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BIOS 203 Lecture 6: Some surprises in the biophysics of protein dynamics

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  • 1. Some  Surprises  in  the  Biophysics  of   Protein  Dynamics Vijay  S.  Pande Departments  of  Chemistry,  Structural  Biology,  and  Computer  Science Program  in  Biophysics Stanford  University 1Friday, March 15, 13 1
  • 2. Friday, March 15, 13 2
  • 3. Crystallography  gives  a  wealth  of  informa>on Collagen Helix P53 Oligomerization Formation (50% of cancers) (Osteogenesis Imperfecta) Ribosome: (Last step of Central Dogma, Antibiotic resistance) Aβ peptide aggregation (Alzheimer’s Disease) Chaperonin Assisted Folding (relevant to cancer: HSP90 inhibitors)Friday, March 15, 13 3
  • 4. Ceci n’est pas une pipe.Friday, March 15, 13 4
  • 5. “This is not a GPCR” (Hibert et al, TIPS Reviews, 1993)Friday, March 15, 13 5
  • 6. “This is not a cell”Friday, March 15, 13 6
  • 7. Age old challenges of molecular simulationFriday, March 15, 13 7
  • 8. Age old challenges of molecular simulation 1. Finding a sufficiently accurate modelFriday, March 15, 13 7
  • 9. Age old challenges of molecular simulation 1. Finding a sufficiently accurate model 2. Sampling sufficiently long timescalesFriday, March 15, 13 7
  • 10. Age old challenges of molecular simulation 1. Finding a sufficiently accurate model 2. Sampling sufficiently long timescales 3. Learning something new from the resulting flood of dataFriday, March 15, 13 7
  • 11. How  do  you  break  a  billion-­‐fold  impasse?       Combine  mul=ple,  powerful,  complementary  technologies   8Friday, March 15, 13 8
  • 12. How  do  you  break  a  billion-­‐fold  impasse?       Combine  mul=ple,  powerful,  complementary  technologies   1)  Folding@home:     very  large-­‐scale   distributed  compu4ng Most  powerful   computer  cluster  in  the   world  (~8  petaflops) 104x  to  105x h#p://folding.stanford.edu Voelz,  et  al,  JACS  (2010) Ensign  et  al,  JMB  (2007) Shirts  and  Pande,  Science  (2000) 8Friday, March 15, 13 8
  • 13. How  do  you  break  a  billion-­‐fold  impasse?       Combine  mul=ple,  powerful,  complementary  technologies   1)  Folding@home:     2)  OpenMM:    Very   very  large-­‐scale   fast  MD  (~1µs/ distributed  compu4ng day)  on  GPUs Most  powerful   ~1µs/day  for  implicit   computer  cluster  in  the   solvent  simulaton  of   world  (~8  petaflops) small  proteins  (~40aa)   104x  to  105x 102x  to  103x h#p://folding.stanford.edu h#p://simtk.org/home/openmm Voelz,  et  al,  JACS  (2010) Elsen,  et  al.  ACM/IEEE  conf.  on   Ensign  et  al,  JMB  (2007) Supercompu=ng  (2006) Friedrichs,  et  al.  J.  Comp.  Chem.,  (2009) Shirts  and  Pande,  Science  (2000) Eastman  and  Pande.    J.  Comp.  Chem.   8 (2009)Friday, March 15, 13 8
  • 14. How  do  you  break  a  billion-­‐fold  impasse?       Combine  mul=ple,  powerful,  complementary  technologies   1)  Folding@home:     2)  OpenMM:    Very   3)  Markov  State  Models:     very  large-­‐scale   fast  MD  (~1µs/ Sta4s4cal  mechanics  of   distributed  compu4ng day)  on  GPUs many  trajectories Most  powerful   ~1µs/day  for  implicit   very  long  4mescale   computer  cluster  in  the   solvent  simulaton  of   dynamics  by  combining   world  (~8  petaflops) small  proteins  (~40aa)   many  simula4ons   104x  to  105x 102x  to  103x 102x  to  103x h#p://folding.stanford.edu h#p://simtk.org/home/openmm h#p://simtk.org/home/msmbuilder Voelz,  et  al,  JACS  (2010) Elsen,  et  al.  ACM/IEEE  conf.  on   Bowman,  et  al,  J.  Chem.  Phys.  (2009) Ensign  et  al,  JMB  (2007) Supercompu=ng  (2006) Singhal  &  Pande,  J.  Chem.  Phys.   Friedrichs,  et  al.  J.  Comp.  Chem.,  (2009) (2005) Shirts  and  Pande,  Science  (2000) Eastman  and  Pande.    J.  Comp.  Chem.   (2009) Singhal,  et  al,  J.  Chem.  Phys.  (2004) 8Friday, March 15, 13 8
  • 15. How  do  you  break  a  billion-­‐fold  impasse?       Combine  mul=ple,  powerful,  complementary  technologies   1)  Folding@home:     2)  OpenMM:    Very   3)  Markov  State  Models:     very  large-­‐scale   fast  MD  (~1µs/ Sta4s4cal  mechanics  of   distributed  compu4ng day)  on  GPUs many  trajectories Most  powerful   ~1µs/day  for  implicit   very  long  4mescale   computer  cluster  in  the   solvent  simulaton  of   dynamics  by  combining   world  (~8  petaflops) small  proteins  (~40aa)   many  simula4ons   104x  to  105x 102x  to  103x 102x  to  103x h#p://folding.stanford.edu h#p://simtk.org/home/openmm h#p://simtk.org/home/msmbuilder Voelz,  et  al,  JACS  (2010) Elsen,  et  al.  ACM/IEEE  conf.  on   Bowman,  et  al,  J.  Chem.  Phys.  (2009) Ensign  et  al,  JMB  (2007) Supercompu=ng  (2006) Singhal  &  Pande,  J.  Chem.  Phys.   Friedrichs,  et  al.  J.  Comp.  Chem.,  (2009) (2005) Shirts  and  Pande,  Science  (2000) Eastman  and  Pande.    J.  Comp.  Chem.   (2009) Singhal,  et  al,  J.  Chem.  Phys.  (2004) 8Friday, March 15, 13 8
  • 16. What  are  Markov  State  Models  (MSMs)? Markov  State  Models  (MSMs)  are  a   theoreOcal  scheme  to  build  models   of  long  Omescale  phenomena (1)  to  aid  simulators  reach  long   Omescales  and  (2)  gain  insight  from   their  simulaOons see  the  work  of:            Andersen,  Caflisch,  Chodera,  Deuflhard,  Dill,  Grubmüller,   Hummer,  Levy,  Noé,  Pande,  Pitera,  Singhal-­‐Heinrichs,  Roux,  SchüDe,  Swope,  Weber  Friday, March 15, 13 9
  • 17. States  avoid  issues  with  projec>ons  and  R.C.’s Synthesis Degraded fragments U Disordered Disordered aggregate aggregate Disordered aggregate I Amyloid Prefibrillar Figure  adapted  from   fibril species Dobson,  et  al,  Nature Oligomer N Fiber CrystalFriday, March 15, 13 10
  • 18. States  avoid  issues  with  projec>ons  and  R.C.’s Master  equaEon: Synthesis dpi X = [kl,i pl ki,l pi ] Degraded dt fragments U l Disordered Disordered aggregate aggregate Disordered aggregate I Amyloid Prefibrillar Figure  adapted  from   fibril species Dobson,  et  al,  Nature Oligomer N Fiber CrystalFriday, March 15, 13 10
  • 19. MSMs  coarse  grain  conformaEon  space  (to  ~3Å)   to  build  a  Master  equaEon Master  equaEon: Synthesis dpi X = [kl,i pl ki,l pi ] Degraded fragments dt U l Disordered Disordered aggregate aggregate Build  from  MD: derive  rate  matrix   Disordered aggregate from  simulaOon  w/   I Bayesian  methods Amyloid fibril Prefibrillar species Figure  adapted  from   Dobson,  et  al,  Nature Oligomer N Fiber Crystal 11Friday, March 15, 13 11
  • 20. but  also  derive  a  coarser  view  for  human  consumpEon Master  equaEon: Synthesis dpi X = [kl,i pl ki,l pi ] Degraded fragments dt U l Disordered Disordered aggregate aggregate Build  from  MD: derive  rate  matrix   Disordered aggregate from  simulaOon  w/   I Bayesian  methods Amyloid fibril Prefibrillar species Coarse  grain  MSM: N Oligomer use  eigenvectors  to  idenOfy   Fiber Crystal collecOve  modesFriday, March 15, 13 12
  • 21. Heart  of  the  power  of  MSMs Systema=cally  idenOfying   intermediate  states  allows  us  to (1)  qualitaOvely  understand  and   (2)  quanOtaOvely  predict   chemical  mechanismsFriday, March 15, 13 13
  • 22. ogy to the quantum mechanical problem, an MSM tor.” Suppose we would like to calculate the impact of amatrix could also be augmented calculated with the “perturbed” Hamiltonian can be by a “perturbation m perturbation on the eigenspectrum of the transition ma- (J.  Weber,  VSP)eigenspectrum perturbation calculate the impact of a like to theory [19]. Suppose we would can  tell  us  which  results  are  robust The  quantum mechanical problem, an MSM MSM   gy to the on the eigenspectrum of the transitionT (to first We could define a perturbed transition matrix turbation ma-)atrix could also be augmented by a “perturbation such that ould define a perturbed transition matrix T (to first 0 ⇥Suppose we would like⇥ T calculate the impact of a [ 3 ] T that PerturbaEons  to  to + ⇥T matrix  can  be   • transiEon   } urbation on the eigenspectrum of the transition ma- 0 ⇥ 0 the original T + ⇥T matrix theory T ⇥ transitione T define a perturbed transition matrix T ⇥ (to a matrix of uld ishandled  like  QM  perturbaEon  and T is first ] slow [3 discrete  region m noise. Transi4on  order correction (T0  =  to noise, ⇥ for each that original transition matrixrror  due⇥ “real”matrix) of • The first matrix  with  e and T is a matrix n s the 0 of the transition matrix is given by the simple inner value n T ⇥ T0 + ⇥T⇥ due to noise, ⇥ for [ 3 ] (rates  of  MSM  states) se. The first order correction each eigenvalue  spectrum ct n 0 n of the transition matrix 0 |T⇥ |e0 ⌃ ⇥ the simple inner [ 4 ] ⇥ is given by the original transition= ⇧en and T is a (ie  rates) of matrix eigenvalues   matrix • We  calculate  perturbed   n n e. The first order correction0due to noise, ⇥ for each ⇥ n n = ⇧e0 |T⇥ |ene0 is e0 is the nth eigenvector⌃ of the zeroth-order transition n n [4] n of the transition matrix is given by the simple inner x [19]. Corrected eigenvectors are given by the formula } is the • and  ⇥perturbed  eigenvectors  (ie  mechanism) nth eigenvector ⇥ of0the zeroth-order transition = ⇧e0 |T ⇤⌃⇧e0 |T⇥ |e0 ⌃the formula 4 ] con=nuous  region . Corrected eigenvectors|en given by n n are j [ n en = e 0 + n e0 j [5] is the nth eigenvector of0 the zeroth-order transition 0 ⇤ ⇧ej |T⇥ |e0 ⌃ 0 j n 0 j⇤=n n Corrected= e0 + en eigenvectors are giveneby the formula [ 5 ] n j 0 0 these Key  result ⇤n to n ⇥ 0 j • corrections j⇤= due 0 perturbation, one could gauge thect of a n = e0 perturbs  (or a|en ⌃ e0 systematic) change infast • error   +noise⇧ej |T more e random n eigenvalues j [5] a tion matrix on its=eigenspectrum. discrete  region  of  the appli- corrections duewill  be  robust  in  the   We illustrate the • results   to perturbation, one could gauge the   0 0 j⇤ n n jn of this perturbationatheory by applying the above analysis a randomeigenvalue  spectrum systematic) change in a noise (or morematrix Relevant  to perturbation, one matrix. the appli-e eigenvalues eigenspectrum. We nd  could gauge the corrections due ftheboth  theory  a illustrate • on its of or   villin transition experimenthis perturbation (or a more systematic)above analysis random noise theory by applying the change in anvalues of the villinPande. TheWe illustrate the appli- used atrixJ.on its and V. S. transitionmethodismore extensively Biophys J. (2011) for a Weber Framework. Proteinmatrix. mechanistically robust. New eigenspectrum. folding s Friday, March 15, 13analogous, though not identical, to classical per- perturbation theory by applying the above analysis study is 14
  • 23. Folding  simulaEon  has  come  a  long  way  in  15  years ACBP 10,000 Shaw (ANTON supercomputer) Pande (Folding@home) Schulten Noe NTL9 NTL9 Kollman 1000 blue = explicit solvent Folding Time (microseconds) Lambda Lambda red = implicit solvent 100 Protein G Lambda BBL NTL9 a3D Pin1 WW GTT WW Lambda BBA 10 BBA5 Trp Zip Fip35 WW Fip35 Fip35 Trp-cage Villin Protein B Homeodomain Trp Cage Villin 1 Villin Villin Villin Chignolin Fs  Peptide Fs Peptide 0.1 1998 2000 2002 2004 2006 2008 2010 2012 YearFriday, March 15, 13 15
  • 24. Folding  simulaEon  has  come  a  long  way  in  15  years ACBP 10,000 Shaw (ANTON supercomputer) Pande (Folding@home) Schulten Noe NTL9 NTL9 Kollman 1000 blue = explicit solvent Folding Time (microseconds) Lambda Lambda red = implicit solvent 100 Protein G Lambda BBL NTL9 a3D Pin1 WW GTT WW Lambda BBA 10 BBA5 Trp Zip Fip35 WW Fip35 Fip35 Trp-cage Villin Protein B Homeodomain Trp Cage Villin 1 Villin Villin Villin Chignolin Fs  Peptide Fs Peptide 0.1 1998 2000 2002 2004 2006 2008 2010 2012 YearFriday, March 15, 13 15
  • 25. Can  we  quan>ta>vely  predict  experiment? 10,000 Implicit Pande Explicit ACBP NTL9 1000 Predicted folding time (μs) Fip35 WW 100 10 WT Villin Trp Zip BBA5 ⋋-repressor Trp-cage 1 0.1 Fs Peptide 0.01 0.1 1 10 100 1000 10,000 Experimental folding time (μs)Friday, March 15, 13 16
  • 26. What  has  the  community  done  so  far? 10,000 Noé Implicit Pande Explicit ACBP Schulten NTL9 Shaw 1000 Predicted folding time (μs) Fip35 WW 100 Protein G ⋋-repressor BBL α3D Fip35 NTL9 Pin1 WW Trp-cage 10 WT Villin Fip35 WW Protein B Trp Zip BBA5 ⋋-repressor Villin Nle Trp-cage Homeodomain Villin Nle 1 0.1 Fs Peptide 0.01 0.1 1 10 100 1000 10,000 Experimental folding time (μs)Friday, March 15, 13 17
  • 27. (Beauchamp,  Das,  VSP) Experiments  can  now  probe  detailed  MSM  aspects ∆G  (kcal/mol) RMSD  (Å) Many  states  have  low  ∆G  and   are  highly  structurally  related Bowman,  Beauchamp,  Boxer,  Pande,  JCP  (2009); Beauchamp,  Das,  Pande,  PNAS  (2011)Friday, March 15, 13 18
  • 28. (Beauchamp,  Das,  VSP) Experiments  can  now  probe  detailed  MSM  aspects ∆G  (kcal/mol) RMSD  (Å) Many  states  have  low  ∆G  and   are  highly  structurally  related Bowman,  Beauchamp,  Boxer,  Pande,  JCP  (2009); Beauchamp,  Das,  Pande,  PNAS  (2011)Friday, March 15, 13 18
  • 29. (Beauchamp,  Das,  VSP) Experiments  can  now  probe  detailed  MSM  aspects ∆G  (kcal/mol) RMSD  (Å) Many  states  have  low  ∆G  and   are  highly  structurally  related Bowman,  Beauchamp,  Boxer,  Pande,  JCP  (2009); Beauchamp,  Das,  Pande,  PNAS  (2011) from  Reiner,  Henklein,  &  Kie`aber  PNAS  (2010)Friday, March 15, 13 18
  • 30. The  challenge  of  simula>ng  vs  understanding “It is nice to know that the computer understands the problem. But I would like to understand it too.” – Eugene Wigner, in response to a large-scale quantum mechanical calculationFriday, March 15, 13 19
  • 31. A  brief  history  of  protein  folding  kine>cs  theoryFriday, March 15, 13 20
  • 32. A  brief  history  of  protein  folding  kine>cs  theory • 1990:      Simple  kineEc  models • Master  equa4on  approaches  (Shakhnovich  et  al;   Orland  et  al;  Wolynes  et  al) • Ladce  model  simula4ons  (Dill;  many  others)Friday, March 15, 13 20
  • 33. A  brief  history  of  protein  folding  kine>cs  theory • 1990:      Simple  kineEc  models • Master  equa4on  approaches  (Shakhnovich  et  al;   Orland  et  al;  Wolynes  et  al) • Ladce  model  simula4ons  (Dill;  many  others) • 2000:    A  naEve-­‐centric  view  dominates • Experiments  suggest  a  two-­‐state  model  for  protein   folding  kine4cs  (Fersht) • Contact  order  (Plaxco,  Simmons,  Baker) • Minimal  frustra4on/protein  design  approach   (Wolynes;  Shakhnovich;  Pande;  others) • Consequence:    Go  model  simula4ons,  funnel   energy  landscape  paradigmFriday, March 15, 13 20
  • 34. A  brief  history  of  protein  folding  kine>cs  theory • 1990:      Simple  kineEc  models • Master  equa4on  approaches  (Shakhnovich  et  al;   Orland  et  al;  Wolynes  et  al) PHE35 • Ladce  model  simula4ons  (Dill;  many  others) PHE11 • 2000:    A  naEve-­‐centric  view  dominates • Experiments  suggest  a  two-­‐state  model  for  protein   folding  kine4cs  (Fersht) PHE18 • Contact  order  (Plaxco,  Simmons,  Baker) • Minimal  frustra4on/protein  design  approach   (Wolynes;  Shakhnovich;  Pande;  others) TRP24 • Consequence:    Go  model  simula4ons,  funnel   energy  landscape  paradigm • What  is  a  Go  model? • Hα  =  -­‐ε  ∑ij  Cαij  CNij   • interac4ons  present  in  the   folded  state  are  ajrac4ve • all  others  are  repulsiveFriday, March 15, 13 20
  • 35. A  brief  history  of  protein  folding  kine>cs  theory • 1990:      Simple  kineEc  models • Master  equa4on  approaches  (Shakhnovich  et  al;   Orland  et  al;  Wolynes  et  al) PHE35 • Ladce  model  simula4ons  (Dill;  many  others) PHE11 • 2000:    A  naEve-­‐centric  view  dominates • Experiments  suggest  a  two-­‐state  model  for  protein   folding  kine4cs  (Fersht) PHE18 • Contact  order  (Plaxco,  Simmons,  Baker) • Minimal  frustra4on/protein  design  approach   (Wolynes;  Shakhnovich;  Pande;  others) TRP24 • Consequence:    Go  model  simula4ons,  funnel   energy  landscape  paradigm • What  is  a  Go  model? • 2010:    The  naEve  centric  view  is  unsaEsfying • Hα  =  -­‐ε  ∑ij  Cαij  CNij   • Structure  in  the  unfolded  state  (eg  Raleigh) • interac4ons  present  in  the   • Slow  diffusion  (eg  Lapidus) folded  state  are  ajrac4ve • non-­‐na4ve  interac4ons  (eg  Majhews) • all  others  are  repulsiveFriday, March 15, 13 20
  • 36. A  key  ques>on  domina>ng  protein  folding  theory How  important  are   non-­‐na=ve   (i.e.  not  present  in  the   folded  state)   interacOons?Friday, March 15, 13 21
  • 37. Folding  simulaEon  has  come  a  long  way  in  15  years ACBP 10,000 Shaw (ANTON supercomputer) Pande (Folding@home) Schulten Noe NTL9 NTL9 Kollman 1000 blue = explicit solvent Folding Time (microseconds) Lambda Lambda red = implicit solvent 100 Protein G Lambda BBL NTL9 a3D Pin1 WW GTT WW Lambda BBA 10 BBA5 Trp Zip Fip35 WW Fip35 Fip35 Trp-cage Villin Protein B Homeodomain Trp Cage Villin 1 Villin Villin Villin Chignolin Fs  Peptide Fs Peptide 0.1 1998 2000 2002 2004 2006 2008 2010 2012 YearFriday, March 15, 13 22
  • 38. Folding  simulaEon  has  come  a  long  way  in  15  years ACBP 10,000 Shaw Pande Schulten Noe NTL9 Kollman 1000 blue = explicit solvent Folding Time (microseconds) Lambda red = implicit solvent 100 Protein G NTL9 Lambda BBL NTL9 a3D Pin1 WW GTT WW Lambda BBA 10 Lambda Fip35 WW Fip35 Fip35 Trp-cage BBA5 Trp Zip Villin Protein B Homeodomain Trp Cage Villin 1 Villin Villin Villin Chignolin Fs Peptide 0.1 1998 2000 2002 2004 2006 2008 2010 2012 YearFriday, March 15, 13 23
  • 39. Friday, March 15, 13 24
  • 40. Pathway  seen  in  the  movie:    Series  of  metastable  states (Voelz,  Bowman,  Beauchamp,  VSP) Voelz, Bowman, Beauchamp, Pande. JACS (2010) snapshots  from  the  movie: starts  in   helix collapse, final  part  of   folded   unfolded forms then  beta   beta  ready  to   structure   state early sheet  forms align forms correspond  to  states  from  our  Markov  State  Model: 25Friday, March 15, 13 25
  • 41. RepeaEng  with  many  more  trajectories  yields  an   MSM:    coarse  visualizaEon (Voelz,  Bowman,  Beauchamp,  VSP) f area  of  each  state  is  propor>onal  to   g macrostate  free  energy l d a n a→l→n    and  a→m→n   i comprise  10%  of  the   b total  flux m width  of  each  arrow  is   c propor>onal  to   transi>on  flux k j h Flux  calcula>on  method:     e TPT:    Vanden-­‐Eijnden,  et  al  (2006) Berezhkovskii,  Hummer,  Szabo  (2009)   Top  10  folding  pathways  shows  us: • A  great  deal  of  pathway  heterogeneity  exists   • non-­‐na4ve  structure  plays  a  key  role  in  many  states • metastability  is  onen  structurally  localized  (analogous  to  the  foldon  concept) 26Friday, March 15, 13 26
  • 42. Contact  map  view  of  the  states  reveals  non-­‐naEve  structure   formaEon  along  the  pathway (Voelz,  Bowman,  Beauchamp,  VSP) h more alpha a k m n more beta unfolded basin transition state region (committor) native basin 27Friday, March 15, 13 27
  • 43. Contact  map  view  of  the  states  reveals  non-­‐naEve  structure   formaEon  along  the  pathway (Voelz,  Bowman,  Beauchamp,  VSP) h more alpha significant   a amount of  non-­‐ k naEve   structure,   even  in  high   m pfold  states n more beta unfolded basin transition state region (committor) native basin 27Friday, March 15, 13 27
  • 44. (Bowman,  Voelz,  VSP) Beta  sheet  states  slow  folding  in  helical  proteins? Lambda G. Bowman, V. Voelz, and V. S. Pande. Atomistic folding simulations of the five helix bundle protein ! λ6-85. Journal of the American Chemical Society 133 664-667 (2011)Friday, March 15, 13 28
  • 45. “Intramolecular  amyloids”? ßsheets in unfolded state Lambda A B C D E F “λ6-85 is not only thermodynamically, but G also kinetically protected from reaching intramolecular analogs of beta sheet H aggregates while folding” without helix5 xtal structure – Prigozhin & GruebeleFriday, March 15, 13 29
  • 46. (Voelz,  VSP) Consequences  of  projec>ons How  can  one  reconcile  this  with  the  simple  picture? V. A. Voelz, et al. JACS (2012)Friday, March 15, 13 30
  • 47. (Voelz,  VSP) Consequences  of  projec>ons How  can  one  reconcile  this  with  the  simple  picture? V. A. Voelz, et al. JACS (2012)Friday, March 15, 13 30
  • 48. (Voelz,  VSP) Consequences  of  projec>ons How  can  one  reconcile  this  with  the  simple  picture? V. A. Voelz, et al. JACS (2012)Friday, March 15, 13 30
  • 49. (Voelz,  VSP) Consequences  of  projec>ons How  can  one  reconcile  this  with  the  simple  picture? V. A. Voelz, et al. JACS (2012)Friday, March 15, 13 30
  • 50. (Voelz,  VSP) Consequences  of  projec>ons How  can  one  reconcile  this  with  the  simple  picture? ‘‘Regarded from two sides’’ by Diet Wiegman (1984) Kruschela & Zagrovic. V. A. Voelz, et al. JACS (2012) DOI:10.1039/b917186jFriday, March 15, 13 30
  • 51. ConclusionsFriday, March 15, 13 31
  • 52. Conclusions ACBP 10,000 Shaw Pande Schulten Noe NTL9 Kollman 1000 Folding Time (microseconds) Lambda 100 Protein G Lambda BBL NTL9 a3D Pin1 WW GTT WW Lambda BBA 10 Trp Zip Fip35 WW Fip35 Fip35 Trp-cage BBA5 Villin Protein B Homeodomain Trp Cage Villin 1 Villin Villin Villin Chignolin Fs Peptide 0.1 1998 2000 2002 2004 2006 2008 2010 2012 Year With MSMs, we can simulate folding on the 10ms timescaleFriday, March 15, 13 31
  • 53. Conclusions ACBP 10,000 10,000 Shaw Noé Implicit Pande Pande Explicit ACBP Schulten Schulten NTL9 Shaw Noe Kollman NTL9 1000 1000 Folding Time (microseconds) Lambda Predicted folding time (μs) Fip35 WW 100 Protein G ⋋-repressor BBL 100 Protein G α3D Fip35 NTL9 Pin1 WW Lambda Trp-cage BBL NTL9 10 WT Villin Fip35 WW a3D GTT WW Lambda Protein B Trp Zip BBA5 Pin1 WW BBA ⋋-repressor Villin Nle 10 Trp Zip Fip35 WW Fip35 Fip35 Trp-cage Villin Nle Trp-cage Homeodomain BBA5 Villin Protein B 1 Homeodomain Trp Cage Villin 1 Villin Villin 0.1 Fs Peptide Villin Chignolin Fs Peptide 0.1 0.01 1998 2000 2002 2004 2006 2008 2010 2012 0.1 1 10 100 1000 10,000 Year Experimental folding time (μs) With MSMs, we can simulate Simulation methods are sufficiently folding on the 10ms timescale accurate to predict experimentFriday, March 15, 13 31
  • 54. Conclusions ACBP 10,000 10,000 Shaw Noé Implicit Pande Pande Explicit ACBP Schulten Schulten NTL9 Shaw Noe Kollman NTL9 1000 1000 Folding Time (microseconds) Lambda Predicted folding time (μs) Fip35 WW 100 Protein G ⋋-repressor BBL 100 Protein G α3D Fip35 NTL9 Pin1 WW Lambda Trp-cage BBL NTL9 10 WT Villin Fip35 WW a3D GTT WW Lambda Protein B Trp Zip BBA5 Pin1 WW BBA ⋋-repressor Villin Nle 10 Trp Zip Fip35 WW Fip35 Fip35 Trp-cage Villin Nle Trp-cage Homeodomain BBA5 Villin Protein B 1 Homeodomain Trp Cage Villin 1 Villin Villin 0.1 Fs Peptide Villin Chignolin Fs Peptide 0.1 0.01 1998 2000 2002 2004 2006 2008 2010 2012 0.1 1 10 100 1000 10,000 Year Experimental folding time (μs) With MSMs, we can simulate Simulation methods are sufficiently folding on the 10ms timescale accurate to predict experiment folding via parallel paths of many metastable statesFriday, March 15, 13 31
  • 55. Conclusions ACBP 10,000 10,000 Shaw Noé Implicit Pande Pande Explicit ACBP Schulten Schulten NTL9 Shaw Noe Kollman NTL9 1000 1000 Folding Time (microseconds) Lambda Predicted folding time (μs) Fip35 WW 100 Protein G ⋋-repressor BBL 100 Protein G α3D Fip35 NTL9 Pin1 WW Lambda Trp-cage BBL NTL9 10 WT Villin Fip35 WW a3D GTT WW Lambda Protein B Trp Zip BBA5 Pin1 WW BBA ⋋-repressor Villin Nle 10 Trp Zip Fip35 WW Fip35 Fip35 Trp-cage Villin Nle Trp-cage Homeodomain BBA5 Villin Protein B 1 Homeodomain Trp Cage Villin 1 Villin Villin 0.1 Fs Peptide Villin Chignolin Fs Peptide 0.1 0.01 1998 2000 2002 2004 2006 2008 2010 2012 0.1 1 10 100 1000 10,000 Year Experimental folding time (μs) With MSMs, we can simulate Simulation methods are sufficiently folding on the 10ms timescale accurate to predict experiment ! folding via parallel paths of intramolecular amyloid many metastable states hypothesisFriday, March 15, 13 31
  • 56. Where  do  we  go  from  here?Friday, March 15, 13 32
  • 57. Petaflops  on  the  cheap  today,  exaflops  soon? There  are  approximately  a  billion  computers  in  the  world Folding@homeFriday, March 15, 13 33
  • 58. Petaflops  on  the  cheap  today,  exaflops  soon? There  are  approximately  a  billion  computers  in  the  world How  many  GPUs?    How  many  GPU  flops? Folding@homeFriday, March 15, 13 33
  • 59. Petaflops  on  the  cheap  today,  exaflops  soon? There  are  approximately  a  billion  computers  in  the  world How  many  GPUs?    How  many  GPU  flops? Folding@home A  million  GPUs  pu]ng  out  1TFLOP  each  gets  us  to  an  exaflop:     we  could  do  this  todayFriday, March 15, 13 33
  • 60. The  combinaOon  of  new  simulaOon   advances  and  chemically  detailed  models   has  suggested  a  paradigm  change  in  how   we  conceptualize  protein  folding.Friday, March 15, 13 34
  • 61. The  combinaOon  of  new  simulaOon   advances  and  chemically  detailed  models   has  suggested  a  paradigm  change  in  how   we  conceptualize  protein  folding. We  are  now  looking  to  apply   MSM  approaches  to  new  areas: 1)  basis  of  signal  transducOon 2)  protein  misfolding  diseases both  involving  issues  of  small  molecules   and  the  role  of  chemical  interacOonsFriday, March 15, 13 35
  • 62. New  interest  in  my  lab:  probing  the  molecular   nature  of  the  mechanism  of  signal  transducEon GPCRs kinasesFriday, March 15, 13 36
  • 63. What  do  we  want  to  do? kinasesFriday, March 15, 13 37
  • 64. What  do  we  want  to  do? • Understand  how  they  funcEon • what  is  the  mechanism  of  ac4va4on  &   inac4va4on? • how  is  the  signal  transduced? • what  is  the  role  of  chemical  interac4ons  in   this  process? kinasesFriday, March 15, 13 37
  • 65. What  do  we  want  to  do? • Understand  how  they  funcEon • what  is  the  mechanism  of  ac4va4on  &   inac4va4on? • how  is  the  signal  transduced? • what  is  the  role  of  chemical  interac4ons  in   this  process? • Use  this  understanding  to  modulate   their  funcEon • design/predict  novel  small  inhibitors  &   ac4vators • design/predict  protein  muta4ons  which   yield  new  func4ons  or  new  behaviors kinasesFriday, March 15, 13 37
  • 66. What  do  we  want  to  do? • Understand  how  they  funcEon • what  is  the  mechanism  of  ac4va4on  &   inac4va4on? • how  is  the  signal  transduced? • what  is  the  role  of  chemical  interac4ons  in   this  process? • Use  this  understanding  to  modulate   their  funcEon • design/predict  novel  small  inhibitors  &   ac4vators • design/predict  protein  muta4ons  which   yield  new  func4ons  or  new  behaviors • Connect  this  new  chemical  insight  to   kinases basic  biology  and  aspects  of  diseaseFriday, March 15, 13 37
  • 67. Protein  Kinases • Protein  Kinases  are  enzymes  that  modify  the  func4on  of  other   proteins  by  ajaching    phosphate  groups  to  them.  Friday, March 15, 13 38
  • 68. (Shukla,  VSP) Conforma>onal  change  in  src  kinase LYS295 C-­‐helix C-­‐helix GLU310 hbond ARG409 ATP A-­‐loop A-­‐loop TYR419 InacEve acEveFriday, March 15, 13 39
  • 69. Kine>c  traces  for  ac>va>on/deac>va>on • We  see  many   acEvaEon  events • MSM  kineEcs  can  be   used  to  predict   experiment • We  get  reasonable   kineEcs • Ac4va4on  4mescales   consistent  with   experiment  (sub-­‐ millisecond   4mescale) • What  does  the   mechanism  look   like?Friday, March 15, 13 40
  • 70. Age old challenges of molecular simulation 1. Finding a sufficiently accurate model 2. Sampling sufficiently long timescales 3. Learning something new from the resulting flood of dataFriday, March 15, 13 41
  • 71. Age old challenges of molecular simulation ✔ 1. Finding a sufficiently accurate model 2. Sampling sufficiently long timescales 3. Learning something new from the resulting flood of dataFriday, March 15, 13 41
  • 72. Age old challenges of molecular simulation ✔ 1. Finding a sufficiently accurate model ✔ 2. Sampling sufficiently long timescales 3. Learning something new from the resulting flood of dataFriday, March 15, 13 41
  • 73. Age old challenges of molecular simulation ✔ 1. Finding a sufficiently accurate model ✔ 2. Sampling sufficiently long timescales ? 3. Learning something new from the resulting flood of dataFriday, March 15, 13 41
  • 74. Age old challenges of molecular simulation ✔ 1. Finding a sufficiently accurate model ✔ 2. Sampling sufficiently long timescales ? 3. Learning something new from the resulting flood of dataFriday, March 15, 13 41
  • 75. (Shukla,  VSP) Kinase  conforma>onal  changeFriday, March 15, 13 42
  • 76. (Shukla,  VSP) Surprise:  an  intermediate  state? InacOveFriday, March 15, 13 43
  • 77. (Shukla,  VSP) Surprise  2:  intermediate  state(s)Friday, March 15, 13 44
  • 78. (Shukla,  VSP) We  find  many  intermediates!Friday, March 15, 13 45
  • 79. Problems  with  projec>onsFriday, March 15, 13 46
  • 80. Problems  with  projec>ons from Chandler (1998)Friday, March 15, 13 46
  • 81. Problems  with  projec>ons from Chandler (1998)Friday, March 15, 13 46
  • 82. Problems  with  projec>ons from Chandler (1998) MSMs can tell us where to look as we have a full modelFriday, March 15, 13 46
  • 83. Heart  of  the  power  of  MSMs Systema=cally  idenOfying   intermediate  states  allows  us  to (1)  qualitaOvely  understand  and   (2)  quanOtaOvely  predict   chemical  mechanismsFriday, March 15, 13 47
  • 84. (McGibbon,  Schwantes,  VSP) New  challenges  with  conforma>onal  change •Building  MSMs  for  conformaEonal  change • much  more  challenging  than  for  protein  folding • as  the  changes  are  much  more  subtle •We  have  developed  novel  theoreEcal  approaches  to  tackle   these  new  challenges •Metric  learning  approaches:  use  Machine  Learning  to   iden4fy  which  degrees  of  freedom  are  important  and   which  are  noise •Dimensionality  reduc4on  approaches:    iden4fy  collec4ve   degrees  of  freedom  systema4cally   •Use  these  new  approaches  to  both  build  bejer  MSMs   but  also  to  ideally  learn  something  new  about  the  systemFriday, March 15, 13 48
  • 85. MSM  reveals  key  intermediates • We  see  many   acEvaEon  events • MSM  kineEcs  can  be   used  to  predict   experiment • We  get  reasonable   kineEcs • Ac4va4on  4mescales   consistent  with   experiment  (sub-­‐ millisecond   4mescale) • What  does  the   mechanism  look   like?Friday, March 15, 13 49
  • 86. MSM  reveals  key  intermediatesFriday, March 15, 13 49
  • 87. MSM  reveals  key  intermediatesFriday, March 15, 13 49
  • 88. (Shukla,  VSP) Characterizing  intermediate  2 C-­‐helix in  inacEve   ConformaEon R409 E310 E310-­‐R409   H-­‐bond  broken A-­‐loop unfolded Intermediate  2  of  c-­‐src  Kinase   (Simula4on)Friday, March 15, 13 50
  • 89. (Shukla,  VSP) Characterizing  intermediate  2 C-­‐helix in  inacEve   ConformaEon E310 R409 E310 E310-­‐R409   R409 H-­‐bond  broken A-­‐loop unfolded Intermediate  2  of  c-­‐src  Kinase   Cyclin-­‐dependent  Kinase  2   (Simula4on) (PDB:  4BCQ)Friday, March 15, 13 50
  • 90. (Shukla,  VSP) SimulaEons  predict  drug  stabilizes  intermediate  2 • ANS  binding  to  the  allosteric  site   adjacent  to  C-­‐helix  in  c-­‐src   kinase  stabilizes  the   intermediate  conformaEon • by  blocking  the  interac4ons   between  K295  and  E310   • h-­‐bond  forma4on  between   K295  and  E310  is  required  for   the  locking  of  the  C-­‐helix  in   the  ac4ve  conforma4on   • sulfonate  group  in  the  ANS   forms  a  hydrogen-­‐bond  with   the  K295  thereby  locking  it  in   its  inac4ve  conforma4onFriday, March 15, 13 51
  • 91. (Shukla,  VSP) SimulaEons  predict  drug  stabilizes  intermediate  2 • ANS  binding  also  pushes  the   C-­‐helix  away  from  the  ATP   binding  pocket • Superimposi4on  of  the   structures  obtained  from  the   simula4ons  reveal  the  dis4nct   conforma4ons  of  the  c-­‐helix   in  presence  of  ANS: • ATP-­‐bound  c-­‐src  kinase  (cyan) • ATP  and  ANS-­‐bound  src-­‐ kinase,  1  molecule  of  ANS  in   the  allosteric  site  (orange)  () • ATP  and  ANS-­‐bound  src-­‐ kinase,  2  molecule  of  ANS  in   the  allosteric  site  (green)Friday, March 15, 13 52
  • 92. Simula>ng  the  kinome c-­‐src  kinase  (2SRC) Fyn  kinase  (2DQ7) Hck  kinase  (2HCK) Lyn  kinase  (2ZV7)Friday, March 15, 13 53
  • 93. Signal  transduc>on  in  G-­‐protein-­‐coupled  receptors   Rosenbaum et. al., Nature, 2009.Friday, March 15, 13 54
  • 94. G-­‐Protein  Coupled  Receptor  Structure Kobilka and coworkers, Nature, 2011.Friday, March 15, 13 55
  • 95. Key  DetailsFriday, March 15, 13 56
  • 96. (Kohlhoff,  Shukla,  Lawrenz,  …,  VSP) Trajectories  of  ß2  behavior:  Agonist  bound !Friday, March 15, 13 57
  • 97. What  do  we  want  to  do? •Understand  how  they  funcEon •what  is  the  mechanism  of  ac4va4on  &  inac4va4on? •how  is  the  signal  transduced? •what  is  the  role  of  chemical  interac4ons  in  this  process? •Use  this  understanding  to  modulate  their  funcEon •design/predict  novel  small  inhibitors  &  ac4vators •design/predict  protein  muta4ons  which  yield  new   func4ons  or  new  behaviors •Connect  this  new  chemical  insight  to  basic  biology   and  aspects  of  diseaseFriday, March 15, 13 58
  • 98. As  in  life,  in  science  it  is  very   dangerous  to  fall  in  love  with   beau=ful  models.Friday, March 15, 13 59
  • 99. Several  different  aspects  of  theore>cal  chemistryFriday, March 15, 13 60
  • 100. Several  different  aspects  of  theore>cal  chemistry Theory (simplicity, transparency)Friday, March 15, 13 60
  • 101. Several  different  aspects  of  theore>cal  chemistry Theory SimulaEon (simplicity, (detail,   transparency) accuracy)Friday, March 15, 13 60
  • 102. Several  different  aspects  of  theore>cal  chemistry Theory SimulaEon (simplicity, (detail,   transparency) accuracy) InformaEcs (experiment, sta=s=cs)Friday, March 15, 13 60
  • 103. My  approach:  unify  theore>cal  approaches Theory SimulaEon (simplicity, (detail,   transparency) accuracy) InformaEcs (experiment, sta=s=cs)Friday, March 15, 13 61
  • 104. My  approach:  unify  theore>cal  approaches My  approach:   Theory SimulaEon to  unify   (simplicity, (detail,   simulaOon,   transparency) accuracy) theory,  and   informaOcs,  to   InformaEcs build  models  of   (experiment, long  Omescale   sta=s=cs) biology  in   chemical  detailFriday, March 15, 13 61
  • 105. “This is not a cell”Friday, March 15, 13 62
  • 106. AcknowledgementsFriday, March 15, 13 63