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
4
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
