Dead-End Elimination for Protein Design with Flexible Rotamers

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Dead-End Elimination for Protein Design with Flexible Rotamers Ivelin Georgiev Donald Lab 02/19/2008

description

Dead-End Elimination for Protein Design with Flexible Rotamers. Ivelin Georgiev Donald Lab 02/19/2008. Computational Design. wildtype. energy function. input structure. rotamer library. protein design algorithm. stability specificity novel function drug design. mutant. …. C. - PowerPoint PPT Presentation

Transcript of Dead-End Elimination for Protein Design with Flexible Rotamers

Page 1: Dead-End Elimination for Protein Design with Flexible Rotamers

Dead-End Eliminationfor Protein Design

with Flexible Rotamers

Ivelin Georgiev

Donald Lab

02/19/2008

Page 2: Dead-End Elimination for Protein Design with Flexible Rotamers
Page 3: Dead-End Elimination for Protein Design with Flexible Rotamers

protein design protein design algorithmalgorithm

energy function rotamer

library

inputinputstructurestructure

stabilityspecificity

novel function

drug designdrug design

wildtypewildtype

mutantmutant

Computational Design

Page 4: Dead-End Elimination for Protein Design with Flexible Rotamers

CCCC

MinDEE

qqqq q*q*q*q*

partition functionε-approximation algorithm

redesign for Leu

Contributions

provable energy minimization

ensembles

Page 5: Dead-End Elimination for Protein Design with Flexible Rotamers

Traditional-DEE

E lowerbound

E upperbound

itir

rotamer pruning

Enumerate

fixed backbone/side-chains

Desmet et al., 1992

GMECGMEC

ir

it

conformations

E

O(qO(q22nn22))

Page 6: Dead-End Elimination for Protein Design with Flexible Rotamers

Traditional-DEE with Rigid Rotamers/Backbone

Conformations

Ene

rgy

it

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Traditional-DEE with Side-chain Dihedral FlexibilityConformations

Ene

rgy

min

max

it

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Traditional-DEETraditional-DEE

CCCC

E minimizationE minimization

Xnot provably-correct

CCCC

MinDEEMinDEE

E minimizationE minimization

provably-correct

Traditional-DEETraditional-DEE

CCCC

rigid energiesrigid energies

√provably-correct

Page 9: Dead-End Elimination for Protein Design with Flexible Rotamers

CCCC

MinDEEMinDEE

E minimizationE minimization

provably-correct

Page 10: Dead-End Elimination for Protein Design with Flexible Rotamers

continuousside-chain

dihedral space

voxelsvoxelsbound rotamer

movement

MinDEE

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lowerlower / / upperupper energy boundsenergy boundsir

js

it

χir

Energy

MinDEE

E(ir , js)

ir

js

js

ir

χjs

Page 12: Dead-End Elimination for Protein Design with Flexible Rotamers

MinDEE:

pruning candidate

competitor

witness

not in trad-DEElowerlower boundbound

on on ir conformation conformationenergiesenergies

-upperupper boundbound

on on it conformation conformationenergiesenergies

-possible energypossible energychanges due tochanges due to

rotamer movementrotamer movement

> 0

lowerlower / / upperupper energy boundsenergy boundsir

js

it

Page 13: Dead-End Elimination for Protein Design with Flexible Rotamers

traditional-DEE

MinDEE

MinDEEMinDEE: Side-chain Dihedral Flexibility

Page 14: Dead-End Elimination for Protein Design with Flexible Rotamers

MinDEE Applications

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∫1Z

K*: provably-accurate approximation to the binding constant via conformational

ensembles

JCB’05

min

GMEC-basedGMEC-based

singlelowest-energyconformation

weightedaverage

Ensemble-basedEnsemble-based

a

sequence K*

TIAAIC 7.3

GIRMQM 3.1

TGIAIV 2.9

LMLAIS 1.7

TWAIGY 0.3

MinDEE Applications

Page 16: Dead-End Elimination for Protein Design with Flexible Rotamers

MinDEE/A*: GMEC-based Method

Page 17: Dead-End Elimination for Protein Design with Flexible Rotamers

full Eminimization

CC

C’C’

MinDEEpruning

A* search(E lower bounds)

MinDEE/A*: GMEC-based Method

O(nO(n22rr22))

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full Eminimization

CC

C’C’

MinDEEpruning

A* search(E lower bounds)

MinDEE/A*: GMEC-based Method

O(nO(n22rr22))

Page 19: Dead-End Elimination for Protein Design with Flexible Rotamers

full Eminimization

CC

C’C’

MinDEEpruning

A* search(E lower bounds)

MinDEE/A*: GMEC-based Method

O(nO(n22rr22))

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full Eminimization

CC

C’C’

MinDEEpruning

A* search(E lower bounds)

……

… …

MinDEE/A*: GMEC-based Method

B(c) > E(best)B(c) > E(best)

O(nO(n22rr22))

minGMEC

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Hybrid-K*: Ensembles Method

Page 22: Dead-End Elimination for Protein Design with Flexible Rotamers

Hybrid-K*: Ensembles Method

CC

C’C’

DEEpruning

A* search(E lower bounds)

q*

full Eminimization

Volumefilter

seq1

p’

Page 23: Dead-End Elimination for Protein Design with Flexible Rotamers

Hybrid-K*: Ensembles Method

CC

C’C’

DEEpruning

A* search(E lower bounds)

q*

full Eminimization

Volumefilter

seq1

p’

Page 24: Dead-End Elimination for Protein Design with Flexible Rotamers

Hybrid-K*: Ensembles Method

CC

C’C’

DEEpruning

A* search(E lower bounds)

q*

full Eminimization

p’

q* < (1-ε)q

Page 25: Dead-End Elimination for Protein Design with Flexible Rotamers

Hybrid-K*: Ensembles Method

CC

DEEpruning

A* search(E lower bounds)

q*

full Eminimization p’

q* ≥ (1-ε)q

repeat search

C’C’

CC

C’C’

DEEpruning

A* search(E lower bounds)

q*

full Eminimization

p’

q* < (1-ε)q

Page 26: Dead-End Elimination for Protein Design with Flexible Rotamers

Hybrid-K*: Ensembles Method

CC

C’C’

DEEpruning

A* search(E lower bounds)

q*

full Eminimization

Volumefilter

seq1

CC

C’C’

DEEpruning

q*

full Eminimization

seqn

p’

A* search(E lower bounds)

q* ≥ (1-ε)qq* ≥ (1-ε)q

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Hybrid-K*: Inter-mutation Pruning

CC

C’C’

DEEpruning

A* search(E lower bounds)

q*

full Eminimization

Volumefilter

seq1

CCseqn

p’

q* ≥ (1-ε)q

Page 28: Dead-End Elimination for Protein Design with Flexible Rotamers

Hybrid-K*: Inter-mutation Pruning

CC

C’C’

DEEpruning

A* search(E lower bounds)

q*

full Eminimization

Volumefilter

seq1

CC

C’C’

DEEpruning

q*

full Eminimization

seqn

p’

A* search(E lower bounds)

K*i

p’

Ķ*n<

Page 29: Dead-End Elimination for Protein Design with Flexible Rotamers

Hybrid-K*: Inter-mutation Pruning

CC

C’C’

DEEpruning

A* search(E lower bounds)

q*

full Eminimization

Volumefilter

seq1

CC

C’C’

DEEpruning

q*

full Eminimization

seqn

p’

A* search(E lower bounds)

K*i

p’

Ķ*n

>>>

q* < (1-ε)q

Page 30: Dead-End Elimination for Protein Design with Flexible Rotamers

Hybrid-K*: Intra-mutation Pruning

CC

C’C’

DEEpruning

A* search(E lower bounds)

q*

full Eminimization

Volumefilter

seq1

CC

C’C’

DEEpruning

q*

full Eminimization

seqn

p’

A* search(E lower bounds)

q* ≥ (1-ε)qq* ≥ (1-ε)q

Page 31: Dead-End Elimination for Protein Design with Flexible Rotamers

E minimizationE minimization

Results

rigid energiesrigid energies single structuresingle structure

ensemblesensembles

MinDEE: EnsemblesMinDEE: Ensembles

Traditional-DEETraditional-DEE

MinDEE: GMEC-basedMinDEE: GMEC-based

K* (RECOMB’04)K* (RECOMB’04)

this workthis work previousprevious

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Structural Model

GrsA-PheA Active Site

235 236 239 278 299 301 322 330 331

D A W T I A A I C

• 1AMU (Conti et al., 1997)• Residues: 39

• flexible: 9• steric shell: 30

• Flexible ligand• AMP• Richardsons’ rotamer library• AMBER (vdW,elect,dihed) + EEF1• 2-point mutation search for Leu• GAVLIFYWM allowed

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Comparison to Traditional-DEE

• trad-GMEC ranked 397th

• E(minGMEC) < E(rigid-GMEC) by ≈ 6 kcal/mol

minGMEC:

* minGMEC rotamer pruned by traditional-DEE

235 236 239 278 299 301 322 330 331

D M W T I A* M I C

2 5 3 3 6 - 9 6 2

-150

-148

-146

-144

-142

-140

-138

-136

-134

0 1000 2000 3000 4000 5000 6000 7000

Conformation Rank

En

erg

y

minGMEC

trad-GMECtrad-GMEC

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Hybrid-K*

Predictions • T278M/A301G (Stachelhaus et al., 1999) ranked 3rd

• G301 in all known natural Leu adenylation domains• Experimental verification

Computational • 9 hrs. on 24 processors• Original K* fully-evaluated 30%

more conformations• K* w/o filters: ≈ 3,263 days

Conf. Remaining

Pruning Factor (%)

Initial 6.8 x 108 -

Volume Filter 2.04 x 108 3.33 (70.0)

MinDEE Filter 4.13 x 106 49.43 (98.0)

Steric Filter 3.86 x 106 1.07 (6.5)

A* Filter 7.82 x 104 49.41 (98.0)

Top 40 Mutations – Hybrid-K*

0

0.001

0.002

0.003

0.004

0.005

0.006

0 5 10 15 20 25 30 35 40 45 50

Log K* Score

Fra

ctio

n E

valu

ated

Co

nfs

(B

ou

nd

)

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• Ew = 12.5 kcal/mol• 4 days on a single processor• 206 of 421 rotamers pruned• over 60,000 extracted conformations• 7,261 conformations (221 unique sequences) within Ew

• minGMEC: A236M/A322M

MinDEE/A*Top 40 Mutations – MinDEE/A*

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

D235 M236 W239 T278 I299 M322 I330 C331

AS Residue

Ro

tam

er F

req

uen

cy (

no

rmal

ized

)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

-150 -148 -146 -144 -142 -140 -138 -136 -134

Energy

RM

SD

Rotamer Diversity for A236M/A322MRotamer Diversity for A236M/A322MConf Energies vs. RMSD for A236M/A322MConf Energies vs. RMSD for A236M/A322M

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Conclusions and Future Work• Traditional-DEE not correct with energy minimization

• MinDEE provably-correct and efficient

• MinDEE capable of returning lower-energy conformations

• Ensemble-based and GMEC-based redesign predictions are substantially different

• MinDEE: Ensembles Method successfully predicts both known and novel redesigns

• Improve MinDEE pruning efficiency

• Improve model accuracy

• Marriage of MinDEE and BD

Top 40 Mutations – Hybrid-K*

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Acknowledgments

• Bruce Donald

• Ryan Lilien

• Amy Anderson

• Serkan Apaydin

• John MacMaster

• Tony Yan

• All members of Donald Lab FundingFunding:: • NIHNIH• NSFNSF

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