MS: regressing to previous infrastructure has been problematic in running SFEs
Looking at Hmix, density for 7 water models, wants to look at SFE but infra issues
Binary mixtures with water, using Sage 2.1
TIP3P “best” water model for alcohols, but still high error
TIP4P/TIP3P best for amines and both alc/amine
DM: Looks like confidence intervals overlap for different RMSE’s, seems hard to tell which is “best” based on RMSE [MS/BM agree]
MS: The errors are bootstrapped over molecule
DM: May find that another analysis would be helpful, maybe paired t-test, to see statistics on by-molecule basis rather than aggregating over whole dataset [recording around 10:30--not confident I captured this correctly]
CC--need a multiple hypothesis correction for paired t test of all pairs
Want to re-train LJ with TIP3P_FB and OPC3 or maybe 4-point model, and check OpenFF 1.0
JW: I would think OPC would be better than TIP4P, but you see OPC is the worst--is that expected?
MS: a bit of a surprise. TIP4P_FB and OPC have similar properties in pure water, but OPC is bad on mixtures
DM: thought this would be pearson R, which should always be positive, do you know which r measure it is?
BM: I’ll look into it
MS: May be worth doing the fit with your own code
DM: looks like this is a coefficient of determination
MS: reason we want to try Parsley 1.3 is because it’s before we re-trained our LJ parameters with TIP3P, so other models might be better
BS: could also train charges
CC: BCC in AM1BCC could be trained
MS: maybe after we do this, would want to co-tune it with LJ
MS: also want to try SFE’s with nonpolar molecules
MS: goal of this is: should we change our water model?
If TIP3P FB and TIP4P are already close to TIP3P, maybe if we reopt LJ for these models, they’d perform even better
MS: doesn’t look like 4 point models are better
BS: depends what you want--TIP3P doesn’t have some properties
MS: yes, TIP4P is more close to real water. TIP3P FB and OPC3 are closer to 4-point model performance than TIP3P
BS: TIP4P doesn’t give right density using Ewald methodologies
MS: some of that is corrected in TIP3P FB and OPC3
JW: I see switching the water model as something we want to do, but only once. Would make sense to align timeline-wise with release of protein FF. Additionally, could think about making our own water model, I think the offxml environment is so alien to people that they won’t care if it’s an existing water model or not
MS: do we want to have an intermediate where we recommend an existing water model?
DM: don’t think it matters, as long as it’s good. Don’t think people will refuse to use our water model
JW: Disagree, I think it’s much easier to release it as one release rather than going through intermediates
DM: sure, I mostly meant I don’t think it matters if we also support another water model. People will use whatever we say to use
CC: Some molecules will exist as charged species in water (eg primary amines), are you doing anything to account for that
BM: no, should look at that
CC: I don’t think we did either for Sage, kind of a gap
MS: Need to look at pKa and see if it’s near pH 7
CC: I think for primary amines, they will be
MS: Not just pKa but how does it change as a result of composition
BS: have some notes about treating this, who should i send it to
MS: either put in slack for everyone or email to me and I’ll forward it
MS: Is there a good pKa predictor for small molecules, or do you have to do QM?
Overall slow diffusion in lipid simulations, much slower lateral diffusion than MacRog and Slipids
Think it’s due to alkane tail behavior
Lipid tails are 6-18 C
Neither Slipids nor MacRog uses HMR
Calculate D from simulations; Sage 2.1, HMR is slower than non-HMR but both are slower than expt
JW: to be clear, even with small tail length, still have head groups with ~10 heavy atoms?
MS/JH: no, we’re just looking at alkane tails
MS: HMR is reducing diffusion constant, COM not affected by HMR but dynamics of things twisting/rotating are affected/slowed down due to moving moment of inertia
JH: Amber’s most recent lipid FF mentions they have to fine tune C-C-C angle for alkanes, which drastically affected lipid diffusion, after tuning the angle they re-trained torsions which helped a lot
D underestimated worse as chain length grows; up to 20% of the diffusion constant for 15 C
MS: expect this if barrier is too high
Density is pretty accurate
TIP3P has results you’d expect for D and density, suggesting it’s not the problem
BS: you left out TIP3P D, it’s 3, you predict 6…
JH: yeah, it’s true. but we’re looking at alkanes for now, shouldn’t affect it too much
Diffusion does not always increase with box size as it would be expected to do, not sure if that’s OK