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Computational Methods for Automated Generation of Enzyme Mutants Kasper Primdal Lauritzen Department of Chemistry, University of Copenhagen June 25, 2012 Slide 1/10 Outline 1 Backgound 2 Methods 3 Results 4 Outlook Kasper Primdal Lauritzen

SLIDE 1

Computational Methods for Automated Generation of Enzyme Mutants

Kasper Primdal Lauritzen

Department of Chemistry, University of Copenhagen

June 25, 2012 — Slide 1/10

SLIDE 2

Outline

1 Backgound 2 Methods 3 Results 4 Outlook

Kasper Primdal Lauritzen (Dept. of Chemistry) — Automated Enzyme Mutation — June 25, 2012 — Slide 2/10

SLIDE 3

Background

Goal

Get from WT structure to activity estimate of mutants in an automated process.

Model

Candida Antarctica lipase B (CalB). An esterase, tested for amidase activity.

Evaluation

Interpolation between ES and TI states. Rough estimate, points to promising candidates.

Kasper Primdal Lauritzen (Dept. of Chemistry) — Automated Enzyme Mutation — June 25, 2012 — Slide 3/10

SLIDE 4

Methods

3 central steps

Structure Preparation
Mutating
Interpolation / Evaluation

Kasper Primdal Lauritzen (Dept. of Chemistry) — Automated Enzyme Mutation — June 25, 2012 — Slide 4/10

SLIDE 5

Methods

Structure Preparation

Crystal Structure obtained from PDB (1LBS)

Preparation steps

Crystal Waters
Protonation
Substrate Placement
Initial Optimization

Kasper Primdal Lauritzen (Dept. of Chemistry) — Automated Enzyme Mutation — June 25, 2012 — Slide 5/10

SLIDE 6

Methods

Substrate Placement

R2 R1 O N H Nε2 HNδ1 H Oγ Nε2 H HNδ1 R1 R2 N H O Oγ ES TI TS

His224 Ser105 Substrate His224 His224 Ser105 Ser105 Substrate Substrate

H Nε2 HNδ1 R1 R2 H O N Oγ

Kasper Primdal Lauritzen (Dept. of Chemistry) — Automated Enzyme Mutation — June 25, 2012 — Slide 6/10

SLIDE 7

Methods

Mutation

PyMOL has a library of rotamers for each amino acid.
Loop over each rotamer, optimize with PyMOL and

evaluate energy with MOPAC.

Choose rotamer with lowest energy as canonical mutant.
Place mutant in TI structure and optimize
Generate ES from optimized TI

Kasper Primdal Lauritzen (Dept. of Chemistry) — Automated Enzyme Mutation — June 25, 2012 — Slide 7/10

SLIDE 8

Methods

Interpolation

10 interpolation frames to represent the reaction scheme.
Constrain the position of Ser105 Oγ and the substrate

carbonyl carbon in each frame.

Optimize the remaining structure
Evaluate the energy of each frame.

Kasper Primdal Lauritzen (Dept. of Chemistry) — Automated Enzyme Mutation — June 25, 2012 — Slide 8/10

SLIDE 9

Results

Time Requirements

A more strict initial optimization does not result in

faster mutant optimizations.

A single mutant optimization can be done in roughly 24

hours.

Wildtype L140K L140R L140N L140Q 2 4 6 8 10 12 14 16

Optimization time

WT vs. mutants

GNORM=5 GNORM=2

T i m e i n d a y s Kasper Primdal Lauritzen (Dept. of Chemistry) — Automated Enzyme Mutation — June 25, 2012 — Slide 9/10

SLIDE 10

Outlook

Link MOPAC with each script (!)
Try every mutation
Multi-fold mutations
Automatic interpolation evaluation

Kasper Primdal Lauritzen (Dept. of Chemistry) — Automated Enzyme Mutation — June 25, 2012 — Slide 10/10