2022-02-24 Protein FF meeting note

BPS poster feedback

• CC – People were very excited about OpenFF poster, asking about when they can use it.

  • Lots of excitement about covalent modifications

  • I was right next to MDAnalysis poster, I think that attracted a nice crowd of folks interested in big simulations.

  • People asked whether interchange was going to support TINKER. I don’t know what our plans are there.

    • MT – I’d be happy to support tinker if they put resources in for my time, but right now it’s not a priority for me. They’re a little lacking in specification and they don’t have as large of a userbase as AMBER/GROMACS, which are still in our plans.

  • A lot of folks working on the CHARMM-DRUDE FF came by, I don’t think we have plans to include DRUDE in our toolkit right now, but I did mention that we’re pursuing some polarizability models. The folks that I talked to were at Justin Lumkul’s group at VT (lead author of 2nd gen DRUDE FF, lab is looking into making parameters for covalently modified AAs).

    • JW – I think DC lab is working on polarizable parameter generation? Not certain, but this could be a way folks to get early access to it before it’s supported in our flagship packages

    • MT – Polarizability is a huge pain infrastructure-wise, this won’t be coming to our infrastructure any time soon.

    • CC – Agree that it’s a huge pain. For now our best path forward is to stick with additive models.

Talk notes

• Heard talk from John Jumper, lead dev on alphafold. A lot of folks said that they just put more data in than anyone else, but John credits the network structure instead.

  • He went into a lot of detail about how the structure prediction works - They treat the backbone as a set of beads (3 beads per AA), and the network learns constraints based the relative positioning of these beads. Then the later, additional atom placement steps are treated as another puzzle. John says that the network doesn’t understand context - It just tries to make a folded structure as hard as it can - so it doesn’t react correctly to mutations like putting a charged AA in the core, which should lead to unfolding.

  • One cool thing they found is that the “confidence score” is well-correlated with the propensity of a protein to form secondary structure. So the confidence score can incidentally be used as a high-performing disordered-region-detector.

  • Also found that alphafold does a good job of predicting dimers. Later trained a model for multimers, this model is pretty good but misses some interfaces.

  • Credited CASP and the surrounding community were critical to solving the problem, by giving a strict definition of the problem and a lot of data.

  • MT – Do you think that they did an adequate job of explaining how the network actually solves the problem?

    • CC – They did mention specific layers in the network in describing different stages of the folding process. I’m not sure that he fully justified his explanations, but it seemed reasonable.

    • MT – This still sounds a bit black-boxy, but it clearly does work well, so maybe understanding all the intricacies isn’t necessary.

  • JW – I wonder if they’re working in a world of biased physics - not just “here’s the entire world of protein physics”, but rather “here’s the world of protein physics for proteins THAT CRYSTALLIZE”

    • CC – They were aware of this bias in many methods. In response, they mention that the confidence score correlates very well with disordered proteins/regions, and the model doesn’t generate predictions for those regions.

  • JW – How did the team dynamics at alphafold look?

    • CC – There were 15-20 authors, so the team was pretty large. They were gracious – Credited CASP community. Not sure what their future plans are business-wise. I think this is seen more as an advertisement “hey, we solved protein folding, we can solve your business problem too”

    • MT – When alphazero was released a few years ago and beat other chess engines, people were excited by a bunch of followup questions. There were a bunch of followup papers about possibiltiies, but the google team moved on and didn’t come back.

• Talk from Phillip Biggins lab from Oxford.

  • Studied lipid-exposed binding pockets, like in bilayers/membranes. So “unbound state” was defined in lipid, not water.

  • Only used one protein target and FF (99sb-ildn, and GAFF2), and 22 ligands. Found that they underestimate binding energies by about 2.8 kcal/mol.

  • Hypothesized that ligands are less flexible in lipids compared to water. This might interfere with protocols that are used in current binding energy calculations. Found that more aggressively forcing sampling of ligand conformational space brought RMSE down from 2.8 to 1.6 kcal/mol.

  • PB – WRT the lipid simultions, which FFs did they use for this?

    • CC – Probably CHARMM FFs. They have an all-atom model, and the coarse-grained MARTINI model

  • CC – People asked whether, in the apo state, did the binding pocket remain in a conformation that allowed the ligand to successfully get lambdad into the binding pocket? Like, if you lipid FF is wrong, maybe your binding pocket is collapsing/going to a different conformation so the ligand is unable to get into its correct conformation? Authors replied that the apo state seemed to be modeled well.

  • CC – This seems like a generally interesting questions to investigate - mostly people look at solvent-exposed binding pockets

  • JW – It’s a little interesting that they look at the lipid-binding pocket transition, instead of solvent → lipid → binding pocket

    • CC – The full cycle for binding would indeed include transtitions from solvent to lipid, that’s an active area of research. But they’re looking at a different part of the cycle, and it would add unnecessary noise to consider the whole cycle instead of just the part that they studied.

• Talk from Lars Bock, from Grobuler lab in Max Planck institute in Gottingen.

  • Was in response to a talk about using cryo-EM grids to predict solvent distribution (By Holger Stark?. So the different classes separated from a cryo-EM ensemble could represent a transition where the solvent rearranges, and you could do math wth the changes in density to calculate thermodynamics.

  • Speaker did some rough math and MD simulations to show that the cooling process before cryo-EM would keep this from being a useful source of solvent info. The cooling process, if it takes more thana few hundred ns, gives the molecules time to relax to a lower energy state, and so they’re not representative of solvent structures. There’s a significant subpopulation of the field that agrees that the cooling process disrupts the solvent distribution to the point where it should be used.

• Cool FF talk from PROSECCO group in prague.

  • ECC stands for Electronic Continuum Correction. Tries to deal with the fact that molecules are parameterized in gas phase, but actualy simulations happens in condensed phase. So there’s a big permittivity difference. Standard way oif dealing with this is to use a polarizable FF, but that’s difficult to implement. RESP2 and iPolQ give ways to adjust fixed charges to represent condensed phase environments. This talk argues that you can separate permittivity into nuclear and electronic contributions.

  • They do some math using a factor of 1/sqrt(permittivity) to correct for this. So if you have a system with zero charges, sometimes you end up modeling components with nonzero total charge.

  • Did some validation but they went through it pretty fast, I”ll need to read the manuscript.

  • JW – Do you know if they have software available to generate these charges? We could experiment with them here.

    • Working on refitting a FF with PROSECCO charges, so they must have software, but the rest of theFF will be CHARMM36. So I’m not sure whether therte’s software to just get the charges out.