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April 1, 2021 11:05
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Making a table of Pyrosetta scorefunctions
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| # 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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