RoseNet

Sarah Coffland, Katie Christensen | Western Washington University, Department of Computer Science, 2024

Studying the structural and functional implications of protein mutations is an important task in computational biology and bioinformatics. We predict energy metrics of proteins with double amino acid insertions or deletions (InDels). We train three of these models on our data sets containing the exhaustive double InDel mutations for three proteins, as well as another three proteins for which roughly 145k random mutants with two InDels have been generated. Our models make use of the identities of the residues, and the positions at which these residues are inserted.

Data

• All train/validation/test1/test2/test3 splits for each protein live in the data splits directory.
• To create new data splits
  • Update the configuration file data.yaml under the config/ directory
  • Run the script to create the data splits

    cd src/
    python3 data_split.py
    

Training

• Update the configuration file main.yaml under the config/ directory
• Run the training pipeline

     cd src/
     python3 pipeline.py
  

Evaluating

• Create the solvation accessible surface area (SASA) and secondary structure (SS) masks.
  • Update the configuration file masks.yaml under the config/ directory
  • Run the script to create the masks

    cd src/
    python3 make_masks.py
    

• Update the configuration file evaluate.yaml under the config/ directory
• Run the evaluation pipeline

     cd src/
     python3 evaluate.py
  

Acknowledgements

Dr. Brian Hutchinson (Western Washington University) and Dr. Filip Jagodzinski (Western Washington University)

Rosetta Scores and Definitions

Rosetta Score	Definition
fa_atr	Lennard-Jones attractive between atoms in different residues
fa_rep	Lennard-Jones repulsive between atoms in different residues
fa_sol	Lazaridis-Karplus solvation energy
fa_intra_sol_xover4	Intra-residue Lazaridis-Karplus solvation energy
lk_ball_wtd	Asymmetric solvation energy
fa_intra_rep	Lennard-Jones repulsive between atoms in the same residue
fa_elec	Coulombic electrostatic potential with a distance-dependent dielectric
pro_close	Proline ring closure energy and energy of psi angle of preceding residue
hbond_sr_bb	Backbone-backbone hbonds close in primary sequence
hbond_lr_bb	Backbone-backbone hbonds distant in primary sequence
hbond_bb_sc	Sidechain-backbone hydrogen bond energy
hbond_sc	Sidechain-sidechain hydrogen bond energy
dslf_fa13	Disulfide geometry potential
rama_prepro	Ramachandran preferences (with separate lookup tables for pre-proline positions and other positions)
omega	Omega dihedral in the backbone. A Harmonic constraint on planarity with standard deviation of ~6 deg.
p_aa_pp	Probability of amino acid, given torsion values for phi and psi
fa_dun	Internal energy of sidechain rotamers as derived from Dunbrack's statistics
yhh_planarity	A special torsional potential to keep the tyrosine hydroxyl in the plane of the aromatic ring
ref	Reference energy for each amino acid. Balances internal energy of amino acid terms. Plays role in design.
METHOD_WEIGHTS	Not an energy term itself, but the parameters for each amino acid used by the ref energy term.