NMR fit | View file |
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| name | 2023-10-05-nmr-fit.pdf |
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| Chapin Cavender | Slides will go here Cross-validation study of regularization parameter alpha shows good performance with alpha ~ 1 Also find improved performance using regularized NMR data vs just QM PB: Are you fitting all parameters? CC: No, only fitting N_param parameters, includes number of cosine terms in torsion. Basically optimizing 6 parameter types, with between 2-4 cosine terms each. Correspond to phi/psi in 3 different peptide groups. Don’t want parameters that are trained to peptide NMR to affect small molecule fits, so have created new params even in Null FF for backbone.
JW: For black rows (NMR fit--slide 12), most chi^2 are in the range of 0.7-0.8, is that difference important? CC: chi^2 is basically first term of loss function, square error from right answer divided by variance in right answer. Would expect chi^2 to be about 1 for a “good” model, anything less than that means you’re matching the reference well but the reference is imprecise, doesn’t matter how much less than 1 you are CC: Based on that, don’t really have a way to decide between these 4 NMR fit force fields.
RMSE from QM torsion drives agree about as well as non-NMR fit FF’s in most cases Free energy for beta → alpha generally still overestimated by NMR-trained FF Re-sampling leads to worse chi^2 vs expt, except specific OPC3 DM: Strategy did work in at least one case, not a total miss on this strategy CC: Decision now is: is specific OPC3 good enough to move forward with larger protein benchmarks DM: if it doesn’t take too much time, just go ahead with benchmarks, and at the same time try to get alpha-beta difference down while waiting. Maybe specific OPC3 will be good enough once we see results
MG: Not clear how much this measure corresponds to what we’re looking for. By these metrics we’re already beating AMBER, why did we think that improving these metrics would improve performance? See recording around ~29 mins CC: Free energy beta → alpha, we’re overestimated compared to ff14sb by 1-2 kcal/mol. Having better chi^2 isn’t everything MG: Does NMR data have to do with alpha helix issue? CC: Peptides do sometimes have alpha helix structures, but don’t spend a lot of time there MG: Is it possible to do NMR refitting but with larger proteins that do spend more time in alpha helix? CC: Yes, that’s the next step if this doesn’t work
JW: TIP3P-FB looks good for most of the metrics except the alpha helix, important to look at all metrics MG: Tough because we’re fitting very small differences in functions
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