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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 Genomics & Proteomics Protein-Protein Interactions.

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Presentation on theme: "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 Genomics & Proteomics Protein-Protein Interactions."— Presentation transcript:

1 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 Genomics & Proteomics Protein-Protein Interactions

2 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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’

3 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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 (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)‏

4 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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)‏

5 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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)‏

6 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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)‏

7 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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’

8 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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  ~ interactions Human: estimated ~ interactions Network simplest: homodimer (two)‏ common: hetero-oligomer (more)‏ holistic: protein network (all)‏

9 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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 ~ J per Å2 Average buried accessible surface area: 12% for dimers 17% for trimers21% for tetramers 83-84% of all interfaces are flat Secondary structure: 50% a-helix20% b-sheet20% coil10% mixed Less hydrophobic than core, more hydrophobic than exterior

10 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [10] - Anton Feenstra - Complexation Reaction A + B  AB K a = [AB]/[A][B]  association K d = [A][B]/[AB]  dissociation

11 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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)‏

12 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [12] - Anton Feenstra - PPI Network

13 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [13] - Anton Feenstra - Protein-protein interactions Complexity: Multibody interaction Diversity: Various interaction types Specificity: Complementarity in shape and binding properties

14 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [14] - Anton Feenstra - Binding vs. Localization Obligate oligomers Non-obligate weak transient Non-obligate triggered transient e.g. GTPPO 4 - Non-obligate co-localised e.g. in membrane Non-obligate permanent e.g. antibody-antigen strong weak co-expresseddifferent places

15 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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)‏

16 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [16] - Anton Feenstra - Strong – medium – weak (Sub-)Nanomolar  K d < Micro– to nanomolar  > K d > Micromolar  > K d > A + B  AB  K d = [A][B]/[AB]  dissociation

17 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [17] - Anton Feenstra - Analysis of 122 Homodimers 70 interfaces single patched 35 have two patches 17 have three or more

18 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [18] - Anton Feenstra - Patches Cluster in different domains structurally defined units often with specific function two domains anticodon-binding catalytic

19 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [19] - Anton Feenstra - Interfaces ~30% polar ~70% non-polar

20 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [20] - Anton Feenstra - Interface Rim is water accessible rim core

21 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [21] - Anton Feenstra - Interface composition Composition of interface essentially the same as core But % surface area can be quite different!

22 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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

23 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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’

24 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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)‏

25 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [25] - Anton Feenstra - The Protein Docking Problem Search space 5 relative degrees of freedom:... and MANY internal degrees! 2 angles1 distance3 angles

26 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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

27 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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  

28 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [28] - Anton Feenstra - Protein-protein docking Correlation function: C  = 1/N 3  o  p  q exp[2  i(o  + p  + q  )/N] C o,p,q

29 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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

30 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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 Å 2 (~11 aa’s)‏ Interface residues: Atomic dist. < 5 Å, or C  -dist. < 9 Å NR sets Seq. Id.’s at 50%, 55%, … 95%, 100%

31 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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

32 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [32] - Anton Feenstra - IFT Clustering Three domains: multiple interactions Distinct faces: D > 0.55 (99%)‏ A B3 B2 B1 f2 f1 f3

33 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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

34 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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

35 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [35] - Anton Feenstra - Conservation Interfaces are conserved, even at low sequence conservation

36 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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

37 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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’

38 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [38] - Anton Feenstra - Predicting PPI’s Coarse-grained mesoscopic modelling Mapping interaction information onto structure:  First: find Functionally (most) Relevant Sites  determining binding specificity DLQPVTYCEPAFWCSIS DLQPVTYCEPAFWCSIS DLQPVTYCEPAFWCSIS DLQPVTYCEPAFWCSIS DLQPVTYCESAFWCSIS DLQPVTYCEPAFWCSIS DLQPVTYCEPAFWCSIS DLQPVTYCEPAFWCSIS TMHPVNYQEPKYWCSIV DVQAVAYEEPKHWCSIV DVQAVAYEEPKHWCSIV DVQAVAYEEPKHWCSIV DVQAVAYEEPKHWCSIV DVQAVAYEEPKHWCSIV DVQAVAYEEPKHWCSIV HASQPSMTVDGFTDPSNS HASQPSMTVDGFTDPSNS HASQPSMTVDGFTDPSNS HASQPSMTVDGFTDPSNS HASQPSLTVDGFTDPSNA HASQPSMTVDGFTDPSNS HASQPSMTVDGFTDPSNS HASQPSMTVDGFTDPSNS NASQLSIIIDGFTDPSNN HASSTSVLVDGFTDPSNN HASSTSVLVDGFTDPSNN HASSTSVLVDGFTDPSNN HASSTSVLVDGFTDPSNN HASSTSVLVDGFTDPSNN HASSTSILVDGFTDPSNN

39 CENTRFORINTEGRATIVE BIOINFORMATICSVU 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 (?)‏ (!)‏

40 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [40] - Anton Feenstra - Comparing Groups: Sequence Harmony Weigh groups A and B equally: Take p A + p B in stead of p AB  Defined on the fixed interval of [0  1] one is complete overlap in composition: Harmony zero is no overlap in composition: No Harmony x p A i,x + p B i,x p A i,x log  SH i AB =  p A i,x

41 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [41] - Anton Feenstra - TGF-β signalling pathway T  R-IIT  R-I TGF-  AR-Smads division, differentiation, motility, adhesion, programmed cell death Nucleus activation/repression TGF-  target genes Smad-association p pp BMPR-IBMPR-II BR-Smads p Nucleus activation/repression BMP target genes BMP Smad-association pp

42 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [42] - Anton Feenstra - Smads: Interactions Miyazawa et al. Genes to Cells (2002) 7, 1191 ARBRnon-R

43 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [43] - Anton Feenstra - Low-harmony sites  300 

44 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [44] - Anton Feenstra - Low Harmony Clusters R462 C463 Q400 R410 W368 Y366 A392 S269 F273 N443 Q294 Q309 L297 L440 N381 A354 V461 S460 Q407 Q364 P360 R365 T267 A272 I341 P295 S308 T298 R337 F346 P378 Q284 V325 A323 R427 M327 T430 R334

45 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [45] - Anton Feenstra - Functional Clusters R462 C463 Q400 R410 W368 Y366 A392 S269 F273 N443 Q294 Q309 L297 L440 N381 A354 V461 S460 Q407 Q364 P360 R365 T267 A272 I341 P295 S308 T298 R337 F346 P378 Q284 V325 A323 R427 M327 T430 R334 FAST1, Mixer, SARA c-Ski/SnoN SARA T β R-I/ALK1/2 T β R-I/BMPR-I ? SARA/ Mixer T β R-I/BMPR-I/ALK1/2 ? receptor-binding retention & transcription factors co-repressors

46 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [46] - Anton Feenstra - Low Harmony Patches

47 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [47] - Anton Feenstra - Predicting PPI’s: DLQPVTYCEPAFWCSIS DLQPVTYCEPAFWCSIS DLQPVTYCEPAFWCSIS DLQPVTYCEPAFWCSIS DLQPVTYCESAFWCSIS DLQPVTYCEPAFWCSIS DLQPVTYCEPAFWCSIS DLQPVTYCEPAFWCSIS TMHPVNYQEPKYWCSIV DVQAVAYEEPKHWCSIV DVQAVAYEEPKHWCSIV DVQAVAYEEPKHWCSIV DVQAVAYEEPKHWCSIV DVQAVAYEEPKHWCSIV DVQAVAYEEPKHWCSIV HASQPSMTVDGFTDPSNS HASQPSMTVDGFTDPSNS HASQPSMTVDGFTDPSNS HASQPSMTVDGFTDPSNS HASQPSLTVDGFTDPSNA HASQPSMTVDGFTDPSNS HASQPSMTVDGFTDPSNS HASQPSMTVDGFTDPSNS NASQLSIIIDGFTDPSNN HASSTSVLVDGFTDPSNN HASSTSVLVDGFTDPSNN HASSTSVLVDGFTDPSNN HASSTSVLVDGFTDPSNN HASSTSVLVDGFTDPSNN HASSTSILVDGFTDPSNN ?

48 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [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

49 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [49] - Anton Feenstra -

50 CENTRFORINTEGRATIVE BIOINFORMATICSVU E [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’


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