Objective | Develop a procedure to extend a general small molecule force field to also model proteins self-consistently. |
Primary Driver | |
Approvers | |
Supporting Drivers | |
Stakeholders (these people will be tagged in project update notifications on Slack) | Jeffrey Wagner Pavan Behara Iván Pulido Lily Wang Joshua Horton David Mobley |
Project Manager | |
Page Owner (only this person can edit this page) | |
Decision authority | Majority of Primary Driver and all Approvers, only in “Biopolymer FF call” meetings Veto authority: Primary Driver, any Approver |
Discussion/notification venue | Fortnightly “Biopolymer FF call” meetings (decision forum). NOT “FF release call” meetings. #ff-biopolymers channel on OpenFF slack and “FF release call” meetings (notification and discussion, no major decisions allowed here. It is not assumed that meeting attendees have read the slack discussions, they must be summarized during meetings to be considered in decisions) |
Meeting notes | |
Due date | 2022-01-01 |
Key outcomes |
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Status |
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
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.
Dataset name | Dataset type | QC method | Molecules | QCA submission | Status |
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OpenFF Protein Capped 1-mers 3-mers Optimization | Optimization | B3LYP-D3BJ/DZVP | Ace-X-Nme and Ace-Y-X-Y-Nme X is 26 canonical amino acids and protomers Y is Ala or Val | OpenFF Protein Capped 1-mers 3-mers Optimization Dataset v1.0 | 756/759 complete |
OpenFF Protein Capped 1-mers Backbones | TorsionDrive | B3LYP-D3BJ/DZVP | Ace-X-Nme X is 26 canonical amino acids and protomers 2-D scan of phi and psi Chi1 and chi2 constrained to most populated rotamer | 25/26 complete | |
OpenFF Protein Capped 1-mers Sidechains | TorsionDrive | B3LYP-D3BJ/DZVP | Ace-X-Nme X is 26 canonical amino acids and protomers 2-D scan of chi1 and chi2 Phi and psi constrained to values in alpha helix or beta strand | 42/46 complete |
These datasets will be used to choose between models for protein-specific parameters (see below).
Dataset name | Dataset type | QC method | Molecules | QCA submission | Status |
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OpenFF Protein Capped 3-mers Backbones | TorsionDrive | B3LYP-D3BJ/DZVP | Ace-Y-X-Y-Nme X is 27 canonical amino acids and protomers including cisPro and transPro Y is Ala or Val 2-D scan of phi and psi Chi1 and chi2 constrained to most populated rotamer | 6/54 complete |
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.
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
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.
Backbone torsions
General backbone torsions for phi and psi
Residue-specific backbone torsions for Gly
Sidechain torsions
No protein-specific sidechain torsions
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)
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
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)
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
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.
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)
Milestone | Owner | Deadline | Status | Notes |
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Generate ELF10 library charges | 2022-06-01 |
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Choose benchmark systems | Biopolymer FF group | 2022-04-01 |
| In parallel with LiveCoMS review |
Generate protein QC datasets | 2022-04-01 |
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Fit parameters for protein-specific models | 2022-10-01 |
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Software for NMR observable benchmarks | 2022-10-01 | |||
Tier 1 NMR benchmarks | 2022-11-01 |
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Tier 2 NMR benchmarks | 2023-01-01 |
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Tier 3 NMR benchmarks | 2023-07-01 |
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