Participants
Goals
Update on NMR reweighting fit
Data for NIH resubmission
Data for Espaloma manuscript
Recording
https://drive.google.com/file/d/12xZk4faxnHe5Gv3AGQwlwuEj79BoqDIe/view?usp=sharing
Discussion topics
Item | Presenter | Notes |
|---|
NMR fit | View file |
|---|
| name | 2023-11-02-nmr-fit.pdf |
|---|
|
| | JC (slide 18): which experiment are these points arising from? Are the simulations also done at 274K? CC: Yes CC: in general, easier to fit the computed dihedrals requirement than the H-bonds. MS: which should match the experimental values more? JC: neither of the dihedral or H-bond measures would correlate smoothly with the chemical shifts MS: where does your exp data come from? CC: in the NMR experiment what they report is the metric of helicity here. Raw chemical shifts aren’t given in the paper MS: your NMR simulations don’t have C=O shift? Perhaps that would be interesting as a measure of the fhelix? CC: Yes, I can do that
KT (slides 18-21): How long are each simulation? CC: 2 us right now, would like to get them out to 10. We’d expect if a peptide is going from an extended to helical region, it would pass through delta, as Espaloma is doing here
MS (slide 23): AF generated some data on GB3 DLM: have you looked at differences between Espaloma assignment and your force fields? CC: yes, I was going to propose this as a next direction DLM: could be typing, could be you’re stuck near a bad minimum JC: wouldn’t bespokefit be a better comparison, of a helical fragment? That could tell you if it’s the torsions that are the problem, or LJ? There’s no guarantee Espaloma is correct CC: I’m not certain our fits to QM data are giving us what we need MG: but training more carefully to local QM may not give us what we need to solve this problem CC: if we follow the Stonybrook line of FFs, they found that training to gas-phase QM did not help condensed-phase peptides very well, and they had to fit to peptide data. In ff19sb they didn’t have to fit to NMR data, but they had CMAPs and continuum solvent QM
KT: … MT (in chat): Naive question … are torsion parameters the only ones being re-fit? And, if so, how much improvement over these long-range properties can be obtained without also fitting vdW parameters? (One could ask the same question about this work, the Stony Brook work, and maybe other efforts) CC: yes, only 6 torsion types. CC: I did run an experiment with NB parameters from AMBER (but SMIRNOFF valence) and didn’t observe differences in behaviour, so not expecting this to lie with NBs MT: What if you refit it? CC: MG and I have discussed this. It is a non-trivial amount of work and it seems that other FFs have been able to solve this problem without fitting vdW parameters to peptide data. MT: … DM: the AMBER people built their FFs without re-fitting LJs in that context CC: only exception is ipolq style FFs
JC which dataset are you using for the (slide 33): CC: Kyle Beauchamp, Pande, …? JC: did you look at https://pubs.acs.org/doi/full/10.1021/ja0660406 ? CC: I’m using Ala3 to Ala5 from this paper CC: I could add Ala6 and Ala7 MG: any idea how helical you’d expect them to be? JC: conclusion is that Rama populations are very similar from Ala3 to Ala7 MS: Pure ala is not that helical
MS: how different are the torsiondrives of ff14sb and proto-Rosemary? Not the parameters but the potentials, but it would include the NB interactions too CC: things I’m doing to move forward:
|
NIH resubmission | | |
Espaloma manuscript | | |
Action items
Decisions
