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Making a table of Pyrosetta scorefunctions
# this was run in a Jupyter notebook.
import pyrosetta
import os
import pandas as pd
from typing import *
pyrosetta.distributed.maybe_init(extra_options='-mute all')
# ------------------------------------------------------------------------------------
# not all these method were used.
def get_weights(scorefxn:Union[str, pyrosetta.ScoreFunction]) -> Dict[str, float]:
"""
Gets weights for scorefxn
"""
if isinstance(scorefxn, str):
#scorefxn = pyrosetta.create_score_function(scorefxn)
# mod:
scorefxn = get_scorefxn(scorefxn)
get_weight = lambda scorefxn, score_type: scorefxn.get_weight(getattr(pyrosetta.rosetta.core.scoring.ScoreType, score_type.name))
return {'name': scorefxn.get_name(),
**{score_type.name: get_weight(scorefxn, score_type) for score_type in scorefxn.get_nonzero_weighted_scoretypes()},
**get_ref_values_badly(scorefxn, prefix=True)}
def get_ref_values_badly(scorefxn: pyrosetta.ScoreFunction,
prefix: Optional[bool]=True) -> Dict[str, float]:
"""
I could not find a direct way to get_method_weights.
Here this horrid way.
"""
# similarity order 'IVLFCMAGTSWYPHNDEQKR'
# name1 alphabetical order 'ACDEFGHIKLMNPQRSTVWY'
# name3 alphabetical order 'ARNDCQEGHILKMFPSTWYV'
pose = pyrosetta.pose_from_sequence('ARNDCQEGHILKMFPSTWYV')
scorefxn(pose)
aa_ref = {}
for res in range(1, pose.total_residue() + 1):
name = pose.residue(res).name3()
value = pose.energies().residue_total_energies(res)[pyrosetta.rosetta.core.scoring.ScoreType.ref]
if prefix:
aa_ref[f'ref_{name}'] = value
else:
aa_ref[name] = value
return aa_ref
def get_scorefxn(scorefxn_name:str) -> pyrosetta.ScoreFunction:
"""
Gets the scorefxn with appropriate corrections.
"""
corrections = {'beta_july15': False,
'beta_nov16': False,
'gen_potential': False,
'restore_talaris_behavior': False
}
if 'beta_july15' in scorefxn_name or 'beta_nov15' in scorefxn_name:
# beta_july15 is ref2015
corrections['beta_july15'] = True
elif 'beta_nov16' in scorefxn_name:
corrections['beta_nov16'] = True
elif 'genpot' in scorefxn_name:
corrections['gen_potential'] = True
pyrosetta.rosetta.basic.options.set_boolean_option('corrections:beta_july15', True)
elif 'talaris' in scorefxn_name: #2013 and 2014
corrections['restore_talaris_behavior'] = True
else:
pass
for corr, value in corrections.items():
pyrosetta.rosetta.basic.options.set_boolean_option(f'corrections:{corr}', value)
return pyrosetta.create_score_function(scorefxn_name)
def get_possible_scorefxn() -> List[str]:
"""
Returns the scorefxn names
"""
folder = os.path.join(os.path.split(pyrosetta.__file__)[0], 'database', 'scoring', 'weights')
return sorted([fn.replace('.wts', '') for fn in os.listdir(folder) if '.wts' == os.path.splitext(fn)[1]])
def get_scorefxn_block(scorefxn_name) -> str:
"""
Read the file given a scorefxn name
"""
filename = os.path.join(os.path.split(pyrosetta.__file__)[0], 'database', 'scoring', 'weights', f'{scorefxn_name}.wts')
with open(filename, 'r') as fh:
return fh.read()
def find_metion(word:str) -> List[str]:
"""
Find all the scorefxn names whose files contain the string word (case insensitive).
>>> find_metion('spades')
returns ``['hydrate_score12']``
"""
mentionants = []
scorefxn_names = get_possible_scorefxn()
for scorefxn_name in scorefxn_names:
block = get_scorefxn_block(scorefxn_name)
if word.lower() in block.lower():
mentionants.append(scorefxn_name)
return mentionants
# ------- main ---------------------------------------------------------------------
scorefxn_names = ['ref2015', 'ref2015_cart', 'beta_nov16', 'beta_nov16_cart']
# 'talaris2013' and 'talaris2014' ommitted due to clutter
# 'franklin2019', 'beta_genpot' will not work due to the ref method weights business.
terms = pd.DataFrame(map(get_weights, scorefxn_names)).fillna(0).transpose()
print(terms.to_markdown())
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@matteoferla matteoferla commented Apr 1, 2021

Output:

name ref2015 ref2015_cart beta_nov16 beta_nov16_cart
fa_atr 1.0 1.0 1.0 1.0
fa_rep 0.55 0.55 0.55 0.55
fa_sol 1.0 1.0 1.0 1.0
fa_intra_rep 0.005 0.005 0.0 0.0
fa_intra_sol_xover4 1.0 1.0 1.0 1.0
lk_ball_wtd 1.0 1.0 0.0 0.0
fa_elec 1.0 1.0 1.0 1.0
pro_close 1.25 0.0 1.25 0.0
hbond_sr_bb 1.0 1.0 1.0 1.0
hbond_lr_bb 1.0 1.0 1.0 1.0
hbond_bb_sc 1.0 1.0 1.0 1.0
hbond_sc 1.0 1.0 1.0 1.0
dslf_fa13 1.25 1.25 1.25 1.25
omega 0.4 0.4 0.48 0.48
fa_dun 0.7 0.7 0.0 0.0
p_aa_pp 0.6 0.6 0.61 0.61
yhh_planarity 0.625 0.625 0.0 0.0
ref 1.0 1.0 1.0 1.0
rama_prepro 0.45 0.45 0.5 0.5
ref_ALA 1.32468 1.32468 2.3386 2.3386
ref_ARG -0.09474 -0.09474 -1.281 -1.281
ref_ASN -1.34026 -1.34026 -0.873554 -0.873554
ref_ASP -2.14574 -2.14574 -2.2837 -2.2837
ref_CYS 3.25479 3.25479 3.2718 3.2718
ref_GLN -1.45095 -1.45095 -1.0644 -1.0644
ref_GLU -2.72453 -2.72453 -2.5358 -2.5358
ref_GLY 0.79816 0.79816 1.2108 1.2108
ref_HIS -0.30065 -0.30065 0.134426 0.134426
ref_ILE 2.30374 2.30374 1.0317 1.0317
ref_LEU 1.66147 1.66147 0.729516 0.729516
ref_LYS -0.71458 -0.71458 -1.6738 -1.6738
ref_MET 1.65735 1.65735 1.2334 1.2334
ref_PHE 1.21829 1.21829 1.4028 1.4028
ref_PRO -1.64321 -1.64321 -5.1227 -5.1227
ref_SER -0.28969 -0.28969 -1.1772 -1.1772
ref_THR 1.15175 1.15175 -1.425 -1.425
ref_TRP 2.26099 2.26099 3.035 3.035
ref_TYR 0.58223 0.58223 0.964136 0.964136
ref_VAL 2.64269 2.64269 2.085 2.085
cart_bonded 0.0 0.5 0.0 0.5
fa_intra_atr_xover4 0.0 0.0 1.0 1.0
fa_intra_rep_xover4 0.0 0.0 0.55 0.55
lk_ball 0.0 0.0 0.92 0.92
lk_ball_iso 0.0 0.0 -0.38 -0.38
lk_ball_bridge 0.0 0.0 -0.33 -0.33
lk_ball_bridge_uncpl 0.0 0.0 -0.33 -0.33
fa_intra_elec 0.0 0.0 1.0 1.0
fa_dun_dev 0.0 0.0 0.69 0.69
fa_dun_rot 0.0 0.0 0.76 0.76
fa_dun_semi 0.0 0.0 0.78 0.78
hxl_tors 0.0 0.0 1.0 1.0
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