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#####################################################################
############# PROTEIN SEQUENCE DIFFUSION SAMPLER ####################
#####################################################################
import sys, os, subprocess, pickle, time, json
script_dir = os.path.dirname(os.path.realpath(__file__))
sys.path = sys.path + [script_dir+'/../model/'] + [script_dir+'/']
import shutil
import glob
import torch
import numpy as np
import copy
import json
import matplotlib.pyplot as plt
from torch import nn
import math
import re
import pickle
import pandas as pd
import random
from copy import deepcopy
import time
from collections import namedtuple
import math
from torch.nn.parallel import DistributedDataParallel as DDP
from RoseTTAFoldModel import RoseTTAFoldModule
from util import *
from inpainting_util import *
from kinematics import get_init_xyz, xyz_to_t2d
import parsers_inference as parsers
import diff_utils
import pickle
import pdb
from utils.calc_dssp import annotate_sse
from potentials import POTENTIALS
from diffusion import GaussianDiffusion_SEQDIFF
MODEL_PARAM ={
"n_extra_block" : 4,
"n_main_block" : 32,
"n_ref_block" : 4,
"d_msa" : 256,
"d_msa_full" : 64,
"d_pair" : 128,
"d_templ" : 64,
"n_head_msa" : 8,
"n_head_pair" : 4,
"n_head_templ" : 4,
"d_hidden" : 32,
"d_hidden_templ" : 32,
"p_drop" : 0.0
}
SE3_PARAMS = {
"num_layers_full" : 1,
"num_layers_topk" : 1,
"num_channels" : 32,
"num_degrees" : 2,
"l0_in_features_full": 8,
"l0_in_features_topk" : 64,
"l0_out_features_full": 8,
"l0_out_features_topk" : 64,
"l1_in_features": 3,
"l1_out_features": 2,
"num_edge_features_full": 32,
"num_edge_features_topk": 64,
"div": 4,
"n_heads": 4
}
SE3_param_full = {}
SE3_param_topk = {}
for param, value in SE3_PARAMS.items():
if "full" in param:
SE3_param_full[param[:-5]] = value
elif "topk" in param:
SE3_param_topk[param[:-5]] = value
else: # common arguments
SE3_param_full[param] = value
SE3_param_topk[param] = value
MODEL_PARAM['SE3_param_full'] = SE3_param_full
MODEL_PARAM['SE3_param_topk'] = SE3_param_topk
DEFAULT_CKPT = '/home/jgershon/models/SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt' #this is the one with good sequences
LOOP_CHECKPOINT = '/home/jgershon/models/SEQDIFF_221202_AB_NOSTATE_fromBASE_mod30.pt'
t1d_29_CKPT = '/home/jgershon/models/SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt'
class SEQDIFF_sampler:
'''
MODULAR SAMPLER FOR SEQUENCE DIFFUSION
- the goal for modularizing this code is to make it as
easy as possible to edit and mix functions around
- in the base implementation here this can handle the standard
inference mode with default passes through the model, different
forms of partial diffusion, and linear symmetry
'''
def __init__(self, args=None):
'''
set args and DEVICE as well as other default params
'''
self.args = args
self.DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
self.conversion = 'ARNDCQEGHILKMFPSTWYVX-'
self.dssp_dict = {'X':3,'H':0,'E':1,'L':2}
self.MODEL_PARAM = MODEL_PARAM
self.SE3_PARAMS = SE3_PARAMS
self.SE3_param_full = SE3_param_full
self.SE3_param_topk = SE3_param_topk
self.use_potentials = False
self.reset_design_num()
def set_args(self, args):
'''
set new arguments if iterating through dictionary of multiple arguments
# NOTE : args pertaining to the model will not be considered as this is
used to sample more efficiently without having to reload model for
different sets of args
'''
self.args = args
self.diffuser_init()
if self.args['potentials'] not in ['', None]:
self.potential_init()
def reset_design_num(self):
'''
reset design num to 0
'''
self.design_num = 0
def diffuser_init(self):
'''
set up diffuser object of GaussianDiffusion_SEQDIFF
'''
self.diffuser = GaussianDiffusion_SEQDIFF(T=self.args['T'],
schedule=self.args['noise_schedule'],
sample_distribution=self.args['sample_distribution'],
sample_distribution_gmm_means=self.args['sample_distribution_gmm_means'],
sample_distribution_gmm_variances=self.args['sample_distribution_gmm_variances'],
)
self.betas = self.diffuser.betas
self.alphas = 1-self.betas
self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
def make_hotspot_features(self):
'''
set up hotspot features
'''
# initialize hotspot features to all 0s
self.features['hotspot_feat'] = torch.zeros(self.features['L'])
# if hotspots exist in args then make hotspot features
if self.args['hotspots'] != None:
self.features['hotspots'] = [(x[0],int(x[1:])) for x in self.args['hotspots'].split(',')]
for n,x in enumerate(self.features['mappings']['complex_con_ref_pdb_idx']):
if x in self.features['hotspots']:
self.features['hotspot_feat'][self.features['mappings']['complex_con_hal_idx0'][n]] = 1.0
def make_dssp_features(self):
'''
set up dssp features
'''
assert not ((self.args['secondary_structure'] != None) and (self.args['dssp_pdb'] != None)), \
f'You are attempting to provide both dssp_pdb and/or secondary_secondary structure, please choose one or the other'
# initialize with all zeros
self.features['dssp_feat'] = torch.zeros(self.features['L'],4)
if self.args['secondary_structure'] != None:
self.features['secondary_structure'] = [self.dssp_dict[x.upper()] for x in self.args['secondary_structure']]
assert len(self.features['secondary_structure']*self.features['sym'])+self.features['cap']*2 == self.features['L'], \
f'You have specified a secondary structure string that does not match your design length'
self.features['dssp_feat'] = torch.nn.functional.one_hot(
torch.tensor(self.features['cap_dssp']+self.features['secondary_structure']*self.features['sym']+self.features['cap_dssp']),
num_classes=4)
elif self.args['dssp_pdb'] != None:
dssp_xyz = torch.from_numpy(parsers.parse_pdb(self.args['dssp_pdb'])['xyz'][:,:,:])
dssp_pdb = annotate_sse(np.array(dssp_xyz[:,1,:].squeeze()), percentage_mask=0)
#we assume binder is chain A
self.features['dssp_feat'][:dssp_pdb.shape[0]] = dssp_pdb
elif (self.args['helix_bias'] + self.args['strand_bias'] + self.args['loop_bias']) > 0.0:
tmp_mask = torch.rand(self.features['L']) < self.args['helix_bias']
self.features['dssp_feat'][tmp_mask,0] = 1.0
tmp_mask = torch.rand(self.features['L']) < self.args['strand_bias']
self.features['dssp_feat'][tmp_mask,1] = 1.0
tmp_mask = torch.rand(self.features['L']) < self.args['loop_bias']
self.features['dssp_feat'][tmp_mask,2] = 1.0
#contigs get mask label
self.features['dssp_feat'][self.features['mask_str'][0],3] = 1.0
#anything not labeled gets mask label
mask_index = torch.where(torch.sum(self.features['dssp_feat'], dim=1) == 0)[0]
self.features['dssp_feat'][mask_index,3] = 1.0
def model_init(self):
'''
get model set up and choose checkpoint
'''
if self.args['checkpoint'] == None:
self.args['checkpoint'] = DEFAULT_CKPT
self.MODEL_PARAM['d_t1d'] = self.args['d_t1d']
# decide based on input args what checkpoint to load
if self.args['hotspots'] != None or self.args['secondary_structure'] != None \
or (self.args['helix_bias'] + self.args['strand_bias'] + self.args['loop_bias']) > 0 \
or self.args['dssp_pdb'] != None and self.args['checkpoint'] == DEFAULT_CKPT:
self.MODEL_PARAM['d_t1d'] = 29
print('You are using features only compatible with a newer model, switching checkpoint...')
self.args['checkpoint'] = t1d_29_CKPT
elif self.args['loop_design'] and self.args['checkpoint'] == DEFAULT_CKPT:
print('Switched to loop design checkpoint')
self.args['checkpoint'] = LOOP_CHECKPOINT
# check to make sure checkpoint chosen exists
if not os.path.exists(self.args['checkpoint']):
print('WARNING: couldn\'t find checkpoint')
self.ckpt = torch.load(self.args['checkpoint'], map_location=self.DEVICE)
# check to see if [loader_param, model_param, loss_param] is in checkpoint
# if so then you are using v2 of inference with t2d bug fixed
self.v2_mode = False
if 'model_param' in self.ckpt.keys():
print('You are running a new v2 model switching into v2 inference mode')
self.v2_mode = True
for k in self.MODEL_PARAM.keys():
if k in self.ckpt['model_param'].keys():
self.MODEL_PARAM[k] = self.ckpt['model_param'][k]
else:
print(f'no match for {k} in loaded model params')
# make model and load checkpoint
print('Loading model checkpoint...')
self.model = RoseTTAFoldModule(**self.MODEL_PARAM).to(self.DEVICE)
model_state = self.ckpt['model_state_dict']
self.model.load_state_dict(model_state, strict=False)
self.model.eval()
print('Successfully loaded model checkpoint')
def feature_init(self):
'''
featurize pdb and contigs and choose type of diffusion
'''
# initialize features dictionary for all example features
self.features = {}
# set up params
self.loader_params = {'MAXCYCLE':self.args['n_cycle'],'TEMPERATURE':self.args['temperature'], 'DISTANCE':self.args['min_decoding_distance']}
# symmetry
self.features['sym'] = self.args['symmetry']
self.features['cap'] = self.args['symmetry_cap']
self.features['cap_dssp'] = [self.dssp_dict[x.upper()] for x in 'H'*self.features['cap']]
if self.features['sym'] > 1:
print(f"Input sequence symmetry {self.features['sym']}")
assert (self.args['contigs'] in [('0'),(0),['0'],[0]] ) ^ (self.args['sequence'] in ['',None]),\
f'You are specifying contigs ({self.args["contigs"]}) and sequence ({self.args["sequence"]}) (or neither), please specify one or the other'
# initialize trb dictionary
self.features['trb_d'] = {}
if self.args['pdb'] == None and self.args['sequence'] not in ['', None]:
print('Preparing sequence input')
allowable_aas = [x for x in self.conversion[:-1]]
for x in self.args['sequence']: assert x in allowable_aas, f'Amino Acid {x} is undefinded, please only use standart 20 AAs'
self.features['seq'] = torch.tensor([self.conversion.index(x) for x in self.args['sequence']])
self.features['xyz_t'] = torch.full((1,1,len(self.args['sequence']),27,3), np.nan)
self.features['mask_str'] = torch.zeros(len(self.args['sequence'])).long()[None,:].bool()
#added check for if in partial diffusion mode will mask
if self.args['sampling_temp'] == 1.0:
self.features['mask_seq'] = torch.tensor([0 if x == 'X' else 1 for x in self.args['sequence']]).long()[None,:].bool()
else:
self.features['mask_seq'] = torch.zeros(len(self.args['sequence'])).long()[None,:].bool()
self.features['blank_mask'] = torch.ones(self.features['mask_str'].size()[-1])[None,:].bool()
self.features['idx_pdb'] = torch.tensor([i for i in range(len(self.args['sequence']))])[None,:]
conf_1d = torch.ones_like(self.features['seq'])
conf_1d[~self.features['mask_str'][0]] = 0
self.features['seq_hot'], self.features['msa'], \
self.features['msa_hot'], self.features['msa_extra_hot'], _ = MSAFeaturize_fixbb(self.features['seq'][None,:],self.loader_params)
self.features['t1d'] = TemplFeaturizeFixbb(self.features['seq'], conf_1d=conf_1d)[None,None,:]
self.features['seq_hot'] = self.features['seq_hot'].unsqueeze(dim=0)
self.features['msa'] = self.features['msa'].unsqueeze(dim=0)
self.features['msa_hot'] = self.features['msa_hot'].unsqueeze(dim=0)
self.features['msa_extra_hot'] = self.features['msa_extra_hot'].unsqueeze(dim=0)
self.max_t = int(self.args['T']*self.args['sampling_temp'])
self.features['pdb_idx'] = [('A',i+1) for i in range(len(self.args['sequence']))]
self.features['trb_d']['inpaint_str'] = self.features['mask_str'][0]
self.features['trb_d']['inpaint_seq'] = self.features['mask_seq'][0]
else:
assert not (self.args['pdb'] == None and self.args['sampling_temp'] != 1.0),\
f'You must specify a pdb if attempting to use contigs with partial diffusion, else partially diffuse sequence input'
if self.args['pdb'] == None:
self.features['parsed_pdb'] = {'seq':np.zeros((1),'int64'),
'xyz':np.zeros((1,27,3),'float32'),
'idx':np.zeros((1),'int64'),
'mask':np.zeros((1,27), bool),
'pdb_idx':['A',1]}
else:
# parse input pdb
self.features['parsed_pdb'] = parsers.parse_pdb(self.args['pdb'])
# generate contig map
self.features['rm'] = ContigMap(self.features['parsed_pdb'], self.args['contigs'],
self.args['inpaint_seq'], self.args['inpaint_str'],
self.args['length'], self.args['ref_idx'],
self.args['hal_idx'], self.args['idx_rf'],
self.args['inpaint_seq_tensor'], self.args['inpaint_str_tensor'])
self.features['mappings'] = get_mappings(self.features['rm'])
self.features['pdb_idx'] = self.features['rm'].hal
### PREPARE FEATURES DEPENDING ON TYPE OF ARGUMENTS SPECIFIED ###
# FULL DIFFUSION MODE
if self.args['trb'] == None and self.args['sampling_temp'] == 1.0:
# process contigs and generate masks
self.features['mask_str'] = torch.from_numpy(self.features['rm'].inpaint_str)[None,:]
self.features['mask_seq'] = torch.from_numpy(self.features['rm'].inpaint_seq)[None,:]
self.features['blank_mask'] = torch.ones(self.features['mask_str'].size()[-1])[None,:].bool()
seq_input = torch.from_numpy(self.features['parsed_pdb']['seq'])
xyz_input = torch.from_numpy(self.features['parsed_pdb']['xyz'][:,:,:])
self.features['xyz_t'] = torch.full((1,1,len(self.features['rm'].ref),27,3), np.nan)
self.features['xyz_t'][:,:,self.features['rm'].hal_idx0,:14,:] = xyz_input[self.features['rm'].ref_idx0,:14,:][None, None,...]
self.features['seq'] = torch.full((1,len(self.features['rm'].ref)),20).squeeze()
self.features['seq'][self.features['rm'].hal_idx0] = seq_input[self.features['rm'].ref_idx0]
# template confidence
conf_1d = torch.ones_like(self.features['seq'])*float(self.args['tmpl_conf'])
conf_1d[~self.features['mask_str'][0]] = 0 # zero confidence for places where structure is masked
seq_masktok = torch.where(self.features['seq'] == 20, 21, self.features['seq'])
# Get sequence and MSA input features
self.features['seq_hot'], self.features['msa'], \
self.features['msa_hot'], self.features['msa_extra_hot'], _ = MSAFeaturize_fixbb(seq_masktok[None,:],self.loader_params)
self.features['t1d'] = TemplFeaturizeFixbb(self.features['seq'], conf_1d=conf_1d)[None,None,:]
self.features['idx_pdb'] = torch.from_numpy(np.array(self.features['rm'].rf)).int()[None,:]
self.features['seq_hot'] = self.features['seq_hot'].unsqueeze(dim=0)
self.features['msa'] = self.features['msa'].unsqueeze(dim=0)
self.features['msa_hot'] = self.features['msa_hot'].unsqueeze(dim=0)
self.features['msa_extra_hot'] = self.features['msa_extra_hot'].unsqueeze(dim=0)
self.max_t = int(self.args['T']*self.args['sampling_temp'])
# PARTIAL DIFFUSION MODE, NO INPUT TRB
elif self.args['trb'] != None:
print('Running in partial diffusion mode . . .')
self.features['trb_d'] = np.load(self.args['trb'], allow_pickle=True)
self.features['mask_str'] = torch.from_numpy(self.features['trb_d']['inpaint_str'])[None,:]
self.features['mask_seq'] = torch.from_numpy(self.features['trb_d']['inpaint_seq'])[None,:]
self.features['blank_mask'] = torch.ones(self.features['mask_str'].size()[-1])[None,:].bool()
self.features['seq'] = torch.from_numpy(self.features['parsed_pdb']['seq'])
self.features['xyz_t'] = torch.from_numpy(self.features['parsed_pdb']['xyz'][:,:,:])[None,None,...]
if self.features['mask_seq'].shape[1] == 0:
self.features['mask_seq'] = torch.zeros(self.features['seq'].shape[0])[None].bool()
if self.features['mask_str'].shape[1] == 0:
self.features['mask_str'] = torch.zeros(self.features['xyz_t'].shape[2])[None].bool()
idx_pdb = []
chains_used = [self.features['parsed_pdb']['pdb_idx'][0][0]]
idx_jump = 0
for i,x in enumerate(self.features['parsed_pdb']['pdb_idx']):
if x[0] not in chains_used:
chains_used.append(x[0])
idx_jump += 200
idx_pdb.append(idx_jump+i)
self.features['idx_pdb'] = torch.tensor(idx_pdb)[None,:]
conf_1d = torch.ones_like(self.features['seq'])
conf_1d[~self.features['mask_str'][0]] = 0
self.features['seq_hot'], self.features['msa'], \
self.features['msa_hot'], self.features['msa_extra_hot'], _ = MSAFeaturize_fixbb(self.features['seq'][None,:],self.loader_params)
self.features['t1d'] = TemplFeaturizeFixbb(self.features['seq'], conf_1d=conf_1d)[None,None,:]
self.features['seq_hot'] = self.features['seq_hot'].unsqueeze(dim=0)
self.features['msa'] = self.features['msa'].unsqueeze(dim=0)
self.features['msa_hot'] = self.features['msa_hot'].unsqueeze(dim=0)
self.features['msa_extra_hot'] = self.features['msa_extra_hot'].unsqueeze(dim=0)
self.max_t = int(self.args['T']*self.args['sampling_temp'])
else:
print('running in partial diffusion mode, with no trb input, diffusing whole input')
self.features['seq'] = torch.from_numpy(self.features['parsed_pdb']['seq'])
self.features['xyz_t'] = torch.from_numpy(self.features['parsed_pdb']['xyz'][:,:,:])[None,None,...]
if self.args['contigs'] in [('0'),(0),['0'],[0]]:
print('no contigs given partially diffusing everything')
self.features['mask_str'] = torch.zeros(self.features['xyz_t'].shape[2]).long()[None,:].bool()
self.features['mask_seq'] = torch.zeros(self.features['seq'].shape[0]).long()[None,:].bool()
self.features['blank_mask'] = torch.ones(self.features['mask_str'].size()[-1])[None,:].bool()
else:
print('found contigs setting up masking for partial diffusion')
self.features['mask_str'] = torch.from_numpy(self.features['rm'].inpaint_str)[None,:]
self.features['mask_seq'] = torch.from_numpy(self.features['rm'].inpaint_seq)[None,:]
self.features['blank_mask'] = torch.ones(self.features['mask_str'].size()[-1])[None,:].bool()
idx_pdb = []
chains_used = [self.features['parsed_pdb']['pdb_idx'][0][0]]
idx_jump = 0
for i,x in enumerate(self.features['parsed_pdb']['pdb_idx']):
if x[0] not in chains_used:
chains_used.append(x[0])
idx_jump += 200
idx_pdb.append(idx_jump+i)
self.features['idx_pdb'] = torch.tensor(idx_pdb)[none,:]
conf_1d = torch.ones_like(self.features['seq'])
conf_1d[~self.features['mask_str'][0]] = 0
self.features['seq_hot'], self.features['msa'], \
self.features['msa_hot'], self.features['msa_extra_hot'], _ = msafeaturize_fixbb(self.features['seq'][none,:],self.loader_params)
self.features['t1d'] = templfeaturizefixbb(self.features['seq'], conf_1d=conf_1d)[none,none,:]
self.features['seq_hot'] = self.features['seq_hot'].unsqueeze(dim=0)
self.features['msa'] = self.features['msa'].unsqueeze(dim=0)
self.features['msa_hot'] = self.features['msa_hot'].unsqueeze(dim=0)
self.features['msa_extra_hot'] = self.features['msa_extra_hot'].unsqueeze(dim=0)
self.max_t = int(self.args['t']*self.args['sampling_temp'])
# set L
self.features['L'] = self.features['seq'].shape[0]
def potential_init(self):
'''
initialize potential functions being used and return list of potentails
'''
potentials = self.args['potentials'].split(',')
potential_scale = [float(x) for x in self.args['potential_scale'].split(',')]
assert len(potentials) == len(potential_scale), \
f'Please make sure number of potentials matches potential scales specified'
self.potential_list = []
for p,s in zip(potentials, potential_scale):
assert p in POTENTIALS.keys(), \
f'The potential specified: {p} , does not match into POTENTIALS dictionary in potentials.py'
print(f'Using potential: {p}')
self.potential_list.append(POTENTIALS[p](self.args, self.features, s, self.DEVICE))
self.use_potentials = True
def setup(self, init_model=True):
'''
run init model and init features to get everything prepped to go into model
'''
# initialize features
self.feature_init()
# initialize potential
if self.args['potentials'] not in ['', None]:
self.potential_init()
# make hostspot features
self.make_hotspot_features()
# make dssp features
self.make_dssp_features()
# diffuse sequence and mask features
self.features['seq'], self.features['msa_masked'], \
self.features['msa_full'], self.features['xyz_t'], self.features['t1d'], \
self.features['seq_diffused'] = diff_utils.mask_inputs(self.features['seq_hot'],
self.features['msa_hot'],
self.features['msa_extra_hot'],
self.features['xyz_t'],
self.features['t1d'],
input_seq_mask=self.features['mask_seq'],
input_str_mask=self.features['mask_str'],
input_t1dconf_mask=self.features['blank_mask'],
diffuser=self.diffuser,
t=self.max_t,
MODEL_PARAM=self.MODEL_PARAM,
hotspots=self.features['hotspot_feat'],
dssp=self.features['dssp_feat'],
v2_mode=self.v2_mode)
# move features to device
self.features['idx_pdb'] = self.features['idx_pdb'].long().to(self.DEVICE, non_blocking=True) # (B, L)
self.features['mask_str'] = self.features['mask_str'][None].to(self.DEVICE, non_blocking=True) # (B, L)
self.features['xyz_t'] = self.features['xyz_t'][None].to(self.DEVICE, non_blocking=True)
self.features['t1d'] = self.features['t1d'][None].to(self.DEVICE, non_blocking=True)
self.features['seq'] = self.features['seq'][None].type(torch.float32).to(self.DEVICE, non_blocking=True)
self.features['msa'] = self.features['msa'].type(torch.float32).to(self.DEVICE, non_blocking=True)
self.features['msa_masked'] = self.features['msa_masked'][None].type(torch.float32).to(self.DEVICE, non_blocking=True)
self.features['msa_full'] = self.features['msa_full'][None].type(torch.float32).to(self.DEVICE, non_blocking=True)
self.ti_dev = torsion_indices.to(self.DEVICE, non_blocking=True)
self.ti_flip = torsion_can_flip.to(self.DEVICE, non_blocking=True)
self.ang_ref = reference_angles.to(self.DEVICE, non_blocking=True)
self.features['xyz_prev'] = torch.clone(self.features['xyz_t'][0])
self.features['seq_diffused'] = self.features['seq_diffused'][None].to(self.DEVICE, non_blocking=True)
self.features['B'], _, self.features['N'], self.features['L'] = self.features['msa'].shape
self.features['t2d'] = xyz_to_t2d(self.features['xyz_t'])
# get alphas
self.features['alpha'], self.features['alpha_t'] = diff_utils.get_alphas(self.features['t1d'], self.features['xyz_t'],
self.features['B'], self.features['L'],
self.ti_dev, self.ti_flip, self.ang_ref)
# processing template coordinates
self.features['xyz_t'] = get_init_xyz(self.features['xyz_t'])
self.features['xyz_prev'] = get_init_xyz(self.features['xyz_prev'][:,None]).reshape(self.features['B'], self.features['L'], 27, 3)
# initialize extra features to none
self.features['xyz'] = None
self.features['pred_lddt'] = None
self.features['logit_s'] = None
self.features['logit_aa_s'] = None
self.features['best_plddt'] = 0
self.features['best_pred_lddt'] = torch.zeros_like(self.features['mask_str'])[0].float()
self.features['msa_prev'] = None
self.features['pair_prev'] = None
self.features['state_prev'] = None
def symmetrize_seq(self, x):
'''
symmetrize x according sym in features
'''
assert (self.features['L']-self.features['cap']*2) % self.features['sym'] == 0, f'symmetry does not match for input length'
assert x.shape[0] == self.features['L'], f'make sure that dimension 0 of input matches to L'
n_cap = torch.clone(x[:self.features['cap']])
c_cap = torch.clone(x[-self.features['cap']+1:])
sym_x = torch.clone(x[self.features['cap']:self.features['sym']]).repeat(self.features['sym'],1)
return torch.cat([n_cap,sym_x,c_cap], dim=0)
def predict_x(self):
'''
take step using X_t-1 features to predict Xo
'''
self.features['seq'], \
self.features['xyz'], \
self.features['pred_lddt'], \
self.features['logit_s'], \
self.features['logit_aa_s'], \
self.features['alpha'], \
self.features['msa_prev'], \
self.features['pair_prev'], \
self.features['state_prev'] \
= diff_utils.take_step_nostate(self.model,
self.features['msa_masked'],
self.features['msa_full'],
self.features['seq'],
self.features['t1d'],
self.features['t2d'],
self.features['idx_pdb'],
self.args['n_cycle'],
self.features['xyz_prev'],
self.features['alpha'],
self.features['xyz_t'],
self.features['alpha_t'],
self.features['seq_diffused'],
self.features['msa_prev'],
self.features['pair_prev'],
self.features['state_prev'])
def self_condition_seq(self):
'''
get previous logits and set at t1d template
'''
self.features['t1d'][:,:,:,:21] = self.features['logit_aa_s'][0,:21,:].permute(1,0)
def self_condition_str_scheduled(self):
'''
unmask random fraction of residues according to timestep
'''
print('self_conditioning on strcuture')
xyz_prev_template = torch.clone(self.features['xyz'])[None]
self_conditioning_mask = torch.rand(self.features['L']) < self.diffuser.alphas_cumprod[t]
xyz_prev_template[:,:,~self_conditioning_mask] = float('nan')
xyz_prev_template[:,:,self.features['mask_str'][0][0]] = float('nan')
xyz_prev_template[:,:,:,3:] = float('nan')
t2d_sc = xyz_to_t2d(xyz_prev_template)
xyz_t_sc = torch.zeros_like(self.features['xyz_t'][:,:1])
xyz_t_sc[:,:,:,:3] = xyz_prev_template[:,:,:,:3]
xyz_t_sc[:,:,:,3:] = float('nan')
t1d_sc = torch.clone(self.features['t1d'][:,:1])
t1d_sc[:,:,~self_conditioning_mask] = 0
t1d_sc[:,:,mask_str[0][0]] = 0
self.features['t1d'] = torch.cat([self.features['t1d'][:,:1],t1d_sc], dim=1)
self.features['t2d'] = torch.cat([self.features['t2d'][:,:1],t2d_sc], dim=1)
self.features['xyz_t'] = torch.cat([self.features['xyz_t'][:,:1],xyz_t_sc], dim=1)
self.features['alpha'], self.features['alpha_t'] = diff_utils.get_alphas(self.features['t1d'], self.features['xyz_t'],
self.features['B'], self.features['L'],
self.ti_dev, self.ti_flip, self.ang_ref)
self.features['xyz_t'] = get_init_xyz(self.features['xyz_t'])
def self_condition_str(self):
'''
conditioining on strucutre in NAR way
'''
print("conditioning on structure for NAR structure noising")
xyz_t_str_sc = torch.zeros_like(self.features['xyz_t'][:,:1])
xyz_t_str_sc[:,:,:,:3] = torch.clone(self.features['xyz'])[None]
xyz_t_str_sc[:,:,:,3:] = float('nan')
t2d_str_sc = xyz_to_t2d(self.features['xyz_t'])
t1d_str_sc = torch.clone(self.features['t1d'])
self.features['xyz_t'] = torch.cat([self.features['xyz_t'],xyz_t_str_sc], dim=1)
self.features['t2d'] = torch.cat([self.features['t2d'],t2d_str_sc], dim=1)
self.features['t1d'] = torch.cat([self.features['t1d'],t1d_str_sc], dim=1)
def save_step(self):
'''
add step to trajectory dictionary
'''
self.trajectory[f'step{self.t}'] = (self.features['xyz'].squeeze().detach().cpu(),
self.features['logit_aa_s'][0,:21,:].permute(1,0).detach().cpu(),
self.features['seq_diffused'][0,:,:21].detach().cpu())
def noise_x(self):
'''
get X_t-1 from predicted Xo
'''
# sample x_t-1
self.features['post_mean'] = self.diffuser.q_sample(self.features['seq_out'], self.t, DEVICE=self.DEVICE)
if self.features['sym'] > 1:
self.features['post_mean'] = self.symmetrize_seq(self.features['post_mean'])
# update seq and masks
self.features['seq_diffused'][0,~self.features['mask_seq'][0],:21] = self.features['post_mean'][~self.features['mask_seq'][0],...]
self.features['seq_diffused'][0,:,21] = 0.0
# did not know we were clamping seq
self.features['seq_diffused'] = torch.clamp(self.features['seq_diffused'], min=-3, max=3)
# match other features to seq diffused
self.features['seq'] = torch.argmax(self.features['seq_diffused'], dim=-1)[None]
self.features['msa_masked'][:,:,:,:,:22] = self.features['seq_diffused']
self.features['msa_masked'][:,:,:,:,22:44] = self.features['seq_diffused']
self.features['msa_full'][:,:,:,:,:22] = self.features['seq_diffused']
self.features['t1d'][:1,:,:,22] = 1-int(self.t)/self.args['T']
def apply_potentials(self):
'''
apply potentials
'''
grads = torch.zeros_like(self.features['seq_out'])
for p in self.potential_list:
grads += p.get_gradients(self.features['seq_out'])
self.features['seq_out'] += (grads/len(self.potential_list))
def generate_sample(self):
'''
sample from the model
this function runs the full sampling loop
'''
# setup example
self.setup()
# start time
self.start_time = time.time()
# set up dictionary to save at each step in trajectory
self.trajectory = {}
# set out prefix
self.out_prefix = self.args['out']+f'_{self.design_num:06}'
print(f'Generating sample {self.design_num:06} ...')
# main sampling loop
for j in range(self.max_t):
self.t = torch.tensor(self.max_t-j-1).to(self.DEVICE)
# run features through the model to get X_o prediction
self.predict_x()
# save step
if self.args['save_all_steps']:
self.save_step()
# get seq out
self.features['seq_out'] = torch.permute(self.features['logit_aa_s'][0], (1,0))
# save best seq
if self.features['pred_lddt'][~self.features['mask_seq']].mean().item() > self.features['best_plddt']:
self.features['best_seq'] = torch.argmax(torch.clone(self.features['seq_out']), dim=-1)
self.features['best_pred_lddt'] = torch.clone(self.features['pred_lddt'])
self.features['best_xyz'] = torch.clone(self.features['xyz'])
self.features['best_plddt'] = self.features['pred_lddt'][~self.features['mask_seq']].mean().item()
# self condition on sequence
self.self_condition_seq()
# self condition on structure
if self.args['scheduled_str_cond']:
self.self_condition_str_scheduled()
if self.args['struc_cond_sc']:
self.self_condition_str()
# sequence alterations
if self.args['softmax_seqout']:
self.features['seq_out'] = torch.softmax(self.features['seq_out'],dim=-1)*2-1
if self.args['clamp_seqout']:
self.features['seq_out'] = torch.clamp(self.features['seq_out'],
min=-((1/self.diffuser.alphas_cumprod[t])*0.25+5),
max=((1/self.diffuser.alphas_cumprod[t])*0.25+5))
# apply potentials
if self.use_potentials:
self.apply_potentials()
# noise to X_t-1
if self.t != 0:
self.noise_x()
print(''.join([self.conversion[i] for i in torch.argmax(self.features['seq_out'],dim=-1)]))
print (" TIMESTEP [%02d/%02d] | current PLDDT: %.4f << >> best PLDDT: %.4f"%(
self.t+1, self.args['T'], self.features['pred_lddt'][~self.features['mask_seq']].mean().item(),
self.features['best_pred_lddt'][~self.features['mask_seq']].mean().item()))
# record time
self.delta_time = time.time() - self.start_time
# save outputs
self.save_outputs()
# increment design num
self.design_num += 1
print(f'Finished design {self.out_prefix} in {self.delta_time/60:.2f} minutes.')
def save_outputs(self):
'''
save the outputs from the model
'''
# save trajectory
if self.args['save_all_steps']:
fname = f'{self.out_prefix}_trajectory.pt'
torch.save(self.trajecotry, fname)
# get items from best plddt step
if self.args['save_best_plddt']:
self.features['seq'] = torch.clone(self.features['best_seq'])
self.features['pred_lddt'] = torch.clone(self.features['best_pred_lddt'])
self.features['xyz'] = torch.clone(self.features['best_xyz'])
# get chain IDs
if (self.args['sampling_temp'] == 1.0 and self.args['trb'] == None) or (self.args['sequence'] not in ['',None]):
chain_ids = [i[0] for i in self.features['pdb_idx']]
elif self.args['dump_pdb']:
chain_ids = [i[0] for i in self.features['parsed_pdb']['pdb_idx']]
# write output pdb
fname = self.out_prefix + '.pdb'
if len(self.features['seq'].shape) == 2:
self.features['seq'] = self.features['seq'].squeeze()
write_pdb(fname,
self.features['seq'].type(torch.int64),
self.features['xyz'].squeeze(),
Bfacts=self.features['pred_lddt'].squeeze(),
chains=chain_ids)
if self.args['dump_trb']:
self.save_trb()
if self.args['save_args']:
self.save_args()
def save_trb(self):
'''
save trb file
'''
lddt = self.features['pred_lddt'].squeeze().cpu().numpy()
strmasktemp = self.features['mask_str'].squeeze().cpu().numpy()
partial_lddt = [lddt[i] for i in range(np.shape(strmasktemp)[0]) if strmasktemp[i] == 0]
trb = {}
trb['lddt'] = lddt
trb['inpaint_lddt'] = partial_lddt
trb['contigs'] = self.args['contigs']
trb['device'] = self.DEVICE
trb['time'] = self.delta_time
trb['args'] = self.args
if self.args['sequence'] != None:
for key, value in self.features['trb_d'].items():
trb[key] = value
else:
for key, value in self.features['mappings'].items():
if key in self.features['trb_d'].keys():
trb[key] = self.features['trb_d'][key]
else:
if len(value) > 0:
if type(value) == list and type(value[0]) != tuple:
value=np.array(value)
trb[key] = value
with open(f'{self.out_prefix}.trb','wb') as f_out:
pickle.dump(trb, f_out)
def save_args(self):
'''
save args
'''
with open(f'{self.out_prefix}_args.json','w') as f_out:
json.dump(self.args, f_out)
#####################################################################
###################### science is cool ##############################
#####################################################################
# making a custom sampler class for HuggingFace app
class HuggingFace_sampler(SEQDIFF_sampler):
def model_init(self):
'''
get model set up and choose checkpoint
'''
if self.args['checkpoint'] == None:
self.args['checkpoint'] = DEFAULT_CKPT
self.MODEL_PARAM['d_t1d'] = self.args['d_t1d']
# check to make sure checkpoint chosen exists
if not os.path.exists(self.args['checkpoint']):
print('WARNING: couldn\'t find checkpoint')
self.ckpt = torch.load(self.args['checkpoint'], map_location=self.DEVICE)
# check to see if [loader_param, model_param, loss_param] is in checkpoint
# if so then you are using v2 of inference with t2d bug fixed
self.v2_mode = False
if 'model_param' in self.ckpt.keys():
print('You are running a new v2 model switching into v2 inference mode')
self.v2_mode = True
for k in self.MODEL_PARAM.keys():
if k in self.ckpt['model_param'].keys():
self.MODEL_PARAM[k] = self.ckpt['model_param'][k]
else:
print(f'no match for {k} in loaded model params')
# make model and load checkpoint
print('Loading model checkpoint...')
self.model = RoseTTAFoldModule(**self.MODEL_PARAM).to(self.DEVICE)
model_state = self.ckpt['model_state_dict']
self.model.load_state_dict(model_state, strict=False)
self.model.eval()
print('Successfully loaded model checkpoint')
def generate_sample(self):
'''
sample from the model
this function runs the full sampling loop
'''
# setup example
self.setup()
# start time
self.start_time = time.time()
# set up dictionary to save at each step in trajectory
self.trajectory = {}
# set out prefix
print(f'Generating sample {self.out_prefix} ...')
# main sampling loop
for j in range(self.max_t):
self.t = torch.tensor(self.max_t-j-1).to(self.DEVICE)
# run features through the model to get X_o prediction
self.predict_x()
# save step
if self.args['save_all_steps']:
self.save_step()
# get seq out
self.features['seq_out'] = torch.permute(self.features['logit_aa_s'][0], (1,0))
# save best seq
if self.features['pred_lddt'].mean().item() > self.features['best_plddt']:
self.features['best_seq'] = torch.argmax(torch.clone(self.features['seq_out']), dim=-1)
self.features['best_pred_lddt'] = torch.clone(self.features['pred_lddt'])
self.features['best_xyz'] = torch.clone(self.features['xyz'])
self.features['best_plddt'] = self.features['pred_lddt'][~self.features['mask_seq']].mean().item()
# self condition on sequence
self.self_condition_seq()
# self condition on structure
if self.args['scheduled_str_cond']:
self.self_condition_str_scheduled()
if self.args['struc_cond_sc']:
self.self_condition_str()
# sequence alterations
if self.args['softmax_seqout']:
self.features['seq_out'] = torch.softmax(self.features['seq_out'],dim=-1)*2-1
if self.args['clamp_seqout']:
self.features['seq_out'] = torch.clamp(self.features['seq_out'],
min=-((1/self.diffuser.alphas_cumprod[t])*0.25+5),
max=((1/self.diffuser.alphas_cumprod[t])*0.25+5))
# apply potentials
if self.use_potentials:
self.apply_potentials()
# noise to X_t-1
if self.t != 0:
self.noise_x()
print(''.join([self.conversion[i] for i in torch.argmax(self.features['seq_out'],dim=-1)]))
print (" TIMESTEP [%02d/%02d] | current PLDDT: %.4f << >> best PLDDT: %.4f"%(
self.t+1, self.args['T'], self.features['pred_lddt'][~self.features['mask_seq']].mean().item(),
self.features['best_pred_lddt'][~self.features['mask_seq']].mean().item()))
# record time
self.delta_time = time.time() - self.start_time
# save outputs
self.save_outputs()
# increment design num
self.design_num += 1
print(f'Finished design {self.out_prefix} in {self.delta_time/60:.2f} minutes.')
def take_step_get_outputs(self, j):
self.t = torch.tensor(self.max_t-j-1).to(self.DEVICE)
# run features through the model to get X_o prediction
self.predict_x()
# save step
if self.args['save_all_steps']:
self.save_step()
# get seq out
self.features['seq_out'] = torch.permute(self.features['logit_aa_s'][0], (1,0))
# save best seq
if self.features['pred_lddt'].mean().item() > self.features['best_plddt']:
self.features['best_seq'] = torch.argmax(torch.clone(self.features['seq_out']), dim=-1)
self.features['best_pred_lddt'] = torch.clone(self.features['pred_lddt'])
self.features['best_xyz'] = torch.clone(self.features['xyz'])
self.features['best_plddt'] = self.features['pred_lddt'].mean().item()
# WRITE OUTPUT TO GET TEMPORARY PDB TO DISPLAY
if self.t != 0:
self.features['seq'] = torch.argmax(torch.clone(self.features['seq_out']), dim=-1)
else:
# prepare final output
if self.args['save_args']:
self.save_args()
# get items from best plddt step
if self.args['save_best_plddt']:
self.features['seq'] = torch.clone(self.features['best_seq'])
self.features['pred_lddt'] = torch.clone(self.features['best_pred_lddt'])
self.features['xyz'] = torch.clone(self.features['best_xyz'])
# get chain IDs
if (self.args['sampling_temp'] == 1.0 and self.args['trb'] == None) or (self.args['sequence'] not in ['',None]):
chain_ids = [i[0] for i in self.features['pdb_idx']]
elif self.args['dump_pdb']:
chain_ids = [i[0] for i in self.features['parsed_pdb']['pdb_idx']]
# write output pdb
if len(self.features['seq'].shape) == 2:
self.features['seq'] = self.features['seq'].squeeze()
fname = f'{self.out_prefix}.pdb'
write_pdb(fname, self.features['seq'].type(torch.int64),
self.features['xyz'].squeeze(),
Bfacts=self.features['pred_lddt'].squeeze(),
chains=chain_ids)
aa_seq = ''.join([self.conversion[x] for x in self.features['seq'].tolist()])
# self condition on sequence
self.self_condition_seq()
# self condition on structure
if self.args['scheduled_str_cond']:
self.self_condition_str_scheduled()
if self.args['struc_cond_sc']:
self.self_condition_str()
# sequence alterations
if self.args['softmax_seqout']:
self.features['seq_out'] = torch.softmax(self.features['seq_out'],dim=-1)*2-1
if self.args['clamp_seqout']:
self.features['seq_out'] = torch.clamp(self.features['seq_out'],
min=-((1/self.diffuser.alphas_cumprod[t])*0.25+5),
max=((1/self.diffuser.alphas_cumprod[t])*0.25+5))
# apply potentials
if self.use_potentials:
self.apply_potentials()
# noise to X_t-1
if self.t != 0:
self.noise_x()
print(''.join([self.conversion[i] for i in torch.argmax(self.features['seq_out'],dim=-1)]))
print (" TIMESTEP [%02d/%02d] | current PLDDT: %.4f << >> best PLDDT: %.4f"%(
self.t+1, self.args['T'], self.features['pred_lddt'][~self.features['mask_seq']].mean().item(),
self.features['best_pred_lddt'][~self.features['mask_seq']].mean().item()))
return aa_seq, fname, self.features['pred_lddt'].mean().item()
def get_outputs(self):
aa_seq = ''.join([self.conversion[x] for x in self.features['seq'].tolist()])
path_to_pdb = self.out_prefix+'.pdb'
return aa_seq, path_to_pdb, self.features['pred_lddt'].mean().item()
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