Spaces:
Build error
Build error
File size: 6,206 Bytes
c145e8a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
import numpy as np
import torch
# ============================================================
def get_pair_dist(a, b):
"""calculate pair distances between two sets of points
Parameters
----------
a,b : pytorch tensors of shape [batch,nres,3]
store Cartesian coordinates of two sets of atoms
Returns
-------
dist : pytorch tensor of shape [batch,nres,nres]
stores paitwise distances between atoms in a and b
"""
dist = torch.cdist(a, b, p=2)
return dist
# ============================================================
def get_ang(a, b, c):
"""calculate planar angles for all consecutive triples (a[i],b[i],c[i])
from Cartesian coordinates of three sets of atoms a,b,c
Parameters
----------
a,b,c : pytorch tensors of shape [batch,nres,3]
store Cartesian coordinates of three sets of atoms
Returns
-------
ang : pytorch tensor of shape [batch,nres]
stores resulting planar angles
"""
v = a - b
w = c - b
v = v / torch.norm(v, dim=-1, keepdim=True)
w = w / torch.norm(w, dim=-1, keepdim=True)
# this is not stable at the poles
#vw = torch.sum(v*w, dim=-1)
#ang = torch.acos(vw)
# this is better
# https://math.stackexchange.com/questions/1143354/numerically-stable-method-for-angle-between-3d-vectors/1782769
y = torch.norm(v-w,dim=-1)
x = torch.norm(v+w,dim=-1)
ang = 2*torch.atan2(y, x)
return ang
# ============================================================
def get_dih(a, b, c, d):
"""calculate dihedral angles for all consecutive quadruples (a[i],b[i],c[i],d[i])
given Cartesian coordinates of four sets of atoms a,b,c,d
Parameters
----------
a,b,c,d : pytorch tensors of shape [batch,nres,3]
store Cartesian coordinates of four sets of atoms
Returns
-------
dih : pytorch tensor of shape [batch,nres]
stores resulting dihedrals
"""
b0 = a - b
b1r = c - b
b2 = d - c
b1 = b1r/torch.norm(b1r, dim=-1, keepdim=True)
v = b0 - torch.sum(b0*b1, dim=-1, keepdim=True)*b1
w = b2 - torch.sum(b2*b1, dim=-1, keepdim=True)*b1
x = torch.sum(v*w, dim=-1)
y = torch.sum(torch.cross(b1,v,dim=-1)*w, dim=-1)
ang = torch.atan2(y, x)
return ang
# ============================================================
def xyz_to_c6d(xyz, params):
"""convert cartesian coordinates into 2d distance
and orientation maps
Parameters
----------
xyz : pytorch tensor of shape [batch,3,nres,3]
stores Cartesian coordinates of backbone N,Ca,C atoms
Returns
-------
c6d : pytorch tensor of shape [batch,nres,nres,4]
stores stacked dist,omega,theta,phi 2D maps
"""
batch = xyz.shape[0]
nres = xyz.shape[2]
# three anchor atoms
N = xyz[:,0]
Ca = xyz[:,1]
C = xyz[:,2]
# recreate Cb given N,Ca,C
b = Ca - N
c = C - Ca
a = torch.cross(b, c, dim=-1)
Cb = -0.58273431*a + 0.56802827*b - 0.54067466*c + Ca
# 6d coordinates order: (dist,omega,theta,phi)
c6d = torch.zeros([batch,nres,nres,4],dtype=xyz.dtype,device=xyz.device)
dist = get_pair_dist(Cb,Cb)
dist[torch.isnan(dist)] = 999.9
c6d[...,0] = dist + 999.9*torch.eye(nres,device=xyz.device)[None,...]
b,i,j = torch.where(c6d[...,0]<params['DMAX'])
c6d[b,i,j,torch.full_like(b,1)] = get_dih(Ca[b,i], Cb[b,i], Cb[b,j], Ca[b,j])
c6d[b,i,j,torch.full_like(b,2)] = get_dih(N[b,i], Ca[b,i], Cb[b,i], Cb[b,j])
c6d[b,i,j,torch.full_like(b,3)] = get_ang(Ca[b,i], Cb[b,i], Cb[b,j])
# fix long-range distances
c6d[...,0][c6d[...,0]>=params['DMAX']] = 999.9
return c6d
# ============================================================
def c6d_to_bins(c6d,params):
"""bin 2d distance and orientation maps
"""
dstep = (params['DMAX'] - params['DMIN']) / params['DBINS']
astep = 2.0*np.pi / params['ABINS']
dbins = torch.linspace(params['DMIN']+dstep, params['DMAX'], params['DBINS'],dtype=c6d.dtype,device=c6d.device)
ab360 = torch.linspace(-np.pi+astep, np.pi, params['ABINS'],dtype=c6d.dtype,device=c6d.device)
ab180 = torch.linspace(astep, np.pi, params['ABINS']//2,dtype=c6d.dtype,device=c6d.device)
db = torch.bucketize(c6d[...,0].contiguous(),dbins)
ob = torch.bucketize(c6d[...,1].contiguous(),ab360)
tb = torch.bucketize(c6d[...,2].contiguous(),ab360)
pb = torch.bucketize(c6d[...,3].contiguous(),ab180)
ob[db==params['DBINS']] = params['ABINS']
tb[db==params['DBINS']] = params['ABINS']
pb[db==params['DBINS']] = params['ABINS']//2
return torch.stack([db,ob,tb,pb],axis=-1).to(torch.uint8)
# ============================================================
def dist_to_bins(dist,params):
"""bin 2d distance maps
"""
dstep = (params['DMAX'] - params['DMIN']) / params['DBINS']
db = torch.round((dist-params['DMIN']-dstep/2)/dstep)
db[db<0] = 0
db[db>params['DBINS']] = params['DBINS']
return db.long()
# ============================================================
def c6d_to_bins2(c6d,params):
"""bin 2d distance and orientation maps
(alternative slightly simpler version)
"""
dstep = (params['DMAX'] - params['DMIN']) / params['DBINS']
astep = 2.0*np.pi / params['ABINS']
db = torch.round((c6d[...,0]-params['DMIN']-dstep/2)/dstep)
ob = torch.round((c6d[...,1]+np.pi-astep/2)/astep)
tb = torch.round((c6d[...,2]+np.pi-astep/2)/astep)
pb = torch.round((c6d[...,3]-astep/2)/astep)
# put all d<dmin into one bin
db[db<0] = 0
# synchronize no-contact bins
db[db>params['DBINS']] = params['DBINS']
ob[db==params['DBINS']] = params['ABINS']
tb[db==params['DBINS']] = params['ABINS']
pb[db==params['DBINS']] = params['ABINS']//2
return torch.stack([db,ob,tb,pb],axis=-1).long()
# ============================================================
def get_cb(N,Ca,C):
"""recreate Cb given N,Ca,C"""
b = Ca - N
c = C - Ca
a = torch.cross(b, c, dim=-1)
Cb = -0.58273431*a + 0.56802827*b - 0.54067466*c + Ca
return Cb
|