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import jax.numpy as jnp
import numpy as np
import re
import copy
import random
import os
import joblib
from .modules import RunModel
from colabdesign.shared.prep import prep_pos
from colabdesign.shared.utils import Key, copy_dict
# borrow some stuff from AfDesign
from colabdesign.af.prep import prep_pdb
from colabdesign.af.alphafold.common import protein, residue_constants
aa_order = residue_constants.restype_order
order_aa = {b:a for a,b in aa_order.items()}
from scipy.special import softmax, log_softmax
class mk_mpnn_model():
def __init__(self, model_name="v_48_020",
backbone_noise=0.0, dropout=0.0,
seed=None, verbose=False, weights="original"): # weights can be set to either original or soluble
# load model
if weights == "original":
from .weights import __file__ as mpnn_path
elif weights == "soluble":
from .weights_soluble import __file__ as mpnn_path
else:
raise ValueError(f'Invalid value {weights} supplied for weights. Value must be either "original" or "soluble".')
path = os.path.join(os.path.dirname(mpnn_path), f'{model_name}.pkl')
checkpoint = joblib.load(path)
config = {'num_letters': 21,
'node_features': 128,
'edge_features': 128,
'hidden_dim': 128,
'num_encoder_layers': 3,
'num_decoder_layers': 3,
'augment_eps': backbone_noise,
'k_neighbors': checkpoint['num_edges'],
'dropout': dropout}
self._model = RunModel(config)
self._model.params = jax.tree_util.tree_map(np.array, checkpoint['model_state_dict'])
self._setup()
self.set_seed(seed)
self._num = 1
self._inputs = {}
self._tied_lengths = False
def prep_inputs(self, pdb_filename=None, chain=None, homooligomer=False,
ignore_missing=True, fix_pos=None, inverse=False,
rm_aa=None, verbose=False, **kwargs):
'''get inputs from input pdb'''
pdb = prep_pdb(pdb_filename, chain, ignore_missing=ignore_missing)
atom_idx = tuple(residue_constants.atom_order[k] for k in ["N","CA","C","O"])
chain_idx = np.concatenate([[n]*l for n,l in enumerate(pdb["lengths"])])
self._lengths = pdb["lengths"]
L = sum(self._lengths)
self._inputs = {"X": pdb["batch"]["all_atom_positions"][:,atom_idx],
"mask": pdb["batch"]["all_atom_mask"][:,1],
"S": pdb["batch"]["aatype"],
"residue_idx": pdb["residue_index"],
"chain_idx": chain_idx,
"lengths": np.array(self._lengths),
"bias": np.zeros((L,20))}
if rm_aa is not None:
for aa in rm_aa.split(","):
self._inputs["bias"][...,aa_order[aa]] -= 1e6
if fix_pos is not None:
p = prep_pos(fix_pos, **pdb["idx"])["pos"]
if inverse:
p = np.delete(np.arange(L),p)
self._inputs["fix_pos"] = p
self._inputs["bias"][p] = 1e7 * np.eye(21)[self._inputs["S"]][p,:20]
if homooligomer:
assert min(self._lengths) == max(self._lengths)
self._tied_lengths = True
self._len = self._lengths[0]
else:
self._tied_lengths = False
self._len = sum(self._lengths)
self.pdb = pdb
if verbose:
print("lengths", self._lengths)
if "fix_pos" in self._inputs:
print("the following positions will be fixed:")
print(self._inputs["fix_pos"])
def get_af_inputs(self, af):
'''get inputs from alphafold model'''
self._lengths = af._lengths
self._len = af._len
self._inputs["residue_idx"] = af._inputs["residue_index"]
self._inputs["chain_idx"] = af._inputs["asym_id"]
self._inputs["lengths"] = np.array(self._lengths)
# set bias
L = sum(self._lengths)
self._inputs["bias"] = np.zeros((L,20))
self._inputs["bias"][-af._len:] = af._inputs["bias"]
if "offset" in af._inputs:
self._inputs["offset"] = af._inputs["offset"]
if "batch" in af._inputs:
atom_idx = tuple(residue_constants.atom_order[k] for k in ["N","CA","C","O"])
batch = af._inputs["batch"]
self._inputs["X"] = batch["all_atom_positions"][:,atom_idx]
self._inputs["mask"] = batch["all_atom_mask"][:,1]
self._inputs["S"] = batch["aatype"]
# fix positions
if af.protocol == "binder":
p = np.arange(af._target_len)
else:
p = af.opt.get("fix_pos",None)
if p is not None:
self._inputs["fix_pos"] = p
self._inputs["bias"][p] = 1e7 * np.eye(21)[self._inputs["S"]][p,:20]
# tie positions
if af._args["homooligomer"]:
assert min(self._lengths) == max(self._lengths)
self._tied_lengths = True
else:
self._tied_lengths = False
def sample(self, num=1, batch=1, temperature=0.1, rescore=False, **kwargs):
'''sample sequence'''
O = []
for _ in range(num):
O.append(self.sample_parallel(batch, temperature, rescore, **kwargs))
return jax.tree_util.tree_map(lambda *x:np.concatenate(x,0),*O)
def sample_parallel(self, batch=10, temperature=0.1, rescore=False, **kwargs):
'''sample new sequence(s) in parallel'''
I = copy_dict(self._inputs)
I.update(kwargs)
key = I.pop("key",self.key())
keys = jax.random.split(key,batch)
O = self._sample_parallel(keys, I, temperature, self._tied_lengths)
if rescore:
O = self._rescore_parallel(keys, I, O["S"], O["decoding_order"])
O = jax.tree_util.tree_map(np.array, O)
# process outputs to human-readable form
O.update(self._get_seq(O))
O.update(self._get_score(I,O))
return O
def _get_seq(self, O):
''' one_hot to amino acid sequence '''
def split_seq(seq):
if len(self._lengths) > 1:
seq = "".join(np.insert(list(seq),np.cumsum(self._lengths[:-1]),"/"))
if self._tied_lengths:
seq = seq.split("/")[0]
return seq
seqs, S = [], O["S"].argmax(-1)
if S.ndim == 1: S = [S]
for s in S:
seq = "".join([order_aa[a] for a in s])
seq = split_seq(seq)
seqs.append(seq)
return {"seq": np.array(seqs)}
def _get_score(self, I, O):
''' logits to score/sequence_recovery '''
mask = I["mask"].copy()
if "fix_pos" in I:
mask[I["fix_pos"]] = 0
log_q = log_softmax(O["logits"],-1)[...,:20]
q = softmax(O["logits"][...,:20],-1)
if "S" in O:
S = O["S"][...,:20]
score = -(S * log_q).sum(-1)
seqid = S.argmax(-1) == self._inputs["S"]
else:
score = -(q * log_q).sum(-1)
seqid = np.zeros_like(score)
score = (score * mask).sum(-1) / (mask.sum() + 1e-8)
seqid = (seqid * mask).sum(-1) / (mask.sum() + 1e-8)
return {"score":score, "seqid":seqid}
def score(self, seq=None, **kwargs):
'''score sequence'''
I = copy_dict(self._inputs)
if seq is not None:
p = np.arange(I["S"].shape[0])
if self._tied_lengths and len(seq) == self._lengths[0]:
seq = seq * len(self._lengths)
if "fix_pos" in I and len(seq) == (I["S"].shape[0] - I["fix_pos"].shape[0]):
p = np.delete(p,I["fix_pos"])
I["S"][p] = np.array([aa_order.get(aa,-1) for aa in seq])
I.update(kwargs)
key = I.pop("key",self.key())
O = jax.tree_util.tree_map(np.array, self._score(**I, key=key))
O.update(self._get_score(I,O))
return O
def get_logits(self, **kwargs):
'''get logits'''
return self.score(**kwargs)["logits"]
def get_unconditional_logits(self, **kwargs):
L = self._inputs["X"].shape[0]
kwargs["ar_mask"] = np.zeros((L,L))
return self.score(**kwargs)["logits"]
def set_seed(self, seed=None):
np.random.seed(seed=seed)
self.key = Key(seed=seed).get
def _setup(self):
def _score(X, mask, residue_idx, chain_idx, key, **kwargs):
I = {'X': X,
'mask': mask,
'residue_idx': residue_idx,
'chain_idx': chain_idx}
I.update(kwargs)
# define decoding order
if "decoding_order" not in I:
key, sub_key = jax.random.split(key)
randn = jax.random.uniform(sub_key, (I["X"].shape[0],))
randn = jnp.where(I["mask"], randn, randn+1)
if "fix_pos" in I: randn = randn.at[I["fix_pos"]].add(-1)
I["decoding_order"] = randn.argsort()
for k in ["S","bias"]:
if k in I: I[k] = _aa_convert(I[k])
O = self._model.score(self._model.params, key, I)
O["S"] = _aa_convert(O["S"], rev=True)
O["logits"] = _aa_convert(O["logits"], rev=True)
return O
def _sample(X, mask, residue_idx, chain_idx, key,
temperature=0.1, tied_lengths=False, **kwargs):
I = {'X': X,
'mask': mask,
'residue_idx': residue_idx,
'chain_idx': chain_idx,
'temperature': temperature}
I.update(kwargs)
# define decoding order
if "decoding_order" in I:
if I["decoding_order"].ndim == 1:
I["decoding_order"] = I["decoding_order"][:,None]
else:
key, sub_key = jax.random.split(key)
randn = jax.random.uniform(sub_key, (I["X"].shape[0],))
randn = jnp.where(I["mask"], randn, randn+1)
if "fix_pos" in I: randn = randn.at[I["fix_pos"]].add(-1)
if tied_lengths:
copies = I["lengths"].shape[0]
decoding_order_tied = randn.reshape(copies,-1).mean(0).argsort()
I["decoding_order"] = jnp.arange(I["X"].shape[0]).reshape(copies,-1).T[decoding_order_tied]
else:
I["decoding_order"] = randn.argsort()[:,None]
for k in ["S","bias"]:
if k in I: I[k] = _aa_convert(I[k])
O = self._model.sample(self._model.params, key, I)
O["S"] = _aa_convert(O["S"], rev=True)
O["logits"] = _aa_convert(O["logits"], rev=True)
return O
self._score = jax.jit(_score)
self._sample = jax.jit(_sample, static_argnames=["tied_lengths"])
def _sample_parallel(key, inputs, temperature, tied_lengths=False):
inputs.pop("temperature",None)
inputs.pop("key",None)
return _sample(**inputs, key=key, temperature=temperature, tied_lengths=tied_lengths)
fn = jax.vmap(_sample_parallel, in_axes=[0,None,None,None])
self._sample_parallel = jax.jit(fn, static_argnames=["tied_lengths"])
def _rescore_parallel(key, inputs, S, decoding_order):
inputs.pop("S",None)
inputs.pop("decoding_order",None)
inputs.pop("key",None)
return _score(**inputs, key=key, S=S, decoding_order=decoding_order)
fn = jax.vmap(_rescore_parallel, in_axes=[0,None,0,0])
self._rescore_parallel = jax.jit(fn)
#######################################################################################
def _aa_convert(x, rev=False):
mpnn_alphabet = 'ACDEFGHIKLMNPQRSTVWYX'
af_alphabet = 'ARNDCQEGHILKMFPSTWYVX'
if x is None:
return x
else:
if rev:
return x[...,tuple(mpnn_alphabet.index(k) for k in af_alphabet)]
else:
x = jax.nn.one_hot(x,21) if jnp.issubdtype(x.dtype, jnp.integer) else x
if x.shape[-1] == 20:
x = jnp.pad(x,[[0,0],[0,1]])
return x[...,tuple(af_alphabet.index(k) for k in mpnn_alphabet)] |