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20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
20120703 girona seminar
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20120703 girona seminar

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Multiscale simulations to identify hotspots in protein interfaces

Multiscale simulations to identify hotspots in protein interfaces

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  • 1. multiscale structure and function Multiscale simulations to identify hotspots in protein interfacesCésar L. Ávila, Nils Drechsel, Michael A. Johnston and Jordi Villà-Freixa Universitat Pompeu Fabra / Universitat de Vic http://cbbl.imim.es Girona Seminar, 03/07/2012
  • 2. multiscale structure and functionGirona Seminar, 03/07/2012
  • 3. multiscale structure and function Girona Seminar, 03/07/2012
  • 4. multiscale structure and functionsequence->structure->function... Guix et al. Brain (2009) Giupponi et al (in preparation) Girona Seminar, 03/07/2012
  • 5. multiscale structure and functionsequence->structure->function... Guix et al. Brain (2009) Giupponi et al (in preparation) Girona Seminar, 03/07/2012
  • 6. multiscale structure and functionsequence->structure->function... Guix et al. Brain (2009) Giupponi et al (in preparation) Girona Seminar, 03/07/2012
  • 7. multiscale structure and function Girona Seminar, 03/07/2012
  • 8. multiscale structure and functionhow do we explore protein movements? Girona Seminar, 03/07/2012
  • 9. multiscale structure and function SP M potentialM energySP surface M reaction coordinate Girona Seminar, 03/07/2012
  • 10. multiscale structure and functionexample of thedynamics of aRING fingerdomain Girona Seminar, 03/07/2012
  • 11. multiscale structure and functionexample of the great... as long asdynamics of a we have a goodRING finger knowledge of thedomain structure but what if not? Girona Seminar, 03/07/2012
  • 12. multiscale structure and functionin collaboration with Joan Sayós 1 11 21 31 41 RMSD 2NMS.pdb, chain A 18 G I P Q I T G P T T VNG L ERGS L T V Q C V Y R S GW E T Y L K WW C R G A I WR D C K I L V K rat.B99990001.pdb 23 G C V P L R G P S S V TGT VGES L N V T CQY E ER F K M N K K YWC R G S LVL LCKD I VR 51 61 71 81 91 RMSD - 2NMS.pdb, chain A 68 T S G S E Q E V K R DRV S I KDNQK NR T F T V TMED L MK T D A D T YW CG I E K TGND L rat.B99990001.pdb 73 T G G S E . E A R N GRV S I RDDRD N L T F T V T L QN L T L EDAGT YM CAVD I P L I DH 101 111 RMSD 2NMS.pdb, chain A118 G V T V Q V T I D P AP rat.B99990001.pdb 122 S F K V E L S V V P GN James Dalton Sergio Rubio Girona Seminar, 03/07/2012
  • 13. multiscale structure and function A more challenging problem: CFTRJames Dalton ! ! Dalton, Kalid, Shushan, Ben-Tal and Villà- Freixa . J. Chem. Inf. Mod. in press Serohijos et al. PNAS 2008 Mornon et al. Cell Mol Lif Sci 2008 Girona Seminar, 03/07/2012
  • 14. multiscale structure and function A more challenging problem: CFTRJames Dalton ! Dalton, Kalid, Shushan, Ben-Tal and Villà- ! Freixa . J. Chem. Inf. Mod. in press Girona Seminar, 03/07/2012
  • 15. multiscale structure and function homology modeling + docking + md Mulero et al. submittedCRIS CHARNECO Girona Seminar, 03/07/2012
  • 16. multiscale structure and function Beyond dynamics: energeticsExplicitmicroscopicMajorconvergencechallenges Semi- macroscopicSimplified (PDLD/S-microscopic LRA)(PDLD) Schutz et al., proteins (2001) Girona Seminar, 03/07/2012
  • 17. multiscale structure and function PDLD/S-LRA Warshel and coworkers Girona Seminar, 03/07/2012
  • 18. multiscale structure and functionresidue differential Solvation Khan et al. (unpublished) Girona Seminar, 03/07/2012
  • 19. multiscale structure and function Girona Seminar, 03/07/2012
  • 20. multiscale structure and function2-steps CDK2/CyclinA Bonet et al. proteins (2006) Girona Seminar, 03/07/2012
  • 21. multiscale structure and function2-steps CDK2/CyclinA Bonet et al. proteins (2006) Girona Seminar, 03/07/2012
  • 22. multiscale structure and function2-steps CDK2/CyclinA Bonet et al. proteins (2006) Girona Seminar, 03/07/2012
  • 23. multiscale structure and function 2-steps CDK2/CyclinA 70 J. BONET ET AL. catalytic regulatory A B A Bfree energy soft complexes A B strong complexes Bonet, et al Proteins (2006) Fig. 3. Bar plots showing the stabilization for all residues in CDK2 in three different conformational states: i,CDK2 (a) unbound CDK2 (⌬⌬Gsolv ); (b) bound CDK2 conformation with calculations performed in the absence of i,CDK2Ј i,CDK2Ј/CyclinA Cyclin A (⌬⌬Gsolv ); and (c) bound CDK2 (⌬⌬Gsolv ). The “ALL” label refers to the sum of all stabilities. Girona Seminar, 03/07/2012 stabilization of Lys33 by its direct interactions to Glu51 tically nonexistent, and the effect of binding to Cyclin A is and Asp145. An interesting feature can be seen in the again irrelevant for its stabilization. This is striking,
  • 24. multiscale structure and functionRING fingers / UBC interactions Schepper, et al Proteins (2009) Girona Seminar, 03/07/2012
  • 25. multiscale structure and functionRING fingers / UBC interactions Schepper, et al Proteins (2009) Girona Seminar, 03/07/2012
  • 26. multiscale structure and functionSchepper, et al Proteins (2009) Girona Seminar, 03/07/2012
  • 27. multiscale structure and function Norma Díaz- Vergara Girona Seminar, 03/07/2012
  • 28. multiscale structure and function Transient Proteins Permanent Proteins 1 1 0.9 0.8 0.8 True Positive Rate 0.7True Positive Rate 0.6 0.6 0.5 0.4 0.4 0.3 GPDLD 0.2 GPDLD 0.2 GRobet GRobet Perm GRobetta ASA Perm GPDLDTran 0.1 ASA Perm GRobetta Tran GPDLD Perm GPDLDTran + ASA 0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 False Negative Rate False Negative Rate Keskin and coworkers; Díaz-Vergara, et al in preparation Girona Seminar, 03/07/2012
  • 29. multiscale structure and function going multiscaleAYTON & VOTH, COSB (2007) Girona Seminar, 03/07/2012
  • 30. multiscale structure and function Coarse grain models• average contribution of groups of atoms• use of the most effective variables (greatest conformational change from the smallest energy change) Marrink et al. (2003-2011) Girona Seminar, 03/07/2012
  • 31. multiscale structure and function energy vs samplingLevitt, NSB (2004) Girona Seminar, 03/07/2012
  • 32. multiscale structure and function energy vs samplingJohnston, Villà-Freixa, in progress Girona Seminar, 03/07/2012
  • 33. multiscale structure and function energy vs samplingJohnston, Villà-Freixa, in progress Girona Seminar, 03/07/2012
  • 34. multiscale structure and function energy vs samplingJohnston, Villà-Freixa, in progress Girona Seminar, 03/07/2012
  • 35. multiscale structure and function energy vs samplingJohnston, Villà-Freixa, in progress Girona Seminar, 03/07/2012
  • 36. multiscale structure and function Potential Energy Surface Coordinates MappingEnergy REFINEMENT All-Atom EXPLORATION Coarse-grained Energy Internal Coordinates Girona Seminar, 03/07/2012
  • 37. J_ID: Z7E Customer A_ID: 00524-2009.R1 Cadmus Art: PROT22640 Date: 21-NOVEMBER-09 Stage: I Page: 9 multiscale structure and function CG Model Simulations of Protein Landscapeses of Chemical Reactions simplified referenceB, potentials J. Phys. Chem. Vol. 104, No. 18, 2000 4583 emarksops and examines a method for calculationspies of chemical reactions in solution. The focused on the entropic contribution of the Reliable evaluation of this contribution is r progress in the understanding of the role n enzyme catalysis. Our approach involvesc cycle of Figure 1, where the activationered for two paths; in one path the reactingained to a single reaction coordinated and inhey are allowed to move in the subspace reaction coordinate. The difference betweenn barriers can provide our -T(∆Sq)!. How-forming a direct calculation along the above ree energies ∆G! and ∆G! obtained by RS TSates where the fragments in the RS and TS,orced to stay at a given R (by a strong h and then allowing the fragments to move h Figure 3. A schematic illustration of the effect of R and the enthalpicstraints. The difference between ∆G! and RS contribution to ∆G!. The et al. JPCB (2000), Villà et al. PNAS (2000) Strajbl figure considers one-dimensional potentials desired -T(∆Sq)! and a residual contribu- of mean force (W) for two systems and the effect of confining the py of the system. This residual contribution e5 system to small segments of R by a strong constraint (K1). The figurefinding restraint coordinates that minimized hermodynamic cycles used to calculate Messer et al. Proteins (2010) the change in free energy of unfolding upon mutation (see text for details). is essentially a rigorous equivalent of Figure 1 where the effect of . h restraining the system in different R’s can be formulated. As explained Girona Seminar, 03/07/2012 n converging results, with essential to have via the free-energy Appendix and illustrated here, the value of ∆G! depends on Rhe change of the parameters associated it isthe X DG f Nsp !Msp uf and DG Nsp !Msp in the perturba- h
  • 38. multiscale structure and function (de)constructing CG-aA Multiscale Analysis of Protein Landscapes be used in the method. The energy function used in the structure that contains positions for all ofÁvila et al. CPPS (2011) SCP step consists of the van der Waals and dihedral energy based on the backbone atom positions. Th terms as defined by the AMBER99 force field.55 ture is constructed by placing the rotamer o representations The method to place the side-chains is a straightforward that has the minimal energy between the hill climbing algorithm. It starts by generating an initial the backbone atoms of the other residues. D cedure any rotamer that has interaction e backbone higher than a user defined cuto and is no longer considered in further energy cutoff helps to improve the efficien ing any side-chains that have steric clashes the backbone. In this work an energy cuto mol was used. Unlike the Xiang and Hon only use this one structure as the initial rather than generating 120 starting confo was done to improve efficiency, even if it slight decrease in accuracy. However, the re the method still performs well. Additional dence that most side-chains can be place only using their interactions with the ba other methods also use this as the initial str Starting from the initial structure an i dure is used to find side-chains with the Each side-chain is selected in turn, and energy between the possible rotamers selected side-chain and all of the other c side-chains and the backbone is consider another rotamer that has a lower energ rotamer is replaced by the lower energy continues until after a full iteration over tein none of the rotamers are replaced o specified maximum number of iterations the results of this paper the maximum nu tions allowed was 10, which was never reconstructing the coarse-grain simulatio and S6. The rotamers can be considere down the chain or in a random order. We RACOGS the proteins studied in this paper the ord did not affect the results. Side-chain minimization step After the SCP a number of side-chains energy conformations were detected. The all zation could only fix a small fraction of t Figure 1 Cartoon illustration of the RACOGS method for a short pept sequence VAL-ASP-SER-LEU-VAL. (1) Starting from the Ca atoms are added. (2) After the backbones are added the side- (3) The first and third amino acids, circled, are clashing and energy interaction. The side-chain minimization step is perfo amino acid and resolves the clash. The last step of adding hy performing an all-atom minimization is not shown. [Color fi in the online issue, which is available at www.interscience.wi DOI 10.1002/prot Heath et al. Proteins (2007) Girona Seminar, 03/07/2012
  • 39. multiscale structure and function Girona Seminar, 03/07/2012
  • 40. toms. (5) sidechain-mainchain vdw. (6) sidechain-mainchain electrostatics. ˜ c aybe CAsar knows more) multiscale structure and function ydrogenbonding. (8) an additional mainchain-mainchain torsional potential avor secondary structures. (9) additional mainchain-mainchain electrostat all atom vs coarse grain force field κb (b − b0 )2 + 2 κθ (θ − θ0 thebut I’m notV (r)amber = exactly. Didn’t find information in ) paper eith sure for what ˜ cmaybe CAsar knows more) bonds angles + (Vn2/2)(1 + cos[nφ − δ]) V (r)amber = κb (b − b0 ) + κθ (θ − θ0 )2 dihedrals bonds angles + 12 (Aij /rij ) 6 − (Bij /rij ) + (qi qj /rij ) + (Vn /2)(1 + cos[nφ − δ]) nonbij dihedrals + GB(r) 12 6 + (Aij /rij ) − (Bij /rij ) + (qi qj /rij ) nonbij nils: is the value 9 in the HB term fixed or is it variable too in th + GB(r) rdi: The 9 is actually a parameter that we optimize nils: is the value 9 in the HB term fixed or is it variable too in the G 0 ef QQ self ef Qq V (r)ambercg =Umm + Uss + Uss + Us + Ums + Umsordi: The 9 is actually a parameter that we optimize HB φ−ψ qq + ∆Umm + ∆Umm + ∆Umm 0 ef QQ self ef Qq V (r)ambercg =Umm + Uss + Uss + Us + Ums + Ums HB φ−ψ qq + ∆Umm + ∆Umm + ∆Umm Girona Seminar, 03/07/2012
  • 41. multiscale structure and function Adunhttp://adun.imim.es Johnston et al. JCC (2005), LNCS (2007) Girona Seminar, 03/07/2012
  • 42. multiscale structure and function AdunDifferent local and remote (P2P)databases http://adun.imim.es Johnston et al. JCC (2005), LNCS (2007) Girona Seminar, 03/07/2012
  • 43. −260 Energ Energ −280 multiscale structure and function tions. The origin of this problem is not the HB pattern, but, as suggested above, th −200 Uphi−psi term. In order to analyze this fact, we project the FES with respect to the tw −300 main chain torsional coordinates and superimpose the expected Ramachandran angle −250 alpha-helix beta hairpin 3 4 5 6 7 8 9 3 4 5 6 RMSD RMSD for both peptides. Figure 4 clearly shows a displacement from the expected angle CG PES and Figure 2: Correlation between RMSD and Energy for the complete sampling spa the model peptides. Hexagon binning for snapshots of the 300K trajectory for (A reconstruction (left) and (Val)5 ProGly(Val)5 (right). of the AA PES CG potential energy landscape for alpha helix CG potential energy landscape for beta hairpin −200 −50 Figure 9 Peptide folding FES projected onto backbone dihedralaspace. Trajectorie 4: carried out in which one slowly transforms the5.3 structure from coarse grain re −220 −100 for polyalanine are displayed on the left-1while those for the hairpin are shown on-1th 8 Energy / kcal mol−1 Energy / kcal mol−1 −240 tationright. all atom representation (see -2 Methods section for details). As t to an the 5.08 -2 7 −150 4.86 -3 involves the appearance of new atoms in the system, it is obvious that care m Rgyr Rgyr −260 6 -3 4.64 -4 taken for the βmake the simulation a less pronounced displacement (fully coarse grai 5 not to –hairpin system, while explode right after the first is seen in the (Ala) −280 −200 -4 4 4.42 -5 -5 −300 peptide. 3 4 5 6 7 8 9 −250 3 4 5 6 window. The final reconstructed 10 atom FES is shown2.4 Figure 5. 6In term 3 5 6 7 8 9 all 4.2 0 1.2 in 3.6 4.8 -6 RMSD RMSD Despite the limitation of the coarse grain potential, especially for β –hairpin stru RMSD(A) RMSD(A)Figure 2: Correlation between RMSD and Energy for the complete sampling space of3: Peptide folding paper is to demonstrate the feasibility in building a comple Figure the objective of this FES. Data for (Ala)15 (left) and (Val)5 ProGly(Val)5 (r tures, 9 5.3 -1 (Ala)15 8 for The RMSD is calculated 0portable model α–helix and β next, we anthe model peptides. Hexagon binning for snapshots of the 300K trajectory forare depicted. multiscale simulations in aagainst a software like Adun, so–hairpin res pipeline 5.08 -2(left) and (Val)5 ProGly(Val)5 (right). tively. 7 -1 all atom 4.86 -3 alyze the ability of the method to reconstruct the free energy surface for the all ato Rgyr Rgyr 6 -2 -4 4.64 system.5 Figure 5 shows the results for -3 corrected FE, obtained as follows. First the -5 4 4.42 series of 8 × 8 (α–helix) and 16 × 16 (β –hairpin) representative structures were ob -4 -6 3 4.2 5 6 7 8 9 10 0 1.2 2.4 3.6 4.8 6 tained from Figure 3. For RMSD(A) 8 each of these a free energy perturbation (FEP) protocol wa RMSD(A) Figure 5: Corrected folding FES for the (Ala)15Seminar, (Val)5 ProGly(Val)5 Girona (left) and 03/07/2012 9 5.3 -1 peptides derived from Figure 3, using the multiscale free energy perturbation ap -1 9
  • 44. multiscale structure and functionTarget optimal Coarse Grain CG to AA Girona Seminar, 03/07/2012
  • 45. multiscale structure and functiontesting in CASP Girona Seminar, 03/07/2012
  • 46. multiscale structure and function 12.1 12.1 11.76 -0.5 -1 11.76 -2 1HRC -4 -1.5 11.42 11.42 Rgyr Rgyr -2 -6 11.08 11.08 -2.5 -8 10.74 -3 10.74 -10 -3.5 10.4 10.4 0.5 2.28 4.06 5.84 7.62 9.4 0.5 2.28 4.06 5.84 7.62 9.4 RMSD(A) RMSD(A) 11.6 11.6 -0.5 -1 10.98 -1 10.98 -2 1PRB -1.5 -3 10.36 10.36 Rgyr Rgyr -2 -4 9.74 9.74 -5 -2.5 -6 9.12 -3 9.12 -7 -3.5 8.5 8.5 0.7 2.82 4.94 7.06 9.18 11.3 0.7 2.82 4.94 7.06 9.18 11.3 RMSD(A) RMSD(A) 11.9 11.9 -1 -0.5 -2 11.54 -1 11.54 -3 -1.5 -4 11.18 11.18 -5 Rgyr Rgyr -2 10.82 10.82 -6 -2.5 -7 10.46 10.46 -8 -3 -9 10.1 1 2.86 4.72 6.58 RMSD(A) 8.44 10.3 -3.5 10.1 1 2.86 4.72 6.58 RMSD(A) 8.44 10.3 3NZLigure 6: Coarse grain FES (left column) and all atom FES reconstruction (right col-mn) for three selected structures of increasing complexity: A fast folder, PDB code Girona Seminar, 03/07/2012
  • 47. Table 1: Cα RMSD in terms of Cα coordinates obtained for the target structures: A after ab-initio folding with Rosetta; B) the best structure compared to functionin multiscale structure and the native the 300K trajectory; C) the best structure in the bin corresponding to the minimum in the corrected free energy surface compared to the native structure; D) all-atomi Testing on CASP 2010 targets representation of the reference structure with which the free energy perturbation wa carried out; The lines correspond to: first, one-state folder; second, two-state folder third, CASP 2008 targets; fourth, CASP 2010 targets sequence PDB-ID RMSD RMSD RMSD RMSD residues (Rosetta) (globally best) (CG, best (AA, reference secondary structure in minimum bin) structure) analysis 1PRB 4.94 5.31 10.32 11.03 53 generate 3aa + 9aa 1HRC 8.24 7.64 11.49 9.77 103 structural fragments 1YCC 6.77 8.11 8.71 8.61 108 2K53 6.64 6.38 6.56 8.20 76 generate initial 2K5C 9.53 8.03 10.77 11.51 95 tertiary structure 2K5E 6.19 5.19 5.36 5.96 73 relax tertiary 2KDL 10.36 9.61 9.89 11.38 56 x 10 structure 3DAI 13.82 12.18 14.06 13.96 130 2KY4 11.39 10.72 10.82 13.32 149 energy score 2L06 8.15 9.38 11.14 9.89 155 structure 2L09 9.26 7.34 9.52 8.39 62 3NEU 9.59 9.64 9.94 10.28 125 choose structure 3NRW 12.49 8.29 11.74 14.22 117 with lowest energy 3NZL 6.89 6.55 7.00 10.38 83used to generate the initial structures for structures in Table 1 with Free energy surfaces Girona Seminar, 03/07/2012
  • 48. multiscale structure and functionImproving the model Girona Seminar, 03/07/2012
  • 49. multiscale structure and function Improving the model• better sampling – statistical potentials as reference – transition path sampling and string method – GPU computing – better global reaction coordinate Girona Seminar, 03/07/2012
  • 50. multiscale structure and function Improving the model• better sampling – statistical potentials as reference – transition path sampling and string method – GPU computing – better global reaction coordinate• better energy – polarizable models – solvation models – simple improvement of the CG parameters Girona Seminar, 03/07/2012
  • 51. multiscale structure and function rethinking the problem: hotspots Native Native Native Unfolded Folded Complex ∆ GN f ∆ GN a∆∆ G N − M = ∆ G M − ∆ G N = ( ∆ G M − ∆ G M ) − ( ∆ G N − ∆ G N ) B B B a f a f ∆ GM f ∆ GM a Mutant Mutant Mutant Unfolded Folded Complex Girona Seminar, 03/07/2012
  • 52. multiscale structure and functionrethinking the problem: hotspots∆ G N − M u ∆ G N − M f ∆ G N − M c∆∆ G N − M = ∆ G N − M − ∆ G N − M − ∆ G N − M B c f u Girona Seminar, 03/07/2012
  • 53. multiscale structure and function changes on binding free energy upon mutation WT MutantA MutantB MutantC Unfolded Unfolded Unfolded Unfolded FoldedFolding Folded Folded Folded Complex ComplexBinding Complex Complex Destabilization Destabilization Mixed of complex of unbound protein Girona Seminar, 03/07/2012
  • 54. multiscale structure and functionextensive optimization Trajectory reconstruction explicit vs CG Pearson correlation Girona Seminar, 03/07/2012
  • 55. multiscale structure and function CG model optimization with GA initialization parallel serial start control process ted s ple collects new parameters sse m co oce s if ck s pr initial che mutate wnparameters spa calculate fitness parent 1 offspring 1 ses parent 2 ces offspring 2 pro calculate fitness ns initial population aw sp calculate fitness parent 1 offspring 1 parent 2 offspring 2 calculate fitness select parents with a rank recombine with a uniform recombination based roulette-wheel selector operator, mutate with a gaussian mutation operator Girona Seminar, 03/07/2012
  • 56. multiscale structure and function CG model optimization with GA simulation 9 1.2 1 0.8 0.6pearson coefficient 0.4 0.2 0 -0.2 mean -0.4 standard deviations best worst -0.6 0 50 100 150 200 250 300 350 generation Girona Seminar, 03/07/2012
  • 57. multiscale structure and function CG model optimization with GA simulation 9 1.1 1 0.9variance 0.8 0.7 0.6 0.5 VD VD H BO C C C C AH BS G LH BO G G G G TU EL H W W EL N X- X- X- X- EE R N D 2 3 C C C C IX IX N D ED T- T- T- T- T N N H C 3 1 bond types Girona Seminar, 03/07/2012
  • 58. multiscale structure and function energy comparison 300 280 260coarse-grained [kcal/mol] 240 220 200 180 160 140 120 150 200 250 300 350 400 450 explicit [kcal/mol] energy comparison 180 160 coarse-grained [kcal/mol] 140 120 100 80 60 80 100 120 140 160 180 200 220 240 260 280 explicit [kcal/mol] Girona Seminar, 03/07/2012
  • 59. multiscale structure and function energy comparison energy comparison -340 -320 -360 -340 -380 -360coarse-grained [kcal/mol] coarse-grained [kcal/mol] -400 -380 GA -420 -400 -440 -420 -460 -440 -480 -460 450 500 550 600 650 700 750 450 500 550 600 650 700 750 explicit [kcal/mol] correlation explicit [kcal/mol] 1 0.8 0.6 we still coarse-grained 0.4 have a 0.2 problem 0 0 0.2 0.4 0.6 0.8 1 explicit Girona Seminar, 03/07/2012
  • 60. multiscale structure and function Still work in progress Hotspot prediction 14 AH102A 12Gbinding (kcal/mol) 10 FP TP AR87A 8 DY29A AK27A DT42A 6 DY29F AR59APredicted DD39A 4 DD35A 2 D DW44F E80A DW38F TN AR83Q FN DE76A 0 -1 0 1 2 3 4 5 6 7 8 Experimental Gbinding (kcal/mol) Girona Seminar, 03/07/2012
  • 61. multiscale structure and function Beyond the current model– using statistical potentials as a reference (collaboration with Janusz Bujnicki, IIMCB Warsaw)– including a semi-explicit solvation model (collaboration with Ken A. Dill, SBNY) Girona Seminar, 03/07/2012
  • 62. multiscale structure and function RNA Statistical potentialRaúl Alcántara Girona Seminar, 03/07/2012
  • 63. multiscale structure and function RNA Statistical potentialRaúl Alcántara Girona Seminar, 03/07/2012
  • 64. multiscale structure and function RNA Statistical potentialRaúl Alcántara Girona Seminar, 03/07/2012
  • 65. multiscale structure and functionNon bonded interactions Girona Seminar, 03/07/2012
  • 66. multiscale structure and function Fitting non-bonded term MatlabInitial guesses from GMM Mathematica Non-linear fit Girona Seminar, 03/07/2012
  • 67. multiscale structure and function force field markup language: FFMLmrow potential=FourierTorsion numberOfAtoms=4 type=energy mrow potential=FourierTorsion numberOfAtoms=4 type=forcevariable name=angle variable name=dAngleparameterdata number=1 name=param1 variable name=angleparameterdata number=2 name=param2 parameterdata number=1 name=param1 apply parameterdata number=2 name=param2 times/ apply ciparam1/ci times/ apply cn2/cn power/ ciparam1/ci apply apply minus/ minus/ ciangle/ci ciangle/ci ciparam2/ci ciparam2/ci /apply /apply cn2/cn cidAngle/ci /apply /apply /apply /mrow/mrow Meneu et al., in progress Girona Seminar, 03/07/2012
  • 68. multiscale structure and functionSECIS elements simulations Girona Seminar, 03/07/2012
  • 69. multiscale structure and functionSECIS elements simulations Girona Seminar, 03/07/2012
  • 70. multiscale structure and functiona b SEA water model Fennell et al. JACS (2010) a b Linear Alkanes Linear Alkynes 4 4 !G (kcal/mol) !G (kcal/mol) 3 3 rw 2 2 Semi-Explicit assembly !A + b 1 TIP3P 1 rw 0 Experiment 0 CH4c d c Linear PAHs d Planar PAHs 5 5 4 4 !G (kcal/mol) !G (kcal/mol) 3 3 2 2 1 1 0 0 -1 -1 ra Figure 3: The nonpolar solvation free energy for a series of a) linear alkanes, b) linear alkynes, c) polyaro- matic hydrocarbons (PAHs) in a linear arrangement, and d) PAHs in a planar arrangement calculated using !ra γA + b, Semi-Explicit assembly, and explicit solvent. For γA + b, the traditional (0.00542 × SAtot ) + 0.92 18 36 a b used, and the TIP3P results are those obtained through explicit free energy calculations. Experi- was c d e mental comparisons to ∆G cannot be drawn with the linear alkynes or PAHs series, because they have a substantial polar term to the overall solvation. Girona Seminar, 03/07/2012 Figure 4: Maps of the collective dispersion attraction about the solvent accessible surface (SAS) of a) n-
  • 71. multiscale structure and functionNILS Girona Seminar, 03/07/2012
  • 72. multiscale structure and functionNILS Girona Seminar, 03/07/2012
  • 73. multiscale structure and function SEA-water vs TIP3P 504molecule cluster, ADUN 4 3 Fernet (kcal/mol) 2 1 0 -1 -1 0 1 2 3 4 TIP3 (kcal/mol) SEA-Water Semi-Analytic SEA-Watercorrelation with explicit correlation with water 0.91 explicit water 0.95 Drechsel et al, in preparation Girona Seminar, 03/07/2012
  • 74. multiscale structure and function summary wt wt ΔG fold ΔG bind wt-m wt-m wt-mΔΔGunfolded ΔΔG unbound ΔΔG bound m m ΔG fold ΔG bind Girona Seminar, 03/07/2012
  • 75. multiscale structure and function aScidea COMPUTATIONAL BIOLOGY SOLUTIONS MICHAEL JAMES CÉSAR NILS Sergio  Rubio James  Dalton  (UAB) aScidea Nils  Drechsel  (SBNY) Michael  A.  Johnston  (IBM) COMPUTATIONAL BIOLOGY SOLUTIONS Norma  Díaz-­‐Vergara  (UAB)NORMA César  L.  Ávila  (U  Tucumán) Janusz  Bujnicki  (IIMCB) SERGIO Ken  Dill  and  Chris  Fennell  (UCSF) Girona Seminar, 03/07/2012
  • 76. multiscale structure and functionGirona Seminar, 03/07/2012
  • 77. multiscale structure and functionACCESS TO INFORMATION Girona Seminar, 03/07/2012
  • 78. multiscale structure and functionINTEGRATION ACCESS TO INFORMATION Girona Seminar, 03/07/2012
  • 79. multiscale structure and function U sp = U mm + U ss + U ss + U sself + U ms + U ms + ∆ U mm + ∆ U mm ψ + ∆ U mm ef QQ ef Qq HB φ− qq 0 ef ijU ss = scale [3(r ij /r ij ) 8 − 4(r ij /r ij ) 6 ] 0 0 ij C ij U sself = i i U np ( N np ) + U polar ( N polar ) i 2 1.5 4exp[− 0.2(N np − 6) 2 ] N np ≤ 6 1 U np = 4 N np 6E (kcal/mol) 0.5 0 − 2exp[− 0.2(N polar − 4) 2 ] N polar ≤ 4 U polar = -0.5 − 2 N polar 4 -1 -1.5 -2 3 4 5 6 7 8 9 10 r ij rij 4∆ U mm ψ = φ− A i g( φ − φi0 , w 0 ) g( ψ − ψ0 , w 0 ) i i i − 9 r ≤ 2.0 HB i =1 ∆ U mm = 2 − 9exp( − 15(r − 2.0) ) r 2.0g( χ , w) = exp( − 0.693(1 − cos( χ )) /sin ( w/ 2)) ( φ, ψ) 0 180 0 -1 120 -2 -2 -3 E (kcal/mol) 60 -4 -4 0 -5 ψ -6 -6 -60 -7 -8 -120 -8 -9 -10 -180 -10 1 1.5 2 2.5 3 -180 -120 -60 0 60 120 180 rOH φ Washel and col. (2009-2010) Girona Seminar, 03/07/2012

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