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d766458 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | import jax
import jax.numpy as jnp
import numpy as np
from colabdesign.shared.utils import copy_dict
from colabdesign.af.alphafold.model import config
def setup(self, crop_len=128, crop_mode="slide", crop_iter=5):
def set_crop(crop_len=128, crop_mode="slide", crop_iter=5):
assert crop_mode in ["slide","roll","pair"]
assert crop_len < sum(self._lengths)
assert self._args["copies"] == 1 or self._args["repeat"]
assert self.protocol not in ["partial","binder"]
self._args["crop"] = {"len":crop_len, "mode":crop_mode, "iter":crop_iter}
#################################################################
# function to apply to inputs
#################################################################
def pre_callback(inputs, opt):
def crop_feat(feat, pos):
'''crop features to specified [pos]itions'''
if feat is None: return None
def find(x,k):
i = []
for j,y in enumerate(x):
if y == k: i.append(j)
return i
shapes = config.CONFIG.data.eval.feat
NUM_RES = "num residues placeholder"
idx = {k:find(v,NUM_RES) for k,v in shapes.items()}
new_feat = copy_dict(feat)
for k in new_feat.keys():
if k in ["batch","prev"]:
new_feat[k] = crop_feat(feat[k], pos)
if k in idx:
for i in idx[k]: new_feat[k] = jnp.take(new_feat[k], pos, i)
return new_feat
p = opt["crop_pos"]
inputs.update(crop_feat(inputs, p))
#################################################################
# function to apply to outputs
#################################################################
def post_callback(aux, opt):
length = sum(self._lengths)
def uncrop_feat(x, pos, pair=False):
'''uncrop features'''
if pair:
p1, p2 = pos[:,None], pos[None,:]
return jnp.zeros((length,length)+x.shape[2:]).at[p1,p2].set(x)
else:
return jnp.zeros((length,)+x.shape[1:]).at[pos].set(x)
p,x = opt["crop_pos"], aux["prev"]
full_aux = {"prev":{
"prev_pos": uncrop_feat(x["prev_pos"], p),
"prev_pair": uncrop_feat(x["prev_pair"], p, pair=True),
"prev_msa_first_row": uncrop_feat(x["prev_msa_first_row"], p)
},
}
aux.update(full_aux)
#################################################################
# function to apply before design step
#################################################################
def pre_design_callback(self):
def update_pos():
c = self._args["crop"]
L = sum(self._lengths)
if c["mode"] == "slide":
i = np.random.randint(0,(L-c["len"])+1)
self.opt["crop_pos"] = np.arange(i,i+c["len"])
if c["mode"] == "roll":
i = np.random.randint(0,L)
self.opt["crop_pos"] = np.sort(np.roll(np.arange(L),L-i)[:c["len"]])
if c["mode"] == "pair":
# pick random pair of interactig crops
max_L = c["len"] // 2
# pick first crop
i_range = np.append(np.arange(0,(L-2*max_L)+1),np.arange(max_L,(L-max_L)+1))
i = np.random.choice(i_range)
# pick second crop
j_range = np.append(np.arange(0,(i-max_L)+1),np.arange(i+max_L,(L-max_L)+1))
if hasattr(self,"_cmap"):
# if contact map defined, bias to interacting pairs
w = np.array([self._cmap[i:i+max_L,j:j+max_L].sum() for j in j_range]) + 1e-8
j = np.random.choice(j_range, p=w/w.sum())
else:
j = np.random.choice(j_range)
self.opt["crop_pos"] = np.sort(np.append(np.arange(i,i+max_L),np.arange(j,j+max_L)))
if "crop" not in self._tmp:
self._tmp["crop"] = {"k":self._k}
update_pos()
if self._k != self._tmp["crop"]["k"]:
if (self._k % self._args["crop"]["iter"]) == 0:
update_pos()
self._tmp["crop"]["k"] = self._k
if hasattr(self,"aux"):
for k in ["cmap","pae","plddt","atom_positions"]:
self._tmp["crop"][k] = self.aux[k]
#################################################################
# function to apply after design step
#################################################################
def post_design_callback(self):
# uncrop/accumulate features
L = sum(self._lengths)
def uncrop_feat(x, pos, pair=False):
if pair:
y = np.full((L,L)+x.shape[2:],np.nan)
y[pos[:,None],pos[None,:]] = x
else:
y = np.full((L,)+x.shape[1:],np.nan)
y[pos] = x
return y
# uncrop features
a, p = self.aux, self.opt["crop_pos"]
vs = {k:uncrop_feat(a[k],p,pair=True) for k in ["cmap","pae"]}
vs.update({k:uncrop_feat(a[k],p) for k in ["plddt","atom_positions"]})
# accumulate features
for k,v in vs.items():
w = self._tmp["crop"].get(k,v)
w = np.where(np.isnan(w),v,w)
vs[k] = np.where(np.isnan(v),w,(v+w)/2)
self.aux.update(vs)
#################################################################
# SETUP
#################################################################
if hasattr(self,"set_crop"):
self.set_crop(crop_len, crop_mode, crop_iter)
else:
self.set_crop = set_crop
self.set_crop(crop_len, crop_mode, crop_iter)
# add distances
if self.protocol == "fixbb":
cb_atoms = self._pdb["cb_feat"]["atoms"]
cb_atoms[self._pdb["cb_feat"]["mask"] == 0,:] = np.nan
cb_dist = np.sqrt(np.square(cb_atoms[:,None] - cb_atoms[None,:]).sum(-1))
self._cmap = cb_dist < self.opt["cmap_cutoff"]
# populate callbacks
self._callbacks["model"]["pre"].append(pre_callback)
self._callbacks["model"]["post"].append(post_callback)
self._callbacks["design"]["pre"].append(pre_design_callback)
self._callbacks["design"]["post"].append(post_design_callback) |