#11thICCS Paul Hawkins on conformational sampling macrocycles in solution and in the solid state
The iron triangle in conformation generation: want fast, good and cheap; i.e. fast, accurate and small ensemble.
MD vs torsion sampling. Slow versus fast. Distance geometry: fast, don't require 3d, stochastic, implicit solvent.
Atoms placed randomly in space, and then minimisation a distance constraint function. MMFF94 afterwards. Energy cutoff, and RMSD de-duplication to create an ensemble.
Validation against the solid-state. Three sources: CSD (train), PDB (test)and BIRD (validate).
Compare simple stats of the three dbs. Ring size distrib and molecular complexity.
How to assess? Whole molecule RMSD? But side-chain support is often low. Ring-only? Too something. In the end, use ring+beta atom (ed: seems to be attached double bonds, plus...?).
Solvent modelling via Poisson-Boltzman, and Sheffield model. (ed:Didn't quite get how this is incorporated.) Accuracy seems to be the same for all, so try to discriminate via speed or ensemble size. Now looking at quality - sheffield model is better than in vacuo model..
Now comparing features of a whole bunch of programs. Macromodel, MD, MOE, Prime, OMEGA. Only MD can be discriminated...
Can identify problem structures for OMEGA: sidechain to backbone interactions cause trouble. Three outliers in performance. Direction for future work.
Can identify problem structures for OMEGA: sidechain to backbone interactions cause trouble. Three outliers in performance. Direction for future work.
Compared to other approaches it's about 10 times faster than the next fastest.
Moving onto discussing solution structure. Example: Lokey peptide. Solid-state easy to reproduce. In soln, intra-molecular H bonds driven by polarity. Shows interactive display for torsion sampling compared to a reference.
It's possible to incorporate NMR constraints directly in DG approach. Should improve results. Works much better - focussed sampling in torsion space.
