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manager.py
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manager.py
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import torch
from rfdiffusion.potentials import potentials as potentials
import numpy as np
def make_contact_matrix(nchain, intra_all=False, inter_all=False, contact_string=None):
"""
Calculate a matrix of inter/intra chain contact indicators
Parameters:
nchain (int, required): How many chains are in this design
contact_str (str, optional): String denoting how to define contacts, comma delimited between pairs of chains
'!' denotes repulsive, '&' denotes attractive
"""
alphabet = [a for a in 'ABCDEFGHIJKLMNOPQRSTUVWXYZ']
letter2num = {a:i for i,a in enumerate(alphabet)}
contacts = np.zeros((nchain,nchain))
written = np.zeros((nchain,nchain))
# intra_all - everything on the diagonal has contact potential
if intra_all:
contacts[np.arange(nchain),np.arange(nchain)] = 1
# inter all - everything off the diagonal has contact potential
if inter_all:
mask2d = np.full_like(contacts,False)
for i in range(len(contacts)):
for j in range(len(contacts)):
if i!=j:
mask2d[i,j] = True
contacts[mask2d.astype(bool)] = 1
# custom contacts/repulsions from user
if contact_string != None:
contact_list = contact_string.split(',')
for c in contact_list:
assert len(c) == 3
i,j = letter2num[c[0]],letter2num[c[2]]
symbol = c[1]
assert symbol in ['!','&']
if symbol == '!':
contacts[i,j] = -1
contacts[j,i] = -1
else:
contacts[i,j] = 1
contacts[j,i] = 1
return contacts
def calc_nchains(symbol, components=1):
"""
Calculates number of chains for given symmetry
"""
S = symbol.lower()
if S.startswith('c'):
return int(S[1:])*components
elif S.startswith('d'):
return 2*int(S[1:])*components
elif S.startswith('o'):
raise NotImplementedError()
elif S.startswith('t'):
return 12*components
else:
raise RuntimeError('Unknown symmetry symbol ',S)
class PotentialManager:
'''
Class to define a set of potentials from the given config object and to apply all of the specified potentials
during each cycle of the inference loop.
Author: NRB
'''
def __init__(self,
potentials_config,
ppi_config,
diffuser_config,
inference_config,
hotspot_0idx,
binderlen,
):
self.potentials_config = potentials_config
self.ppi_config = ppi_config
self.inference_config = inference_config
self.guide_scale = potentials_config.guide_scale
self.guide_decay = potentials_config.guide_decay
if potentials_config.guiding_potentials is None:
setting_list = []
else:
setting_list = [self.parse_potential_string(potstr) for potstr in potentials_config.guiding_potentials]
# PPI potentials require knowledge about the binderlen which may be detected at runtime
# This is a mechanism to still allow this info to be used in potentials - NRB
if binderlen > 0:
binderlen_update = { 'binderlen': binderlen }
hotspot_res_update = { 'hotspot_res': hotspot_0idx }
for setting in setting_list:
if setting['type'] in potentials.require_binderlen:
setting.update(binderlen_update)
if setting['type'] in potentials.require_hotspot_res:
setting.update(hotspot_res_update)
self.potentials_to_apply = self.initialize_all_potentials(setting_list)
self.T = diffuser_config.T
def is_empty(self):
'''
Check whether this instance of PotentialManager actually contains any potentials
'''
return len(self.potentials_to_apply) == 0
def parse_potential_string(self, potstr):
'''
Parse a single entry in the list of potentials to be run to a dictionary of settings for that potential.
An example of how this parsing is done:
'setting1:val1,setting2:val2,setting3:val3' -> {setting1:val1,setting2:val2,setting3:val3}
'''
setting_dict = {entry.split(':')[0]:entry.split(':')[1] for entry in potstr.split(',')}
for key in setting_dict:
if not key == 'type': setting_dict[key] = float(setting_dict[key])
return setting_dict
def initialize_all_potentials(self, setting_list):
'''
Given a list of potential dictionaries where each dictionary defines the configurations for a single potential,
initialize all potentials and add to the list of potentials to be applies
'''
to_apply = []
for potential_dict in setting_list:
assert(potential_dict['type'] in potentials.implemented_potentials), f'potential with name: {potential_dict["type"]} is not one of the implemented potentials: {potentials.implemented_potentials.keys()}'
kwargs = {k: potential_dict[k] for k in potential_dict.keys() - {'type'}}
# symmetric oligomer contact potential args
if self.inference_config.symmetry:
num_chains = calc_nchains(symbol=self.inference_config.symmetry, components=1) # hard code 1 for now
contact_kwargs={'nchain':num_chains,
'intra_all':self.potentials_config.olig_intra_all,
'inter_all':self.potentials_config.olig_inter_all,
'contact_string':self.potentials_config.olig_custom_contact }
contact_matrix = make_contact_matrix(**contact_kwargs)
kwargs.update({'contact_matrix':contact_matrix})
to_apply.append(potentials.implemented_potentials[potential_dict['type']](**kwargs))
return to_apply
def compute_all_potentials(self, xyz):
'''
This is the money call. Take the current sequence and structure information and get the sum of all of the potentials that are being used
'''
potential_list = [potential.compute(xyz) for potential in self.potentials_to_apply]
potential_stack = torch.stack(potential_list, dim=0)
return torch.sum(potential_stack, dim=0)
def get_guide_scale(self, t):
'''
Given a timestep and a decay type, get the appropriate scale factor to use for applying guiding potentials
Inputs:
t (int, required): The current timestep
Output:
scale (int): The scale factor to use for applying guiding potentials
'''
implemented_decay_types = {
'constant': lambda t: self.guide_scale,
# Linear interpolation with y2: 0, y1: guide_scale, x2: 0, x1: T, x: t
'linear' : lambda t: t/self.T * self.guide_scale,
'quadratic' : lambda t: t**2/self.T**2 * self.guide_scale,
'cubic' : lambda t: t**3/self.T**3
}
if self.guide_decay not in implemented_decay_types:
sys.exit(f'decay_type must be one of {implemented_decay_types.keys()}. Received decay_type={self.guide_decay}. Exiting.')
return implemented_decay_types[self.guide_decay](t)