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Loop Refinement by Localized Sampling and Minimization (SLAM) Vageli Coutsias, Matt Jacobson, Michael Wester, Lan Hua.

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Presentation on theme: "Loop Refinement by Localized Sampling and Minimization (SLAM) Vageli Coutsias, Matt Jacobson, Michael Wester, Lan Hua."— Presentation transcript:

1 Loop Refinement by Localized Sampling and Minimization (SLAM) Vageli Coutsias, Matt Jacobson, Michael Wester, Lan Hua

2 (1) loops to be refined - given or by comparison with homologous proteins [ for homology modeling only: use PRIME to get templates. Identify loops that show most variability among different templates. ] (2) use SPLAT to sample around the loops identified in (1). Sampling is restricted to Ramachandran allowed regions (these are sampled with probability-usually.001 threshold) and screened for sterics with variable steric cutoff. Too large screens exclude interesting contacts from forming. Too small give high clash energies. (3) use PLOP to minimize energy of the loop regions (including residues within a certain cutoff distance, mostly set at 7A). (4) select lowest energy candidates without any obvious flaws (i.e. loops away in solvent, obvious burials of polar residues or hydrophobics blatantly pointing outwards) and resample around them with windowed sampling - usually +/-30 degrees, except for certain residues I wanted to maintain. I tried different strategies for the windowed sampling. (5) iterate steps 3-4. Typically looked for a funnel-like distribution of RMSD from a reasonable minimum vs. energy to decide convergence. Only used systematically (but not completely due to time pressure) for R488.

3 Results R432: rmsd to native 1.0 (vs. 1.3 template) R453: higher rmsd / correct secondary R488: higher rmsd T0479: rmsd to native 1.3

4 Conclusion Although sampling seems to have revealed the shape of the loop including occasional secondary structure elements, special contacts & salt bridges formed that minimized well but forced the sampling into ranges far from the native. In subsequent work we should: (1)include a stage of localized move MC to properly weigh minimum states by free energy estimates. Various constraints will allow to focus sampling and raise efficiency. (2) Must include a Jacobian-based pruning of data to increase efficiency by cutting down on minimizations. (3) Explore other global search & optimization methods to produce a map of the low minima. (4) Add enforcing of position and orientation constraints

5 R432 = 3dai Refinement of loop 85-92 Small helix end perturbation Broad sampling of loop Cyan (model 1) is at 1.0A to native (purple) Template (white) is at 1.3A Loop 85-92 is at 2.4 A (cyan) and 3.8 A (white) Strategy: lowest energy structures from 1 st batch start own narrower sampling branches. Best results of second iteration sampled again. Now low energy extension of helix at 90 was found, and it was selected in some of the new seed runs. Lowest energy results from the fourth stage were chosen. R432



8 R453 = 3ded Loop in space. Found structure with a single-turn helix. Appears in chain A Template, although closer in RMSD, lacks this structure. W:native, P:template, C:best model Fig. 1: matched to chain D Fig. 2: matched to chain A R453



11 Small twist around res35 (which is a Proline and res 37 is also a Proline) in both native and model structures. Interestingly, in the model structures the carbonyl oxygen of Pro37 seems to form hydrogen bond with OH of Tyr53, however no such hydrogen bond is formed in native structure and the initial best model. Probably such hydrogen bond formation pulls the loop turn (res35-40) in the model structure away from native structure. R453

12 R488 with ligand (D). 6k set, full 360 sampling of all residues (11-19) of TR488 with 6-ALA ligand (E)3k set, 360 at pivots (1-5-7), +/-30 at LEU18, GLY19 (to preserve orientation of LEU sidechain), +/-60 at other residues (F) 6k set, 360 at pivots (same), +/-30 at all other residues Structure of minimum stable; a few structures with slightly lower energy ~2kcal, but ruined secondary structures (helices or strands un-made) TOP candidate: T488_f_04930-opt.pdb Shown with its precursors, d-06993 and e-01016 Together with other f-batch structures of similar shape, but slightly higher energies R488




16 d-, e-1016, f-4930 and other low-energy F-structures; well converged shape R488



19 Although this model represents the height of the preformance of the algorithm, it had a twist, which made the effort totally speculative: as a PDZ domain, it ought to have a binding pocket. My first set of efforts did not take that into account, and I simply sampled/minimized quite extensively, iterating several times until I arrived at a very robust-seeming loop. Then we noticed that the binding pocket was too tight, so we introduced a putative ligand and sampled again to allow for its presense. I also restricted sampling to conformations that had the LEU18 pointing inward. The sidechain of LEU18 is pointing inward in native structure, initial best model and model structures. Asp13 points inward in both refined models, probably forming salt bridges with Lys11 (the distance between OD of Asp13 and NZ of Lys 11 is around 2.7 angstrom), which twist the loop inward. However Asp13 points outward in native structure and initial best model, and does not form salt bridge with Lys11. Native (W), model 1 (C), model 2 (P), template (Y)



22 TO479 = 3dkz Model T0479_3 (model 3) has the smallest rmsd (1.3 A) and T0479_4 (mod 4) the largest (2.3 A). Refining three loops: used various combinations of minima from each refinement, on the assumption that the loops did not interact. Homology modeling using PRIME. Subsequent loop refinement using SPLAT(sampling)/PLOP(minimization). (fig1) Native (W) vs models 3 (Y), 4 (R) (fig2) Native (W), 1(P), 2(C), 3(Y), 4(R), 5(B) T0479



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