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CENTR
FORINTEGRATIVE
BIOINFORMATICSVU
E
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’
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
[3] - Anton Feenstra -
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)
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
[4] - Anton Feenstra -
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)
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
[5] - Anton Feenstra -
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)
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
[6] - Anton Feenstra -
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)
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
[7] - 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’
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
[8] - Anton Feenstra -
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)
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
[9] - Anton Feenstra -
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
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
[10] - Anton Feenstra -
Complexation Reaction• A + B AB
• Ka = [AB]/[A]•[B]
association
• Kd = [A]•[B]/[AB]
dissociation
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
[11] - Anton Feenstra -
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)
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
[12] - Anton Feenstra -
PPI Network
http://www.phy.auckland.ac.nz/staff/prw/biocomplexity/protein_network.htm
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
[13] - Anton Feenstra -
Protein-protein interactions• Complexity:
• Multibody interaction
• Diversity:
• Various interaction types
• Specificity:
• Complementarity in shape and binding properties
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
[14] - Anton Feenstra -
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
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
[15] - Anton Feenstra -
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)
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
[16] - Anton Feenstra -
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
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
[17] - Anton Feenstra -
Analysis of 122 Homodimers• 70 interfaces
single patched
• 35 have two patches
• 17 have three or more
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
[18] - Anton Feenstra -
Patches• Cluster in different domains
• structurally defined units often with specific function
two domains anticodon-binding
catalytic
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
[19] - Anton Feenstra -
Interfaces• ~30% polar
• ~70% non-polar
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
[20] - Anton Feenstra -
Interface• Rim is water accessible
rimcore
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
[21] - Anton Feenstra -
Interface composition• Composition of interface essentially the same as core
• But % surface area can be quite different!
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
[22] - Anton Feenstra -
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
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
[23] - 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’
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
[24] - Anton Feenstra -
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)
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
[25] - Anton Feenstra -
The Protein Docking
Problem• Search space
• 5 relative degrees of freedom:
• ... and MANY internal degrees!
2 angles 1 distance 3 angles
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
[26] - Anton Feenstra -
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
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
[27] - Anton Feenstra -
Protein-protein docking• Finding correct
surface match
• Systematic search:• 2 times 3D space!
• Define functions:• ‘1’ on surface
• ‘’ or ‘’ inside
• ‘0’ outside
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
[28] - Anton Feenstra -
Protein-protein docking• Correlation function:
C = 1/N3 o p q exp[2i(o + p + q)/N] • Co,p,q
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
[29] - Anton Feenstra -
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
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
[30] - Anton Feenstra -
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%
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
[31] - Anton Feenstra -
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
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
[32] - Anton Feenstra -
IFT Clustering• Three domains: multiple interactions
• Distinct faces: D > 0.55 (99%)
A
B3
B2
B1
f2
f1
f3
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
[33] - Anton Feenstra -
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
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
[34] - Anton Feenstra -
Faces and Types• Same face, same type (same)
• Same face, different type (competitive)
• Different face
reflected in differences between IFT’s
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
[35] - Anton Feenstra -
Conservation• Interfaces are conserved, even at low
sequence conservation
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
[36] - Anton Feenstra -
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
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
[37] - 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’
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
[38] - Anton Feenstra -
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 P A F W C S I S
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 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
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 P A F W C S I S
D L Q P V T Y C E P 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
D V Q A V A Y E E P K H W C S I V
D V Q A V A Y E E P K H W C S I V
D V Q A V A Y E E P K H W C S I V
D V Q A V A Y E E P K H 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 M T V D G F T D P S N S
H A S Q P S M T V D G F T D P S N S
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
H A S Q P S M T V D G F T D P S N S
H A S Q P S M T V D G F T D P S N S
H A S Q P S M T V D G F T D P S N S
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 V L V 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 V L V 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
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
[39] - Anton Feenstra -
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
(?)
(!)
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
[40] - Anton Feenstra -
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
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
[41] - Anton Feenstra -
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
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
[42] - Anton Feenstra -
Smads: Interactions
Miyazawa et al. Genes to Cells (2002) 7, 1191
AR BRnon-R
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
[43] - Anton Feenstra -
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 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 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 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 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 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 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 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 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 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
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 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 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
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 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 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 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 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 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 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 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
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
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[44] - Anton Feenstra -
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
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
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[45] - Anton Feenstra -
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
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
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[46] - Anton Feenstra -
Low Harmony Patches
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
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[47] - Anton Feenstra -
Predicting PPI’s:
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 P A F W C S I S
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 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
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 P A F W C S I S
D L Q P V T Y C E P 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
D V Q A V A Y E E P K H W C S I V
D V Q A V A Y E E P K H W C S I V
D V Q A V A Y E E P K H W C S I V
D V Q A V A Y E E P K H 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 M T V D G F T D P S N S
H A S Q P S M T V D G F T D P S N S
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
H A S Q P S M T V D G F T D P S N S
H A S Q P S M T V D G F T D P S N S
H A S Q P S M T V D G F T D P S N S
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 V L V 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 V L V 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
?
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
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[48] - Anton Feenstra -
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
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
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[49] - Anton Feenstra -
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
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[50] - 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’