bioexcel webinar series #7: prodigy, a web server to predict binding affinities in protein-protein...

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PRODIGY, a web server to predict binding affinities

in protein-protein complexes

Presenters: Anna Vangone

Host: Adam Carter

BioExcel Educational Webinar Series #7

12 October, 2016

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This webinar is being recorded

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Today’s Presenter

Anna Vangone studied Chemistry at University of Salerno

(Italy) and received her MSc cum laude in 2009. She then

joined the doctoral program in bioinformatics at University of

Salerno and worked as visiting PhD student at University of

Oxford (UK), obtaining her PhD in 2013.

After a few visits at King Abdullah University of Science and

Technology (Saudi Arabia) as an invited scientist, she joined

the computational structural biology group at Utrecht

University as a post-doc researcher. In 2015 she obtained a

prestigious Marie Curie individual fellowship that has

supported her research at Utrecht University since October

2015.

Her research interests are focused on the study of protein-

protein interactions, characterization and analysis of biological

complexes by computational modeling and docking in

particular. Her work has resulted in 15 peer-reviewed

publications and several international conference invitations.

5

Structural Biology Groupwww.bonvinlab.org

[email protected]

Anna Vangone

Computational Structural Biology group

Utrecht University

PRODIGY

A web-server to predict binding affinity

in protein-protein complexes

1. INTRODUCTION

2. THE METHOD

3. RESULTS

4. CONCLUSION

Binding affinity

Computational prediction methods

1. INTRODUCTION

2. THE METHOD

3. RESULTS

4. CONCLUSION

1. Introduction - 2. Method - 3. Results - 4. Conclusion

DNA replication

Immune response

Signaling cascade… many more

Protein-protein interactions


Protein-protein interactions: binding energy

ΔG

En

ergy

Conformational space

+

ΔGbind = Gbound – Gfree = RT ln(K)


Protein-protein interactions: binding energy

ΔG

En

ergy

Conformational space

+

ΔGbind = Gbound – Gfree = RT ln(K)

EXACT METHODS

EMPIRICAL SCORING

FUNCTIONS

STRUCTURAL PROPERTIES

MD direct counting

Umbrella Sampling

Combination of Energetics terms (En)

• Electrostatic

• Van der Waals

• Exclusion solvent

• ……

Inaccurate/fast

NIS: Kastritis et al. J Mol Biol (2014)

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

1. INTRODUCTION

2. THE METHOD

3. RESULTS

4. CONCLUSION

Interfacial contacts (ICs):

number of pair-residues within a distance cut-off


Contacts at the interface

5.5 Å

Vangone and Bonvin, eLife (2015)

Interfacial contacts (ICs):

number of pair-residues within a distance cut-off


Contacts at the interface

5.5 Å


ICstotal

ICsProperty P1

ICs Property P2

r

6 2 4 …

Performance: reported as Pearson’s Correlation

Coefficient

Classification of residues based on their

physico-chemical properties

P1 is #ICs between charged-polar residues

P2 is #ICs between polar-apolar residues

……

ΔGpredicted = w1P1 + w2P2 + …LINEAR REGRESSION MODEL

P1Charged/Charged

P2Charged/Polar

P3Charged/Apolar

P4Polar/Polar

P5Polar/Apola

r

P6Apolar/Apolar

r

w1 w2 w3 w4 w5 w6 N


Training the predictor

P1Charged/Charged

P2Charged/Polar

P3Charged/Apolar

P4Polar/Polar

P5Polar/Apola

r

P6Apolar/Apolar

r

w1 w2 w3 w4 w5 w6 M


P1Charged/Charged

P2Charged/Polar

P3Charged/Apolar

P4Polar/Polar

P5Polar/Apola

r

P6Apolar/Apolar

r

w1 w2 w3 w4 w5 w6 N

FEATURE SELECTION (AIC)(Akaike Information Criterion)



P1Charged/Charged

P2Charged/Polar

P3Charged/Apolar

P4Polar/Polar

P5Polar/Apola

r

P6Apolar/Apolar

r

w1 w2 w3 w4 w5 w6 M


25% prediction

P1Charged/Charged

P2Charged/Polar

P3Charged/Apolar

P4Polar/Polar

P5Polar/Apola

r

P6Apolar/Apolar

r

w1 w2 w3 w4 w5 w6 N

FEATURE SELECTION (AIC)(Akaike Information Criterion)

CROSS-VALIDATION: 4-fold cross-validation

75% trainingDataset:

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



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


The dataset: protein-protein binding affinity benchmark

i_rmds: 3.28 Åi_rmds: 0.42 Å

Rigid complex Flexible complex


Conformational changes upon binding

i_rmsd > 1.0 Å

i_rmds: 3.28 Åi_rmds: 0.42 Å

Rigid complex Flexible complexi_rmsd > 1.0 Å


Conformational changes upon binding

1. INTRODUCTION

2. THE METHOD

3. RESULTS

4. CONCLUSION

Experimental ΔGs (kcal mol-1)

R=-0.50p-value<0.0001

ICs

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

ICs and impact of experimental techniques1. Introduction - 2. Method - 3. Results - 4. Conclusion

10

20

30

40

50

60

70

80

-20 -18 -16 -14 -12 -10 -8 -6 -4

ICs

R=0.05p-value<0.4

Experimental ΔGs (kcal mol-1) Experimental ΔGs (kcal mol-1)

R=-0.50p-value<0.0001

ICs


All -0.50 -0.32 122



ITC -0.55 -0.64 20

SPR -0.53 -0.44 39



Inhibition Assay + Fluorescence10

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

ICs

R=0.05p-value<0.4

Experimental ΔGs (kcal mol-1) Experimental ΔGs (kcal mol-1)


R=-0.59p-value<0.0001

R=-0.50p-value<0.0001

ICs

ICs


All -0.50 -0.32 122



ITC -0.55 -0.64 20

SPR -0.53 -0.44 39



Inhibition Assay + Fluorescence10

20

30

40

50

60

70

80

-20 -18 -16 -14 -12 -10 -8 -6 -4


ICs

total

ICs Property-

basedNIS r

✓ 0.59


ΔGpred=w1P1+w2P2+….

The predictor1. Introduction - 2. Method - 3. Results - 4. Conclusion

ICs

total

ICs Property-

basedNIS r

✓ 0.59

✓ 0.67




ICs

total

ICs Property-

basedNIS r

✓ 0.59

✓ 0.67

✓ ✓ 0.73




ICs

total

ICs Property-

basedNIS 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


Pre

dic

ted

ΔG

s (k

cal

mo

l-1)

-20

-18

-16

-14

-12

-10

-8

-6

-4

-20 -18 -16 -14 -12 -10 -8 -6 -4


r = 0.73

RMSE= 1.89 kcal mol-1



Comparison with other methods1. Introduction - 2. Method - 3. Results - 4. Conclusion

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 methods1. Introduction - 2. Method - 3. Results - 4. Conclusion

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

ALL

RIGID

FLEXIBLE

1. INTRODUCTION

2. THE METHOD

3. RESULTS

4. CONCLUSION

PRODIGY: the web-server1. Introduction - 2. Method - 3. Results - 4. Conclusion

Xue, Rodrigues, Kastritis, Bonvin, Vangone. Bioinformatics (2016)

1. INTRODUCTION

2. THE METHOD

3. RESULTS

4. CONCLUSION

Take home message1. Introduction - 2. Method - 3. Results - 4. Conclusion

The number and nature

of interface contacts

is a simple but robust

predictor of

binding affinity

http://milou.science.uu.nl/services/PRODIGY/

ACKNOWLEDGMENTS

CSB group @ UU

Li Xue

João Rodrigues

Panagiotis Kastritis

Alexandre Bonvin

Further info

Cross-validation data

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


ICs BSA

• ICs performs better than BSA

BSA of each residue greatly depends from its ASA in the free form

BSAaa = ASAaa_free −ASAaa_complex

For aa at interface: ASAaa_complex ~ 0 BSAaa ∼ASAaa_free

The advantage of using contacts instead of surfaces?1. Introduction - 2. Method - 3. Results - 4. Conclusion

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