SMIRNOFF format

We need SMARTS strings that can specify protein-specific terms for general amino acids. To summarize the discussion here: https://openforcefield.atlassian.net/wiki/pages/createpage.action?spaceKey=MEET&title=2020-04-01 AMBER FF porting meeting notes01%20AMBER%20FF%20porting%20meeting%20notes

• Amber ff14SB was ported to SMIRNOFF format by using SMARTS strings that capture an entire amino acid, differentiating between main chain and terminal residues and between protonation/tautomeric states

• Previous approach is not extensible for modified or synthetic amino acids

• Need general SMARTS strings for backbone and side chain torsions in polypeptide chains

• [#6X3](=O)-[#7X3:1]-[#6X4:2]-[#6X3:3](=O)-[#7X3H1:4]-[#6X4] will tag ψ for all residues except proline

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Needs to be determined. In particular, did Parsley train on dipeptides or tripeptides for any of the 20 canonical amino acids?

Protein-specific datasets

Cerutti tetrapeptides are a set of 185 tetrapeptides with sidechains X-Y-X, X in [Ala, Gly, Ser, Val], and Y in [Ala, Arg, Ash, Asn, Asp, Cys, Glh, Gln, Glu, Gly, Hid, Hie] excluding (X == Ser && Y == Glu). David Cerutti selected multiple conformers for each tetrapeptide.

Model

We envision several tiers of models, presented below in order of increasing anticipated effort. We will generate and benchmark lower-effort models first and use the results of the benchmarks to inform decisions about higher-effort models.

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• Copy library charges from existing protein force field: Amber ff14SB (RESP, unchanged from Amber ff99) or Amber ff15ipq (IPolQ)

  • Pro: easy to implement

  • Pro: we know these work pretty well in the Amber context

  • Con: we are no longer in the Amber context

• Generate library charges for the 20 canonical amino acids (main chain and terminal) using AM1-BCC (RESP2)

  • Pro: consistent with Parsley; for example, we want the parameters of a serine side chain to look a lot like those of ethanol, since we have reason to believe these parameters play well with the other parameters in the FF

  • Pro: Lily Wang has evidence that AM1-BCC charges of fragments are similar (< 0.1 e) to the charges from a larger polymer (see https://zenodo.org/record/4977401#.YNuCk34pCpp )

  • Con: more effort to generate

• Generate charges on-the-fly using graph convolutional networks (see https://openforcefield.atlassian.net/wiki/pages/createpage.action?spaceKey=MEET&title=2020-04-01 AMBER FF porting meeting notes01%20AMBER%20FF%20porting%20meeting%20notes)

  • Maybe don’t take this approach unless/until it is also being used for the small molecules

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A major decision for this model is which sidechains should have unique torsions that override the general peptide backbone torsion. We envision using the Protein Fragments Optimization and TorsionDrive datasets as the primary training data. Then, other datasets such as PEPCONF can be used as validation data to make decisions about the model. Alternatively, automated chemical perception (Chemical perceptionPerception) may be used to identify dihedrals that are not described well in the Parsley training set. The resulting model will likely be the candidate for the first protein force field release.

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Experimental datasets are being curated to evaluate protein force fields. These datasets will be published as a LiveCoMS review, described here: /wiki/spaces/COMMS/pages/1927413777. It will be useful to identify a small number of key benchmarks that can interrogate distinct physical properties of proteins and that can be completed relatively quickly (~1 month). These key benchmarks will be used to evaluate force field models and make decisions about more complex models.

Milestones and deadlines