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What I will tell

DECONSTRUCTING EVOLUTION TO PERTURB PROTEINS AND NETWORKS Olivier Lichtarge MD, PhD Cullen Professor of Human and Molecular Genetics Baylor College of Medicine Houston, Texas USA. PROLOGUE. What I will tell. What I want to tell. What I tried to tell.

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What I will tell

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  1. DECONSTRUCTING EVOLUTION TO PERTURB PROTEINS AND NETWORKSOlivier Lichtarge MD, PhDCullen Professor of Human and Molecular GeneticsBaylor College of MedicineHouston, TexasUSA

  2. PROLOGUE What I will tell What I want to tell What I tried to tell

  3. Morbidity and Mortality of Protein Diseases • Alzheimer’s • Cancers • Sickle cell • HIV entry • Autoimmune diseases • Amyloidosis • Type II diabetes • Bleeding diathesis • Molecular mimicry • Cardiomyopaties • Cystic Fibrosis • Huntington’s chorea • Ataxias…. Protein dysfunction is linked to many ailments

  4. FUNCTIONAL SITES MEDIATE PROTEIN FUNCTION UNDERSTAND FUNCTIONAL SITES FOCUS EXPERIMENTS RELEVANT TARGETS Molecular recognition protein-small molecule protein-peptide protein-protein protein-nucleic acid Function catalysis signaling motion metabolism immunity transport … • Engineer • drugs • peptide mimics • binding sites • catalytic sites • Modulatepathways • signaling • transcription • development • apoptosis • … Functional site identification has widespread applications Lichtarge Lab Baylor College of Medicine

  5. HOW DO PROTEINS WORK? To control proteins, know their functional determinants

  6. RELEVANT PATTERNS OF EVOLUTIONARY VARIATIONS functional determinants

  7. FUNCTIONAL DETERMINANTS IN PROTEINS Lichtarge Lab Baylor College of Medicine

  8. RATIONAL PROTEIN DESIGN Block, separate function GalphaBourne OnrustScience (1997) RGS Wensel Sowa PNAS (2000) Nucl. TranspMooreCushman JMB ‘04 Nucl. Recept. SmithRaviscioniProteins “06 Ku70/80 BertuchRibes-Zamora NSMB ’07 GRK ClarkBaamaeurMol. Pharm‘10 RecA-LexALichtarge Adikesavan (submitted) Lichtarge Lab Baylor College of Medicine

  9. RATIONAL PROTEIN DESIGN Peptide inhibitor or trigger CohesinPati GRK Clarke Ku70/80 Bertuch Lichtarge Lab Baylor College of Medicine

  10. RATIONAL PROTEIN DESIGN Rewire function RGS Wensel Sowa NSMB (2001) ProneuralTxHassan Quan Develop. ‘04 Nucl. Recept. Cooney RaviscioniJMB“06 GPCR WenselRodriguez PNAS ‘10 Lichtarge Lab Baylor College of Medicine

  11. RATIONAL PROTEIN DESIGN Constitutive activity “internal reprogramming” GPCR WenselMadabushiJBC ’04 GPCR LefkowitzShenoyJBC ‘06 GPCR WenselRodriguez PNAS ’10 Lichtarge Lab Baylor College of Medicine

  12. MUTATIONAL IMPACT Item Mol. Genet. Metab. ‘07 ShaibaniArch.Neurol. ’09 HaberleHum. Mutations ‘10 Katsonis in prep Lichtarge Lab Baylor College of Medicine

  13. PROTEIN FUNCTION PREDICTION Lichtarge Lab Baylor College of Medicine

  14. PROTEIN FUNCTION PREDICTION KristensenProt Sci ‘06 KristensenBMC Bioinfo ‘08 Ward PLoS One ‘09 KristensenJ Mol Biol ‘09 VennerPLoS One ‘10 Lichtarge Lab Baylor College of Medicine

  15. 1. Amino acids may be ranked by importance 2. Top-ranked residues cluster Match known sites 3. Clusters predict functional sites Predict and guide experiments Not random Scalable Robust PATTERNS AND EMERGING PROTEOMIC RULES EVOLUTION Sequence Structure 4. ET quality measures (Clustering, Rank Information) correlate withprediction quality Function 5. ET Clusters exchange water more slowly more H-bonds and salt bridges 6. Importance symmetry across interface Three classes of automated accurate ET ranking functions Three ET servers: http://mammoth.bcm.tmc.edu 7. Top-ranked residue variations: specificity key Lichtarge Lab Baylor College of Medicine

  16. – LECTURE 1 – What I will tell What I want to tell What I tried to tell

  17. EVOLUTION: A COMPUTATIONAL TOOL FOR PROTEINFUNCTIONAL SITE DISCOVERY PROBLEM METHOD • Given a structure • Where is the active site ? • What are the key residue determinants of function? • Evolutionary Trace (ET) • Use evolution’s mutations and assays • Overview SH2, SH3, ZnF • Functional sites, 4º structure RGS, Ga • Functional annotation ZnF, GPCRs • Remote homology and alignmentsGPCRs • Generality Lichtarge Lab Baylor College of Medicine

  18. Deterministic process Non-deterministic process A FUNDAMENTAL CHALLENGE SEQUENCE STRUCTURE FUNCTION INTEGRATING SEQUENCE-STRUCTURE-FUNCTION INFORMATION Lichtarge Lab Baylor College of Medicine

  19. ? SEQUENCE EXPERIMENTS FUNCTIONAL SITE THEORY A SIMPLER PROBLEM STRUCTURE X Given structure x, where are its functional sites? Lichtarge Lab Baylor College of Medicine

  20. FUNCTIONAL SITE CHARACTERIZATION:A LIMITING STEP IN EXPLOITING STRUCTURES • What is important in the structure ? • Where are the functional sites? • How is specificity encoded ? • Mutational analysis is precise, but protein specific, costly, and requires assays. • Structural Genomics producing vast numbers of new structures. NEED a CHEAP, SCALABLE method to characterize the key residue determinants of protein function Lichtarge Lab Baylor College of Medicine

  21. Very basic Evolutionary Tracing (ET)

  22. FUNCTIONAL SITES EVOLVE THROUGH VARIATIONS ON A CONSERVED ARCHITECTURE A L GAALF…….RT…W…KL A F L K R G W T A L GAALY…….RT…W…KD A Y D K R G W T Q L A F D GAQLF…….FT…W…RE R F G W T • Location, architecture and function of active sites are conserved • Specific variations impart novel and unique functional modulations IF these macroscopic observations apply to proteins THEN active site residues will be invariant within functional classes, THEREFORE identify functional sites by looking for such classspecific residues. Lichtarge Lab Baylor College of Medicine

  23. 0. GATHER SEQUENCES KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM Lichtarge Lab Baylor College of Medicine

  24. 1. SPLIT THEM INTO FUNCTIONALSUBGROUPS KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM Lichtarge Lab Baylor College of Medicine

  25. 2. IDENTIFY KEY RESIDUES IN EACH SUBGROUP GROUPS 1 2 3 4 CONSENSUS SEQUENCES KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM KE-TFT-HK-LM Consensus sequence: residues that are invariant within that group VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM VERT-TG-K-QM ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM ASR.YTGHKKNM Lichtarge Lab Baylor College of Medicine

  26. 3. COMPARE KEY RESIDUES ACROSS GROUPS GROUPS 1 2 3 4 EVOLUTIONARY TRACE CONSENSUS SEQUENCES KE-TFT-HK-LM VERT-TG-K-QM ASR.YTGVKKNV ASR.YTGHKKNM KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM KE-TFT-HK-LM VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM VERT-TG-K-QM Compare consensus sequences ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM ASR.YTGHKKNM Lichtarge Lab Baylor College of Medicine

  27. 4. IDENTIFY CLASS SPECIFIC RESIDUESX GROUPS 1 2 3 4 EVOLUTIONARY TRACE CONSENSUS SEQUENCES KE-TFT-HK-LM VERT-TG-K-QM ASR.YTGVKKNV ASR.YTGHKKNM X KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM KE-TFT-HK-LM VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM VERT-TG-K-QM By definition: ifX varies, function changes the sine qua non of importance ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM ASR.YTGHKKNM Lichtarge Lab Baylor College of Medicine

  28. 5. MAP TRACE RESIDUES ON THE STRUCTURE GROUPS 1 2 3 4 EVOLUTIONARY TRACE ACTIVE SITE CONSENSUS SEQUENCES KE-TFT-HK-LM VERT-TG-K-QM ASR.YTGVKKNV ASR.YTGHKKNM XX___T__K_XX KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM KE-TFT-HK-LM VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM VERT-TG-K-QM A site where any variation is linked to functional change. ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM ASR.YTGHKKNM Lichtarge Lab Baylor College of Medicine

  29. HOW TO DEFINE FUNCTIONAL SUBGROUPS? KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM Expert bias VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM Experiments ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM Approximation Lichtarge Lab Baylor College of Medicine

  30. APPROXIMATE FUNCTIONAL SUBGROUPS FROM EVOLUTIONARY INFORMATION GROUPS 1 2 3 4 KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM Hypothesis A sequence identity tree approximates a functional classification. If so, each node is a virtual assay that defines functional subgroups. 4 branches 4 functional groups. VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM Lichtarge Lab Baylor College of Medicine

  31. 2. IDENTIFY KEY RESIDUES IN EACH SUBGROUP GROUPS 1 2 3 4 CONSENSUS SEQUENCES KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM KE-TFT-HK-LM VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM VERT-TG-K-QM ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM ASR.YTGHKKNM Lichtarge Lab Baylor College of Medicine

  32. 3. COMPARE KEY RESIDUES ACROSS GROUPS GROUPS 1 2 3 4 EVOLUTIONARY TRACE CONSENSUS SEQUENCES KE-TFT-HK-LM VERT-TG-K-QM ASR.YTGVKKNV ASR.YTGHKKNM KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM KE-TFT-HK-LM VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM VERT-TG-K-QM ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM ASR.YTGHKKNM Lichtarge Lab Baylor College of Medicine

  33. 4. IDENTIFY TRACE RESIDUES X GROUPS 1 2 3 4 EVOLUTIONARY TRACE CONSENSUS SEQUENCES KE-TFT-HK-LM VERT-TG-K-QM ASR.YTGVKKNV ASR.YTGHKKNM X KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM KE-TFT-HK-LM VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM VERT-TG-K-QM Definition A traceresidueis one that does NOT vary within branches. Generically this property is also called class specificity. ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM ASR.YTGHKKNM Lichtarge Lab Baylor College of Medicine

  34. 5. MAP TRACE RESIDUES ON THE STRUCTURE GROUPS 1 2 3 4 EVOLUTIONARY TRACE ACTIVE SITE CONSENSUS SEQUENCES KE-TFT-HK-LM VERT-TG-K-QM ASR.YTGVKKNV ASR.YTGHKKNM XX___T__K_XX KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM KE-TFT-HK-LM VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM VERT-TG-K-QM ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM ASR.YTGHKKNM Lichtarge Lab Baylor College of Medicine

  35. 6. EVOLUTIONARY IMPORTANCE RANK EVOLUTIONARY TRACE ACTIVE SITE CONSENSUS SEQUENCES GROUPS 1 -----T--K--- _____T__K___ KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM 1 1 VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM rank 1 Definition Thetracerankis the fewest number of branches at which a residue first becomes class specific. ASR.YTGVKKNM ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM ---.-T--K--- Lichtarge Lab Baylor College of Medicine

  36. 6. RANK OF EVOLUTIONARY IMPORTANCE GROUPS 1 2 EVOLUTIONARY TRACE ACTIVE SITE CONSENSUS SEQUENCES -E-T-T--K--M ASR.YTG-KKN- _X___T__K___ KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM 1 2 1 VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM -E-T-T--K--M rank 2 ASR.YTGVKKNM ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM ASR.YTG-KKN- Lichtarge Lab Baylor College of Medicine

  37. KERTFTGHKKLM KERTFTGHKRLM KERTFTVHKRLM KEKTFTGHKKLM KE-TFT-HK-LM VERTFTGHKSQM VERTDTGHKRQM VERTFTGMKRQM VERT-TG-K-QM ASR.YTGVKKNM ASR.YTGVKKNV ASR.YTGHKKNM ASR.YTGHKKNM ASR.YTG-KKN- A WELL DEFINED ALGORITHMIC PROCEDURE GROUPS 1 2 3 EVOLUTIONARY TRACE ACTIVE SITE CONSENSUS SEQUENCES KE-TFT-HK-LM VERT-TG-K-QM ASR.YTG-KKN- XX___T__K_X_ 1 3 2 1 rank 3 Use the tree’s intrinsic hierarchy to assign an evolutionary trace rank to every residues. Lichtarge Lab Baylor College of Medicine

  38. EVOLUTIONARY TRACE ANNOTATION OF PROTEINFUNCTIONAL SITES PROBLEM METHOD • Given a protein structure • Where is the active site ? • What are the key residue determinants of function? • the Evolutionary Trace (ET): • Use evolution’s mutation and assays • Overview SH2, SH3, ZnF • Functional sites, 4º structure RGS, Ga • Functional annotationZnF, GPCRs • Remote homology and alignments GPCRs • Generality Lichtarge Lab Baylor College of Medicine

  39. SH2 DOMAIN Get an SH2 structure Extract the sequence Gather homologs: Blast, FASTA... Align: PILEUP, CLUSTALW... Construct a tree: PHYLIP,… Trace! Lichtarge Lab Baylor College of Medicine

  40. 0° 90° 180° 270° SH2 DOMAIN A B C D E F G Lichtarge Lab Baylor College of Medicine

  41. 0° 90° 180° 270° SH2 DOMAIN A B C D E F G • Trace residues (colored) • exist • Accumulate with more branches • map unevenly on the structure, • up until they scatter Lichtarge Lab Baylor College of Medicine

  42. 0° 90° 180° 270° SH2 DOMAIN A B C D E F G • Mutations of residues ranked • best kill function • lower modulate it • worst no effect Lichtarge Lab Baylor College of Medicine

  43. 80 sequences SH2 DOMAIN 40 sequences Binding site (Waksman et al.) Trace cluster matches binding site Lichtarge Lab Baylor College of Medicine

  44. SH3 DOMAIN Trace cluster matches the binding site (cyan). But it matches the functional site (red) even better. Evolution’s experiments agree with laboratory experiments Lichtarge Lab Baylor College of Medicine

  45. INTRACELLULAR HORMONE RECEPTORS Trace residue clusters match the protein-DNA interface Lichtarge Lab Baylor College of Medicine

  46. PROOF OF PRINCIPLE • If • The dendrogram approximates a functional tree. • The active site evolves through variations on a conserved architecture. • Then • Class specific residues can be found • They cluster at functional sites (protein-protein, protein-DNA interfaces) • They are ranked following a functional hierarchy: • functionally essential residues are first, • modulators of specificity follow, • then noise appears, unlike signal it is scattered rather than clustered. Lichtarge et al. J. Mol. Biol. (1996) Lichtarge Lab Baylor College of Medicine

  47. EVOLUTIONARY TRACE ANNOTATION OF PROTEINFUNCTIONAL SITES PROBLEM METHOD • Given a protein structure • Where is the active site ? • What are the key residue determinants of function? • the Evolutionary Trace (ET): • Use evolution’s mutation and assays • Overview: control studies SH2, SH3, ZnF • Bona fidepredictions of functional sites Galpha • and 4º structureRGS • Functional annotationZnF, GPCRs • Remote homology and alignments GPCRs • Generality Lichtarge Lab Baylor College of Medicine

  48. 1 Effector G PROTEIN SIGNALING Light Calcium Epinephrine 7TMR Angiotensin Thrombin LH, FSH >1000 GDP G a G G bg • Ubiquitous in eukaryotes • Sight smelltaste pain reward inflammation • ≥ 80% of neuroendocrine signaling, • 100% of autonomic physiology. • 40-60% of all drugs Lichtarge Lab Baylor College of Medicine

  49. 1 Effector G PROTEIN-COUPLED RECEPTOR ACTIVATION Light Calcium Epinephrine 7TMR Angiotensin Thrombin LH, FSH >1000 GDP G a G bg Lichtarge Lab Baylor College of Medicine

  50. Effector GTP 2 G bg G PROTEIN ACTIVATION Light Calcium Epinephrine 7TMR Angiotensin Thrombin LH, FSH >1000 G GDP a a G a Activation G bg Lichtarge Lab Baylor College of Medicine

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