import os import multiprocessing from multiprocessing import Pool from turtle import width import numpy as np from moleculekit.molecule import Molecule from scipy.spatial import KDTree from sklearn.cluster import AgglomerativeClustering def create_grid_fromBB(boundingBox, voxelSize=1): """Create a grid from a bounding box. Parameters ---------- boundingBox : list List of the form [xmin, xmax, ymin, ymax, zmin, zmax] voxelSize : float Size of the voxels in Angstrom Returns ------- grid : numpy.ndarray Grid of shape (nx, ny, nz) box_N : numpy.ndarray Number of voxels in each dimension """ # increase grid by 0.5 to sample everything xrange = np.arange(boundingBox[0][0], boundingBox[1][0] + 0.5, step=voxelSize) yrange = np.arange(boundingBox[0][1], boundingBox[1][1] + 0.5, step=voxelSize) zrange = np.arange(boundingBox[0][2], boundingBox[1][2] + 0.5, step=voxelSize) gridpoints = np.zeros((xrange.shape[0] * yrange.shape[0] * zrange.shape[0], 3)) i = 0 for x in xrange: for y in yrange: for z in zrange: gridpoints[i][0] = x gridpoints[i][1] = y gridpoints[i][2] = z i += 1 return gridpoints, (xrange.shape[0], yrange.shape[0], zrange.shape[0]) def get_bb(points): """Return bounding box from a set of points (N,3) Parameters ---------- points : numpy.ndarray Set of points (N,3) Returns ------- boundingBox : list List of the form [xmin, xmax, ymin, ymax, zmin, zmax] """ minx = np.min(points[:, 0]) maxx = np.max(points[:, 0]) miny = np.min(points[:, 1]) maxy = np.max(points[:, 1]) minz = np.min(points[:, 2]) maxz = np.max(points[:, 2]) bb = [[minx, miny, minz], [maxx, maxy, maxz]] return bb def get_all_protein_resids(pdb_file): """Return all protein residues from a pdb file Parameters ---------- pdb_file : str Path to pdb file Returns ------- resids : numpy.ndarray indexes of ca atoms """ try: prot = Molecule(pdb_file) except: exit("could not read file") prot.filter("protein and not hydrogen") return prot.get("index", sel="name CA") def get_all_metalbinding_resids(pdb_file): """Return all metal binding residues from a pdb file Parameters ---------- pdb_file : str Path to pdb file Returns ------- resids : numpy.ndarray indexes of name CA that are metal binding """ try: prot = Molecule(pdb_file) except: exit("could not read file") prot.filter("protein and not hydrogen") return prot.get( "index", sel="name CA and resname HIS HID HIE HIP CYS CYX GLU GLH GLN ASP ASH ASN GLN MET", ) def get_all_resids_from_list(pdb_file, resids): """Return all metal binding residues from a pdb file Parameters ---------- pdb_file : str Path to pdb file resids : list id of resids that are metal binding Returns ------- resids : numpy.ndarray indexes of name CA resids """ try: prot = Molecule(pdb_file) except: exit("could not read file") prot.filter("protein and not hydrogen") return prot.get( "index", sel=f"name CA and resid {resids}", ) def compute_average_p_fast(point, cutoff=1): """Using KDTree find the closest gridpoints Parameters ---------- point : numpy.ndarray Point of shape (3,) cutoff : float Cutoff distance in Angstrom Returns ------- average_p : numpy.ndarray Average probability of shape (1,)""" p = 0 nearest_neighbors, indices = tree.query( point, k=15, distance_upper_bound=cutoff, workers=1 ) if np.min(nearest_neighbors) != np.inf: p = np.mean(output_v[indices[nearest_neighbors != np.inf]]) return p def get_probability_mean(grid, prot_centers, pvalues): """Compute the mean probability of all gridpoints from the globalgrid based on the individual boxes Parameters ---------- grid : numpy.ndarray Grid of shape (nx, ny, nz) prot_centers : numpy.ndarray Protein centers of shape (N,3) pvalues : numpy.ndarray Probability values of shape (N,1) Returns ------- mean_p : numpy.ndarray Mean probability over grid of shape (nx, ny, nz) """ global output_v output_v = pvalues global prot_v prot_v = prot_centers cpuCount = multiprocessing.cpu_count() global tree tree = KDTree(prot_v) p = Pool(cpuCount) results = p.map(compute_average_p_fast, grid) return np.array(results) def write_cubefile(bb, pvalues, box_N, outname="Metal3D_pmap.cube", gridres=1): """Write a cube file from a probability map The cube specification from gaussian is used, distance are converted to bohr Parameters ---------- bb : list List of the form [xmin, xmax, ymin, ymax, zmin, zmax] pvalues : numpy.ndarray Probability values of shape (nx, ny, nz) box_N : tuple Number of voxels in each dimension outname : str Name of the output file gridres:float Resolution of the grid used for writing the voxels """ with open(outname, "w") as cube: cube.write(" Metal3D Cube File\n") cube.write(" Outer Loop: X, Middle Loop y, inner Loop z\n") angstromToBohr = 1.89 cube.write( f" 1 {bb[0][0]*angstromToBohr: .6f} {bb[0][1]*angstromToBohr: .6f} {bb[0][2]*angstromToBohr: .6f}\n" ) cube.write( f"{str(box_N[0]).rjust(5)} {1.890000*gridres:.9f} 0.000000 0.000000\n" ) cube.write( f"{str(box_N[1]).rjust(5)} 0.000000 {1.890000*gridres:.9f} 0.000000\n" ) cube.write( f"{str(box_N[2]).rjust(5)} 0.000000 0.000000 {1.890000*gridres:.9f}\n" ) cube.write(" 1 1.000000 0.000000 0.000000 0.000000\n") o = pvalues.reshape(box_N) for x in range(box_N[0]): for y in range(box_N[1]): for z in range(box_N[2]): cube.write(f" {o[x][y][z]: .5E}") if z % 6 == 5: cube.write("\n") cube.write("\n") def find_unique_sites( pvalues, grid, writeprobes=False, probefile="probes.pdb", threshold=5, p=0.75 ): """The probability voxels are points and the voxel clouds may contain multiple metals This function finds the unique sites and returns the coordinates of the unique sites with the highest p for each cluster. It uses the AgglomerativeClustering algorithm to find the unique sites. The threshold is the maximum distance between two points in the same cluster it can be changed to get more metal points. Parameters ---------- pvalues : numpy.ndarray Probability values of shape (N, 1) grid : numpy.ndarray Grid of shape (N, 3) writeprobes : bool If True, write the probes to a pdb file probefile : str Name of the output file threshold : float Maximum distance between two points in the same cluster p : float Minimum probability of a point to be considered a unique site """ points = grid[pvalues > p] point_p = pvalues[pvalues > p] if len(points) == 0: return "no metals found" clustering = AgglomerativeClustering( n_clusters=None, linkage="complete", distance_threshold=threshold ).fit(points) message = f"min metal p={p}, n(metals) found: {clustering.n_clusters_}" sites = [] for i in range(clustering.n_clusters_): c_points = points[clustering.labels_ == i] c_points_p = point_p[clustering.labels_ == i] position = c_points[np.argmax(c_points_p)] sites.append((position, np.max(c_points_p))) if writeprobes: print(f"writing probes to {probefile}") with open(probefile, "w") as f: for i, site in enumerate(sites): f.write( f"HETATM {i+1:3} ZN ZN A {i+1:3} {site[0][0]: 8.3f}{site[0][1]: 8.3f}{site[0][2]: 8.3f} {site[1]:.2f} 0.0 ZN2+\n" ) return message