Content Comparison

Review fitting strategy

Chapin Cavender

• Charges

  • ELF-10

  • Library charges for water and 26 canonical amino acids

• Lennard-Jones

  • Fit Sage types

  • Target Sage physical property training dataset

• Valence and torsions

  • Fit Sage types (null model) or new protein-specific torsions

  • Target Sage QC training dataset and new QC datasets

    • Optimization dataset for capped 1-mers

    • TorsionDrives on (phi, psi) and (chi1, chi2) for capped 1-mers

  • Fit torsions/valence simultaneously or sequentially

Rosemary benchmark - small molecules

Chapin Cavender

• Sage benchmarks

  • QC test dataset

  • Physical property test dataset - solvation and transfer free energies

Rosemary benchmark - proteins

Chapin Cavender

• All trajectories in triplicate

• Force fields

  • Need

    • Rosemary/TIP3P

    • Rosemary/OPC

    • Amber ff14SB/TIP3P

  • Want

    • Amber ff14SB/OPC

    • CHARMM36m/TIP3P

    • Amber ff19SB/OPC

    • a99SB-disp TIP4P-D

• Beauchamp/Pande dataset

  • 32 small peptides (2 to 5 residues)

  • Chemical shifts and scalar couplings

  • 500 ns trajectories

  • Use as validation dataset to choose between models for protein torsions

• Need: Robustelli/Shaw a99SB-disp dataset

  • 4 folded proteins (largest 120 residues)

    • Scalar couplings

    • 10 μs trajectories

  • 10 disordered proteins (largest 140 residues)

    • Chemical shifts and scalar couplings

    • 30 μs trajectories

• Want: Mao/Montelione dataset

  • 41 folded proteins

  • Chemical shifts and NOEs

  • 10 μs trajectories

• Aggregate sampling

  • Need: 3 ms

  • Want: 3.7 ms

Compute for protein benchmarks

Jeffrey Wagner

• Talked to SB - If protein observable benchmark sims will be run on F@H, there’s little benefit to having the analysis implemented in Evaluator. So we’ll want to think about which repo we could keep the submission, trajectory pulldown, and analysis scripts in, and how that will be reproducible (record software versions etc)