Overview of strategy

• Generate protein QC datasets for training and validation

• Fit multiple models to the same training dataset

  • Models vary in types of torsion parameters

  • All models optimize valence parameters and torsion amplitudes

• Benchmark protein models

  • Tier 1 benchmarks for all models

  • Tier 2 benchmarks for models that perform well in Tier 1. Specific failures in Tier 1 may lead to new models to address problems.

  • Tier 3 benchmarks for release candidate

SMIRNOFF format

We need SMARTS strings that can specify protein-specific terms for general amino acids. To summarize the full discussion here: 2022-06-30 Protein FF meeting note

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We envision several tiers of models, presented below in order of increasing anticipated effortnumber of parameter types. We will generate and benchmark lower-effort simpler models first and use the results of the benchmarks to inform decisions about higher-effort modelshow to prioritize fitting and benchmarking of more complex models. Benchmarking results for simpler models may also inspire new models not listed below to address specific benchmarking failures.

The same set of training datasets will be used for each model: Sage QC training dataset and protein QC datasets described above. Protein-specific datasets will be weighted equally for each of the twenty canonical sidechains (i.e. weights of all protomers for the same sidechain will sum to one), and the total weight for protein-specific datasets and small molecule datasets will be equal.

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The null model is that the small molecule force field already describes proteins well and needs no protein-specific parameters. Parsley was trained on compounds that resemble protein backbone and sidechain analogs, so these parameters are likely a good first pass at describing polypeptide chains.

Amber ff99SB typed model

• Backbone torsions

  • General backbone torsions for phi and psi

  • Residue-specific backbone torsions for Gly

• Sidechain torsions

  • No protein-specific sidechain torsions

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• Backbone torsions

  • General backbone torsions for phi and psi

  • Residue-specific backbone torsions for Gly

• Sidechain torsions

  • General sidechain torsions for chi1 and chi2

  • Residue-specific sidechain torsions for beta-branched sidechains (Ile, Thr, and Val)

Amber ff14SB typed model

• Backbone torsions

  • General backbone torsions for phi and psi

  • Residue-specific backbone torsions for Gly

• Sidechain torsions

  • General sidechain torsions for chi1 and chi2

  • Residue-specific sidechain torsions for 11 groups of sidechains from Amber ff14SB

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• Backbone torsions

  • General backbone torsions for phi and psi

  • Residue-specific backbone torsions for Gly, Pro, and beta-branched sidechains (Ile, Thr, and Val)

• Sidechain torsions

  • General sidechain torsions for chi1 and chi2

  • Residue-specific sidechain torsions for beta-branched sidechains (Ile, Thr, and Val)

Beta-branched backbones and Amber ff14SB typed sidechains model

• Backbone torsions

  • General backbone torsions for phi and psi

  • Residue-specific backbone torsions for Gly, Pro, and beta-branched sidechains (Ile, Thr, and Val)

• Sidechain torsions

  • General sidechain torsions for chi1 and chi2

  • Residue-specific sidechain torsions for 11 groups of sidechains from Amber ff14SB

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