Protein Force Field Project Plan

Objective

Develop a procedure to extend a general small molecule force field to also model proteins self-consistently.

Primary Driver

@Chapin Cavender

Approvers

@Michael Gilson @Michael Shirts

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

@Diego Nolasco (Deactivated)

Page Owner (only this person can edit this page)

@Chapin Cavender

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

https://openforcefield.atlassian.net/wiki/spaces/MEET/pages/2250932319

Due date

2022-01-01

Key outcomes

  • Extensible SMIRNOFF format for amino acid residues

  • Training datasets for 20 natural amino acids

  • Selection of FF model for proteins

  • One or more sets of OpenFF parameters for the 20 canonical amino acids

  • Identification of key benchmark systems

Status

PLANNING PHASE

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: https://openforcefield.atlassian.net/wiki/spaces/MEET/pages/2348285957

  • 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.

Dataset name

Dataset type

QC method

Molecules

QCA submission

Status

Dataset name

Dataset type

QC method

Molecules

QCA submission

Status

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

OpenFF Protein Dipeptide 2-D TorsionDrive v2.1

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

OpenFF Protein Capped 1-mer Sidechains v1.2

42/46 complete

Validation QC datasets

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

Dataset name

Dataset type

QC method

Molecules

QCA submission

Status

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

OpenFF Protein Capped 3-mer Backbones v1.0

6/54 complete

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: https://openforcefield.atlassian.net/wiki/spaces/MEET/pages/2386788361

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: . 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

Milestone

Owner

Deadline

Status

Notes

Milestone

Owner

Deadline

Status

Notes

Generate ELF10 library charges

@Chapin Cavender

2022-06-01

Completed

 

Choose benchmark systems

Biopolymer FF group

2022-04-01

COMPLETED

In parallel with LiveCoMS review

Generate protein QC datasets

@Chapin Cavender

2022-04-01

COMPLETED

 

Fit parameters for at least two models (null and Amber ff99SB typed)

@Chapin Cavender

2022-10-01

IN PROGRESS

 

Software for NMR observable benchmarks

@Chapin Cavender

2022-10-01

IN PROGRESS

 

Tier 1 NMR benchmarks

@Chapin Cavender

2022-11-01

NOT STARTED

 

Tier 2 NMR benchmarks

@Chapin Cavender

2023-01-01

NOT STARTED

 

Tier 3 NMR benchmarks

@Chapin Cavender

2023-07-01

NOT STARTED