint – a frequency/count based fingerprint with a ‘Harlequin’ nature

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RDKit via KNIME (v2.11) RDKit via Python (v2.7) StarDrop (v6.2)

Atom Pairs (AP) {256, 512, 1024bit} - StarDrop {257 descriptors}

Topological Torsions (TT) {ditto} - -

Morgan radius 2 & 3 {ditto} Morgan radius 2 & 3 {256bit only} -

Feature Morgan radius 2 & 3 {ditto} Feature Morgan radius 2 & 3 {ditto} -

RDKit topological {ditto} RDKit topological {ditto} -

- MACCS {167 descriptors} -


Results

• SVM model produces an ordered list of probabilities for all 7 isoforms

• Measure how successful a prediction is by seeing if a MAJOR isoform is one of the top-k guesses

• Reporting the % of the 196 molecule/isoform test set is predicted correctly

− 140 unique compounds in test set

• Also calculated a “random” prediction and a “guided random” biased by the known proportion of compounds that each isoform metabolises

3A4 = 0.35, 2D6 = 0.23, 2C9 = 0.15, 1A2 = 0.10, 2C19 = 0.08, 2C8 = 0.06, 2E1 = 0.04

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Results for Morgan 256bit FPs in detail (KNIME)

• % correct prediction of a MAJOR isoform

− Top-1 (blue), Top-2 (orange), or Top-3 (grey) criteria

• The models produced perform much better than random or educated guess (mostly)

• Incorporation of the MINOR isoform data has a dramatic deleterious effect on success

− Could be considered as ‘noise’ to the MAJOR signal

− Generally the Morgan FPs (radius 2 & 3) suffer the most

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% c

orr

ect

FeatM Morgan Randoms


Results for other 256bit FPs in detail (KNIME)

• MINOR noise doesn’t affect the RDKit or TT fingerprints

− RDKit FP is the poorest for Top-2 or Top-3 performance

− TT is the only FP that out-performs random for all points & all criteria

• Oversampling helps to overcome this noise and helps the Top1 prediction

− Not so for Top2 or Top3 criteria

• StarDrop FP is the best performer with ~85% success for Top-2 criterion

− This is a slightly longer FP (257bits) and it is a frequency based rather than binary FP

− Hence do longer FP’s help..?

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% c

orr

ect

RDKit TT AP StarDrop


Results comparison of 512 & 1024bit FPs (KNIME)

• 512bit fingerprint, little different from 256bit

− although Feature Morgan radius 2 performance improves

• Morgan fingerprints are still the most affected by the MINOR ‘noise’

− RDKit now affected

• Longer FP − 1024Bit Helps the Morgan FPs

− 512 or 1024bit for the AP’s helps to reduce the effect of the MINOR noise

− Brings the AP & Feature Morgan FP up to StarDrop FP performance levels

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% c

orr

ect

AP TT RDKit FeatM3 Morgan3 FeatM2 Morgan2 StarDrop


Results summary comparison for each FP

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• Feature Morgan radius 2 = better tolerates the MINOR noise in going from 256 to 512 but no gain to 1024

• Feature Morgan radius 3 = only improvements seen at the 1024bit length & not before

• Morgan radius 2 = some improvement in MINOR noise tolerance from 256 to 512 and on to 1024 but no effect on

performance with any of the other oversampling

• Morgan radius 3 = similar to the Feature Morgan effect only improvements seen at 1024bit level

• Topological Torsions = virtually no effect on performance at FP increases

• Atom Pairs = Tolerance of the MINOR noise improves 256 to 512 but not 1024 only in top2 or top3 predictions not in

top1

• RDKit = MINOR noise tolerance gets worse in moving away from 256bit whilst other success improves (no difference

between the 512 & 1024 bit FPs)


Results comparison KNIME with Python versions

• MACCS gave >90% success in Top-2 criterion with inclusion of MINOR data & oversampling

• Surprising for such a simple descriptor

• Performances generally were better for what should have been equivalent FPs

• The FP was reversed in one generation method compared to the other

− ie Bit1-Bit256 was = Bit256-Bit1 in the other method

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% c

orr

ect

RDKit FeatM3 Morgan3 FeatM2 Morgan2 MACCS


Results reversing Python fingerprints

• Specifically reversing the order of the bits in the Python derived FP reduces the predictive ability of the system

− With the ‘e1071’ SVM package

• So reversing the KNIME derived FP’s should be beneficial

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% c

orr

ect

RDKit FeatM3 Morgan3 FeatM2 Morgan2 MACCS


Results comparison of KNIME forward with reversed versions

• Reversed versions of the KNIME FPs didn’t produce consistently better results

• Fingerprints shown for illustration

− Feature Morgan 1024bit,

− RDKit 256bit,

− StarDrop

• Why the difference..?

− Different order of compounds in each training set

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% c

orr

ect

RDKit StarDrop StarDrop rev

FeatM2 RDKit rev

FeatM2 rev


Results comparison of KNIME forward, reversed, & reordered versions

• The reordered descriptor files gave a 0-5% improvement in predictive success

− depending on the fingerprint type and the length

• Reversing AND reordering did not reach the predictive success of the MACCS FP

• No consistent pattern for the improvement

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% c

orr

ect

AP512 AP256 TT512 TT1024 TT256 MACCS AP1024


Our MACCS model vs WhichCYP web model

• The test set predicted using the WhichCYP Version 1.2 web resource

− http://www.farma.ku.dk/whichcyp/index.php

− libSVM and CDK libraries used

− Individual 2 category predictions for 1A2, 2C9, 2C19, 2D6, 3A4

− Built on inhibition data rather than substrate data

• The predictions were converted into probabilities

− one CYP probability was 1.0, two CYPs were 0.5 each…

• Results show that our SVM with MACCS fingerprint performs very well by comparison.

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% c

orr

ect

WhichCYP MACCS

http://www.farma.ku.dk/whichcyp/index.php




Conclusions

• MACCS keys perform surprisingly well

− although other fingerprints eg StarDrop, or Atom Pairs, are good performers

• SVM package appears to have some sensitivity to order of columns and/or order of rows

• Over sampled MAJOR data can provide better models for the most stringent Top1 criteria with some fingerprints (eg Atom Pairs).

− Mixing MAJOR & MINOR data equally simply provides noise

− Not all descriptor fingerprints are sensitive to the ‘MINOR’ noise.

• Fingerprint length (256, 512 or 1024 bits) – longer was not always better

− Morgan FP performances benefitted from longer fingerprints.

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Acknowledgements

• Hepatic and Cardio Toxicity Systems modelling

− The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under the grant agreement no 602156

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Documents

WhichP450: Predicting which CYP450 isoforms are involved ...€¦ · which P450 from a list of 7 isoforms 2D6, 2C8, 2C9, 1A2, 2E1, 2C19, 3A4 − Not a modelling exercise that predicts