Challenges and accomplishments in molecular prediction - PowerPoint PPT Presentation

challenges and accomplishments in molecular prediction n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Challenges and accomplishments in molecular prediction PowerPoint Presentation
Download Presentation
Challenges and accomplishments in molecular prediction

play fullscreen
1 / 31
Challenges and accomplishments in molecular prediction
122 Views
Download Presentation
jariah
Download Presentation

Challenges and accomplishments in molecular prediction

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Challenges and accomplishments in molecular prediction Yanay Ofran

  2. accumulation of data not knowledge >70 million (as of 4-2007) Off chart since 1997

  3. DNA RNA Protein Structure Function Central dogma – it’s all in the sequence

  4. - Structure- Function- Interaction

  5. PDB similar structure dissimilar structure Annotation transfer: structure Rost (1999) Protein Engineering 12: 85-94

  6. 35% 150aa Structure prediction by homology P1 P2 >P2 METLILTQEEVESLISMDEAMNAVEEAFRLYALGKAQMPPKV YLEFEKGDLRAMPAHLMGYAGLKWVNSHPGNPDKGLPTVMAL MILNSPETGFPLAVMDATYTTSLRTGAAGGIAAKYL >P1 MEDLVSVGITHKEAEVEELEKARFESDEAVRDIVESFGLSGS VLLQTSNRVEVYASGARDRAEELGDLIHDDAWVKRGSEAVRH LFRVASGLESMMVGEQEILRQVKKAYDRAARLGTLDEALKIV FRRAINLGKRAREETRISEGAVSI Score = 83.2 bits (205), Expect = 9e-17 Identities = 18/101 (X%), Positives = 36/101 (35%), Gaps = 2/101 (1%) Query: 111 AAGGIAAKYLARKNSSVFGFIGCGTQAYFQLEALRRVFDIGEVKAYDVREKAAKKF 170 AA +A + L + +G G ++L + V + + A + Sbjct: 153 AAVELAERELGSLHDKTVLVVGAGEMGKTVAKSLVD-RGVRAVLVANRTYERAVEL 211 Query: 171 EDRGISASVQPAEEASRCDVLVTTTPSRKPVVKAEWVEEGT 211 + + +R DV+V+ T + PV+ + V E Sbjct: 212 GGEAVRFDE-LVDHLARSDVVVSATAAPHPVIHVDDVREAL 251

  7. 40% 50aa Structure prediction by homology P2 P1 >P2 MLELLPTAVEGVSQAQITGRPEWIWLALGTALMGLGTLYFLV KGMGVSDPDAKKFYAITTLVPAIAFTMYLSMLLGYGLTMVPF GGEQNPIYWARYADWLFTTPLLLLDLALLVDADQGTILALVG ADGIMIGTGLVGALTK >P1 MEDLVSVGITHKEAEVEELEKARFESDEAVRDIVESFGLSGS VLLQTSNRVEVYASGARDRAEELGDLIHDDAWVKRGSEAVRH LFRVASGLESMMVGEQEILRQVKKAYDRAARLGTLDEALKIV FRRAINLGKRAREETRISEGAVSI Score = 33.9 bits (77), Expect = 0.068 Identities = 14/58 (y%), Positives = 28/58 (48%), Gaps = 2/58 (3%) Query: 178 SVQPAEEASRCDVLVTTTPSRKPVVKAEWVEEGTHINAIGADGPGKQELD-VEILKKA 234 + EE ++ D+LV T + +VK EW++ G + G + ++ E ++A Sbjct: 198 TAHLDEEVNKGDILVVATGQPE-MVKGEWIKPGAIVIDCGINYKVVGDVAYDEAKERA 254

  8. Structure prediction from sequence Liu & Rost (2002) Bioinformatics 18: 922-933

  9. Annotation Transfer Annotation transfer: Function E val %seq id. Hssp val Rost et al. (2003) CMLS 60:2637-2650

  10. Annotation transfer: interaction Protein A and protein B bind each other. Do A’ and B’, their respective homologues, interact as well? Mika et al. (2006) PLoS CB

  11. Interaction sites by homology

  12. Annotation Transfer Limit of annotation transfer Seq ID Blind annotation transfer 100% structure Function interaction Ab initio 0%

  13. Annotation Transfer Template and model

  14. Combine (I-TASSER, by Zhang)

  15. Annotation Transfer Limit of annotation transfer

  16. Annotation Transfer Some methods can do it

  17. Local vs. non local interaction Levinthal “Paradox”: • A protein with 100 amino acid has ~1048 possible conformations => calculation unfeasible. • Let’s assumes (generously): A protein can sample 1014 structures per second. It would take this protein about 1034 seconds ~ 1026 years to try out all the possible conformations. (Time since the big bang ~1010 years).

  18. Local vs. non local interaction

  19. Annotation Transfer Low RMSD Witsow & Piatigorsky (1999) Science

  20. Annotation Transfer High RMSD Chymotrypsin(5cha) Subtilin(5sic)

  21. Annotation Transfer Challenges for next CASP • modeling the structure of single-residue mutants. • modeling structure changes associated with specificity changes within protein families. • devise scoring functions that will reliably pick the most accurate models from a set of candidate structures produced by current new fold methods.

  22. Annotation Transfer Olympic games of predictions Structure – CASP Interaction – CAPRI Function – AFP, CASP

  23. http://cmol.nbi.dk/models/info/info.html

  24. Combine interaction + seq. analysis to predict function Li et al (2005) Nature biotech

  25. Combine interaction + seq. analysis to predict function Li et al (2005) Nature biotech

  26. - Structure- Function- Interaction

  27. Predicting DNA binding sites Ofran et al (2007) in press

  28. Predicting DNA binding sites Ofran et al (2007) in press

  29. c-Myb + C/EBPβ bound to DNA

  30. Identifying novel DNA binding proteins accuracy