C E N T R F O R I N T E G R A T I V E B I O I N F O R M A T I C S V U E Anton Feenstra Computational...

CENTR

FORINTEGRATIVE

BIOINFORMATICSVU

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Anton Feenstra

Computational Genomics & Proteomics

Protein-Protein Interactions

C E N T R F O R I N T E G R A T I V EB I O I N F O R M A T I C S V U

E

[2] - Anton Feenstra -

Computational Genomics and Proteomics• Protein-protein Interaction (PPI) and Docking:

• Protein-protein Interaction• Interfaces• Solvation• Energetics• Conformational

change• Allostery

• Docking• Search space• Docking methods

• Sequence Analysis & PPI• functional specificity• ‘Sequence Harmony’


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PPI & Docking: CAPRI• Critical Assessment of PRedicted Interactions

Modeled after CASP (CA of Structure Prediction)

• Special issue of ‘Proteins’:Volume 69, Issue 4, Pages 697-872 (December 2007)

• From the Mediterranean coast to the shores of Lake Ontario: CAPRI's premiere on the American continent (Shoshana J. Wodak)

• The targets of CAPRI rounds 6-12 (Joël Janin)

• Docking and scoring protein complexes: CAPRI 3rd Edition (Marc F. Lensink, Raúl Méndez, Shoshana J. Wodak)

• The performance of ZDOCK and ZRANK in rounds 6-11 of CAPRI (Kevin Wiehe, Brian Pierce, Wei Wei Tong, Howook Hwang, Julian Mintseris, Zhiping Weng)

• HADDOCK versus HADDOCK: New features and performance of HADDOCK2.0 on the CAPRI targets (Sjoerd J. de Vries, Aalt D. J. van Dijk, Mickaël Krzeminski, Mark van Dijk, Aurelien Thureau, Victor Hsu, Tsjerk Wassenaar, Alexandre M. J. J. Bonvin)

• Docking with PIPER and refinement with SDU in rounds 6-11 of CAPRI (Yang Shen, Ryan Brenke, Dima Kozakov, Stephen R. Comeau, Dmitri Beglov, Sandor Vajda)


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More from CAPRI...• A holistic approach to protein docking (Sanbo Qin, Huan-Xiang Zhou)

• Implicit flexibility in protein docking: Cross-docking and local refinement (Marcin Król, Raphael A.

G. Chaleil, Alexander L. Tournier, Paul A. Bates)

• RosettaDock in CAPRI rounds 6-12 (Chu Wang, Ora Schueler-Furman, Ingemar Andre, Nir

London, Sarel J. Fleishman, Philip Bradley, Bin Qian, David Baker)

• Automatic prediction of protein interactions with large scale motion (Dina Schneidman-Duhovny,

Ruth Nussinov, Haim J. Wolfson)

• Protein-protein docking in CAPRI using ATTRACT to account for global and local flexibility

(Andreas May, Martin Zacharias)

• ClusPro: Performance in CAPRI rounds 6-11 and the new server (Stephen R. Comeau, Dima

Kozakov, Ryan Brenke, Yang Shen, Dmitri Beglov, Sandor Vajda)

• Acidic groups docked to well defined wetted pockets at the core of the binding interface: A tale

of scoring and missing protein interactions in CAPRI (Marta Bueno, Carlos J. Camacho)


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More from CAPRI....• Incorporating biochemical information and backbone flexibility in RosettaDock for CAPRI rounds

6-12 (Sidhartha Chaudhury, Aroop Sircar, Arvind Sivasubramanian, Monica Berrondo, Jeffrey J.

Gray)

• SOFTDOCK application to protein-protein interaction benchmark and CAPRI (Nan Li, Zhonghua

Sun, Fan Jiang)

• Assessing the energy landscape of CAPRI targets by FunHunt (Nir London, Ora Schueler-

Furman)

• Protein-protein docking: Progress in CAPRI rounds 6-12 using a combination of methods: The

introduction of steered solvated molecular dynamics (Alexander Heifetz, Sandeep Pal, Graham

R. Smith)

• A general approach for developing system-specific functions to score protein-ligand docked

complexes using support vector inductive logic programming (Ata Amini, Paul J. Shrimpton,

Stephen H. Muggleton, Michael J. E. Sternberg)

• Docking of protein molecular surfaces with evolutionary trace analysis (Eiji Kanamori, Yoichi

Murakami, Yuko Tsuchiya, Daron M. Standley, Haruki Nakamura, Kengo Kinoshita)


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More from CAPRI.....• Docking without docking: ISEARCH - prediction of interactions using known interfaces (Stefan

Günther, Patrick May, Andreas Hoppe, Cornelius Frömmel, Robert Preissner)

• DOCKGROUND system of databases for protein recognition studies: Unbound structures for

docking (Ying Gao, Dominique Douguet, Andrey Tovchigrechko, Ilya A. Vakser)

• Prediction and scoring of docking poses with pyDock (Solène Grosdidier, Carles Pons, Albert

Solernou, Juan Fernández-Recio)

• A filter enhanced sampling and combinatorial scoring study for protein docking in CAPRI (Xin Qi

Gong, Shan Chang, Qing Hua Zhang, Chun Hua Li, Long Zhu Shen, Xiao Hui Ma, Ming Hui

Wang, Bin Liu, Hong Qiu He, Wei Zu Chen, Cun Xin Wang)

• The SKE-DOCK server and human teams based on a combined method of shape

complementarity and free energy estimation (Genki Terashi, Mayuko Takeda-Shitaka, Kazuhiko

Kanou, Mitsuo Iwadate, Daisuke Takaya, Hideaki Umeyama)


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change• Allostery




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PPI Characteristics• Universal

• Cell functionality based on protein-protein interactions• Cyto-skeleton• Ribosome• RNA polymerase

• Numerous• Yeast:

• ~6.000 proteins• at least 3 interactions each ~18.000 interactions

• Human:• estimated ~100.000 interactions

• Network• simplest: homodimer (two)• common: hetero-oligomer (more)• holistic: protein network (all)


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Interface Area• Contact area

• usually >1100 Å2• each partner >550 Å2

• each partner loses ~800 Å2 of solvent accessible surface area• ~20 amino acids lose ~40 Å2• ~100-200 J per Å2

• Average buried accessible surface area:• 12% for dimers 17% for trimers 21% for tetramers

• 83-84% of all interfaces are flat• Secondary structure:

• 50% a-helix20% b-sheet 20% coil 10% mixed• Less hydrophobic than core, more hydrophobic than exterior


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Complexation Reaction• A + B AB

• Ka = [AB]/[A]•[B]

association

• Kd = [A]•[B]/[AB]

dissociation


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Experimental Methods• 2D (poly-acrylamide) gel electrophoresis mass spectrometry

• Liquid chromatography• e.g. gel permeation chromatography

• Binding study with one immobilized partner• e.g. surface plasmon resonance

• In vivo by two-hybrid systems or FRET

• Binding constants by ultra-centrifugation, micro-calorimetry or competition

• experiments with labelled ligand (e.g. fluorescence, radioactivity)

• Role of individual amino acids by site directed mutagenesis

• Structural studies (e.g. NMR or X-ray)


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PPI Network

http://www.phy.auckland.ac.nz/staff/prw/biocomplexity/protein_network.htm


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Protein-protein interactions• Complexity:

• Multibody interaction

• Diversity:

• Various interaction types

• Specificity:

• Complementarity in shape and binding properties


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Binding vs. Localization

Obligateoligomers

Non-obligateweak transient

Non-obligatetriggered transient

e.g. GTP•PO4-

Non-obligateco-localised

e.g. in membrane

Non-obligatepermanent

e.g. antibody-antigen

strong

weak

co-expressed different places


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Some terminology• Transient interactions:

• Associate and dissociate in vivo

• Weak transient:

• dynamic oligomeric equilibrium

• Strong transient:

• require a molecular trigger to shift the equilibrium

• Obligate PPI:

• protomers not stable structures on their own

• (functionally obligate)


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Strong – medium – weak• (Sub-)Nanomolar Kd < 10-9

• Micro– to nanomolar 10-6 > Kd > 10-9

• Micromolar 10-3 > Kd > 10-6

• A + B AB

Kd = [A]•[B]/[AB]

dissociation


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Analysis of 122 Homodimers• 70 interfaces

single patched

• 35 have two patches

• 17 have three or more


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Patches• Cluster in different domains

• structurally defined units often with specific function

two domains anticodon-binding

catalytic


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Interfaces• ~30% polar

• ~70% non-polar


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Interface• Rim is water accessible

rimcore


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Interface composition• Composition of interface essentially the same as core

• But % surface area can be quite different!


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Conformational Change• Chaperones

• extreme conformational changes upon complexation

ligand unfolds within the chaperone GroEL/GroES

• Allosteric proteins

• conformational change at 'active' site

• ligand binds to 'regulating' site

• Peptides

• often adopt 'bound' conformation

• different from the 'free' conformation


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change• Allostery




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Docking Programs• The performance of ZDOCK and ZRANK in rounds 6-11 of CAPRI

(Kevin Wiehe, Brian Pierce, Wei Wei Tong, Howook Hwang, Julian Mintseris, Zhiping Weng)

• HADDOCK versus HADDOCK: New features and performance of HADDOCK2.0 on the CAPRI targets (Sjoerd J. de Vries, Aalt D. J. van Dijk, Mickaël Krzeminski, Mark van Dijk, Aurelien Thureau, Victor Hsu, Tsjerk Wassenaar, Alexandre M. J. J. Bonvin)

• Docking with PIPER and refinement with SDU in rounds 6-11 of CAPRI (Yang Shen, Ryan Brenke, Dima Kozakov, Stephen R. Comeau, Dmitri Beglov, Sandor Vajda)

• A holistic approach to protein docking (Sanbo Qin, Huan-Xiang Zhou)

• Implicit flexibility in protein docking: Cross-docking and local refinement (Marcin Król, Raphael A. G. Chaleil, Alexander L. Tournier, Paul A. Bates)

• RosettaDock in CAPRI rounds 6-12 (Chu Wang, Ora Schueler-Furman, Ingemar Andre, Nir London, Sarel J. Fleishman, Philip Bradley, Bin Qian, David Baker)

• Automatic prediction of protein interactions with large scale motion (Dina Schneidman-Duhovny, Ruth Nussinov, Haim J. Wolfson)

• Protein-protein docking in CAPRI using ATTRACT to account for global and local flexibility (Andreas May, Martin Zacharias)

• ClusPro: Performance in CAPRI rounds 6-11 and the new server (Stephen R. Comeau, Dima Kozakov, Ryan Brenke, Yang Shen, Dmitri Beglov, Sandor Vajda)

• Acidic groups docked to well defined wetted pockets at the core of the binding interface: A tale of scoring and missing protein interactions in CAPRI (Marta Bueno, Carlos J. Camacho)

• Incorporating biochemical information and backbone flexibility in RosettaDock for CAPRI rounds 6-12 (Sidhartha Chaudhury, Aroop Sircar, Arvind Sivasubramanian, Monica Berrondo, Jeffrey J. Gray)

• SOFTDOCK application to protein-protein interaction benchmark and CAPRI (Nan Li, Zhonghua Sun, Fan Jiang)

• Assessing the energy landscape of CAPRI targets by FunHunt (Nir

London, Ora Schueler-Furman)

• Protein-protein docking: Progress in CAPRI rounds 6-12 using a

combination of methods: The introduction of steered solvated molecular

dynamics (Alexander Heifetz, Sandeep Pal, Graham R. Smith)

• A general approach for developing system-specific functions to score

protein-ligand docked complexes using support vector inductive logic

programming (Ata Amini, Paul J. Shrimpton, Stephen H. Muggleton,

Michael J. E. Sternberg)

• Docking of protein molecular surfaces with evolutionary trace analysis

(Eiji Kanamori, Yoichi Murakami, Yuko Tsuchiya, Daron M. Standley,

Haruki Nakamura, Kengo Kinoshita)

• Docking without docking: ISEARCH - prediction of interactions using

known interfaces (Stefan Günther, Patrick May, Andreas Hoppe,

Cornelius Frömmel, Robert Preissner)

• DOCKGROUND system of databases for protein recognition studies:

Unbound structures for docking (Ying Gao, Dominique Douguet, Andrey

Tovchigrechko, Ilya A. Vakser)

• Prediction and scoring of docking poses with pyDock (Solène Grosdidier,

Carles Pons, Albert Solernou, Juan Fernández-Recio)

• A filter enhanced sampling and combinatorial scoring study for protein

docking in CAPRI (Xin Qi Gong, Shan Chang, Qing Hua Zhang, Chun

Hua Li, Long Zhu Shen, Xiao Hui Ma, Ming Hui Wang, Bin Liu, Hong Qiu

He, Wei Zu Chen, Cun Xin Wang)

• The SKE-DOCK server and human teams based on a combined method

of shape complementarity and free energy estimation (Genki Terashi,

Mayuko Takeda-Shitaka, Kazuhiko Kanou, Mitsuo Iwadate, Daisuke

Takaya, Hideaki Umeyama)


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The Protein Docking

Problem• Search space

• 5 relative degrees of freedom:

• ... and MANY internal degrees!

2 angles 1 distance 3 angles


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Docking - ZDOCK• Protein-protein docking

• 3-dimensional (3D) structure of protein complex • starting from 3D structures of receptor and ligand

• Rigid-body docking algorithm (ZDOCK) • pairwise shape complementarity function• all possible binding modes • using Fast Fourier Transform algorithm

• Refinement algorithm (RDOCK)• top 2000 predicted structures • three-stage energy minimization • electrostatic and desolvation energies

• molecular mechanical software (CHARMM)• statistical energy method (Atomic Contact Energy)

• 49 non-redundant unbound test cases:• near-native structure (<2.5Å) for 37% test cases

• for 49% within top 4


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Protein-protein docking• Finding correct

surface match

• Systematic search:• 2 times 3D space!

• Define functions:• ‘1’ on surface

• ‘’ or ‘’ inside

• ‘0’ outside


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Protein-protein docking• Correlation function:

C = 1/N3 o p q exp[2i(o + p + q)/N] • Co,p,q


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Characterization of Interfaces• ‘Survey of the Geometric Association of Domain–Domain

Interfaces’• Wan Kyu Kim and Jon C. Ison, Proteins 61:1075 (2005)

• Physicochemical Properties• Shape• Packing density• Binding Energy• Geometry:

• small sets of proteins • sequence on genome-scale• Classification from Hashing:

• e.g. similar interfaces from different folds


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Extract Interfaces• Structures 3.5 Å

• X-ray structures from PQS• NMR (and others) from PDB

• Group according to SCOP• Interface:

• buried surface area >800 Å2 (~11 aa’s)• Interface residues:

• Atomic dist. < 5 Å, or C-dist. < 9 Å• NR sets

• Seq. Id.’s at 50%, 55%, … 95%, 100%


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Some numbers• 48,708 interacting domain pairs

• 2,118 SCOP family–family pairs

• 1,506 superfamily–superfamily pairs

• 78% (1,714) intermolecular

• 22% (640) intramolecular


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IFT Clustering• Three domains: multiple interactions

• Distinct faces: D > 0.55 (99%)

A

B3

B2

B1

f2

f1

f3


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Classification of Distinct Surfaces• 1,746 families -> 100,000 IFTs

• less than 6 h on a PC• days to months by 3D comparison

• IFT’s are ‘patchy’ insensitive to alignment quality

• 70% of families use two or more surfaces• Typically interact with various families


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Faces and Types• Same face, same type (same)

• Same face, different type (competitive)

• Different face

reflected in differences between IFT’s


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Conservation• Interfaces are conserved, even at low

sequence conservation


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Conclusions• Cataloging interfaces

• Basis for predicting protein association• Docking is time consuming and success is limited

• Accuracy less than manual (but much faster…)

• Docking by sampling candidate known interfaces

• Genome-wide docking?

• Predict interface by IFT mapping


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change• Allostery




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Predicting PPI’s• Coarse-grained mesoscopic modelling

• Mapping interaction information onto structure:

First: find Functionally (most) Relevant Sitesdetermining binding specificity

D L Q P V T Y C E P A F W C S I S




D L Q P V T Y C E S A F W C S I S




T M H P V N Y Q E P K Y W C S I V

D V Q A V A Y E E P K H W C S I V






H A S Q P S M T V D G F T D P S N S




H A S Q P S L T V D G F T D P S N A




N A S Q L S I I I D G F T D P S N N

H A S S T S V L V D G F T D P S N N





H A S S T S I L V D G F T D P S N N


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Identification of Functional Sites• Functional differences between Protein (sub-)families

Knowledge from Comparative Genomics

• Current practice:• use Multiple Sequence Alignment

• look for Conserved Sites within (sub-)families• (ignore sites that are overall conserved)

• Example Binders vs. Non-Binders:• sites crucial for binding: conserved

• sites determining ‘non-binding’: not conserved

Take into account Non-Conserved Sites as well!• comparing Amino-acid Compositions

(?)

(!)


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Comparing Groups: Sequence Harmony• Weigh groups A and B equally:

• Take pA + pB in stead of pAB

•

Defined on the fixed interval of [01]

• one is complete overlap in composition: Harmony

• zero is no overlap in composition: No Harmony

xpA

i,x + pBi,x

pAi,x logSHi

AB = pAi,x


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TGF-β signalling pathway

TR-II TR-I

TGF-

AR-Smads

division, differentiation, motility, adhesion,

programmed cell death

Nucleusactivation/repressionTGF- target genes

Smad-associationp

p p

BMPR-I BMPR-IIBR-Smads

p

Nucleusactivation/repression

BMP target genes

BMP

Smad-association

p p


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Smads: Interactions

Miyazawa et al. Genes to Cells (2002) 7, 1191

AR BRnon-R


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Low-harmony sites

ALK1/2NLMq0H1M327

::::::

c-Ski/SnoNKrsE0L1E309

ALK1/2SAe0H1A323

?? (putative)

IV0H1V325

c-Ski/SnoNNsd–0L1–

c-Ski/SnoNNSa0L1S308

c-Ski/SnoNLiT0B3T298

c-Ski/SnoNViLMi0.11

B3L297

c-Ski/SnoNTrlP0B3P295

c-Ski/SnoNSqQ0.16

loopQ294

TβR-INQt0B2Q284

?? (putative)

HyF0loopF273

?? (putative)

KqlsA0loopA272

?? (putative)

EqCSh0loopS269

SARAAcenTm0B1’T267

SARAVfmLa0B1’L263

InteractionBRARSHSec.str.

Pos.

26

2 27

0 28

0 29

0

D L Q P V T Y S E P A F W C S I A Y Y E L N Q R V G E T F H A S Q P S L T V D G

D A A P V M Y H E P A F W C S I S Y Y E L N T R V G E T F H A S Q P S I T V D G






D L Q P V T Y C E P A F W C S I S Y Y E L N Q R V G E T F H A S Q P S M T V D G




D L Q P V T Y C E S A F W C S I S Y Y E L N Q R V G E T F H A S Q P S L T V D G




T M H P V N Y Q E P K Y W C S I V Y Y E L N N R V G E A F N A S Q L S I I I D G

D V Q A V A Y E E P K H W C S I V Y Y E L N N R V G E A F H A S S T S V L V D G





D V Q A V A Y E E P K H W C S I V Y Y E L N N R V G E A F H A S S T S I L V D G

D V H P V A Y Q E P K H W C S I V Y Y E L N N R V G E A F L A S S T S V L V D G

D V Q A V A Y E E P K H W C S I V Y Y E L N N R V G E A F H A S S T S I L V D G

D V Q P V A Y E E P K H W C S I V Y Y E L N N R V G E A F H A S S T S V L V D G




D V Q P V E Y Q E P S H W C S I V Y Y E L N N R V G E A Y H A S S T S V L V D G

D F R P V C Y E E P Q H W C S V A Y Y E L N N R V G E T F Q A S S R S V L I D G

D F R P V C Y E E P L H W C S V A Y Y E L N N R V G E T F Q A S S R S V L I D G

N F R P V C Y E E P Q H W C S V A Y Y E L N N R V G E T F Q A S S R S I L I D G

30

0


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Low Harmony Clusters

R462C463

Q400

R410W368

Y366

A392

S269

F273

N443

Q294

Q309L297

L440N381

A354

V461

S460 Q407

Q364

P360

R365

T267

A272

I341

P295S308

T298R337F346

P378

Q284V325

A323

R427

M327

T430

R334


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Functional Clusters

R462C463

Q400

R410W368

Y366

A392

S269

F273

N443

Q294

Q309L297

L440N381

A354

V461

S460 Q407

Q364

P360

R365

T267

A272

I341

P295S308

T298R337F346

P378

Q284V325

A323

R427

M327

T430

R334

FAST1, Mixer, SARA

c-Ski/SnoN

SARA

TβR-I/ALK1/2

TβR-I/BMPR-I

?SARA/Mixer

TβR-I/BMPR-I/ALK1/2

?

receptor-binding

retention & transcription factors

co-repressors


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Low Harmony Patches


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Predicting PPI’s:





D L Q P V T Y C E S A F W C S I S




T M H P V N Y Q E P K Y W C S I V











H A S Q P S L T V D G F T D P S N A




N A S Q L S I I I D G F T D P S N N






H A S S T S I L V D G F T D P S N N

?


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Conclusions• 40 Sites of Low Sequence Harmony in Smad-MH2

• different between the AR (TGF-β) and BR (BMP) sub-type Smads

• Low Harmony sites in Smad-MH2 are functionally relevant

• Very sharp separation between High- and Low-Harmony sites

• Intuitive scale: more or less likely functional importance

• 14 Low Harmony Sites in Smad-MH2 of unknown function• 11 putative functions from structural considerations

• promising candidates that determine TGF-β/BMP specificity

• confirm (or rebuke) putative functions?

Sequence information maps to structure: Next: Analyze Protein-Protein Interactions


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change• Allostery



C E N T R F O R I N T E G R A T I V E B I O I N F O R M A T I C S V U E Anton Feenstra Computational...

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Transcript of C E N T R F O R I N T E G R A T I V E B I O I N F O R M A T I C S V U E Anton Feenstra Computational...