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

• Start with a general SMARTS for backbone and sidechain torsions, then overwrite with residue-specific SMARTS for exceptions

• Backbone SMARTS should include the alpha carbon of adjacent residues to exclude uncapped termini

• Sidechain SMARTS should not include atoms in adjacent residues to include both capped and uncapped termini

Protein QC datasets

Training QC datasets

These QC datasets will be used to supplement Sage QC training datasets.

Failures in TorsionDrives are caused by failure to converge the rotational component of the gradient. This will be fixed in the upcoming release of geomeTRIC.

Validation QC datasets

These datasets will be used to choose between models for protein-specific parameters (see below).

Protein-specific parameter models

We envision several tiers of models, presented below in order of increasing number of parameter types. We will generate and benchmark simpler models first and use the results of the benchmarks to inform decisions about how 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.

All valence parameters and torsion amplitudes will be optimized for each model, while Lennard-Jones parameters will be identical to Sage 2.0.0. The only difference between the models is the set of SMIRNOFF parameter types in the model. Initially, models will differ only in torsion parameter types. Additional changes, e.g. to Lennard-Jones types, will be considered only if major failures are observed in benchmarks with these models.

Charges will be modeled using ELF10 library charges derived by averaging over the flanking residues X and Z in Ace-Val-X-Y-Z-Nme. Library charges will be derived for the caps Ace and Nme and for the main chain, charged terminal, and uncharged terminal positions of the 26 canonical amino acids and common protomers. See additional details here: 2022-08-11 Protein FF meeting note

Null Model

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

Beta-branched sidechains 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 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

Beta-branched backbone 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 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

Benchmarks

Experimental datasets are being curated to evaluate protein force fields. These datasets will be published as a LiveCoMS review, described here: https://openforcefield.atlassian.net/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.

Protein force field benchmarks will target NMR observables. Three tiers of systems will be used to progressively assess force field models. Models that perform well in Tier 1 will progress to Tier 2, and only the release candidate will progress to Tier 3. Amber ff14SB will be benchmarked alongside OpenFF force fields for all tiers.

• NMR observables

  • Tier 1

    • 19 capped 1-mers (no Pro)

    • 11 uncapped 3-mers (AAA, GGG, VVV, GAG, GEG ,GFG, GKG, GLG, GMG, GSG, GVG)

    • 1 uncapped 4-mer (AAAA)

    • 1 uncapped 5-mer (AAAAA)

    • K19 peptide (alpha helix)

    • CLN025 peptide (beta hairpin)

  • Tier 2

    • 4 folded proteins (Ubiquitin, Lysozyme, GB3, BPTI)

    • 10 disordered proteins (a99SB-disp benchmark dataset)

  • Tier 3

    • Additional 40 folded proteins (Mao benchmark dataset)

Milestones and deadlines