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Computational design of protein function Loren Looger Hellinga lab
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Computational design of protein function

Jan 08, 2016

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Computational design of protein function. Loren Looger Hellinga lab. 1. Allowable structures for proteins, DNA, small molecules. Progesterone. 2. Pseudo-geometric potential. electrostatics. H-bonds. sterics. solvation. Pretty much like CHARMM. a ~ 1.1. E. r. Hydrogen bonds, too. - PowerPoint PPT Presentation
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Page 1: Computational design of protein function

Computational design of protein function

Loren Looger

Hellinga lab

Page 2: Computational design of protein function

1. Allowable structures for proteins, DNA, small molecules

Progesterone

Page 3: Computational design of protein function

2. Pseudo-geometric potential

H-bonds

electrostatics

sterics

solvation

Page 4: Computational design of protein function

Pretty much like CHARMM...

E

r

E=Ar12 −

Br6

E'(r)=E(rmin −rmin −rα

),r <rmin

~ 1.1

Page 5: Computational design of protein function

Hydrogen bonds, too...

D

HA

anchorr

5roptr

⎝ ⎜

⎠ ⎟

12

−6roptr

⎝ ⎜

⎠ ⎟

10

cos2θ cos2 φopt−φ( )-8 · { } · ·

Page 6: Computational design of protein function

Area-based solvation energy

P P

H H

H

P = polarH = hydrophobic

Page 7: Computational design of protein function

Electrostatic potential

E =q1q2

εr

is a function of atom-type pair &protein environment.

Parameterized to fit experimental data.

Page 8: Computational design of protein function

3. Algorithm for choosing best structure(s) from all available

Page 9: Computational design of protein function

Complementary Surface Construction:Complementary Surface Construction:

Molten zone

Evolving zone

Fixed zone

Ligandcoordinates

Proteincoordinates

Poly-alaninePCS

Rotationalligand

ensembleDocking

grid

Force field

Placedligand

ensemble

Fixed ligand ensemble

Side-chainrotamers

EvolvedPCS

ensemble

Ranked PCS ensemble

Experiments

Periplasmic Binding Protein (PBP) scaffolds

Page 10: Computational design of protein function

MetabolitesMetabolites

ExplosivesExplosives PollutantsPollutants

DrugsDrugsNeurotransmittersNeurotransmitters

Chemical ThreatsChemical Threats

NH

HO

NH3+

TNT RDX

MTBE

D-lactateL-lactate

5-fluorouracil

ibuprofen

PMPA~soman

serotonin

NH3+HO

HOdopamine

N

N

NNO2

-

NO2-

NO2-

NO2-

NO2-

CH3

NO2-

O

OH

O

CH3

CH3

CH3

CH3

CH3

O

H3C

CH3

CH3

CH3

NH

NH

O

O

F

CH3

H-OOC

H3C

CH3

H

O

CH3C

O

O

H

&

[L-lactate] (µM) 0

0.5

1

40 80

Fx

Kd = 2 µM

Fx

0

0.5

1

52.5 100

Kd = 6 µM

[serotonin] (µM)

0

0.5

1

12.5 25

Kd = 2 nMxF

[TNT] (nM)

0

0.5

1

0 150 300

Fx

Kd = 4 nM

[5-fluorouracil] (nM)

Fx

0

0.5

1

50 100

Kd = 6 µM

[MTBE] (µM) 0

0.5

1

0.25 0.5

Fx

Kd = 45 nM

[PMPA] (µM)

Page 11: Computational design of protein function

QSAR Results for binding affinities QSAR Results for binding affinities for L-lactate & TNT Receptorsfor L-lactate & TNT Receptors

Calculated affinities from

log K

d

( ) = c

+ c

Δ G

elec

+ c

A + c

4

N

unsat

+ c

5

N

clash

+ c

6

s − s

0.

linear regressi oncoefficients, c…c6,obtained by a least-square s f it of t he experimenta l data; ΔGelec electrostat ic contribution;A nonpolar contact area between receptor and ligand;Nunsat number of unsatisfied hydrogen bonds i nthe ligand;Nclash number of steric clas hes between t heli gandand receptor (defi neda s contacts >5 kca/l mo );l s rati o of the volumes of the wild-type li gandto t hetarge t ligand;s0 apparen toptimum value of s for a particular li .gand

-10

-8

-6

-4

-2

0

log Kd (obs) -8 -4-6 -2

Page 12: Computational design of protein function

QBP

GBP

ABP

HBP

1100µM

10

101

100mM

0.1

RBP

L-lactate designs

Page 13: Computational design of protein function

QBP

GBP

ABP

HBP

1100µM

10

101

100mM

0.1

RBP

The use of QSARs in the predictions improves the designs: D-lactate

Page 14: Computational design of protein function

Construction of biological sentinels for Construction of biological sentinels for chemical threats and pollutantschemical threats and pollutants

[inducer]

exp

ress

ion modulation binary

Page 15: Computational design of protein function

Unicellular sentinels for chemical threats and pollutants

- + - +

Ribose

Lactate

5 Fluoro-uracil

TNT

MTBE

Page 16: Computational design of protein function

100 M 10 M 1 M 0.1 M 0.01 M 0.001 M

IPTG 0 M TNT

[TNT]

100 M2,4-DNT

100 M2,6-DNT

Dose Response of TNT Signaling

Page 17: Computational design of protein function

-1

-0.5

0

0.5

1

0 10 20 30 40 50 60

Ab

sorb

ance

210n

m

-1

-0.5

0

0.5

1

-1.5

-1

-0.5

0

0.5

1

1.5

0 10 20 30 40 50 60

Fraction #

Wt Gbp

L-Lac.G1

D-Lac.G1

KdlactateD L

none none

200µM 3µM

0.8µM 10µM

Immobilized receptors

Racemic mix

Optically pure enantiomers

0 10 20 30 40 50 60

LD

L

D

Page 18: Computational design of protein function

Computational design of ligand-binding sitesStrategy #2: predefined geometries

{ l, 1, 2, 1, 2, 3 }n

geometrical description of

essential features in the

complementary surface

side-chain rotamer library

+...

Site 1

Site 2

Combinatorial search

(108 sequence1012 rotamers)

Calculation #1Initial placement of PCS

on scaffold backbone

Design scaffold coordinates

Pairwise of atomic interactions

Complementarysurface

construction(1010-10200

rotamers)

Site 1

Site 2

+...

Calculation #2Complementary

surface construction(PCS + SCS)

Page 19: Computational design of protein function

Triose phosphate isomerase chemistry

Page 20: Computational design of protein function

Acknowledgements

• Mary Dwyer

• Jeff Smith

• Shahir Rizk