1. Structure-Function Analysis 117 Jan 2006 DNA/Protein structure-function analysis and prediction Protein-protein Interaction (PPI): Protein-protein Interaction –Interfaces –Solvation –Energetics –Conformational change –Allostery Prediction: –Gene Cluster –Phylogenetic Profile: –Rosetta Stone –Sequence co-evolution –Random Decision forest Docking [ Examples ]

2. Structure-Function Analysis 217 Jan 2006 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)‏

3. Structure-Function Analysis 317 Jan 2006 Interface Area Contact area –usually >1100 Å 2 –each partner >550 Å 2 each partner loses ~800 Å 2 of solvent accessible surface area –~20 amino acids lose ~40 Å 2 –~100-200 J per Å 2 Average buried accessible surface area: –12% for dimers –17% for trimers –21% for tetramers 83-84% of all interfaces are flat Secondary structure: –50%  -helix –20%  -sheet –20% coil –10% mixed Less hydrophobic than core, more hydrophobic than exterior

4. Structure-Function Analysis 417 Jan 2006 Complexation Reaction A + B  AB K a = [AB]/[A][B]  association K d = [A][B]/[AB]  dissociation Free energy:  G d = -RTln K d Kd = exp(-  G d / RT)‏ (R = 8.3144 J mol -1 K -1 )‏

5. Structure-Function Analysis 517 Jan 2006 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

6. Structure-Function Analysis 617 Jan 2006 PPI Network http://www.phy.auckland.ac.nz/staff/prw/biocomplexity/protein_network.htm

7. Structure-Function Analysis 717 Jan 2006 Protein-protein interactions Complexity: –Multibody interaction Diversity: –Various interaction types Specificity: –Complementarity in shape and binding properties

8. Structure-Function Analysis 817 Jan 2006 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

9. Structure-Function Analysis 917 Jan 2006 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)‏

10. Structure-Function Analysis 1017 Jan 2006 Strong – medium – weak Nanomolar to sub-nanomolar  K d < 10 -9 Micromolar to nanomolar  10 -6 > K d > 10 -9 Micromolar  10 -3 > K d > 10 -6 A + B  AB  K d = [A][B]/[AB]  dissociation

11. Structure-Function Analysis 1117 Jan 2006 Analysis of 122 Homodimers 70 interfaces single patched 35 have two patches 17 have three or more

12. Structure-Function Analysis 1217 Jan 2006 Patches Cluster in different domains –(structurally defined units often with specific function)‏ two domains anticodon-binding catalytic

13. Structure-Function Analysis 1317 Jan 2006 Interfaces ~30% polar ~70% non-polar

14. Structure-Function Analysis 1417 Jan 2006 Interface Rim is water accessible rim core

15. Structure-Function Analysis 1517 Jan 2006 Interface composition Composition of interface essentially the same as core But % surface area can be quite different!

16. Structure-Function Analysis 1617 Jan 2006 Propensities Interface vs. surface propensities –as ln(f int /f surf )‏

17. Structure-Function Analysis 1717 Jan 2006 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

18. Structure-Function Analysis 1817 Jan 2006 Allostery 1 Regulation by 'remote' modulation of binding affinity (complex strength)‏ www.blc.arizona.edu/courses/181gh/rick/energy/allostery.html

19. Structure-Function Analysis 1917 Jan 2006 Allostery 2 Substrate binding is cooperative Binding of first substrate at first active site –stimulates active shape –promotes binding of second substrate

20. Structure-Function Analysis 2017 Jan 2006 Allostery 3 Committed step of metabolic pathway –regulated by an allosteric enzyme Pathway end product –can regulate the allosteric enzyme for the first committed step Inhibitor binding favors inactive form

21. Structure-Function Analysis 2117 Jan 2006 DNA/Protein structure-function analysis and prediction Protein-protein Interaction (PPI): Protein-protein Interaction –Interfaces –Solvation –Energetics –Conformational change –Allostery Prediction: –Gene Cluster –Phylogenetic Profile: –Rosetta Stone –Sequence co-evolution –Random Decision forest Docking [ Examples ]

22. Structure-Function Analysis 2217 Jan 2006 Predicting Protein-Protein Interactions: Gene Cluster: –Gene neighborhood Phylogenetic Profile: –Co-occurrence across species/genomes Rosetta Stone: –Occurrence of protein with domains linked Sequence co-evolution: –Tree correlation indicated functional relation Random Decision forest: –Using data on domain interactions [ Shoemaker & Panchenko, PLOS-CB 2007 3 e43 ]

23. Structure-Function Analysis 2317 Jan 2006 Gene Cluster / Neighborhood

24. Structure-Function Analysis 2417 Jan 2006 Gene Cluster / Neighborhood Genes with closely related functions encoding potentially interacting proteins: –transcribed as a single unit (operon) in bacteria –co-regulated in eukaryotes Operons can be predicted from intergenic distance Neutral evolution tends to shuffle gene order between distantly related organisms –but gene clusters or operons that encode co- regulated genes are usually conserved –operons found by gene neighbor methods provide additional evidence about functional linkage

25. Structure-Function Analysis 2517 Jan 2006 Phylogenetic Profile

26. Structure-Function Analysis 2617 Jan 2006 Phylogenetic Profile hypothesis that functionally linked and potentially interacting nonhomologous proteins co-evolve and have orthologs in the same subset of organisms –components of complexes and pathways should be present simultaneously in order to perform their functions. phylogenetic profile is a vector of N elements (number of genomes) –presence/absence of protein in genome is ‘‘1’’ or ‘‘0’’ at each position of a profile. clustered using bit-distance measure –proteins in a cluster are considered functionally related. also for protein domains instead of entire proteins

27. Structure-Function Analysis 2717 Jan 2006 “Rosetta Stone”

28. Structure-Function Analysis 2817 Jan 2006 “Rosetta Stone” infer protein interactions from sequences in different genomes –some interacting proteins/domains have homologs that are fused into one protein  a so-called “Rosetta Stone” protein Apparently, gene fusion can occur to optimize co- expression of genes encoding for interacting proteins.

29. Structure-Function Analysis 2917 Jan 2006 Sequence co-evolution

30. Structure-Function Analysis 3017 Jan 2006 Sequence co-evolution interacting proteins often co-evolve so changes in one protein leading to the loss of function or interaction can be compensated by changes in the other –orthologs of coevolving proteins also tend to interact infer unknown interactions in other genomes similarity between phylogenetic trees of two non- homologous interacting protein families –correlation coefficient between the distance matrices –requires correspondence between the matrix elements / tree branches (i.e. ortholog relations)‏ align distance matrices to minimize difference predicted interactions correspond to aligned col’s max. ~30 proteins in a family

31. Structure-Function Analysis 3117 Jan 2006 Classification / Random Decision Forest

32. Structure-Function Analysis 3217 Jan 2006 Random Forest Decision Decision trees based on domains of interacting and non- interacting proteins –All possible combinations of interacting domains –vector of length N (different domain types or features)‏ 2, 1, or 0: found in both, one, or no protein of pair experimental training set of interacting protein pairs –decision tree (many trees) –defines the best splitting feature at each node from a randomly selected feature subspace –best feature is selected based on ‘‘goodness of fit,’’ can discriminate interacting and non-interacting –stops growing the tree when all pairs at a given node are well-separated Traverse the tree to classify an unknown protein pair

33. Structure-Function Analysis 3317 Jan 2006 DNA/Protein structure-function analysis and prediction Protein-protein Interaction (PPI) and Docking: Protein-protein Interaction –Interfaces –Solvation –Energetics –Conformational change –Allostery Prediction Docking –Search space –Docking methods [ Examples ]

34. Structure-Function Analysis 3417 Jan 2006 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

35. Structure-Function Analysis 3517 Jan 2006 Protein-protein docking Finding correct surface match Systematic search: –2 times 3D space! Define functions: –‘1’ on surface –‘  ’ or ‘  ’ inside –‘0’ outside  

36. Structure-Function Analysis 3617 Jan 2006 Protein-protein docking Correlation function: C  = 1/N 3  o  p  q exp[2  i(o  + p  + q  )/N] C o,p,q

37. Structure-Function Analysis 3717 Jan 2006 Docking Programs ZDOCK, RDOCK AutoDock Bielefeld Protein Docking DOCK DOT FTDock, RPScore and MultiDock GRAMM Hex 3.0 ICM Protein-Protein docking KORDO MolFit MPI Protein Docking Nussinov-Wolfson Structural Bioinformatics Group …