Modeling of amide torsions | Chapin Cavender | Slides will be uploaded CC (slide 20) – Hypothesis: energy mismatch between QM and SMIRNOFF profiles arises from different charges. In training data, charges come from OpenEye ELF10. In test data, charges come from LibraryCharges CC – are we worried about these energy profiles as a problem? If so, we could re-fit the FF and include the current test data as training data CC – let me summarise discussion outcomes and to dos Look at magnitude of charge differences between ELF10 and LibraryCharges Look at m values from ForceBalance for proper torsion amplitude If those diagnostics point to the problem being with the torsion parameter, I’ll do a new fit targeting the validation data I’m using here MG – if it’s overly restrained to the wrong value, wouldn’t it be a good idea to relax this and re-fit? I’m not disagreeing with you, just thinking aloud CC – Yes, that’s a more precise statement of what i said MG – Ok, so if it turns out to be overly restrained, you’ll re-fit without additional data? CC – yes MG – and if charges are significantly different? CC – that tells me that having one parameter will not be enough, I’ll split it off into a different parameter just for proteins MG – charges may be different but not responsible. Could you just look at the electrostatic component of the energy to see if it’s in the right direction to be the problem? It’s conceivable they could even be helping you, or only wrong by a fraction of kcal/mol. CC – yes PB – could you pick one or two torsions from this set and just see how much the k values change from changing the priors?
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GB3 trajectories | Chapin Cavender | CC – in v0.0.1, an alpha helix unfolds in a trajectory CC – same in v0.0.2 CC – the unfolding happens around the C-terminus CC – I’m trying to diagnose the exact failure mode. I’m looking at populations of Ramachandran clusters in phi/psi angles, and clusters that show up in crystallographic data, and seeing how often simulations sample those clusters MG – Could it be the omegas? CC – I don’t see any omegas deviating from 180 TG – Could you look at the total torsion force on some of these torsions, and compare them to 14sb? We mix torsions in a way that could be surprising, so looking at the total torsion force we might see that it’s pushing it somewhere we don’t expect TG – We want to look at the sum of torsion profiles for different torsions, e.g. phi/psi/omega. I’m not familiar with the space of parameters Sage assigns to these torsions. Sometimes we’re surprised a particular torsion gets applied to a peptide bond. For me, Sage looks like it’s about k = 10 for the torsion (sum of 2 torsions with k= 6 and k = 4). TG – could tie into previous issues. e.g. omega issue is the barriers are low, maybe that’s a systemic issue. MG – clarification about the energies to look at TG – just the torsion energy MG – ultimately its placement on the Ramachandran map is from the total energy TG – yes, but torsion energy could identify outliers, or surprising torsions. What I’m saying is that we may be looking at one torsion term for diagnostics, but the problem may lie in another TG – going back to FB, when looking at the mvals, you’ll want to look at all the mvals for the all the torsions
CC – once we’re happy I’ll pass it off to Lily for larger benchmarks on lilac, we will iterate on a release candidate, and then I’ll pass the release candidate back off to Lily PB – why do you think Sage-CC is doing better than Specific on slide 6, with the geometry targets? CC – We don’t see this happening on the ddE targets. I’m not very worried about this PB – just wondering why adding more specific torsions degrades performance CC – I added a bespoke parameter for phi/psi/chi1 but all the other parameters are shared. I tried to only add one bespoke parameter per rotatable bond. I also upweighted protein data to get these – maybe we should have the small molecule data be more dominant.
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