Participants
Pavan Behara
Chapin Cavender
Jennifer A Clark
Anika Friedman
Michael Shirts
David Mobley
Matt Thompson
Jeffrey Wagner
Lily Wang
Recording
https://drive.google.com/file/d/1gFtblmqv8TgO2CzyelSHD7XVnmz7X5r5/view?usp=drive_link
Goals
GB3 NMR fits
Protein QM fit experiments
Protein QM datasets
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Item | Presenter | Notes |
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GB3 NMR fits | View file |
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| name | 2025-01-23-nmr-fit.pdf |
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| Chapin Cavender | (Discussion of slide 2) JW – (generally missing the point) MS: takeaway is that the dotted green and solid green lines should overlap, happy to take a look at the code and help debug.
Slide 8 MS – Feasibility of doing umbrella sampling on native contacts? Umbrella sampling on RMSD tends to slow things down a lot becasue the space is so large. AF – We’ve seen loss of beta sheets as well, but not to the same extent as alpha helices. CC – From what I recall, the beta part is two stacked hairpins, and the main feature is that they fray, and that motion is consistent with the NMR data. So that’s something we should sample and the NMR data can tell us how much. But with alpha helix loss that’s not covered in the NMR data, and what follows is that the beta sheets nearby are also lost. MS – Since these are running in OpenMM, what is preventing us from doing repex? CC – It’s a solvable problem, but will require additional scripting. MS – Will improve sampling, but not sure how much.
MS – Other possibility is that the changes in parameters are too small. It’s a tradeoff between noise and … CC – I use log_10(alpha) = 4… MS – Could be try doing something between 3 and 4… It’s more likely than not that you’ll see significant differences since you’ll be taking bigger steps. The big question there will be whether the predicted curves end up being more accurate. But still worth working on the cumulative stuff too. Will reduce noise, increase n_eff samples, and likely allow you to take biger steps.
CC – So I’ll keep the additional iterations working, and will focus on debugging cumulative fits.
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Protein QM fit experiments | Lily Wang | DM (chat) – I wonder if there’s a way to tell WHICH small molecule data wants to drive it that direction (away from AMBER)… like, maybe it’s different chemistry, or weird sterics causing problems, or… Slide 16 AF How different do these torsions look when they appear in small molecules? LW – Great Q. But worth mentioning that the olive/brown curve started from the amber values and stayed relatively close. CC – What’s being plotted here is the raw dihedral term, not the total torsion energy. LW – Right, next step is to plot total energy profiles for torsiondrives of these CC – Can share scripts on how to do this.
Slide 17 MS – FF14SB doesn’t have sequence specific phi and psi, so why is phenylalanine barrier different? CC – The way that AMBER14 solves this is that they have a general parameter that goes into all backbones, then a special one that goes into non-glycine  residues MS – That peak seems really big LW – Possible that Amber overstabilizes proteins. But the fact that the shapes are different might mean a lot.
MS – Is it clear that there’s a “right” answer that we can use for comparison? LW – … … CC – Could compare to training torsiondrives. But generally the SMIRNOFF FFs are closer to QM. I think the big bump in the ff14sb profile in this torsiondrive comes from just QM fitting. MS – … LW – Will plot against torsiondrive total energy.
Slide 23 |
Protein QM datasets | Anika Friedman | JW – Are these planned for submission as opts or torsiondrives? MS – Would be good to compare dataset composition to what amber used for fitting. CC – This looks good, this is what i was hoping to see, and the distribution on the ramachandram map AF: can I talk to you, JW, about getting these submitted? JW: JC + LW are the peopl AF – This will be my first submission, could use pointers. LW – Want to join next week’s QC submission meeting? AF – Yes, that’d be great.
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Action items
Decisions