myociss

cutoff_a = 2.5

l_u_copy = deepcopy(all_residues_dict["l_u"])
close_irmsds = []

for score_rank in 1:length(top_scores)
    rotation_idx, t, score = top_scores[score_rank]
    translation = voxel_size * ([t[1],t[2],t[3]] - [center_val+1,center_val+1,center_val+1])
    all_residues_dict["l_u"] = apply_transformation(l_u_copy, get_rotation(rotation_vectors[rotation_idx,:]), translation)
    i_rmsd = calc_interface_rmsd(all_residues_dict)

myociss

using Rotations
using LinearAlgebra
using ImageTransformations
using CoordinateTransformations
using Interpolations
using FFTW
using StatsBase

function scan_6d(rotation_vectors::Matrix{Float64}, receptor_volume::Array{ComplexF64, 3}, ligand_volume::Array{ComplexF64, 3})
    top_scores = []

myociss

volume_dim = 128
receptor_volume, ligand_volume = zeros(volume_dim, volume_dim, volume_dim), zeros(volume_dim, volume_dim, volume_dim)
vols = Dict('l' => ligand_volume, 'r' => receptor_volume)

asa_min = 1.0

# receptor -- iteration 1: [core: sqrt(1.5)*r, surface: sqrt(0.8)*r], iteration 2: [core: 0.0 (skip), surface: r + 3.4]
rr_iter1 = [receptor_atoms_asa[i] < asa_min ? sqrt(1.5)*receptor_radius_list[i] : sqrt(0.8)*receptor_radius_list[i] for i in 1:length(receptor_atoms_asa)]
rr_iter2 = [receptor_atoms_asa[i] < asa_min ? 0.0 : receptor_radius_list[i]+3.4 for i in 1:length(receptor_atoms_asa)]

myociss

function get_sphere_points(n::Int)
    dl = pi * (3.0 - sqrt(5.0))
    dz = 2.0 / n

    longitude = 0
    z = 1 - dz / 2

    coords = zeros(n, 3)
    for k in 1:n
        r = sqrt((1 - z * z))

myociss

# determine if each amino acid is part of the interface of the complex
interface_max_dist = 10.0

for res_key_l in keys(all_residues_dict["l_b"])
    atom_l_coords = all_residues_dict["l_b"][res_key_l]["coords"]

    for res_key_r in keys(all_residues_dict["r_b"])
        atom_r_coords = all_residues_dict["r_b"][res_key_r]["coords"]
        is_interface = any([norm(coord2 - coord1) <= interface_max_dist for coord2 in atom_r_coords for coord1 in atom_l_coords])
        

myociss

using BioStructures

proteins_dict = Dict()
protein_complex_pdb_id = "3WD5"
figures_path = mkpath(string(protein_complex_pdb_id, "_figures"))

for designation in ("l_u", "r_u", "l_b", "r_b")
    protein = read(string("./protein_data/", protein_complex_pdb_id, '_', designation, ".pdb"), PDBFormat)
    length(protein.models) == 1 || throw(DomainError(protein.models), "pdb files should only contain one model")
    proteins_dict[designation] = protein[1]

myociss

using BioStructures
using LinearAlgebra
using PlotlyJS
using DataFrames
using StaticArrays
using Rotations
using ImageTransformations
using CoordinateTransformations
using Interpolations
using FFTW

myociss

#include <CGAL/Simple_cartesian.h>
#include <CGAL/Surface_mesh.h>
#include <CGAL/Heat_method_3/Surface_mesh_geodesic_distances_3.h>
#include <fstream>
#include <iostream>

#include <boost/foreach.hpp>

typedef CGAL::Simple_cartesian<double>                       Kernel;
typedef Kernel::Point_3                                      Point_3;