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Structural Biology Group www.bonvinlab.org
Anna Vangone
Computational Structural Biology group Utrecht University
PRODIGY: a binding affinity prediction server
1. INTRODUCTION
2. THE METHOD
3. RESULTS
4. CONCLUSION
Interaction between proteins: binding affinity
DNA replication Immune response Signaling cascade … many more
DNA replication Immune response Signaling cascade … many more ΔG = RT lnKd
Kd=dissociation constant
Interaction between proteins: binding affinity
DNA replication Immune response Signaling cascade … many more ΔG = RT lnKd
Kd=dissociation constant
Interaction between proteins: binding affinity
Prediction of bingind affinity: why?
Experimental determination is difficult Control/engineering interactions
Rational drug design
How: exact methods, empirical scoring functions, …
BSA: Chothia & Janin. Nature(1975), Horton & Lewis (1992)
ΔG= f(BSA)
Structural properties
Non Interacting Surface (NIS)
NIS: Kastritis et al. J Mol Biol (2014)
BSA: Chothia & Janin. Nature(1975), Horton & Lewis (1992)
ΔG= f(BSA)
ΔG = f(BSA, NIS)
Structural properties
Non Interacting Surface (NIS)
NIS: Kastritis et al. J Mol Biol (2014)
BSA: Chothia & Janin. Nature(1975), Horton & Lewis (1992)
ΔG= f(BSA)
ΔG = f(BSA, NIS)
Structural properties
1. INTRODUCTION
2. THE METHOD
3. RESULTS
4. CONCLUSION
Vangone and Bonvin, eLife (2015)
Interfacial contacts (ICs): number of pair-residues within a distance cut-off
5.5 Å
After optimization
Contacts: the method
ICs total
6
Vangone and Bonvin, eLife (2015)
Interfacial contacts (ICs): number of pair-residues within a distance cut-off
5.5 Å
After optimization
Contacts: the method
ICs total
6
Classification of residues based on their physico-chemical properties
Vangone and Bonvin, eLife (2015)
Interfacial contacts (ICs): number of pair-residues within a distance cut-off
5.5 Å
After optimization
Contacts: the method
ICs total
ICs Property P1
ICs Property P2
r
6 2 4 …
Performance: reported as Pearson’s Correlation Coefficient
P1 is #ICs between charged-polar residues P2 is #ICs between polar-apolar residues ……
Example:
The predictor
ΔGpredicted = w1P1 + w2P2 + … LINEAR REGRESSION MODEL
P1 Charged/Charged
P2 Charged/Polar
P3 Charged/Apolar
P4 Polar/Polar
P5 Polar/Apola
r
P6 Apolar/Apolar
r
w1 w2 w3 w4 w5 w6 N
P1 Charged/Charged
P2 Charged/Polar
P3 Charged/Apolar
P4 Polar/Polar
P5 Polar/Apola
r
P6 Apolar/Apolar
r
w1 w2 w3 w4 w5 w6 M
The predictor
ΔGpredicted = w1P1 + w2P2 + … LINEAR REGRESSION MODEL
P1 Charged/Charged
P2 Charged/Polar
P3 Charged/Apolar
P4 Polar/Polar
P5 Polar/Apola
r
P6 Apolar/Apolar
r
w1 w2 w3 w4 w5 w6 N
FEATURE SELECTION (AIC) (Akaike Information Criterion)
P1 Charged/Charged
P2 Charged/Polar
P3 Charged/Apolar
P4 Polar/Polar
P5 Polar/Apola
r
P6 Apolar/Apolar
r
w1 w2 w3 w4 w5 w6 M
The predictor
ΔGpredicted = w1P1 + w2P2 + … LINEAR REGRESSION MODEL
25% prediction
P1 Charged/Charged
P2 Charged/Polar
P3 Charged/Apolar
P4 Polar/Polar
P5 Polar/Apola
r
P6 Apolar/Apolar
r
w1 w2 w3 w4 w5 w6 N
FEATURE SELECTION (AIC) (Akaike Information Criterion)
CROSS-VALIDATION: 4-fold cross-validation
75% training Dataset:
Fold_1 Fold_2 Fold_3 Fold_4
Fold_1 Fold_2 Fold_3 Fold_4
Fold_1 Fold_2 Fold_3 Fold_4
Fold_1 Fold_2 Fold_3 Fold_4
X 10
The dataset: protein-protein binding affinity benchmark
Binding affinity
Stronger Weaker
• Functional classes (antibody 12%, enzymes 41%, other 47%) • ΔG (-4.3 / -18.6) kcal mol-1
• BSA (808 – 3370) Å2
• Methods (Kd) (SPR, florescence, ITC…) • Conformational changes (0.17-4.90) Å
Benchmark in: Kastritis at al., Protein Sci 2011
122 complexes with complete crystallographic
structure
-18.6 kcal mol-1 -4.3 kcal mol-1
1. INTRODUCTION
2. THE METHOD
3. RESULTS
4. CONCLUSION
Experimental ΔGs (kcal mol-1)
R=-0.50 p-value<0.0001
Performance and sorting by (experimental) techniques IC
s
Technique r_ICs r_BSA #cases
All -0.50 -0.32 122
Stopped-flow -0.70 -0.55 8
Spectroscopy -0.65 -0.27 14
ITC -0.55 -0.64 20
SPR -0.53 -0.44 39
Inhibition Assay 0.05 -0.08 17
Fluorescence 0.04 0.34 19
10
20
30
40
50
60
70
80
-20 -18 -16 -14 -12 -10 -8 -6 -4
Experimental ΔGs (kcal mol-1)
R=-0.50 p-value<0.0001
Performance and sorting by (experimental) techniques IC
s
Technique r_ICs r_BSA #cases
All -0.50 -0.32 122
Stopped-flow -0.70 -0.55 8
Spectroscopy -0.65 -0.27 14
ITC -0.55 -0.64 20
SPR -0.53 -0.44 39
Inhibition Assay 0.05 -0.08 17
Fluorescence 0.04 0.34 19
10
20
30
40
50
60
70
80
-20 -18 -16 -14 -12 -10 -8 -6 -4
10
20
30
40
50
60
70
80
-20 -18 -16 -14 -12 -10 -8 -6 -4
IC
s
R=0.05 p-value<0.4
Experimental ΔGs (kcal mol-1) Experimental ΔGs (kcal mol-1)
R=-0.50 p-value<0.0001
Performance and sorting by (experimental) techniques IC
s
Technique r_ICs r_BSA #cases
All -0.50 -0.32 122
Stopped-flow -0.70 -0.55 8
Spectroscopy -0.65 -0.27 14
ITC -0.55 -0.64 20
SPR -0.53 -0.44 39
Inhibition Assay 0.05 -0.08 17
Fluorescence 0.04 0.34 19
Inhibition Assay + Fluorescence 10
20
30
40
50
60
70
80
-20 -18 -16 -14 -12 -10 -8 -6 -4
10
20
30
40
50
60
70
80
-20 -18 -16 -14 -12 -10 -8 -6 -4
10
20
30
40
50
60
70
80
-20 -18 -16 -14 -12 -10 -8 -6 -4
IC
s
R=0.05 p-value<0.4
Experimental ΔGs (kcal mol-1) Experimental ΔGs (kcal mol-1)
Experimental ΔGs (kcal mol-1)
R=-0.59 p-value<0.0001
R=-0.50 p-value<0.0001
Performance and sorting by (experimental) techniques IC
s
IC
s
Technique r_ICs r_BSA #cases
All -0.50 -0.32 122
Stopped-flow -0.70 -0.55 8
Spectroscopy -0.65 -0.27 14
ITC -0.55 -0.64 20
SPR -0.53 -0.44 39
Inhibition Assay 0.05 -0.08 17
Fluorescence 0.04 0.34 19
Inhibition Assay + Fluorescence 10
20
30
40
50
60
70
80
-20 -18 -16 -14 -12 -10 -8 -6 -4
Predictive Models
ICs total
ICs Property-based
NIS r
✓ 0.59
Vangone and Bonvin, eLife (2015)
ΔGpred=wICs
Predictive Models
ICs total
ICs Property-based
NIS r
✓ 0.59
✓ 0.67
Vangone and Bonvin, eLife (2015)
ΔGpred=w1P1+w2P2+….
Predictive Models
ICs total
ICs Property-based
NIS r
✓ 0.59
✓ 0.67
✓ ✓ 0.73
Vangone and Bonvin, eLife (2015)
ΔGpred=w1P1+w2P2+….
Predictive Models
ICs total
ICs Property-based
NIS r
✓ 0.59
✓ 0.67
✓ ✓ 0.73
ΔGpredicted= - 0.09459 ICscharged/charged
- 0.10007 ICscharged/apolar
+ 0.19577 ICspolar/polar
- 0.22671 ICspolar/apolar
+ 0.18681 %NISapolar
+ 0.3810 %NIScharged - 15.9433
Vangone and Bonvin, eLife (2015)
Pre
dic
ted Δ
Gs (
kcal m
ol-
1)
-20
-18
-16
-14
-12
-10
-8
-6
-4
-20 -18 -16 -14 -12 -10 -8 -6 -4
Experimental ΔGs (kcal mol-1)
r = 0.73 RMSE= 1.89 kcal mol-1
ΔGpred=w1P1+w2P2+….
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
Pears
on
's C
orr
ela
tio
n
Comparison with other methods
Vangone and Bonvin, eLife (2015) 1CCharPPI web-server: Moal et al., Bioinformatics 2015
Performance compared with 105 functions reported in CCharPPI1, calculated on the same set of structures (“composite scoring functions” reported in the plot)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
Pears
on
's C
orr
ela
tio
n
Comparison with other methods
Vangone and Bonvin, eLife (2015) 1CCharPPI web-server: Moal et al., Bioinformatics 2015
ALL RIGID FLEXIBLE
Performance compared with 105 functions reported in CCharPPI1, calculated on the same set of structures (“composite scoring functions” reported in the plot)
1. INTRODUCTION
2. THE METHOD
3. RESULTS
4. CONCLUSION
Take home message
Web-server: PRODIGY (PROtein binDIng enerGY prediciton)
The number of ICs correlates with binding affinity
Including waters Applying in scoring
Work in progress:
Take home message
http://milou.science.uu.nl/services/PRODIGY/
Web-server: PRODIGY (PROtein binDIng enerGY prediciton)
The number of ICs correlates with binding affinity
Including waters Applying in scoring
Work in progress:
Structural Biology Group www.bonvinlab.org
Alexandre Bonvin
Li Xue (poster #24)
João Rodrigues Panagiotis Kastritis
Gydo van Zundert
#28
Zeynep Kurkcuoglu
#15
Li Xue #24
