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import torch
from icecream import ic
import random
import numpy as np
from kinematics import get_init_xyz
import torch.nn as nn
from util_module import ComputeAllAtomCoords
from util import *
from inpainting_util import MSAFeaturize_fixbb, TemplFeaturizeFixbb, lddt_unbin
from kinematics import xyz_to_t2d
def mask_inputs(seq, msa_masked, msa_full, xyz_t, t1d, input_seq_mask=None,
input_str_mask=None, input_t1dconf_mask=None, diffuser=None, t=None,
MODEL_PARAM=None, hotspots=None, dssp=None, v2_mode=False):
"""
JG - adapted slightly for the inference case
Parameters:
seq (torch.tensor, required): (I,L) integer sequence
msa_masked (torch.tensor, required): (I,N_short,L,48)
msa_full (torch,.tensor, required): (I,N_long,L,25)
xyz_t (torch,tensor): (T,L,27,3) template crds BEFORE they go into get_init_xyz
t1d (torch.tensor, required): (I,L,22) this is the t1d before tacking on the chi angles
str_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where structure is masked at False positions
seq_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where seq is masked at False positions
t1d_24: is there an extra dimension to input structure confidence?
diffuser: diffuser class
t: time step
NOTE: in the MSA, the order is 20aa, 1x unknown, 1x mask token. We set the masked region to 22 (masked).
For the t1d, this has 20aa, 1x unkown, and 1x template conf. Here, we set the masked region to 21 (unknown).
This, we think, makes sense, as the template in normal RF training does not perfectly correspond to the MSA.
"""
assert diffuser != None, 'please choose a diffuser'
###########
seq = seq[0,:1]
msa_masked = msa_masked[0,:1]
msa_full = msa_full[0,:1]
t1d = t1d[0]
xyz_t = xyz_t[0]
seq_mask = input_seq_mask[0]
######################
###sequence diffusion###
######################
"""
#muate some percentage of sequence to have model be able to mutate residues later in denoising trajectory
if True:
masked_values=input_seq_mask[0].nonzero()[:,0]
print(masked_values)
mut_p=math.floor(masked_values.shape[0]*.05)
print(mut_p)
mutate_indices = torch.randperm(len(masked_values))[:mut_p]
print(mutate_indices)
for i in range(len(mutate_indices)):
seq[0,masked_values[mutate_indices[i]]] = torch.randint(0, 21, (1,))
"""
str_mask = input_str_mask[0]
x_0 = torch.nn.functional.one_hot(seq[0,...],num_classes=22).float()*2-1
#ic(seq_mask)
seq_diffused = diffuser.q_sample(x_0,torch.tensor([t-1]),mask=seq_mask)
#seq_diffused = torch.clamp(seq_diffused, min=-1, max=1)
seq_tmp=torch.argmax(seq_diffused,axis=-1).to(device=seq.device)
seq=seq_tmp.repeat(seq.shape[0], 1)
###################
###msa diffusion###
###################
### msa_masked ###
#ic(msa_masked.shape)
B,N,L,_=msa_masked.shape
msa_masked[:,0,:,:22] = seq_diffused
x_0_msa = msa_masked[0,1:,:,:22].float()*2-1
msa_seq_mask = seq_mask.unsqueeze(0).repeat(N-1, 1)
msa_diffused = diffuser.q_sample(x_0_msa,torch.tensor([t-1]),mask=msa_seq_mask)
#msa_diffused = torch.clamp(msa_diffused, min=-1, max=1)
msa_masked[:,1:,:,:22] = torch.clone(msa_diffused)
# index 44/45 is insertion/deletion
# index 43 is the masked token NOTE check this
# index 42 is the unknown token
msa_masked[:,0,:,22:44] = seq_diffused
msa_masked[:,1:,:,22:44] = msa_diffused
# insertion/deletion stuff
msa_masked[:,0,~seq_mask,44:46] = 0
### msa_full ###
################
#msa_full[:,0,:,:22] = seq_diffused
#make msa_full same size as msa_masked
msa_full = msa_full[:,:msa_masked.shape[1],:,:]
msa_full[:,0,:,:22] = seq_diffused
msa_full[:,1:,:,:22] = msa_diffused
### t1d ###
###########
# NOTE: adjusting t1d last dim (confidence) from sequence mask
t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],2)).float()), -1).to(seq.device)
t1d[:,:,:21] = seq_diffused[...,:21]
#t1d[:,:,21] *= input_t1dconf_mask
#set diffused conf to 0 and everything else to 1
t1d[:,~seq_mask,21] = 0.0
t1d[:,seq_mask,21] = 1.0
t1d[:1,:,22] = 1-t/diffuser.num_timesteps
t1d[:,~str_mask,23] = 0.0
t1d[:,str_mask,23] = 1.0
# EXPAND t1d to match model params
if MODEL_PARAM['d_t1d'] == 29:
## added t1d features ##
# 24 -- dssp helix
# 25 -- dssp sheet
# 26 -- dssp loop
# 27 -- dssp mask
# 28 -- hotspot resi on target
t1d = torch.cat((t1d,torch.zeros(t1d.shape[0],t1d.shape[1],5)),dim=-1)
t1d[:,:,24:28] = dssp
t1d[:,:,28] = hotspots
t1d[:,str_mask,24:27] = 0.0
t1d[:,str_mask,27] = 1.0
xyz_t = get_init_xyz(xyz_t[None])
xyz_t = xyz_t[0]
xyz_t[:,~seq_mask,3:,:] = float('nan')
# Structure masking
xyz_t[:,~str_mask,:,:] = float('nan')
if not v2_mode:
xyz_t = get_init_xyz(xyz_t[None])
xyz_t = xyz_t[0]
assert torch.sum(torch.isnan(xyz_t[:,:,:3,:]))==0
return seq, msa_masked, msa_full, xyz_t, t1d, seq_diffused
conversion = 'ARNDCQEGHILKMFPSTWYVX-'
def take_step(model, msa, msa_extra, seq, t1d, t2d, idx_pdb, N_cycle, xyz_prev, alpha, xyz_t,
alpha_t, seq_diffused, msa_prev, pair_prev, state_prev):
"""
Single step in the diffusion process
"""
compute_allatom_coords=ComputeAllAtomCoords().to(seq.device)
#ic(msa.shape)
B, _, N, L, _ = msa.shape
with torch.no_grad():
with torch.cuda.amp.autocast(True):
for i_cycle in range(N_cycle-1):
msa_prev, pair_prev, xyz_prev, state_prev, alpha = model(msa[:,0],
msa_extra[:,0],
seq[:,0], xyz_prev,
idx_pdb,
seq1hot=seq_diffused,
t1d=t1d, t2d=t2d,
xyz_t=xyz_t, alpha_t=alpha_t,
msa_prev=msa_prev,
pair_prev=pair_prev,
state_prev=state_prev,
return_raw=True)
logit_s, logit_aa_s, logits_exp, xyz_prev, pred_lddt, msa_prev, pair_prev, state_prev, alpha = model(msa[:,0],
msa_extra[:,0],
seq[:,0], xyz_prev,
idx_pdb,
seq1hot=seq_diffused,
t1d=t1d, t2d=t2d, xyz_t=xyz_t, alpha_t=alpha_t,
msa_prev=msa_prev,
pair_prev=pair_prev,
state_prev=state_prev,
return_infer=True)
logit_aa_s_msa = torch.clone(logit_aa_s)
logit_aa_s = logit_aa_s.reshape(B,-1,N,L)[:,:,0,:]
logit_aa_s = logit_aa_s.reshape(B,-1,L)
seq_out = torch.argmax(logit_aa_s, dim=-2)
pred_lddt_unbinned = lddt_unbin(pred_lddt)
_, xyz_prev = compute_allatom_coords(seq_out, xyz_prev, alpha)
if N>1:
return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, logit_aa_s_msa, alpha, msa_prev, pair_prev, state_prev
else:
return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, alpha, msa_prev, pair_prev, state_prev
def take_step_nostate(model, msa, msa_extra, seq, t1d, t2d, idx_pdb, N_cycle, xyz_prev, alpha, xyz_t,
alpha_t, seq_diffused, msa_prev, pair_prev, state_prev):
"""
Single step in the diffusion process, with no conditioning on state
"""
compute_allatom_coords=ComputeAllAtomCoords().to(seq.device)
msa_prev = None
pair_prev = None
state_prev = None
B, _, N, L, _ = msa.shape
with torch.no_grad():
with torch.cuda.amp.autocast(True):
for i_cycle in range(N_cycle-1):
msa_prev, pair_prev, xyz_prev, state_prev, alpha = model(msa[:,0],
msa_extra[:,0],
seq[:,0], xyz_prev,
idx_pdb,
seq1hot=seq_diffused,
t1d=t1d, t2d=t2d,
xyz_t=xyz_t, alpha_t=alpha_t,
msa_prev=msa_prev,
pair_prev=pair_prev,
state_prev=state_prev,
return_raw=True)
logit_s, logit_aa_s, logits_exp, xyz_prev, pred_lddt, msa_prev, pair_prev, state_prev, alpha = model(msa[:,0],
msa_extra[:,0],
seq[:,0], xyz_prev,
idx_pdb,
seq1hot=seq_diffused,
t1d=t1d, t2d=t2d, xyz_t=xyz_t, alpha_t=alpha_t,
msa_prev=msa_prev,
pair_prev=pair_prev,
state_prev=state_prev,
return_infer=True)
logit_aa_s_msa = torch.clone(logit_aa_s)
logit_aa_s = logit_aa_s.reshape(B,-1,N,L)[:,:,0,:]
logit_aa_s = logit_aa_s.reshape(B,-1,L)
seq_out = torch.argmax(logit_aa_s, dim=-2)
pred_lddt_unbinned = lddt_unbin(pred_lddt)
_, xyz_prev = compute_allatom_coords(seq_out, xyz_prev, alpha)
if N>1:
return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, logit_aa_s_msa, alpha, msa_prev, pair_prev, state_prev
else:
return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, alpha, msa_prev, pair_prev, state_prev
def get_alphas(t1d, xyz_t, B, L, ti_dev, ti_flip, ang_ref):
# get torsion angles from templates
seq_tmp = t1d[...,:21].argmax(dim=-1).reshape(-1,L)
alpha, _, alpha_mask, _ = get_torsions(xyz_t.reshape(-1,L,27,3), seq_tmp, ti_dev, ti_flip, ang_ref)
alpha_mask = torch.logical_and(alpha_mask, ~torch.isnan(alpha[...,0]))
alpha[torch.isnan(alpha)] = 0.0
alpha = alpha.reshape(B,-1,L,10,2)
alpha_mask = alpha_mask.reshape(B,-1,L,10,1)
alpha_t = torch.cat((alpha, alpha_mask), dim=-1).reshape(B, -1, L, 30)
return alpha, alpha_t
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