utils/model/util.py

import sys

import numpy as np
import torch

import scipy.sparse

from chemical import *
from scoring import *

def th_ang_v(ab,bc,eps:float=1e-8):
    def th_norm(x,eps:float=1e-8):
        return x.square().sum(-1,keepdim=True).add(eps).sqrt()
    def th_N(x,alpha:float=0):
        return x/th_norm(x).add(alpha)
    ab, bc = th_N(ab),th_N(bc)
    cos_angle = torch.clamp( (ab*bc).sum(-1), -1, 1)
    sin_angle = torch.sqrt(1-cos_angle.square() + eps)
    dih = torch.stack((cos_angle,sin_angle),-1)
    return dih

def th_dih_v(ab,bc,cd):
    def th_cross(a,b):
        a,b = torch.broadcast_tensors(a,b)
        return torch.cross(a,b, dim=-1)
    def th_norm(x,eps:float=1e-8):
        return x.square().sum(-1,keepdim=True).add(eps).sqrt()
    def th_N(x,alpha:float=0):
        return x/th_norm(x).add(alpha)

    ab, bc, cd = th_N(ab),th_N(bc),th_N(cd)
    n1 = th_N( th_cross(ab,bc) )
    n2 = th_N( th_cross(bc,cd) )
    sin_angle = (th_cross(n1,bc)*n2).sum(-1)
    cos_angle = (n1*n2).sum(-1)
    dih = torch.stack((cos_angle,sin_angle),-1)
    return dih

def th_dih(a,b,c,d):
    return th_dih_v(a-b,b-c,c-d)

# More complicated version splits error in CA-N and CA-C (giving more accurate CB position)
# It returns the rigid transformation from local frame to global frame
def rigid_from_3_points(N, Ca, C, non_ideal=False, eps=1e-8):
    #N, Ca, C - [B,L, 3]
    #R - [B,L, 3, 3], det(R)=1, inv(R) = R.T, R is a rotation matrix
    B,L = N.shape[:2]
    
    v1 = C-Ca
    v2 = N-Ca
    e1 = v1/(torch.norm(v1, dim=-1, keepdim=True)+eps)
    u2 = v2-(torch.einsum('bli, bli -> bl', e1, v2)[...,None]*e1)
    e2 = u2/(torch.norm(u2, dim=-1, keepdim=True)+eps)
    e3 = torch.cross(e1, e2, dim=-1)
    R = torch.cat([e1[...,None], e2[...,None], e3[...,None]], axis=-1) #[B,L,3,3] - rotation matrix
    
    if non_ideal:
        v2 = v2/(torch.norm(v2, dim=-1, keepdim=True)+eps)
        cosref = torch.sum(e1*v2, dim=-1) # cosine of current N-CA-C bond angle
        costgt = cos_ideal_NCAC.item()
        cos2del = torch.clamp( cosref*costgt + torch.sqrt((1-cosref*cosref)*(1-costgt*costgt)+eps), min=-1.0, max=1.0 )
        cosdel = torch.sqrt(0.5*(1+cos2del)+eps)
        sindel = torch.sign(costgt-cosref) * torch.sqrt(1-0.5*(1+cos2del)+eps)
        Rp = torch.eye(3, device=N.device).repeat(B,L,1,1)
        Rp[:,:,0,0] = cosdel
        Rp[:,:,0,1] = -sindel
        Rp[:,:,1,0] = sindel
        Rp[:,:,1,1] = cosdel
    
        R = torch.einsum('blij,bljk->blik', R,Rp)

    return R, Ca

def get_tor_mask(seq, torsion_indices, mask_in=None):
    B,L = seq.shape[:2]
    tors_mask = torch.ones((B,L,10), dtype=torch.bool, device=seq.device)
    tors_mask[...,3:7] = torsion_indices[seq,:,-1] > 0
    tors_mask[:,0,1] = False
    tors_mask[:,-1,0] = False

    # mask for additional angles
    tors_mask[:,:,7] = seq!=aa2num['GLY']
    tors_mask[:,:,8] = seq!=aa2num['GLY']
    tors_mask[:,:,9] = torch.logical_and( seq!=aa2num['GLY'], seq!=aa2num['ALA'] )
    tors_mask[:,:,9] = torch.logical_and( tors_mask[:,:,9], seq!=aa2num['UNK'] )
    tors_mask[:,:,9] = torch.logical_and( tors_mask[:,:,9], seq!=aa2num['MAS'] )

    if mask_in != None:
        # mask for missing atoms
        # chis
        ti0 = torch.gather(mask_in,2,torsion_indices[seq,:,0])
        ti1 = torch.gather(mask_in,2,torsion_indices[seq,:,1])
        ti2 = torch.gather(mask_in,2,torsion_indices[seq,:,2])
        ti3 = torch.gather(mask_in,2,torsion_indices[seq,:,3])
        is_valid = torch.stack((ti0, ti1, ti2, ti3), dim=-2).all(dim=-1)
        tors_mask[...,3:7] = torch.logical_and(tors_mask[...,3:7], is_valid)
        tors_mask[:,:,7] = torch.logical_and(tors_mask[:,:,7], mask_in[:,:,4]) # CB exist?
        tors_mask[:,:,8] = torch.logical_and(tors_mask[:,:,8], mask_in[:,:,4]) # CB exist?
        tors_mask[:,:,9] = torch.logical_and(tors_mask[:,:,9], mask_in[:,:,5]) # XG exist?

    return tors_mask

def get_torsions(xyz_in, seq, torsion_indices, torsion_can_flip, ref_angles, mask_in=None):
    B,L = xyz_in.shape[:2]
    
    tors_mask = get_tor_mask(seq, torsion_indices, mask_in)
    
    # torsions to restrain to 0 or 180degree
    tors_planar = torch.zeros((B, L, 10), dtype=torch.bool, device=xyz_in.device)
    tors_planar[:,:,5] = seq == aa2num['TYR'] # TYR chi 3 should be planar

    # idealize given xyz coordinates before computing torsion angles
    xyz = xyz_in.clone()
    Rs, Ts = rigid_from_3_points(xyz[...,0,:],xyz[...,1,:],xyz[...,2,:])
    Nideal = torch.tensor([-0.5272, 1.3593, 0.000], device=xyz_in.device)
    Cideal = torch.tensor([1.5233, 0.000, 0.000], device=xyz_in.device)
    xyz[...,0,:] = torch.einsum('brij,j->bri', Rs, Nideal) + Ts
    xyz[...,2,:] = torch.einsum('brij,j->bri', Rs, Cideal) + Ts

    torsions = torch.zeros( (B,L,10,2), device=xyz.device )
    # avoid undefined angles for H generation
    torsions[:,0,1,0] = 1.0
    torsions[:,-1,0,0] = 1.0

    # omega
    torsions[:,:-1,0,:] = th_dih(xyz[:,:-1,1,:],xyz[:,:-1,2,:],xyz[:,1:,0,:],xyz[:,1:,1,:])
    # phi
    torsions[:,1:,1,:] = th_dih(xyz[:,:-1,2,:],xyz[:,1:,0,:],xyz[:,1:,1,:],xyz[:,1:,2,:])
    # psi
    torsions[:,:,2,:] = -1 * th_dih(xyz[:,:,0,:],xyz[:,:,1,:],xyz[:,:,2,:],xyz[:,:,3,:])

    # chis
    ti0 = torch.gather(xyz,2,torsion_indices[seq,:,0,None].repeat(1,1,1,3))
    ti1 = torch.gather(xyz,2,torsion_indices[seq,:,1,None].repeat(1,1,1,3))
    ti2 = torch.gather(xyz,2,torsion_indices[seq,:,2,None].repeat(1,1,1,3))
    ti3 = torch.gather(xyz,2,torsion_indices[seq,:,3,None].repeat(1,1,1,3))
    torsions[:,:,3:7,:] = th_dih(ti0,ti1,ti2,ti3)
    
    # CB bend
    NC = 0.5*( xyz[:,:,0,:3] + xyz[:,:,2,:3] )
    CA = xyz[:,:,1,:3]
    CB = xyz[:,:,4,:3]
    t = th_ang_v(CB-CA,NC-CA)
    t0 = ref_angles[seq][...,0,:]
    torsions[:,:,7,:] = torch.stack( 
        (torch.sum(t*t0,dim=-1),t[...,0]*t0[...,1]-t[...,1]*t0[...,0]),
        dim=-1 )
    
    # CB twist
    NCCA = NC-CA
    NCp = xyz[:,:,2,:3] - xyz[:,:,0,:3]
    NCpp = NCp - torch.sum(NCp*NCCA, dim=-1, keepdim=True)/ torch.sum(NCCA*NCCA, dim=-1, keepdim=True) * NCCA
    t = th_ang_v(CB-CA,NCpp)
    t0 = ref_angles[seq][...,1,:]
    torsions[:,:,8,:] = torch.stack( 
        (torch.sum(t*t0,dim=-1),t[...,0]*t0[...,1]-t[...,1]*t0[...,0]),
        dim=-1 )

    # CG bend
    CG = xyz[:,:,5,:3]
    t = th_ang_v(CG-CB,CA-CB)
    t0 = ref_angles[seq][...,2,:]
    torsions[:,:,9,:] = torch.stack( 
        (torch.sum(t*t0,dim=-1),t[...,0]*t0[...,1]-t[...,1]*t0[...,0]),
        dim=-1 )
    
    mask0 = torch.isnan(torsions[...,0]).nonzero()
    mask1 = torch.isnan(torsions[...,1]).nonzero()
    torsions[mask0[:,0],mask0[:,1],mask0[:,2],0] = 1.0
    torsions[mask1[:,0],mask1[:,1],mask1[:,2],1] = 0.0

    # alt chis
    torsions_alt = torsions.clone()
    torsions_alt[torsion_can_flip[seq,:]] *= -1

    return torsions, torsions_alt, tors_mask, tors_planar

def get_tips(xyz, seq):
    B,L = xyz.shape[:2]

    xyz_tips = torch.gather(xyz, 2, tip_indices.to(xyz.device)[seq][:,:,None,None].expand(-1,-1,-1,3)).reshape(B, L, 3)
    mask = ~(torch.isnan(xyz_tips[:,:,0]))
    if torch.isnan(xyz_tips).any(): # replace NaN tip atom with virtual Cb atom
        # 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    

        xyz_tips = torch.where(torch.isnan(xyz_tips), Cb, xyz_tips)
    return xyz_tips, mask

# process ideal frames
def make_frame(X, Y):
    Xn = X / torch.linalg.norm(X)
    Y = Y - torch.dot(Y, Xn) * Xn
    Yn = Y / torch.linalg.norm(Y)
    Z = torch.cross(Xn,Yn)
    Zn =  Z / torch.linalg.norm(Z)

    return torch.stack((Xn,Yn,Zn), dim=-1)

def cross_product_matrix(u):
    B, L = u.shape[:2]
    matrix = torch.zeros((B, L, 3, 3), device=u.device)
    matrix[:,:,0,1] = -u[...,2]
    matrix[:,:,0,2] = u[...,1]
    matrix[:,:,1,0] = u[...,2]
    matrix[:,:,1,2] = -u[...,0]
    matrix[:,:,2,0] = -u[...,1]
    matrix[:,:,2,1] = u[...,0]
    return matrix

# writepdb
def writepdb(filename, atoms, seq, idx_pdb=None, bfacts=None):
    f = open(filename,"w")
    ctr = 1
    scpu = seq.cpu().squeeze()
    atomscpu = atoms.cpu().squeeze()
    if bfacts is None:
        bfacts = torch.zeros(atomscpu.shape[0])
    if idx_pdb is None:
        idx_pdb = 1 + torch.arange(atomscpu.shape[0])

    Bfacts = torch.clamp( bfacts.cpu(), 0, 1)
    for i,s in enumerate(scpu):
        if (len(atomscpu.shape)==2):
            f.write ("%-6s%5s %4s %3s %s%4d    %8.3f%8.3f%8.3f%6.2f%6.2f\n"%(
                    "ATOM", ctr, " CA ", num2aa[s], 
                    "A", idx_pdb[i], atomscpu[i,0], atomscpu[i,1], atomscpu[i,2],
                    1.0, Bfacts[i] ) )
            ctr += 1
        elif atomscpu.shape[1]==3:
            for j,atm_j in enumerate([" N  "," CA "," C  "]):
                f.write ("%-6s%5s %4s %3s %s%4d    %8.3f%8.3f%8.3f%6.2f%6.2f\n"%(
                        "ATOM", ctr, atm_j, num2aa[s], 
                        "A", idx_pdb[i], atomscpu[i,j,0], atomscpu[i,j,1], atomscpu[i,j,2],
                        1.0, Bfacts[i] ) )
                ctr += 1
        else: 
            natoms = atomscpu.shape[1]
            if (natoms!=14 and natoms!=27):
                print ('bad size!', atoms.shape)
                assert(False)
            atms = aa2long[s]
            # his prot hack
            if (s==8 and torch.linalg.norm( atomscpu[i,9,:]-atomscpu[i,5,:] ) < 1.7):
                atms = (
                    " N  "," CA "," C  "," O  "," CB "," CG "," NE2"," CD2"," CE1"," ND1",
                      None,  None,  None,  None," H  "," HA ","1HB ","2HB "," HD2"," HE1",
                    " HD1",  None,  None,  None,  None,  None,  None) # his_d

            for j,atm_j in enumerate(atms):
                if (j<natoms and atm_j is not None): # and not torch.isnan(atomscpu[i,j,:]).any()):
                    f.write ("%-6s%5s %4s %3s %s%4d    %8.3f%8.3f%8.3f%6.2f%6.2f\n"%(
                        "ATOM", ctr, atm_j, num2aa[s], 
                        "A", idx_pdb[i], atomscpu[i,j,0], atomscpu[i,j,1], atomscpu[i,j,2],
                        1.0, Bfacts[i] ) )
                    ctr += 1


# resolve tip atom indices
tip_indices = torch.full((22,), 0)
for i in range(22):
    tip_atm = aa2tip[i]
    atm_long = aa2long[i]
    tip_indices[i] = atm_long.index(tip_atm)

# resolve torsion indices
torsion_indices = torch.full((22,4,4),0)
torsion_can_flip = torch.full((22,10),False,dtype=torch.bool)
for i in range(22):
    i_l, i_a = aa2long[i], aa2longalt[i]
    for j in range(4):
        if torsions[i][j] is None:
            continue
        for k in range(4):
            a = torsions[i][j][k]
            torsion_indices[i,j,k] = i_l.index(a)
            if (i_l.index(a) != i_a.index(a)):
                torsion_can_flip[i,3+j] = True ##bb tors never flip
# HIS is a special case
torsion_can_flip[8,4]=False

# build the mapping from atoms in the full rep (Nx27) to the "alternate" rep
allatom_mask = torch.zeros((22,27), dtype=torch.bool)
long2alt = torch.zeros((22,27), dtype=torch.long)
for i in range(22):
    i_l, i_lalt = aa2long[i],  aa2longalt[i]
    for j,a in enumerate(i_l):
        if (a is None):
            long2alt[i,j] = j
        else:
            long2alt[i,j] = i_lalt.index(a)
            allatom_mask[i,j] = True

# bond graph traversal
num_bonds = torch.zeros((22,27,27), dtype=torch.long)
for i in range(22):
    num_bonds_i = np.zeros((27,27))
    for (bnamei,bnamej) in aabonds[i]:
        bi,bj = aa2long[i].index(bnamei),aa2long[i].index(bnamej)
        num_bonds_i[bi,bj] = 1
    num_bonds_i = scipy.sparse.csgraph.shortest_path (num_bonds_i,directed=False)
    num_bonds_i[num_bonds_i>=4] = 4
    num_bonds[i,...] = torch.tensor(num_bonds_i)


# LJ/LK scoring parameters
ljlk_parameters = torch.zeros((22,27,5), dtype=torch.float)
lj_correction_parameters = torch.zeros((22,27,4), dtype=bool) # donor/acceptor/hpol/disulf
for i in range(22):
    for j,a in enumerate(aa2type[i]):
        if (a is not None):
            ljlk_parameters[i,j,:] = torch.tensor( type2ljlk[a] )
            lj_correction_parameters[i,j,0] = (type2hb[a]==HbAtom.DO)+(type2hb[a]==HbAtom.DA)
            lj_correction_parameters[i,j,1] = (type2hb[a]==HbAtom.AC)+(type2hb[a]==HbAtom.DA)
            lj_correction_parameters[i,j,2] = (type2hb[a]==HbAtom.HP)
            lj_correction_parameters[i,j,3] = (a=="SH1" or a=="HS")

# hbond scoring parameters
def donorHs(D,bonds,atoms):
    dHs = []
    for (i,j) in bonds:
        if (i==D):
            idx_j = atoms.index(j)
            if (idx_j>=14):  # if atom j is a hydrogen
                dHs.append(idx_j)
        if (j==D):
            idx_i = atoms.index(i)
            if (idx_i>=14):  # if atom j is a hydrogen
                dHs.append(idx_i)
    assert (len(dHs)>0)
    return dHs

def acceptorBB0(A,hyb,bonds,atoms):
    if (hyb == HbHybType.SP2):
        for (i,j) in bonds:
            if (i==A):
                B = atoms.index(j)
                if (B<14):
                    break
            if (j==A):
                B = atoms.index(i)
                if (B<14):
                    break
        for (i,j) in bonds:
            if (i==atoms[B]):
                B0 = atoms.index(j)
                if (B0<14):
                    break
            if (j==atoms[B]):
                B0 = atoms.index(i)
                if (B0<14):
                    break
    elif (hyb == HbHybType.SP3 or hyb == HbHybType.RING):
        for (i,j) in bonds:
            if (i==A):
                B = atoms.index(j)
                if (B<14):
                    break
            if (j==A):
                B = atoms.index(i)
                if (B<14):
                    break
        for (i,j) in bonds:
            if (i==A and j!=atoms[B]):
                B0 = atoms.index(j)
                break
            if (j==A and i!=atoms[B]):
                B0 = atoms.index(i)
                break

    return B,B0


hbtypes = torch.full((22,27,3),-1, dtype=torch.long) # (donortype, acceptortype, acchybtype)
hbbaseatoms = torch.full((22,27,2),-1, dtype=torch.long) # (B,B0) for acc; (D,-1) for don
hbpolys = torch.zeros((HbDonType.NTYPES,HbAccType.NTYPES,3,15)) # weight,xmin,xmax,ymin,ymax,c9,...,c0

for i in range(22):
    for j,a in enumerate(aa2type[i]):
        if (a in type2dontype):
            j_hs = donorHs(aa2long[i][j],aabonds[i],aa2long[i])
            for j_h in j_hs:
                hbtypes[i,j_h,0] = type2dontype[a]
                hbbaseatoms[i,j_h,0] = j
        if (a in type2acctype):
            j_b, j_b0 = acceptorBB0(aa2long[i][j],type2hybtype[a],aabonds[i],aa2long[i])
            hbtypes[i,j,1] = type2acctype[a]
            hbtypes[i,j,2] = type2hybtype[a]
            hbbaseatoms[i,j,0] = j_b
            hbbaseatoms[i,j,1] = j_b0

for i in range(HbDonType.NTYPES):
    for j in range(HbAccType.NTYPES):
        weight = dontype2wt[i]*acctype2wt[j]

        pdist,pbah,pahd = hbtypepair2poly[(i,j)]
        xrange,yrange,coeffs = hbpolytype2coeffs[pdist]
        hbpolys[i,j,0,0] = weight
        hbpolys[i,j,0,1:3] = torch.tensor(xrange)
        hbpolys[i,j,0,3:5] = torch.tensor(yrange)
        hbpolys[i,j,0,5:] = torch.tensor(coeffs)
        xrange,yrange,coeffs = hbpolytype2coeffs[pahd]
        hbpolys[i,j,1,0] = weight
        hbpolys[i,j,1,1:3] = torch.tensor(xrange)
        hbpolys[i,j,1,3:5] = torch.tensor(yrange)
        hbpolys[i,j,1,5:] = torch.tensor(coeffs)
        xrange,yrange,coeffs = hbpolytype2coeffs[pbah]
        hbpolys[i,j,2,0] = weight
        hbpolys[i,j,2,1:3] = torch.tensor(xrange)
        hbpolys[i,j,2,3:5] = torch.tensor(yrange)
        hbpolys[i,j,2,5:] = torch.tensor(coeffs)

# kinematic parameters
base_indices = torch.full((22,27),0, dtype=torch.long)
xyzs_in_base_frame = torch.ones((22,27,4))
RTs_by_torsion = torch.eye(4).repeat(22,7,1,1)
reference_angles = torch.ones((22,3,2))

for i in range(22):
    i_l = aa2long[i]
    for name, base, coords in ideal_coords[i]:
        idx = i_l.index(name)
        base_indices[i,idx] = base
        xyzs_in_base_frame[i,idx,:3] = torch.tensor(coords)

    # omega frame
    RTs_by_torsion[i,0,:3,:3] = torch.eye(3)
    RTs_by_torsion[i,0,:3,3] = torch.zeros(3)

    # phi frame
    RTs_by_torsion[i,1,:3,:3] = make_frame(
        xyzs_in_base_frame[i,0,:3] - xyzs_in_base_frame[i,1,:3],
        torch.tensor([1.,0.,0.])
    )
    RTs_by_torsion[i,1,:3,3] = xyzs_in_base_frame[i,0,:3]

    # psi frame
    RTs_by_torsion[i,2,:3,:3] = make_frame(
        xyzs_in_base_frame[i,2,:3] - xyzs_in_base_frame[i,1,:3],
        xyzs_in_base_frame[i,1,:3] - xyzs_in_base_frame[i,0,:3]
    )
    RTs_by_torsion[i,2,:3,3] = xyzs_in_base_frame[i,2,:3]

    # chi1 frame
    if torsions[i][0] is not None:
        a0,a1,a2 = torsion_indices[i,0,0:3]
        RTs_by_torsion[i,3,:3,:3] = make_frame(
            xyzs_in_base_frame[i,a2,:3]-xyzs_in_base_frame[i,a1,:3],
            xyzs_in_base_frame[i,a0,:3]-xyzs_in_base_frame[i,a1,:3],
        )
        RTs_by_torsion[i,3,:3,3] = xyzs_in_base_frame[i,a2,:3]

    # chi2~4 frame
    for j in range(1,4):
        if torsions[i][j] is not None:
            a2 = torsion_indices[i,j,2]
            if ((i==18 and j==2) or (i==8 and j==2)):  # TYR CZ-OH & HIS CE1-HE1 a special case
                a0,a1 = torsion_indices[i,j,0:2]
                RTs_by_torsion[i,3+j,:3,:3] = make_frame(
                    xyzs_in_base_frame[i,a2,:3]-xyzs_in_base_frame[i,a1,:3],
                    xyzs_in_base_frame[i,a0,:3]-xyzs_in_base_frame[i,a1,:3] )
            else:
                RTs_by_torsion[i,3+j,:3,:3] = make_frame(
                    xyzs_in_base_frame[i,a2,:3],
                    torch.tensor([-1.,0.,0.]), )
            RTs_by_torsion[i,3+j,:3,3] = xyzs_in_base_frame[i,a2,:3]
            

    # CB/CG angles
    NCr = 0.5*(xyzs_in_base_frame[i,0,:3]+xyzs_in_base_frame[i,2,:3])
    CAr = xyzs_in_base_frame[i,1,:3]
    CBr = xyzs_in_base_frame[i,4,:3]
    CGr = xyzs_in_base_frame[i,5,:3]
    reference_angles[i,0,:]=th_ang_v(CBr-CAr,NCr-CAr)
    NCp = xyzs_in_base_frame[i,2,:3]-xyzs_in_base_frame[i,0,:3]
    NCpp = NCp - torch.dot(NCp,NCr)/ torch.dot(NCr,NCr) * NCr
    reference_angles[i,1,:]=th_ang_v(CBr-CAr,NCpp)
    reference_angles[i,2,:]=th_ang_v(CGr,torch.tensor([-1.,0.,0.]))

def get_rmsd(a, b, eps=1e-6):
    '''
    align crds b to a : always use all alphas
    expexted tensor of shape (L,3)
    jake's torch adapted version
    '''
    assert a.shape == b.shape, 'make sure tensors are the same size'
    L = a.shape[0]
    assert a.shape == torch.Size([L,3]), 'make sure tensors are in format [L,3]'

    # center to CA centroid
    a = a - a.mean(dim=0)
    b = b - b.mean(dim=0)

    # Computation of the covariance matrix
    C = torch.einsum('kj,ji->ki', torch.transpose(b.type(torch.float32),0,1), a.type(torch.float32))

    # Compute optimal rotation matrix using SVD
    V, S, W = torch.linalg.svd(C)

    # get sign to ensure right-handedness
    d = torch.ones([3,3])
    d[:,-1] = torch.sign(torch.linalg.det(V)*torch.linalg.det(W))

    # Rotation matrix U
    U = torch.einsum('kj,ji->ki',(d*V),W)

    # Rotate xyz_hal
    rP = torch.einsum('kj,ji->ki',b.type(torch.float32),U.type(torch.float32))

    L = rP.shape[0]
    rmsd = torch.sqrt(torch.sum((rP-a)*(rP-a), axis=(0,1)) / L + eps)

    return rmsd, U