1

1

Computational Biophysics @ GW

II. Biowulf Cluster (64 nodes)

I. Comp. Biophys Group

3 faculty, 1 postdoc, 5 grads

Multi-Scale Approach

Protein Modeling

Bio-Networks

Protein Complex

Network Evolution

HIV MA (Bukrinsky)CDK2 HIV/Cancer (Kashanchi)

Metabolic Control in Plants (Turano)Hypoxia Sensing (Simha, Donaldson)

Gene RegulatoryImmunodominance

2

Single Protein Modeling: StructuresMAQTDVILCPDTHQKAVCLEKIR EYFDCGLPSAQWCVIKNALLTFR

PNMDKILYVVACQGGARASLTLRGAWETRKLHCMQPIVYTTLNGLT

MNSAKPLVVTIYSQYNVHLRFDDGFDSKLACVNPMRTYEIWLETFRMLKSPQCNYIAVRIHGRYLDFGS

CVNKMYHGFDAALLTRQVLPSLTQIVLTAGNYIGRGPNIPCLDIGSEFIINCAQLVRENHWGVSGLRAN

LAGTRVNIMPCDEWSILSLMKIHFHDISAQVYTERPQMVKRLAFRA

TCNMRWDPSIVYTWQFGHLCVHEWMTVINEDSAPILCWHGGLMFGNVVEERPAADIMNWGLRCSLKELT

ILKNETVGGAPQWYIVHNQFNAKNQKDIETRYPMKSLVSCILHIKMLMKIHYDTFREWQVNSCKLDDVS

MSKALLVPQWIVRCSYTPLKWPSTCNMRWDPSIVYTWQFGHLSLVT

CAKVINEDSAPILCTRSGLRLTDVNYIPPAADIAQREMRCSLTNQLVDGHETVGGAPQWYFAKRLCRGA

DEYLIETRYPMKSLILSTAEEKLATDIHYDAGREWQVNSFELITSVETLDLLVPQWIVRCSHTFDIPQS

100,000 proteins in human alone! 1000 folds!

φ

ψ

A

K

R

W

2

3

Design Procedure

100

101

102

103

104

105

Designability (Number of "Compatible" Sequences)

100

101

102

Num

ber

of S

truc

ture

s

II. Pick Top Folds

III. Sequence Design and Verification

I. Model Computation

βαβ motif

2nac 2bnh

4

Single Protein Modeling: Drug Design

ATP-analogInhibitors

New Class of KinaseInhibitors

DoublingDNA

Cell Growth

Cell Division (Mitosis)

R

Cell Cycle & Cancer HIV-1 Replication

3

5

Experimental Verification (Fatah Kashanchi’s group)

cdk2cdk2/E

ATP (µm) - 0 1 10 50 100 0 1 10 50 100

Biotin-Tat (41/44) Biotin-Purv.A

cdk2cdk2

1 2 3 4 5 6 7 8 9 10

cyc EWB

cyc E

cdk2cdk2/E

ATP (µm) - 0 1 10 50 100 0 1 10 50 100

Biotin-Tat (41/44) Biotin-Purv.A

cdk2cdk2

1 2 3 4 5 6 7 8 9 10

cyc EWB

cyc E

PBMC Infection (HIV-1 UG/92/029, SI)

0

200

400

600

800

1000

1200

0 6 12 18 24 Days Post Infection

p24

(pg/

ml)

PBMC

PBMC + SI

PBMC + SI + WT peptide

PBMC + SI + 41/44 peptide

p24

(pg/

ml)

PBMC Infection (HIV-1 THA/92/00, NSI)

0

500

1000

1500

2000

2500

0 6 12 18 24

Days Post Infection

PBMC

PBMC + NSI

PBMC + NSI + WT peptide

PBMC + NSI + 41/44 peptide

0

1000

2000

3000

4000

5000

6000

7000

8000

1 2 3 4 5 6 7 8 9 10 110

1000

2000

3000

4000

5000

6000

7000

8000

1 2 3 4 5 6 7 8 9 10 11

126

WT

WT

WT

134

150

234

180

178

WT

WT

GST-Cdk2

GST-Cdk9 - - - - - - - --

B)

--

1 8765432 11109

8765432 9

Tat Peptide

Cdk2

Tat Peptide (wt) - + - - - - - - - - +

-Tat Peptide (41/44) - + + + + + + + + -

- - - - - - -- + +GST -Cdk9 -GST -Cdk2 + + + + + + + + + - -

WT WT

126

134

150

234

180

178

1 8765432 11109

8765432 9

Tat Peptide

Cdk2

Tat Peptide (wt) - + - - - - - - - - +

-Tat Peptide (41/44) - + + + + + + + + -

- - - - - - -- + +GST -Cdk9 -GST -Cdk2 + + + + + + + + + - -

WT WT

126

134

150

234

180

178A)

Phos

phor

oIm

ager

Uni

ts

0

1000

2000

3000

4000

5000

6000

7000

8000

1 2 3 4 5 6 7 8 9 10 110

1000

2000

3000

4000

5000

6000

7000

8000

1 2 3 4 5 6 7 8 9 10 11

126

WT

WT

WT

134

150

234

180

178

WT

WT

GST-Cdk2

GST-Cdk9 - - - - - - - --

B)

--

1 8765432 11109

8765432 9

Tat Peptide

Cdk2

Tat Peptide (wt) - + - - - - - - - - +

-Tat Peptide (41/44) - + + + + + + + + -

- - - - - - -- + +GST -Cdk9 -GST -Cdk2 + + + + + + + + + - -

WT WT

126

134

150

234

180

178

1 8765432 11109

8765432 9

Tat Peptide

Cdk2

Tat Peptide (wt) - + - - - - - - - - +

-Tat Peptide (41/44) - + + + + + + + + -

- - - - - - -- + +GST -Cdk9 -GST -Cdk2 + + + + + + + + + - -

WT WT

126

134

150

234

180

178A)

Phos

phor

oIm

ager

Uni

ts

Mode of Action !

in vitro binding in vivo ChIP

in vitro mutagenesis cell culture vital suppression

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From Nodes to Network Cdk2 knockout mice are viable.

P. Kaldis et al, NCI, Frederick, MD, Curr Biol 2003, 13:1775-1785

TAALD -8.04 kcal/mol TAALE -6.35 kcal/mol LAALS -5.85 kcal/mol TAACS -5.85 kcal/mol FAALS -5.83 kcal/mol

TAALS -5.30 kcal/mol

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7

Oxygen Sensing (Hypoxia Reponse Network)

Biologist Engineer Mathematician

Flux Analysis

Control Theory –

• Kirchhoff’s Conservation Law

• Second Law of Thermodynamics: positive entropy production(dc current and voltage, I V = Heat Dissapation)

3. Flux and Chemical Potential -> nonlinear relation(low external flux -> linear Hill-Onsager’s Theory)

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Oxygen Low – 90% of the flux Oxygen High – 90% of the flux

Pathway Switch Leads to Sharp Oxygen Response