From 9b94e119b3c7d8bbcc3603e2153d5562186397c2 Mon Sep 17 00:00:00 2001 From: Henry Date: Thu, 29 Jul 2021 11:47:13 +0200 Subject: [PATCH 01/16] :construction: get imports to run in environment --- compute_similarity.py | 155 ++++++++++++++++++++++++++++++++++++++++++ environment.yml | 12 ++++ group_into_regions.py | 54 +++++++++++++++ 3 files changed, 221 insertions(+) create mode 100644 compute_similarity.py create mode 100644 environment.yml create mode 100644 group_into_regions.py diff --git a/compute_similarity.py b/compute_similarity.py new file mode 100644 index 0000000..d8d5fd5 --- /dev/null +++ b/compute_similarity.py @@ -0,0 +1,155 @@ +#!/usr/bin/env python + +import numpy as np +import matplotlib +import matplotlib.pyplot as plt +import seaborn as sns +from sklearn.metrics import pairwise_distances +from sklearn.metrics.pairwise import pairwise_kernels +from sklearn.metrics.pairwise import cosine_similarity +from scipy.spatial.distance import cosine +import sys +import time +start_time = time.time() + + +# Script to compute similarity matrices for subregion electrostatics of each PDB. Will need to plot them too. Hmmm -> matrix heatmap. + +## Load the data ## + +pdbs = [] +potentials = {} +a = open('all_strs_regions_pot.csv', 'r') +for line in a: + mm = line.split(',') + if len(mm) == 3 and mm[0] != 'PDB ID': + if mm[1] == 'region_1': + pdbs.append(mm[0]) + temp_potential = [float(mm[2])] + + elif mm[1] != 'region_1': + temp_potential.append(float(mm[2])) + + if mm[1] == 'region_21': + potentials[mm[0]] = np.array(temp_potential) + + +a.close() + + +## Compute similarity distances ## +## How do I compute uncertainty? ## + +## Sklearn implementation ## + + +cosine_distances = [] +euclidean_distances = [] +l2_distances = [] +manhattan_distances = [] +l1_distances = [] +hamming_distances = [] +chebyshev_distances = [] +jaccard_distances = [] + +similarity_distances = {} +normalized_similarity_distances = {} +mean_similarity_distances = {} +ci95_similarity_distances = {} + +for sys1 in potentials: + for sys2 in potentials: + + cosine_distances.append(pairwise_distances(potentials[sys1].reshape( + 1, -1), potentials[sys2].reshape(1, -1), metric='cosine')[0][0]) + euclidean_distances.append(pairwise_distances(potentials[sys1].reshape( + 1, -1), potentials[sys2].reshape(1, -1), metric='euclidean')[0][0]) + l2_distances.append(pairwise_distances(potentials[sys1].reshape( + 1, -1), potentials[sys2].reshape(1, -1), metric='l2')[0][0]) + manhattan_distances.append(pairwise_distances(potentials[sys1].reshape( + 1, -1), potentials[sys2].reshape(1, -1), metric='manhattan')[0][0]) + l1_distances.append(pairwise_distances(potentials[sys1].reshape( + 1, -1), potentials[sys2].reshape(1, -1), metric='l1')[0][0]) + hamming_distances.append(pairwise_distances(potentials[sys1].reshape( + 1, -1), potentials[sys2].reshape(1, -1), metric='hamming')[0][0]) + chebyshev_distances.append(pairwise_distances(potentials[sys1].reshape( + 1, -1), potentials[sys2].reshape(1, -1), metric='chebyshev')[0][0]) + jaccard_distances.append(pairwise_distances(potentials[sys1].reshape( + 1, -1), potentials[sys2].reshape(1, -1), metric='jaccard')[0][0]) + + similarity_distances[sys1, sys2] = [cosine_distances[-1], euclidean_distances[-1], l2_distances[-1], + manhattan_distances[-1], l1_distances[-1], hamming_distances[-1], chebyshev_distances[-1], jaccard_distances[-1]] + + +# normalization loop + +for sys1 in potentials: + for sys2 in potentials: + + normalized_similarity_distances[sys1, sys2] = (np.array(similarity_distances[sys1, sys2]) - np.array([min(cosine_distances), min(euclidean_distances), min(l2_distances), min(manhattan_distances), min(l1_distances), min(hamming_distances), min(chebyshev_distances), min(jaccard_distances)])) / (np.array([max(cosine_distances), max(euclidean_distances), max( + l2_distances), max(manhattan_distances), max(l1_distances), max(hamming_distances), max(chebyshev_distances), max(jaccard_distances)]) - np.array([min(cosine_distances), min(euclidean_distances), min(l2_distances), min(manhattan_distances), min(l1_distances), min(hamming_distances), min(chebyshev_distances), min(jaccard_distances)])) + + mean_similarity_distances[sys1, sys2] = np.mean( + normalized_similarity_distances[sys1, sys2]) + ci95_similarity_distances[sys1, sys2] = 1.96 * np.std( + normalized_similarity_distances[sys1, sys2]) / np.sqrt(len(normalized_similarity_distances[sys1, sys2])) + + +# Plot the data + +heatmap_matrix = [] +annotations_matrix = [] + +for sys1 in pdbs: + heatmap_row = [] + annotations_row = [] + for sys2 in pdbs: + heatmap_row.append(mean_similarity_distances[sys1, sys2]) + annotations_row.append('{}\n+/- {}'.format(round( + mean_similarity_distances[sys1, sys2], 2), round(ci95_similarity_distances[sys1, sys2], 2))) + + heatmap_matrix.append(heatmap_row) + annotations_matrix.append(annotations_row) + +labels = np.array(annotations_matrix) +print(labels) + +# Generate a mask for the upper triangle +mask = np.triu(np.ones_like(heatmap_matrix, dtype=bool)) + +# Set up the matplotlib figure +matplotlib.rc('xtick', labelsize=16) +matplotlib.rc('ytick', labelsize=16) + +fig, ax = plt.subplots() + +ax = sns.heatmap(heatmap_matrix, mask=mask, annot=labels, fmt='', annot_kws={ + "size": 14}, cmap="RdBu_r") # fmt="0.2f", cmap="RdBu_r") + +row_labels = pdbs +column_labels = pdbs + +# put the major ticks at the middle of each cell +ax.set_yticks(np.arange(len(heatmap_matrix))+0.5, minor=True) +ax.set_xticks(np.arange(len(heatmap_matrix))+0.5, minor=True) + +ax.set_xticklabels(column_labels, minor=False, rotation=30, + fontsize=16, ha="right", rotation_mode="anchor") # rotation=-30 +ax.set_yticklabels(row_labels, minor=False, rotation=0, fontsize=16) +ax.tick_params(axis='x', which='major') # , pad=10) +ax.tick_params(axis='y', which='major') # , pad=10) +ax.set_xlabel('Antibodies', fontsize=20) +# ax.xaxis.tick_top() ## for labels at top + +cbar = ax.collections[0].colorbar +cbar.ax.set_ylabel(r'Similarity score (normalized)', + rotation=270, fontsize=18, labelpad=25) # labelpad=25) +cbar.ax.tick_params(labelsize=13) + +plt.tight_layout() +plt.savefig('similarity_distances.pdf') +plt.close() + + +end_time = time.time() +print("Elapsed time was %g seconds" % (end_time - start_time)) diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..62be764 --- /dev/null +++ b/environment.yml @@ -0,0 +1,12 @@ +name: isbm2021hack +channels: + - defaults +dependencies: + - nodejs + - python>=3.7 + - numpy + - scipy + - matplotlib + - scikit-learn + - seaborn +prefix: C:\Users\kzl465\Anaconda3\envs\isbm2021hack diff --git a/group_into_regions.py b/group_into_regions.py new file mode 100644 index 0000000..4ec0493 --- /dev/null +++ b/group_into_regions.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python + +IFH1 = open('list_residue_pot', 'r') +lines1 = IFH1.readlines() +for line1 in lines1: + line1 = line1.strip("\n") + name = line1.split("_")[0] + res_pot_dict = {} + IFH2 = open(line1, 'r') + lines2 = IFH2.readlines() + for line2 in lines2: + line2 = line2.strip("\n") + res_id = line2.split(",")[0] + pot_value = line2.split(",")[1] + if res_id not in res_pot_dict: + res_pot_dict[res_id] = pot_value + print(res_pot_dict) + + res_surface_area = {} + IFH3 = open(name+"_surface", 'r') + lines3 = IFH3.readlines() + for i, line3 in enumerate(lines3): + if i > 0: + line3 = line3.strip("\n") + fields = line3.split(":") + surface_area = fields[2].strip() + res_details = fields[1] + res_details_list = res_details.split(" ") + res_full_id = res_details_list[1] + res_full_id_list = res_full_id.split("_") + print(res_full_id_list) + residue_id = res_full_id_list[2]+"_"+res_full_id_list[3] + if residue_id not in res_surface_area: + res_surface_area[residue_id] = surface_area + print(res_surface_area) + """ + regions_dict = {} + region_pot = 0 + for res_id in res_pot_dict.keys(): + res_no = res_id.split("_")[0] + + + res_no = int(res_no) + if res_no >= 320 and res_no <= 530: + if res_no <=340: + + if res_surface_area[res_id] > 80: + region_pot = region_pot + res_pot_dict[res_id] + + regions_dict['region_1'] + + elif res_no >340 and res_no < + for i in range(320,531): + """ From 25f4d93fa63480c38532ed5687c15d84bea69a41 Mon Sep 17 00:00:00 2001 From: Henry Date: Thu, 29 Jul 2021 13:13:32 +0200 Subject: [PATCH 02/16] :bug: pdb list or potential keys. use potential keys - avoid key-errors due to change of loop units - changed input (to be committed in a next step) - plot is still gibberish --- compute_similarity.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/compute_similarity.py b/compute_similarity.py index d8d5fd5..c6bca16 100644 --- a/compute_similarity.py +++ b/compute_similarity.py @@ -19,7 +19,7 @@ pdbs = [] potentials = {} -a = open('all_strs_regions_pot.csv', 'r') +a = open('all_spike_strs_regions_pot.csv', 'r') for line in a: mm = line.split(',') if len(mm) == 3 and mm[0] != 'PDB ID': @@ -100,14 +100,14 @@ heatmap_matrix = [] annotations_matrix = [] -for sys1 in pdbs: +for sys1 in potentials.keys(): heatmap_row = [] annotations_row = [] - for sys2 in pdbs: + for sys2 in potentials.keys(): heatmap_row.append(mean_similarity_distances[sys1, sys2]) annotations_row.append('{}\n+/- {}'.format(round( mean_similarity_distances[sys1, sys2], 2), round(ci95_similarity_distances[sys1, sys2], 2))) - + heatmap_matrix.append(heatmap_row) annotations_matrix.append(annotations_row) @@ -126,8 +126,8 @@ ax = sns.heatmap(heatmap_matrix, mask=mask, annot=labels, fmt='', annot_kws={ "size": 14}, cmap="RdBu_r") # fmt="0.2f", cmap="RdBu_r") -row_labels = pdbs -column_labels = pdbs +row_labels = list(potentials.keys()) #pdbs +column_labels = list(potentials.keys()) #pdbs # put the major ticks at the middle of each cell ax.set_yticks(np.arange(len(heatmap_matrix))+0.5, minor=True) From 29c30226d4ddb841c747fc11328eb2265e0badb3 Mon Sep 17 00:00:00 2001 From: Henry Date: Thu, 29 Jul 2021 13:17:33 +0200 Subject: [PATCH 03/16] :sparkles: add changes of David: residual potentials dict with - key as comb. of pos. and resdidual - value: surface potential --- group_into_regions.py | 57 +++++++++++++++++++++++++++++-------------- 1 file changed, 39 insertions(+), 18 deletions(-) diff --git a/group_into_regions.py b/group_into_regions.py index 4ec0493..c710ea2 100644 --- a/group_into_regions.py +++ b/group_into_regions.py @@ -13,7 +13,7 @@ res_id = line2.split(",")[0] pot_value = line2.split(",")[1] if res_id not in res_pot_dict: - res_pot_dict[res_id] = pot_value + res_pot_dict[res_id] = float(pot_value) print(res_pot_dict) res_surface_area = {} @@ -28,27 +28,48 @@ res_details_list = res_details.split(" ") res_full_id = res_details_list[1] res_full_id_list = res_full_id.split("_") - print(res_full_id_list) + # print(res_full_id_list) residue_id = res_full_id_list[2]+"_"+res_full_id_list[3] if residue_id not in res_surface_area: - res_surface_area[residue_id] = surface_area + res_surface_area[residue_id] = float(surface_area) print(res_surface_area) - """ - regions_dict = {} - region_pot = 0 - for res_id in res_pot_dict.keys(): - res_no = res_id.split("_")[0] - - res_no = int(res_no) - if res_no >= 320 and res_no <= 530: - if res_no <=340: + regions_dict = {} + region_pot = 0 + region_number = 0 + for res_id in res_pot_dict.keys(): + # for some reason threw errors where some residues were not in surface area calculation + if res_id not in res_surface_area.keys(): + res_no = int(res_id.split("_")[0]) + if res_no > 320 and res_no <= 530 and res_no % 10 == 0: + region_number = region_number + 1 + regions_dict['region_{}'.format(region_number)] = region_pot + region_pot = 0 - if res_surface_area[res_id] > 80: - region_pot = region_pot + res_pot_dict[res_id] + continue - regions_dict['region_1'] + res_no = res_id.split("_")[0] - elif res_no >340 and res_no < - for i in range(320,531): - """ + res_no = int(res_no) + if res_no >= 320 and res_no <= 530: + # if res_no <=340: + + if res_no > 320 and res_no % 10 == 0: + if res_surface_area[res_id] >= 80: + region_pot = region_pot + res_pot_dict[res_id] + + elif res_surface_area[res_id] < 80: + region_pot = region_pot + 0 + + region_number = region_number + 1 + regions_dict['region_{}'.format(region_number)] = region_pot + region_pot = 0 + + elif res_no % 10 != 0: + if res_surface_area[res_id] >= 80: + region_pot = region_pot + res_pot_dict[res_id] + + elif res_surface_area[res_id] < 80: + region_pot = region_pot + 0 + + print(regions_dict) From 04ec73f35fd92e11cdf552223e2b97d700a5df83 Mon Sep 17 00:00:00 2001 From: Henry Date: Thu, 29 Jul 2021 13:32:46 +0200 Subject: [PATCH 04/16] :art: add data and shift to line by line processing --- data/7DDD_res_pot.csv | 1087 +++++++++++++++++++++++++++++++++++++++++ data/list_residue_pot | 1 + group_into_regions.py | 122 ++--- 3 files changed, 1149 insertions(+), 61 deletions(-) create mode 100644 data/7DDD_res_pot.csv create mode 100644 data/list_residue_pot diff --git a/data/7DDD_res_pot.csv b/data/7DDD_res_pot.csv new file mode 100644 index 0000000..b18d6ae --- /dev/null +++ b/data/7DDD_res_pot.csv @@ -0,0 +1,1087 @@ +14_GLN,-9.01368901172878 +15_CYS,-18.94037098059941 +16_VAL,-17.067395846932214 +17_ASN,-52.6633491546148 +18_LEU,-29.44918832201956 +19_THR,14.219910734619706 +20_THR,16.61740848650453 +21_ARG,45.150087181469 +22_THR,16.192284945332855 +23_GLN,7.874206654718551 +24_LEU,7.300534563694834 +25_PRO,7.0082607219155815 +26_PRO,3.218016149329255 +27_ALA,-3.2639350869708776 +28_TYR,-39.00538833440264 +29_THR,-20.97888392266748 +30_ASN,2.3372356004637496 +31_SER,20.212787521813816 +32_PHE,23.147049307540634 +33_THR,8.707783365071581 +34_ARG,76.662979239567 +35_GLY,-7.939313942927015 +36_VAL,-31.027866103361532 +37_TYR,-12.199026938333962 +38_TYR,-20.011791368353755 +39_PRO,3.587578011108741 +40_ASP,-18.029666839407874 +41_LYS,14.537595332823594 +42_VAL,-7.482795546046475 +43_PHE,-12.787782068160277 +44_ARG,41.61078854132212 +45_SER,8.891638357211413 +46_SER,12.336817054136386 +47_VAL,44.61664283472547 +48_LEU,78.1411731857443 +49_HIS,138.58476274482757 +50_SER,42.648203716952274 +51_THR,32.91987127486146 +52_GLN,13.309116645719353 +53_ASP,5.879023401994681 +54_LEU,11.479858644877979 +55_PHE,-27.11901733403466 +56_LEU,-41.63973137661443 +57_PRO,-37.43114388949077 +58_PHE,-78.20927463037329 +59_PHE,-26.258778120560986 +60_SER,-20.472197261269283 +61_ASN,-34.292715634137274 +62_VAL,-22.454379718934355 +63_THR,15.026171643279138 +64_TRP,15.045678244103147 +65_PHE,16.082100923778754 +66_HIS,60.86150687518432 +67_ALA,26.66690672502804 +68_ILE,40.38842267254243 +69_HIS,44.44892932690589 +77_LYS,32.525318304953316 +78_ARG,63.417411275725115 +79_PHE,-58.070906268817524 +80_ASP,-15.332458273830023 +81_ASN,-49.279290958868835 +82_PRO,2.1153840934579398 +83_VAL,5.690520657540932 +84_LEU,25.889160182858237 +85_PRO,11.684237683942282 +86_PHE,38.40449276066071 +87_ASN,10.38894255517133 +88_ASP,4.559667549343379 +89_GLY,6.74410622177838 +90_VAL,4.447912187165659 +91_TYR,-33.539623071415065 +92_PHE,27.678789602392506 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a/group_into_regions.py +++ b/group_into_regions.py @@ -1,75 +1,75 @@ #!/usr/bin/env python +from pathlib import Path -IFH1 = open('list_residue_pot', 'r') -lines1 = IFH1.readlines() -for line1 in lines1: - line1 = line1.strip("\n") - name = line1.split("_")[0] - res_pot_dict = {} - IFH2 = open(line1, 'r') - lines2 = IFH2.readlines() - for line2 in lines2: - line2 = line2.strip("\n") - res_id = line2.split(",")[0] - pot_value = line2.split(",")[1] - if res_id not in res_pot_dict: - res_pot_dict[res_id] = float(pot_value) - print(res_pot_dict) +data_folder = Path('data') + +with open (data_folder / 'list_residue_pot', 'r') as IFH1: + for file in IFH1: + file = file.strip("\n") + name = file.split("_")[0] + res_pot_dict = {} + with open(data_folder / file, 'r') as IFH2: + for line in IFH2: + line = line.strip("\n") + res_id = line.split(",")[0] + pot_value = line.split(",")[1] + if res_id not in res_pot_dict: + res_pot_dict[res_id] = float(pot_value) + print(res_pot_dict) res_surface_area = {} - IFH3 = open(name+"_surface", 'r') - lines3 = IFH3.readlines() - for i, line3 in enumerate(lines3): - if i > 0: - line3 = line3.strip("\n") - fields = line3.split(":") - surface_area = fields[2].strip() - res_details = fields[1] - res_details_list = res_details.split(" ") - res_full_id = res_details_list[1] - res_full_id_list = res_full_id.split("_") - # print(res_full_id_list) - residue_id = res_full_id_list[2]+"_"+res_full_id_list[3] - if residue_id not in res_surface_area: - res_surface_area[residue_id] = float(surface_area) - print(res_surface_area) + with open(data_folder / f"{name}_surface", 'r') as IFH3: + for i, line in enumerate(IFH3): + if i > 0: + line = line.strip("\n") + fields = line.split(":") + surface_area = fields[2].strip() + res_details = fields[1] + res_details_list = res_details.split(" ") + res_full_id = res_details_list[1] + res_full_id_list = res_full_id.split("_") + # print(res_full_id_list) + residue_id = res_full_id_list[2]+"_"+res_full_id_list[3] + if residue_id not in res_surface_area: + res_surface_area[residue_id] = float(surface_area) + print(res_surface_area) - regions_dict = {} - region_pot = 0 - region_number = 0 - for res_id in res_pot_dict.keys(): - # for some reason threw errors where some residues were not in surface area calculation - if res_id not in res_surface_area.keys(): - res_no = int(res_id.split("_")[0]) - if res_no > 320 and res_no <= 530 and res_no % 10 == 0: - region_number = region_number + 1 - regions_dict['region_{}'.format(region_number)] = region_pot - region_pot = 0 + regions_dict = {} + region_pot = 0 + region_number = 0 + for res_id in res_pot_dict.keys(): + # for some reason threw errors where some residues were not in surface area calculation + if res_id not in res_surface_area.keys(): + res_no = int(res_id.split("_")[0]) + if res_no > 320 and res_no <= 530 and res_no % 10 == 0: + region_number = region_number + 1 + regions_dict['region_{}'.format(region_number)] = region_pot + region_pot = 0 - continue + continue - res_no = res_id.split("_")[0] + res_no = res_id.split("_")[0] - res_no = int(res_no) - if res_no >= 320 and res_no <= 530: - # if res_no <=340: + res_no = int(res_no) + if res_no >= 320 and res_no <= 530: + # if res_no <=340: - if res_no > 320 and res_no % 10 == 0: - if res_surface_area[res_id] >= 80: - region_pot = region_pot + res_pot_dict[res_id] + if res_no > 320 and res_no % 10 == 0: + if res_surface_area[res_id] >= 80: + region_pot = region_pot + res_pot_dict[res_id] - elif res_surface_area[res_id] < 80: - region_pot = region_pot + 0 + elif res_surface_area[res_id] < 80: + region_pot = region_pot + 0 - region_number = region_number + 1 - regions_dict['region_{}'.format(region_number)] = region_pot - region_pot = 0 + region_number = region_number + 1 + regions_dict['region_{}'.format(region_number)] = region_pot + region_pot = 0 - elif res_no % 10 != 0: - if res_surface_area[res_id] >= 80: - region_pot = region_pot + res_pot_dict[res_id] + elif res_no % 10 != 0: + if res_surface_area[res_id] >= 80: + region_pot = region_pot + res_pot_dict[res_id] - elif res_surface_area[res_id] < 80: - region_pot = region_pot + 0 + elif res_surface_area[res_id] < 80: + region_pot = region_pot + 0 - print(regions_dict) + print(regions_dict) From c0f064ca56c5c08aabb7c6795666807dcae7a447 Mon Sep 17 00:00:00 2001 From: Henry Date: Thu, 29 Jul 2021 16:41:52 +0200 Subject: [PATCH 05/16] :construction: Visualize unique dist in DataFrame - similarity DataFrame. Features are similarites/distances - normalization can be added now vectorized - only unique distances/similiarities, no self-correlation --- compute_similarity.ipynb | 688 +++++ compute_similarity.py | 16 +- data/all_spike_strs_regions_pot.csv | 4278 +++++++++++++++++++++++++++ environment.yml | 2 +- 4 files changed, 4977 insertions(+), 7 deletions(-) create mode 100644 compute_similarity.ipynb create mode 100644 data/all_spike_strs_regions_pot.csv diff --git a/compute_similarity.ipynb b/compute_similarity.ipynb new file mode 100644 index 0000000..fa5eb9a --- /dev/null +++ b/compute_similarity.ipynb @@ -0,0 +1,688 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#!/usr/bin/env python\n", + "\n", + "import sys\n", + "import time\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.metrics import pairwise_distances\n", + "from sklearn.metrics.pairwise import pairwise_kernels\n", + "from sklearn.metrics.pairwise import cosine_similarity\n", + "from scipy.spatial.distance import cosine" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "start_time = time.time()\n", + "# Script to compute similarity matrices for subregion electrostatics of each PDB. Will need to plot them too. Hmmm -> matrix heatmap.\n", + "\n", + "## Load the data ##\n", + "\n", + "pdbs = []\n", + "potentials = {}\n", + "data_folder = Path('data')\n", + "a = open(data_folder / 'all_spike_strs_regions_pot.csv', 'r')\n", + "for line in a:\n", + " mm = line.split(',')\n", + " if len(mm) == 3 and mm[0] != 'PDB ID':\n", + " if mm[1] == 'region_1':\n", + " pdbs.append(mm[0])\n", + " temp_potential = [float(mm[2])]\n", + "\n", + " elif mm[1] != 'region_1':\n", + " temp_potential.append(float(mm[2]))\n", + "\n", + " if mm[1] == 'region_21':\n", + " potentials[mm[0]] = np.array(temp_potential)\n", + "\n", + "\n", + "a.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "... ... ... \n", + "7ntc 7nd8 0.952381 99.833570 \n", + " 7nd9 0.952381 100.327947 \n", + " 7ndb 0.952381 119.389193 \n", + " 7ndc 0.952381 150.770188 \n", + " 7ndd 0.952381 110.475924 \n", + "\n", + "[4753 rows x 7 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dict_dist= {}\n", + "metrics = ['cosine', 'euclidean', 'l2', 'manhattan', 'l1', 'hamming', 'chebyshev'] # 'jaccard' excluded as it's for binary data\n", + "for _metric in metrics:\n", + " dict_dist[_metric] = pd.DataFrame(pairwise_distances(X=df, metric=_metric), index=df.index, columns=df.index)\n", + " dict_dist[_metric] = lower_triangle(dict_dist[_metric]).stack()\n", + "df_metrics = pd.DataFrame(dict_dist)\n", + "df_metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "isbm2021hack", + "language": "python", + "name": "isbm2021hack" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/compute_similarity.py b/compute_similarity.py index c6bca16..268b186 100644 --- a/compute_similarity.py +++ b/compute_similarity.py @@ -1,5 +1,9 @@ #!/usr/bin/env python +import sys +import time +from pathlib import Path + import numpy as np import matplotlib import matplotlib.pyplot as plt @@ -8,8 +12,7 @@ from sklearn.metrics.pairwise import pairwise_kernels from sklearn.metrics.pairwise import cosine_similarity from scipy.spatial.distance import cosine -import sys -import time + start_time = time.time() @@ -19,7 +22,8 @@ pdbs = [] potentials = {} -a = open('all_spike_strs_regions_pot.csv', 'r') +data_folder = Path('data') +a = open(data_folder / 'all_spike_strs_regions_pot.csv', 'r') for line in a: mm = line.split(',') if len(mm) == 3 and mm[0] != 'PDB ID': @@ -107,7 +111,7 @@ heatmap_row.append(mean_similarity_distances[sys1, sys2]) annotations_row.append('{}\n+/- {}'.format(round( mean_similarity_distances[sys1, sys2], 2), round(ci95_similarity_distances[sys1, sys2], 2))) - + heatmap_matrix.append(heatmap_row) annotations_matrix.append(annotations_row) @@ -126,8 +130,8 @@ ax = sns.heatmap(heatmap_matrix, mask=mask, annot=labels, fmt='', annot_kws={ "size": 14}, cmap="RdBu_r") # fmt="0.2f", cmap="RdBu_r") -row_labels = list(potentials.keys()) #pdbs -column_labels = list(potentials.keys()) #pdbs +row_labels = list(potentials.keys()) # pdbs +column_labels = list(potentials.keys()) # pdbs # put the major ticks at the middle of each cell ax.set_yticks(np.arange(len(heatmap_matrix))+0.5, minor=True) diff --git a/data/all_spike_strs_regions_pot.csv b/data/all_spike_strs_regions_pot.csv new file mode 100644 index 0000000..9914678 --- /dev/null +++ b/data/all_spike_strs_regions_pot.csv @@ -0,0 +1,4278 @@ +PDB ID, Region within RBD, Delphi Electrostatic Potential +2dd8,region_1,-70.1911148022218 +2dd8,region_2,39.0681912888814 +2dd8,region_3,78.7505594456147 +2dd8,region_4,95.70349637120809 +2dd8,region_5,70.87924143111984 +2dd8,region_6,-76.44971315362164 +2dd8,region_7,0 +2dd8,region_8,93.51659821821428 +2dd8,region_9,-55.84554746679434 +2dd8,region_10,-121.0908881961484 +2dd8,region_11,-55.05637447925392 +2dd8,region_12,-1.0836144136456252 +2dd8,region_13,75.81059747003823 +2dd8,region_14,131.83318110562263 +2dd8,region_15,69.7294855676766 +2dd8,region_16,31.898551121326612 +2dd8,region_17,22.288215517962726 +2dd8,region_18,-3.203085163355062 +2dd8,region_19,-13.742084014800778 +2ghw,region_1,-106.78550709307862 +2ghw,region_2,19.609687675463675 +2ghw,region_3,-1.795815138732749 +2ghw,region_4,-39.132424662052024 +2ghw,region_5,44.01706172943557 +2ghw,region_6,-105.50653982703722 +2ghw,region_7,0 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+7ora,region_3,62.25173480740537 +7ora,region_4,-115.62993561553665 +7ora,region_5,92.6547307630282 +7ora,region_6,-24.808859192340602 +7ora,region_7,0 +7ora,region_8,56.91390521901372 +7ora,region_9,-14.503087212211195 +7ora,region_10,-89.22498385981564 +7ora,region_11,-11.822923395866367 +7ora,region_12,-161.69062111572168 +7ora,region_13,44.40544048424216 +7ora,region_14,41.70258870310625 +7ora,region_15,48.55598721288081 +7ora,region_16,-121.84977038546597 +7ora,region_17,-71.58665126456921 +7ora,region_18,-10.086030155024549 +7ora,region_19,52.54094692436391 +7orb,region_1,-64.06572889529322 +7orb,region_2,61.9119571464172 +7orb,region_3,77.06487667536825 +7orb,region_4,-64.94334039517648 +7orb,region_5,12.360913539588942 +7orb,region_6,-85.27784955100444 +7orb,region_7,-59.660744304286546 +7orb,region_8,9.026049633262227 +7orb,region_9,22.38266176511335 +7orb,region_10,-104.34243428362288 +7orb,region_11,3.2960384090458295 +7orb,region_12,128.76439561949542 +7orb,region_13,14.050218171410293 +7orb,region_14,1.2544568352360876 +7orb,region_15,-28.472693304693667 +7orb,region_16,-33.79695773194336 +7orb,region_17,19.477022882872426 +7orb,region_18,8.134584512293724 +7r6w,region_1,-67.15057183852485 +7r6w,region_2,116.22150911570606 +7r6w,region_3,4.690350714478129 +7r6w,region_4,-129.2354575678353 +7r6w,region_5,-5.786718351731727 +7r6w,region_6,-54.35123811292644 +7r6w,region_7,0 +7r6w,region_8,64.98385280666652 +7r6w,region_9,14.63188753511141 +7r6w,region_10,-107.45627270353432 +7r6w,region_11,-8.481618985141221 +7r6w,region_12,0.033576249702285565 +7r6w,region_13,5.7498717607815095 +7r6w,region_14,4.9850546895888215 +7r6w,region_15,-43.86530377675549 +7r6w,region_16,-220.85561000630997 +7r6w,region_17,36.62154960432997 +7r6w,region_18,50.967934380857976 +7r6w,region_19,-24.92898575675919 +7r6x,region_1,-34.06412912461704 +7r6x,region_2,-116.64608509988896 +7r6x,region_3,-3.2360601502871233 +7r6x,region_4,-79.87091719195516 +7r6x,region_5,-35.885819426326776 +7r6x,region_6,29.373127290119584 +7r6x,region_7,71.47355021378114 +7r6x,region_8,0 +7r6x,region_9,28.990869188056287 +7r6x,region_10,47.64807832581878 +7r6x,region_11,-180.16035323423625 +7r6x,region_12,-1.9562748449586087 +7r6x,region_13,-51.73614300887689 +7r6x,region_14,127.17399158486703 +7r6x,region_15,-49.09224863733236 +7r6x,region_16,-56.13303581198382 +7r6x,region_17,-65.85132764549286 +7r6x,region_18,16.456864394861498 +7r6x,region_19,-28.19696153989726 +7r6x,region_20,-46.13797045113668 +7r7n,region_1,-37.94401403472772 +7r7n,region_2,21.84374665586807 +7r7n,region_3,395.7515404084706 +7r7n,region_4,22.940592703577394 +7r7n,region_5,28.88600121047027 +7r7n,region_6,74.58675094821365 +7r7n,region_7,0 +7r7n,region_8,197.13596129996577 +7r7n,region_9,62.37002135042477 +7r7n,region_10,-100.26856733667466 +7r7n,region_11,18.696421980861142 +7r7n,region_12,-3.98835962456938 +7r7n,region_13,77.950102257055 +7r7n,region_14,-21.4433830789431 +7r7n,region_15,-71.51753420909492 +7r7n,region_16,42.1854933101978 +7r7n,region_17,43.99655896836559 +7r7n,region_18,0.3955335345636537 diff --git a/environment.yml b/environment.yml index 62be764..303a8b0 100644 --- a/environment.yml +++ b/environment.yml @@ -4,7 +4,7 @@ channels: dependencies: - nodejs - python>=3.7 - - numpy + - numpy=1.19 - scipy - matplotlib - scikit-learn From 660d2bdcc15ccf5efac6905d2f31124457e9124f Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 30 Jul 2021 16:14:34 +0200 Subject: [PATCH 06/16] :construction: scripts to integrate --- bin/README.md | 22 ++++++++++ bin/automate_delphical.sh | 19 +++++++++ bin/map_atom_to_res.py | 57 +++++++++++++++++++++++++ bin/remove_undefined.py | 50 ++++++++++++++++++++++ group_into_regions.py | 10 +++++ temp/delphipot_surface.js | 88 +++++++++++++++++++++++++++++++++++++++ 6 files changed, 246 insertions(+) create mode 100644 bin/README.md create mode 100644 bin/automate_delphical.sh create mode 100644 bin/map_atom_to_res.py create mode 100644 bin/remove_undefined.py create mode 100644 temp/delphipot_surface.js diff --git a/bin/README.md b/bin/README.md new file mode 100644 index 0000000..59d6b76 --- /dev/null +++ b/bin/README.md @@ -0,0 +1,22 @@ + + +## Workflow + +1. Extract a single Ag chain from Ab-Ag complex using custom Biopython script +2. Rename PDB containing single Ag chain to include pdb id and chain id +3. Create list of all PDB files created in step 2 (`list_pdb_files`) +4. Run `automate_delphical.sh` in the icn3d folder (for potential calculation) on all these files + - after commenting out the code used for surface area calculation in the `delphipot_surface.js` script +5. Run `automate_delphical.sh` (for surface area/residue calculation) on all these files (in the icn3d folder) + - after commenting out the code used for electrostatic potential calculation in the `delphipot_surface.js` script +6. Make list of output files generated in step 4 (`input_list_out`) +7. Make list of output files generated in step 5 (`list_surface_files`) +8. Run `remove_undefined.py` on list from step 6 +9. Make list of output files generated in step 8 (`list_clean_pot`) +10. Run `map_atom_to_res.py` using lists from step 9 after keeping output files and pdb files in the same folder +11. make list of csv files generated in step 10 +12. keep surface files (filename format `_surface`) and .csv files in a single folder +13. run `group_into_regions.py` - creates an output file `all_spike_strs_regions_pot.csv` + + +- currently: start at step 4 () \ No newline at end of file diff --git a/bin/automate_delphical.sh b/bin/automate_delphical.sh new file mode 100644 index 0000000..af4a8d8 --- /dev/null +++ b/bin/automate_delphical.sh @@ -0,0 +1,19 @@ +#!/usr/bin/bash + +for i in $(cat list_pdb_files); +do + + #${A}= "$(cut -d'_' -f1 <<<"$i")" + #echo "$A" + + + #${B}= "$(cut -d'_' -f4 <<<"$i")" + #echo "$B" + + A=$(awk -F_ '{print $1}' <<< ${i}) + B=$(awk -F_ '{print $3}' <<< ${i}) + + echo "delphipot_surface.js ${A} ${B} > ${A}_out" + node delphipot_surface.js ${A} ${B} > ${A}_surface + +done \ No newline at end of file diff --git a/bin/map_atom_to_res.py b/bin/map_atom_to_res.py new file mode 100644 index 0000000..75757e8 --- /dev/null +++ b/bin/map_atom_to_res.py @@ -0,0 +1,57 @@ +#!/usr/bin/env python + +""" +Extract residual information from PDB files + +Map atoms to residual. +""" +__author__ = 'Mahita Jarapu' + +IFH1 = open('list_of_pdb_files', 'r') +lines1 = IFH1.readlines() +for line1 in lines1: + line1 = line1.strip("\n") + pdb_name = line1.split("_")[0].lower() + IFH2 = open(line1, 'r') + res_dict = {} + lines2 = IFH2.readlines() + for i, line2 in enumerate(lines2): + print(line2[0:4]) + print(line2[16:20].strip()) + print(line2[22:28].strip()) + res_dict[i] = line2[22:28].strip()+"_"+line2[16:20].strip() + print(res_dict) + IFH3 = open(pdb_name+"_clean", 'r') + OFH3 = open(pdb_name+"_res_pot.csv", 'w') + lines3 = IFH3.readlines() + residue_pot = 0 + residue_pot_dict = {} + for j, line3 in enumerate(lines3): + if j == 0 and float(line3) == 0: + + for k in res_dict.keys(): + if j == k + 1: + line3 = line3.strip("\n") + residue_pot = residue_pot + float(line3) + # if k < len(res_dict.keys())-1: + if res_dict[k] != res_dict[k + 1]: + res_id = res_dict[k] + if res_id not in residue_pot_dict: + residue_pot_dict[res_id] = residue_pot + residue_pot = 0 + else: + for k in res_dict.keys(): + if j == k + 1: + line3 = line3.strip("\n") + residue_pot = residue_pot + float(line3) + # if k < len(res_dict.keys())-1: + if res_dict[k] != res_dict[k + 1]: + res_id = res_dict[k] + if res_id not in residue_pot_dict: + residue_pot_dict[res_id] = residue_pot + residue_pot = 0 + + print(residue_pot_dict) + for res_id in residue_pot_dict.keys(): + OFH3.write("%s,%s\n" % (res_id, residue_pot_dict[res_id])) + OFH3.close() diff --git a/bin/remove_undefined.py b/bin/remove_undefined.py new file mode 100644 index 0000000..2b691d2 --- /dev/null +++ b/bin/remove_undefined.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python + +""" +Extract residual information from PDB files + +Map atoms to residual. +""" +__author__ = 'Mahita Jarapu' + +IFH1 = open('input_list_out', 'r') +lines1 = IFH1.readlines() +for line1 in lines1: + line1 = line1.strip("\n") + IFH2 = open(line1, 'r') + OFH2 = open(line1.split("_")[0]+'_'+'clean', 'w') + IFH2 = open(line1, 'r') + lines2 = IFH2.readlines() + print(line1) + delphi_value_dict = {} + + for i,line2 in enumerate(lines2): + if i > 0: + line2 = line2.strip("\n").split(":") + delphi_value = line2[1] + delphi_value_dict[i] = delphi_value + #print(delphi_value) + #if delphi_value == ' undefined': + #if previous.strip("\n").split(":")[1] != ' undefined': + #OFH2.write("%s\n"%(delphi_value)) + print(len(delphi_value_dict.keys())) + for line_no in delphi_value_dict.keys(): + #print(line_no) + + + + if delphi_value_dict[line_no] == ' undefined': + if (line_no + 1) < (len(delphi_value_dict.keys())): + if delphi_value_dict[line_no + 1] != ' undefined': + delphi_value_dict[line_no + 1] = delphi_value_dict[line_no + 1].strip() + OFH2.write("%s\n"%(0)) + elif delphi_value_dict[line_no - 1] != ' undefined': + delphi_value_dict[line_no - 1] = delphi_value_dict[line_no - 1].strip() + OFH2.write("%s\n"%(0)) + elif delphi_value_dict[line_no] != ' undefined': + delphi_value_dict[line_no] = delphi_value_dict[line_no].strip() + OFH2.write("%s\n"%(delphi_value_dict[line_no])) + + OFH2.close() + IFH2.close() +IFH1.close() \ No newline at end of file diff --git a/group_into_regions.py b/group_into_regions.py index 4352f43..ebf0613 100644 --- a/group_into_regions.py +++ b/group_into_regions.py @@ -1,6 +1,16 @@ #!/usr/bin/env python + +""" +Extract residual information from PDB files + +Map atoms to residual. +""" +__author__ = 'Mahita Jarapu' + + from pathlib import Path + data_folder = Path('data') with open (data_folder / 'list_residue_pot', 'r') as IFH1: diff --git a/temp/delphipot_surface.js b/temp/delphipot_surface.js new file mode 100644 index 0000000..fa1fe83 --- /dev/null +++ b/temp/delphipot_surface.js @@ -0,0 +1,88 @@ +/* +Please install the following three packages in your directory with the file interaction.js +npm install three +npm install jquery +npm install jsdom +npm install icn3d +npm install axios +npm install querystring +*/ +// https://github.com/Jam3/three-buffer-vertex-data/issues/2 +global.THREE = require('three'); +let jsdom = require('jsdom'); +global.$ = require('jquery')(new jsdom.JSDOM().window); +let icn3d = require('icn3d'); +let me = new icn3d.iCn3DUI({}); +let https = require('https'); +let axios = require('axios'); +let qs = require('querystring'); +let utils = require('./utils.js'); +let myArgs = process.argv.slice(2); +if(myArgs.length != 2) { + console.log("Usage: node delphipot.js [PDB ID] [comma-separated Chain IDs]"); + return; +} +let pdbid = myArgs[0].toUpperCase(); //'6jxr'; //myArgs[0]; +let chainArray = myArgs[1].split(','); +let baseUrlMmdb = "https://www.ncbi.nlm.nih.gov/Structure/mmdb/mmdb_strview.cgi?v=2&program=icn3d&b=1&s=1&ft=1&complexity=2&uid="; +let urlMmdb = baseUrlMmdb + pdbid; +https.get(urlMmdb, function(res1) { + let response1 = []; + res1.on('data', function (chunk) { + response1.push(chunk); + }); + res1.on('end', function(){ + let dataStr1 = response1.join(''); + let dataJson = JSON.parse(dataStr1); + me.setIcn3d(); + let ic = me.icn3d; + ic.bRender = false; + ic.mmdbParserCls.parseMmdbData(dataJson); + // select chains + ic.hAtoms = {}; + for(let i = 0, il = chainArray.length; i < il; ++i) { + let chainid = pdbid + '_' + chainArray[i]; + ic.hAtoms = me.hashUtilsCls.unionHash(ic.hAtoms, ic.chains[chainid]); + } + let pdbstr = ic.delphiCls.getPdbStr(true); + ic.loadPhiFrom = 'delphi'; + let url = "https://www.ncbi.nlm.nih.gov/Structure/delphi/delphi.fcgi"; + let gsize = 65; + let salt = 0.15; + let contour = 3; + let bSurface = true; + ic.phisurftype = 22; + let dataObj = {'pdb2phi': pdbstr, 'gsize': gsize, 'salt': salt, 'pdbid': pdbid, 'cube': 1, 'json': 1} + //https://attacomsian.com/blog/node-http-post-request + // 'https' didn't work for posting PDB data, use 'application/x-www-form-urlencoded' + const config = { headers: { 'Content-Type': 'application/x-www-form-urlencoded' } }; + axios.post(url, qs.stringify(dataObj), config) + .then(function(res) { + //console.log(`Status: ${res.status}`); + //console.log('Body: ', res.data); + let data = res.data.data.replace(/\\n/g, '\n'); + ic.delphiCls.loadCubeData(data, contour, bSurface); + ic.bAjaxPhi = true; + ic.setOptionCls.setOption('phisurface', 'phi'); + // calculate surface area + ic.analysisCls.calculateArea(); + ic.drawCls.draw(); + //console.log("Electrostatic potential: (kt/e)"); + //for(var i in ic.atoms) { + // console.log(i + ': ' + ic.atoms[i].pot); //if(i < 10) ; + //} + console.log("Solvent accessible surface area: (angstrom square)"); + for(var resid in ic.resid2area) { + console.log("resid: " + resid + ' area: ' + ic.resid2area[resid]); + } + }) + .catch(function(err) { + utils.dumpError(err); + }); + }); +}).on('error', function(e) { + console.error("Error: " + pdbid + " has no MMDB data..."); +}); + + + From 4410864b87222b286078b497242e7baa3269096e Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 30 Jul 2021 19:20:56 +0200 Subject: [PATCH 07/16] :construction: Add David's latest changes --- compute_similarity.py | 83 +++++++++++++++++++++++++------------------ 1 file changed, 48 insertions(+), 35 deletions(-) diff --git a/compute_similarity.py b/compute_similarity.py index 268b186..62f3651 100644 --- a/compute_similarity.py +++ b/compute_similarity.py @@ -28,15 +28,17 @@ mm = line.split(',') if len(mm) == 3 and mm[0] != 'PDB ID': if mm[1] == 'region_1': + if len(pdbs) != 0: + potentials[pdbs[-1]] = np.array(temp_potential) + pdbs.append(mm[0]) temp_potential = [float(mm[2])] elif mm[1] != 'region_1': temp_potential.append(float(mm[2])) - if mm[1] == 'region_21': - potentials[mm[0]] = np.array(temp_potential) +potentials[pdbs[-1]] = np.array(temp_potential) # for last pdb a.close() @@ -54,7 +56,7 @@ l1_distances = [] hamming_distances = [] chebyshev_distances = [] -jaccard_distances = [] +cityblock_distances = [] similarity_distances = {} normalized_similarity_distances = {} @@ -64,34 +66,44 @@ for sys1 in potentials: for sys2 in potentials: - cosine_distances.append(pairwise_distances(potentials[sys1].reshape( - 1, -1), potentials[sys2].reshape(1, -1), metric='cosine')[0][0]) - euclidean_distances.append(pairwise_distances(potentials[sys1].reshape( - 1, -1), potentials[sys2].reshape(1, -1), metric='euclidean')[0][0]) - l2_distances.append(pairwise_distances(potentials[sys1].reshape( - 1, -1), potentials[sys2].reshape(1, -1), metric='l2')[0][0]) - manhattan_distances.append(pairwise_distances(potentials[sys1].reshape( - 1, -1), potentials[sys2].reshape(1, -1), metric='manhattan')[0][0]) - l1_distances.append(pairwise_distances(potentials[sys1].reshape( - 1, -1), potentials[sys2].reshape(1, -1), metric='l1')[0][0]) - hamming_distances.append(pairwise_distances(potentials[sys1].reshape( - 1, -1), potentials[sys2].reshape(1, -1), metric='hamming')[0][0]) - chebyshev_distances.append(pairwise_distances(potentials[sys1].reshape( - 1, -1), potentials[sys2].reshape(1, -1), metric='chebyshev')[0][0]) - jaccard_distances.append(pairwise_distances(potentials[sys1].reshape( - 1, -1), potentials[sys2].reshape(1, -1), metric='jaccard')[0][0]) + shape_1 = potentials[sys1].reshape(1, -1).shape[1] + shape_2 = potentials[sys2].reshape(1, -1).shape[1] + min_len = min(shape_1, shape_2) + + # print(min_len,[potentials[sys1].reshape(1,-1)[0][0:min_len-1]],[potentials[sys2].reshape(1,-1)[0][0:min_len-1]]) + + cosine_distances.append(pairwise_distances([potentials[sys1].reshape( + 1, -1)[0][0:min_len-1]], [potentials[sys2].reshape(1, -1)[0][0:min_len-1]], metric='cosine')[0][0]) + euclidean_distances.append(pairwise_distances([potentials[sys1].reshape( + 1, -1)[0][0:min_len-1]], [potentials[sys2].reshape(1, -1)[0][0:min_len-1]], metric='euclidean')[0][0]) + l2_distances.append(pairwise_distances([potentials[sys1].reshape( + 1, -1)[0][0:min_len-1]], [potentials[sys2].reshape(1, -1)[0][0:min_len-1]], metric='l2')[0][0]) + manhattan_distances.append(pairwise_distances([potentials[sys1].reshape( + 1, -1)[0][0:min_len-1]], [potentials[sys2].reshape(1, -1)[0][0:min_len-1]], metric='manhattan')[0][0]) + l1_distances.append(pairwise_distances([potentials[sys1].reshape( + 1, -1)[0][0:min_len-1]], [potentials[sys2].reshape(1, -1)[0][0:min_len-1]], metric='l1')[0][0]) + hamming_distances.append(pairwise_distances([potentials[sys1].reshape( + 1, -1)[0][0:min_len-1]], [potentials[sys2].reshape(1, -1)[0][0:min_len-1]], metric='hamming')[0][0]) + chebyshev_distances.append(pairwise_distances([potentials[sys1].reshape( + 1, -1)[0][0:min_len-1]], [potentials[sys2].reshape(1, -1)[0][0:min_len-1]], metric='chebyshev')[0][0]) + cityblock_distances.append(pairwise_distances([potentials[sys1].reshape( + 1, -1)[0][0:min_len-1]], [potentials[sys2].reshape(1, -1)[0][0:min_len-1]], metric='cityblock')[0][0]) similarity_distances[sys1, sys2] = [cosine_distances[-1], euclidean_distances[-1], l2_distances[-1], - manhattan_distances[-1], l1_distances[-1], hamming_distances[-1], chebyshev_distances[-1], jaccard_distances[-1]] - + manhattan_distances[-1], l1_distances[-1], hamming_distances[-1], chebyshev_distances[-1], cityblock_distances[-1]] +print("line 83") # normalization loop - +iterate = 0 for sys1 in potentials: for sys2 in potentials: + iterate = iterate + 1 + print(iterate) - normalized_similarity_distances[sys1, sys2] = (np.array(similarity_distances[sys1, sys2]) - np.array([min(cosine_distances), min(euclidean_distances), min(l2_distances), min(manhattan_distances), min(l1_distances), min(hamming_distances), min(chebyshev_distances), min(jaccard_distances)])) / (np.array([max(cosine_distances), max(euclidean_distances), max( - l2_distances), max(manhattan_distances), max(l1_distances), max(hamming_distances), max(chebyshev_distances), max(jaccard_distances)]) - np.array([min(cosine_distances), min(euclidean_distances), min(l2_distances), min(manhattan_distances), min(l1_distances), min(hamming_distances), min(chebyshev_distances), min(jaccard_distances)])) + normalized_similarity_distances[sys1, sys2] = (np.array(similarity_distances[sys1, sys2]) - + np.array([min(cosine_distances), min(euclidean_distances), min(l2_distances), min(manhattan_distances), min(l1_distances), min(hamming_distances), min(chebyshev_distances), min(cityblock_distances)])) / ( + np.array([max(cosine_distances), max(euclidean_distances), max(l2_distances), max(manhattan_distances), max(l1_distances), max(hamming_distances), max(chebyshev_distances), max(cityblock_distances)] + ) - np.array([min(cosine_distances), min(euclidean_distances), min(l2_distances), min(manhattan_distances), min(l1_distances), min(hamming_distances), min(chebyshev_distances), min(cityblock_distances)])) mean_similarity_distances[sys1, sys2] = np.mean( normalized_similarity_distances[sys1, sys2]) @@ -99,15 +111,17 @@ normalized_similarity_distances[sys1, sys2]) / np.sqrt(len(normalized_similarity_distances[sys1, sys2])) +print("line96") + # Plot the data heatmap_matrix = [] annotations_matrix = [] -for sys1 in potentials.keys(): +for sys1 in pdbs: heatmap_row = [] annotations_row = [] - for sys2 in potentials.keys(): + for sys2 in pdbs: heatmap_row.append(mean_similarity_distances[sys1, sys2]) annotations_row.append('{}\n+/- {}'.format(round( mean_similarity_distances[sys1, sys2], 2), round(ci95_similarity_distances[sys1, sys2], 2))) @@ -127,19 +141,18 @@ fig, ax = plt.subplots() -ax = sns.heatmap(heatmap_matrix, mask=mask, annot=labels, fmt='', annot_kws={ - "size": 14}, cmap="RdBu_r") # fmt="0.2f", cmap="RdBu_r") +# annot=labels, fmt='',annot_kws={"size": 14}, cmap="RdBu_r") #fmt="0.2f", cmap="RdBu_r") +ax = sns.heatmap(heatmap_matrix, mask=mask, annot=False, cmap="RdBu_r") -row_labels = list(potentials.keys()) # pdbs -column_labels = list(potentials.keys()) # pdbs +#row_labels = pdbs +#column_labels = pdbs # put the major ticks at the middle of each cell -ax.set_yticks(np.arange(len(heatmap_matrix))+0.5, minor=True) -ax.set_xticks(np.arange(len(heatmap_matrix))+0.5, minor=True) +#ax.set_yticks(np.arange(len(heatmap_matrix))+0.5, minor=True) +#ax.set_xticks(np.arange(len(heatmap_matrix))+0.5, minor=True) -ax.set_xticklabels(column_labels, minor=False, rotation=30, - fontsize=16, ha="right", rotation_mode="anchor") # rotation=-30 -ax.set_yticklabels(row_labels, minor=False, rotation=0, fontsize=16) +# ax.set_xticklabels(column_labels, minor=False, rotation=30, fontsize=16,ha="right",rotation_mode="anchor") #rotation=-30 +#ax.set_yticklabels(row_labels, minor=False, rotation=0, fontsize=16) ax.tick_params(axis='x', which='major') # , pad=10) ax.tick_params(axis='y', which='major') # , pad=10) ax.set_xlabel('Antibodies', fontsize=20) From 89c6079b77ea5a99745b234947815563e84685a8 Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 30 Jul 2021 21:07:18 +0200 Subject: [PATCH 08/16] :sparkles: David's last figure, super fast;) - old data file processing used - new one has additional checks --- .gitignore | 4 +- compute_similarity.ipynb | 780 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 783 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 0ae6756..3744545 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,6 @@ node_modules/ package-lock.json -data/ \ No newline at end of file +data/ + +*ipynb_checkpoints \ No newline at end of file diff --git a/compute_similarity.ipynb b/compute_similarity.ipynb index fa5eb9a..f17351f 100644 --- a/compute_similarity.ipynb +++ b/compute_similarity.ipynb @@ -656,6 +656,786 @@ "df_metrics" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Normalization\n", + "\n", + "$ z = \\frac{x - min(X)}{max(X)-min(X)}$\n", + "\n", + "where\n", + "- $x$: a single correlation value of a metric\n", + "- $X$: the set of correlations for a single metric\n", + "- $z$: a singe *normalized* correlation value of a metric\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cosineeuclideanl2manhattanl1hammingchebyshev
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mean0.652209227.366947227.366947790.757367790.7573670.960446121.723782
std0.31188768.99822868.998228245.058471245.0584710.01831143.365576
min0.06154347.96454147.964541134.773140134.7731400.80952426.415275
25%0.426062179.426617179.426617619.666864619.6668640.95238191.395450
50%0.580068213.896251213.896251741.311852741.3118520.952381112.831046
75%0.824549261.265226261.265226916.887897916.8878970.952381141.552472
max1.782301523.050773523.0507731888.5369631888.5369631.000000336.504365
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" + ], + "text/plain": [ + " cosine euclidean l2 manhattan l1 \\\n", + "count 4753.000000 4753.000000 4753.000000 4753.000000 4753.000000 \n", + "mean 0.652209 227.366947 227.366947 790.757367 790.757367 \n", + "std 0.311887 68.998228 68.998228 245.058471 245.058471 \n", + "min 0.061543 47.964541 47.964541 134.773140 134.773140 \n", + "25% 0.426062 179.426617 179.426617 619.666864 619.666864 \n", + "50% 0.580068 213.896251 213.896251 741.311852 741.311852 \n", + "75% 0.824549 261.265226 261.265226 916.887897 916.887897 \n", + "max 1.782301 523.050773 523.050773 1888.536963 1888.536963 \n", + "\n", + " hamming chebyshev \n", + "count 4753.000000 4753.000000 \n", + "mean 0.960446 121.723782 \n", + "std 0.018311 43.365576 \n", + "min 0.809524 26.415275 \n", + "25% 0.952381 91.395450 \n", + "50% 0.952381 112.831046 \n", + "75% 0.952381 141.552472 \n", + "max 1.000000 336.504365 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats_metrics = df_metrics.describe()\n", + "stats_metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "X_min = stats_metrics.loc['min']\n", + "X_max = stats_metrics.loc['max']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cosineeuclideanl2manhattanl1hammingchebyshev
6nb46nb30.1317130.2055170.2055170.1701440.1701440.500.171331
6nb76nb30.7171790.4236090.4236090.4260780.4260781.000.285944
6nb40.7122550.3598770.3598770.3666520.3666521.000.265897
6xcn6nb30.5737250.4953230.4953230.5229880.5229881.000.385125
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...........................
7ntc7nd80.2731610.3150480.3150480.2704940.2704940.750.236765
7nd90.1495330.2326860.2326860.1971530.1971530.750.238359
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4753 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " cosine euclidean l2 manhattan l1 hamming \\\n", + "6nb4 6nb3 0.131713 0.205517 0.205517 0.170144 0.170144 0.50 \n", + "6nb7 6nb3 0.717179 0.423609 0.423609 0.426078 0.426078 1.00 \n", + " 6nb4 0.712255 0.359877 0.359877 0.366652 0.366652 1.00 \n", + "6xcn 6nb3 0.573725 0.495323 0.495323 0.522988 0.522988 1.00 \n", + " 6nb4 0.622463 0.467607 0.467607 0.439854 0.439854 1.00 \n", + "... ... ... ... ... ... ... \n", + "7ntc 7nd8 0.273161 0.315048 0.315048 0.270494 0.270494 0.75 \n", + " 7nd9 0.149533 0.232686 0.232686 0.197153 0.197153 0.75 \n", + " 7ndb 0.393152 0.373109 0.373109 0.308811 0.308811 0.75 \n", + " 7ndc 0.442506 0.415508 0.415508 0.374974 0.374974 0.75 \n", + " 7ndd 0.294154 0.329176 0.329176 0.334474 0.334474 0.75 \n", + "\n", + " chebyshev \n", + "6nb4 6nb3 0.171331 \n", + "6nb7 6nb3 0.285944 \n", + " 6nb4 0.265897 \n", + "6xcn 6nb3 0.385125 \n", + " 6nb4 0.453328 \n", + "... ... \n", + "7ntc 7nd8 0.236765 \n", + " 7nd9 0.238359 \n", + " 7ndb 0.299830 \n", + " 7ndc 0.401030 \n", + " 7ndd 0.271085 \n", + "\n", + "[4753 rows x 7 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_metrics_normalized = (df_metrics - X_min) / (X_max - X_min)\n", + "df_metrics_normalized" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting the mean metrics heatmap" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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6nb36nb46nb76xcn6xe16zdh7DDD7a5r7a5s7akj...7n8h7nd47nd57nd67nd77nd87nd97ndb7ndc7ndd
6nb40.222052NaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6nb70.5289280.490173NaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6xcn0.5707820.5558160.507191NaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6xe10.7130240.6847430.5660410.416526NaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6zdh0.6107990.6319110.5658630.5655530.689677NaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
..................................................................
7nd90.6271620.5771830.3887950.3878630.3858350.6157160.2756050.4295890.3841170.571683...0.2430260.3013870.3076560.2640370.3541940.236932NaNNaNNaNNaN
7ndb0.5885850.5643590.4140800.2839180.3796920.6616860.3328400.4975670.4744820.660595...0.2545740.3491810.2763870.2945740.2588990.3402710.306191NaNNaNNaN
7ndc0.5817460.5506460.3595440.4993240.4182260.5663890.3809690.4432880.4636810.665655...0.3950830.3772930.2887130.3889780.2088230.3232720.3956570.354349NaNNaN
7ndd0.6205250.5896980.3613220.4266040.3656950.5995450.3270040.4236180.3623050.648582...0.3068930.3570780.2754060.2824000.2135320.2812700.2967310.3091490.20568NaN
7ntc0.6727490.6068940.4907750.4175730.5085990.6367550.4503700.4076010.4202490.538860...0.3551270.3656840.4541810.3763140.4152060.3472870.2853670.4009750.453500.377505
\n", + "

97 rows × 97 columns

\n", + "
" + ], + "text/plain": [ + " 6nb3 6nb4 6nb7 6xcn 6xe1 6zdh 7DDD \\\n", + "6nb4 0.222052 NaN NaN NaN NaN NaN NaN \n", + "6nb7 0.528928 0.490173 NaN NaN NaN NaN NaN \n", + "6xcn 0.570782 0.555816 0.507191 NaN NaN NaN NaN \n", + "6xe1 0.713024 0.684743 0.566041 0.416526 NaN NaN NaN \n", + "6zdh 0.610799 0.631911 0.565863 0.565553 0.689677 NaN NaN \n", + "... ... ... ... ... ... ... ... \n", + "7nd9 0.627162 0.577183 0.388795 0.387863 0.385835 0.615716 0.275605 \n", + "7ndb 0.588585 0.564359 0.414080 0.283918 0.379692 0.661686 0.332840 \n", + "7ndc 0.581746 0.550646 0.359544 0.499324 0.418226 0.566389 0.380969 \n", + "7ndd 0.620525 0.589698 0.361322 0.426604 0.365695 0.599545 0.327004 \n", + "7ntc 0.672749 0.606894 0.490775 0.417573 0.508599 0.636755 0.450370 \n", + "\n", + " 7a5r 7a5s 7akj ... 7n8h 7nd4 7nd5 \\\n", + "6nb4 NaN NaN NaN ... NaN NaN NaN \n", + "6nb7 NaN NaN NaN ... NaN NaN NaN \n", + "6xcn NaN NaN NaN ... NaN NaN NaN \n", + "6xe1 NaN NaN NaN ... NaN NaN NaN \n", + "6zdh NaN NaN NaN ... NaN NaN NaN \n", + "... ... ... ... ... ... ... ... \n", + "7nd9 0.429589 0.384117 0.571683 ... 0.243026 0.301387 0.307656 \n", + "7ndb 0.497567 0.474482 0.660595 ... 0.254574 0.349181 0.276387 \n", + "7ndc 0.443288 0.463681 0.665655 ... 0.395083 0.377293 0.288713 \n", + "7ndd 0.423618 0.362305 0.648582 ... 0.306893 0.357078 0.275406 \n", + "7ntc 0.407601 0.420249 0.538860 ... 0.355127 0.365684 0.454181 \n", + "\n", + " 7nd6 7nd7 7nd8 7nd9 7ndb 7ndc 7ndd \n", + "6nb4 NaN NaN NaN NaN NaN NaN NaN \n", + "6nb7 NaN NaN NaN NaN NaN NaN NaN \n", + "6xcn NaN NaN NaN NaN NaN NaN NaN \n", + "6xe1 NaN NaN NaN NaN NaN NaN NaN \n", + "6zdh NaN NaN NaN NaN NaN NaN NaN \n", + "... ... ... ... ... ... ... ... \n", + "7nd9 0.264037 0.354194 0.236932 NaN NaN NaN NaN \n", + "7ndb 0.294574 0.258899 0.340271 0.306191 NaN NaN NaN \n", + "7ndc 0.388978 0.208823 0.323272 0.395657 0.354349 NaN NaN \n", + "7ndd 0.282400 0.213532 0.281270 0.296731 0.309149 0.20568 NaN \n", + "7ntc 0.376314 0.415206 0.347287 0.285367 0.400975 0.45350 0.377505 \n", + "\n", + "[97 rows x 97 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_metrics = df_metrics_normalized.mean(axis=1).unstack()\n", + "mean_metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Set up the matplotlib figure\n", + "matplotlib.rc('xtick', labelsize=16)\n", + "matplotlib.rc('ytick', labelsize=16)\n", + "\n", + "fig, ax = plt.subplots(figsize=(20,10)) \n", + "\n", + "ax = sns.heatmap(mean_metrics, annot=False, cmap=\"RdBu_r\", ax=ax) #annot=labels, fmt='',annot_kws={\"size\": 14}, cmap=\"RdBu_r\") #fmt=\"0.2f\", cmap=\"RdBu_r\")" + ] + }, { "cell_type": "code", "execution_count": null, From d8fc1952bb1c3ad665837fe712f3f00fc22d4cef Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 30 Jul 2021 22:11:16 +0200 Subject: [PATCH 09/16] :memo: update workflow notes - notes after meeting - start in this repository is provided in list_pdb_files --- bin/README.md | 42 +++++++++++++++++++++++++++--------------- 1 file changed, 27 insertions(+), 15 deletions(-) diff --git a/bin/README.md b/bin/README.md index 59d6b76..428c961 100644 --- a/bin/README.md +++ b/bin/README.md @@ -2,21 +2,33 @@ ## Workflow -1. Extract a single Ag chain from Ab-Ag complex using custom Biopython script -2. Rename PDB containing single Ag chain to include pdb id and chain id -3. Create list of all PDB files created in step 2 (`list_pdb_files`) -4. Run `automate_delphical.sh` in the icn3d folder (for potential calculation) on all these files +0. Extract a single Ag chain from Ab-Ag complex using custom workflow + - not provided is an automated way (biopython script/some other tools) to create a + list of `PDB_ID` and `Chain` pairs to store. + ``` + # current format + # _Ag__rbd.pdb # pdbid and chain need to be extracted + 2dd8_Ag_S_rbd.pdb # example + ``` +1. Create a currated list of all PDB files with their side-chain informatin + - see [`data/list_pdb_files`](../data/list_pdb_files) +2. Run `automate_delphical.sh` in the icn3d folder (for potential calculation) on all these files - after commenting out the code used for surface area calculation in the `delphipot_surface.js` script -5. Run `automate_delphical.sh` (for surface area/residue calculation) on all these files (in the icn3d folder) + > [`delphipot.js`](delphipot.js) needs adaption to accomodate several options + > script has to be adapted. potentially change to snakemake +3. Run `automate_delphical.sh` (for surface area/residue calculation) on all these files (in the icn3d folder) - after commenting out the code used for electrostatic potential calculation in the `delphipot_surface.js` script -6. Make list of output files generated in step 4 (`input_list_out`) -7. Make list of output files generated in step 5 (`list_surface_files`) -8. Run `remove_undefined.py` on list from step 6 -9. Make list of output files generated in step 8 (`list_clean_pot`) -10. Run `map_atom_to_res.py` using lists from step 9 after keeping output files and pdb files in the same folder -11. make list of csv files generated in step 10 +4. Make list of output files generated in step 2 (`input_list_out`) + - having suffix `*_out` +5. Make list of output files generated in step 3 (`list_surface_files`) + - having suffix `*_surface` +6. Run `remove_undefined.py` on list from step 3 +7. Make list of output files generated in step 6 (`list_clean_pot`) +8. Run `map_atom_to_res.py` using lists from step 7 + - after keeping output files and pdb files in the same folder +11. make list of csv files generated in step 8 12. keep surface files (filename format `_surface`) and .csv files in a single folder -13. run `group_into_regions.py` - creates an output file `all_spike_strs_regions_pot.csv` - - -- currently: start at step 4 () \ No newline at end of file +13. run `group_into_regions.py` + - creates an output file `all_spike_strs_regions_pot.csv` + - only keeps residues 320 to 530 + > potentially previous processing could be restricted to region of interest \ No newline at end of file From 6c5480c078fc0deb8f00474b9c9b905988cba6eb Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 30 Jul 2021 23:41:34 +0200 Subject: [PATCH 10/16] :construction: set up snakemake workflow for step1 - get potentials for further processing (step 1) in workflow described in bin/README.md --- README.md | 17 +++++++++++++++-- Snakefile | 38 ++++++++++++++++++++++++++++++++++++++ config.yaml | 3 +++ environment.yml | 5 ++++- 4 files changed, 60 insertions(+), 3 deletions(-) create mode 100644 Snakefile create mode 100644 config.yaml diff --git a/README.md b/README.md index 4cf6964..0ce0a74 100644 --- a/README.md +++ b/README.md @@ -34,14 +34,15 @@ Most of these mutations have appeared on the ACE2-binding region on Spike, calle ## ## Installation -Assuming you have cloned the git repository and are in the main repository folder. These steps are also found in the setup.md file. +Assuming you have cloned the git repository and are in the main repository folder. +These steps are also found in the setup.md file. ### Setup a working node js version One option to do this is to create a conda environment and install nodejs into a new environment ```cmd -conda create -n isbm2021hack nodejs +conda env create -n isbm2021hack -f environment conda activate isbm2021hack ``` @@ -58,6 +59,8 @@ npm install three jquery axios querystring npm install icn3d ``` +## Single Scripts + ### DelPhi calculation Calculate the DelPhi potential map using a nodejs script. @@ -68,3 +71,13 @@ node bin/delphipot.js [PDB ID] [comma-separated Chain IDs] > data/[PBID] The DelPhi potential map is now located in the data folder. +## Snakemake Worflow + +The [Snakemake](https://snakemake.readthedocs.io) workflow executes all necessary scripts +(in development, tbc) + +``` +snakemake -c1 -n # dry-run +#snakemake -c1 -n -p # dry-run with rule execution preview +snakemake --cores 3 # execute three jobs in parallel +``` \ No newline at end of file diff --git a/Snakefile b/Snakefile new file mode 100644 index 0000000..69b10d7 --- /dev/null +++ b/Snakefile @@ -0,0 +1,38 @@ +""" +Snakemake workflow file +https://snakemake.readthedocs.io/ + +Run all scripts to produce final results +""" + +from pathlib import Path +configfile: 'config.yaml' # access using config['key'] + + +config['DATADIR'] = Path(config['DATADIR']) +config['SCRIPTDIR'] = Path(config['SCRIPTDIR']) +with open(config['DATADIR'] / config['INFILE']) as f: + # format: 2dd8_Ag_S_rbd.pdb + PDB_IDS = [] + CHAINS = [] + for line in f: + line = line.split('_') + PDB_IDS.append(line[0]) + CHAINS.append(line[2]) + + +rule target: + input: + expand(config['DATADIR'] / 'potentials' / "{pdb_id}_{chain}_out.txt", + zip, + pdb_id=PDB_IDS, + chain=CHAINS) + + +rule calculate_potential: + output: + config['DATADIR'] / 'potentials' / "{pdb_id}_{chain}_out.txt" + params: + config['SCRIPTDIR'] / 'delphipot.js' + shell: + "node {params} {wildcards.pdb_id} {wildcards.chain} > {output}" diff --git a/config.yaml b/config.yaml new file mode 100644 index 0000000..5872a5c --- /dev/null +++ b/config.yaml @@ -0,0 +1,3 @@ +DATADIR: data +SCRIPTDIR: bin +INFILE: list_pdb_files \ No newline at end of file diff --git a/environment.yml b/environment.yml index 303a8b0..e2c4e98 100644 --- a/environment.yml +++ b/environment.yml @@ -1,6 +1,8 @@ name: isbm2021hack channels: - defaults + - bioconda + - conda-forge dependencies: - nodejs - python>=3.7 @@ -9,4 +11,5 @@ dependencies: - matplotlib - scikit-learn - seaborn -prefix: C:\Users\kzl465\Anaconda3\envs\isbm2021hack + - snakemake-minimal + From 41d35542047b4869d71e37a2cfa2e2a4a87ce168 Mon Sep 17 00:00:00 2001 From: Henry Date: Sat, 31 Jul 2021 18:14:40 +0200 Subject: [PATCH 11/16] :sparkles: add switch to delphipot.js - produce either residual or surface potentials - add more "help" to delphipot.js script - add surface potential creation to snakemake - idea: best would be to adapt the js.script to write to disk to avoid repeated downloads and invocations of the node script... --- .gitignore | 3 ++- Snakefile | 14 +++++++++++++- bin/delphipot.js | 35 +++++++++++++++++++++++++---------- bin/utils.js | 23 +++++++++++++++++++++++ 4 files changed, 63 insertions(+), 12 deletions(-) create mode 100644 bin/utils.js diff --git a/.gitignore b/.gitignore index 3744545..3469342 100644 --- a/.gitignore +++ b/.gitignore @@ -3,4 +3,5 @@ package-lock.json data/ -*ipynb_checkpoints \ No newline at end of file +*ipynb_checkpoints +.snakemake \ No newline at end of file diff --git a/Snakefile b/Snakefile index 69b10d7..3ec10de 100644 --- a/Snakefile +++ b/Snakefile @@ -24,6 +24,10 @@ with open(config['DATADIR'] / config['INFILE']) as f: rule target: input: expand(config['DATADIR'] / 'potentials' / "{pdb_id}_{chain}_out.txt", + zip, + pdb_id=PDB_IDS, + chain=CHAINS), + expand(config['DATADIR'] / 'potentials_surface' / "{pdb_id}_{chain}_surface.txt", zip, pdb_id=PDB_IDS, chain=CHAINS) @@ -35,4 +39,12 @@ rule calculate_potential: params: config['SCRIPTDIR'] / 'delphipot.js' shell: - "node {params} {wildcards.pdb_id} {wildcards.chain} > {output}" + "node {params} {wildcards.pdb_id} {wildcards.chain} res > {output}" + +rule calculate_surface_potential: + output: + config['DATADIR'] / 'potentials_surface' / "{pdb_id}_{chain}_surface.txt" + params: + config['SCRIPTDIR'] / 'delphipot.js' + shell: + "node {params} {wildcards.pdb_id} {wildcards.chain} surface > {output}" \ No newline at end of file diff --git a/bin/delphipot.js b/bin/delphipot.js index cd32548..c10fafc 100644 --- a/bin/delphipot.js +++ b/bin/delphipot.js @@ -17,12 +17,21 @@ let axios = require('axios'); let qs = require('querystring'); let utils = require('./utils.js'); let myArgs = process.argv.slice(2); -if(myArgs.length != 2) { - console.log("Usage: node delphipot.js [PDB ID] [comma-separated Chain IDs]"); +if(myArgs.length != 3) { + console.error( + `Usage: node delphipot.js [PDB ID] [comma-separated Chain IDs mode] + + Console script to calcualte residual or surface potential + + PDB ID: pdb id + chain: selected side-chain(s) comma seperated + mode: res or surface` + ); return; } let pdbid = myArgs[0].toUpperCase(); //'6jxr'; //myArgs[0]; let chainArray = myArgs[1].split(','); +let mode = myArgs[2].toLowerCase(); // res or surface let baseUrlMmdb = "https://www.ncbi.nlm.nih.gov/Structure/mmdb/mmdb_strview.cgi?v=2&program=icn3d&b=1&s=1&ft=1&complexity=2&uid="; let urlMmdb = baseUrlMmdb + pdbid; https.get(urlMmdb, function(res1) { @@ -77,14 +86,20 @@ https.get(urlMmdb, function(res1) { resid2pot[resid] += atom.pot; } } - console.log("Electrostatic potential: (kt/e)"); - for (var resid in resid2pot){ - console.log(resid + " : " + resid2pot[resid]); - } - console.log("Solvent accessible surface area: (angstrom square)"); - for(var resid in ic.resid2area) { - console.log("resid: " + resid + ' area: ' + ic.resid2area[resid]); - } + if (mode === 'res') { + console.log("Electrostatic potential: (kt/e)"); + for (var resid in resid2pot){ + console.log(resid + " : " + resid2pot[resid]); + } + } else if (mode === 'surface') { + console.log("Solvent accessible surface area: (angstrom square)"); + for(var resid in ic.resid2area) { + console.log("resid: " + resid + ' area: ' + ic.resid2area[resid]); + } + } else { + console.error("Choose one of the two modes: res or surface"); + return; + } }) .catch(function(err) { utils.dumpError(err); diff --git a/bin/utils.js b/bin/utils.js new file mode 100644 index 0000000..97d9791 --- /dev/null +++ b/bin/utils.js @@ -0,0 +1,23 @@ +// https://www.geeksforgeeks.org/how-to-share-code-between-node-js-and-the-browser/ +// https://stackoverflow.com/questions/3225251/how-can-i-share-code-between-node-js-and-the-browser +(function(exports) { +// 'use strict'; + +let dumpError = function(err) { + if (typeof err === 'object') { + if (err.message) { + console.log('\nMessage: ' + err.message) + } + if (err.stack) { + console.log('\nStacktrace:') + console.log('====================') + console.log(err.stack); + } + } else { + console.log('dumpError :: argument is not an object'); + } +} + +exports.dumpError = dumpError; + +})(typeof exports === 'undefined'? this : exports); //this['share']={}: exports); From e4d4bbd3ec5e02a62f47949de9538cf80aa87922 Mon Sep 17 00:00:00 2001 From: Henry Date: Sat, 31 Jul 2021 18:30:58 +0200 Subject: [PATCH 12/16] :art: format --- bin/remove_undefined.py | 76 ++++++++++++++++++++--------------------- 1 file changed, 38 insertions(+), 38 deletions(-) diff --git a/bin/remove_undefined.py b/bin/remove_undefined.py index 2b691d2..b76c2bd 100644 --- a/bin/remove_undefined.py +++ b/bin/remove_undefined.py @@ -7,44 +7,44 @@ """ __author__ = 'Mahita Jarapu' -IFH1 = open('input_list_out', 'r') +IFH1 = open('input_list_out', 'r') lines1 = IFH1.readlines() for line1 in lines1: - line1 = line1.strip("\n") - IFH2 = open(line1, 'r') - OFH2 = open(line1.split("_")[0]+'_'+'clean', 'w') - IFH2 = open(line1, 'r') - lines2 = IFH2.readlines() - print(line1) - delphi_value_dict = {} - - for i,line2 in enumerate(lines2): - if i > 0: - line2 = line2.strip("\n").split(":") - delphi_value = line2[1] - delphi_value_dict[i] = delphi_value - #print(delphi_value) - #if delphi_value == ' undefined': - #if previous.strip("\n").split(":")[1] != ' undefined': - #OFH2.write("%s\n"%(delphi_value)) - print(len(delphi_value_dict.keys())) - for line_no in delphi_value_dict.keys(): - #print(line_no) - + line1 = line1.strip("\n") + IFH2 = open(line1, 'r') + OFH2 = open(line1.split("_")[0]+'_'+'clean', 'w') + IFH2 = open(line1, 'r') + lines2 = IFH2.readlines() + print(line1) + delphi_value_dict = {} - - if delphi_value_dict[line_no] == ' undefined': - if (line_no + 1) < (len(delphi_value_dict.keys())): - if delphi_value_dict[line_no + 1] != ' undefined': - delphi_value_dict[line_no + 1] = delphi_value_dict[line_no + 1].strip() - OFH2.write("%s\n"%(0)) - elif delphi_value_dict[line_no - 1] != ' undefined': - delphi_value_dict[line_no - 1] = delphi_value_dict[line_no - 1].strip() - OFH2.write("%s\n"%(0)) - elif delphi_value_dict[line_no] != ' undefined': - delphi_value_dict[line_no] = delphi_value_dict[line_no].strip() - OFH2.write("%s\n"%(delphi_value_dict[line_no])) - - OFH2.close() - IFH2.close() -IFH1.close() \ No newline at end of file + for i, line2 in enumerate(lines2): + if i > 0: + line2 = line2.strip("\n").split(":") + delphi_value = line2[1] + delphi_value_dict[i] = delphi_value + # print(delphi_value) + # if delphi_value == ' undefined': + # if previous.strip("\n").split(":")[1] != ' undefined': + # OFH2.write("%s\n"%(delphi_value)) + print(len(delphi_value_dict.keys())) + for line_no in delphi_value_dict.keys(): + # print(line_no) + + if delphi_value_dict[line_no] == ' undefined': + if (line_no + 1) < (len(delphi_value_dict.keys())): + if delphi_value_dict[line_no + 1] != ' undefined': + delphi_value_dict[line_no + + 1] = delphi_value_dict[line_no + 1].strip() + OFH2.write("%s\n" % (0)) + elif delphi_value_dict[line_no - 1] != ' undefined': + delphi_value_dict[line_no - + 1] = delphi_value_dict[line_no - 1].strip() + OFH2.write("%s\n" % (0)) + elif delphi_value_dict[line_no] != ' undefined': + delphi_value_dict[line_no] = delphi_value_dict[line_no].strip() + OFH2.write("%s\n" % (delphi_value_dict[line_no])) + + OFH2.close() + IFH2.close() +IFH1.close() From b2dbf05acf415eb5af2aa78da95d61aad9505001 Mon Sep 17 00:00:00 2001 From: Henry Date: Sat, 31 Jul 2021 19:20:24 +0200 Subject: [PATCH 13/16] :construction: begin cleaning scripts - get feedback from Mahita on logic - think about output format --- bin/README.md | 6 ++- bin/remove_undefined.py | 91 ++++++++++++++++++++++------------------- 2 files changed, 55 insertions(+), 42 deletions(-) diff --git a/bin/README.md b/bin/README.md index 428c961..6597212 100644 --- a/bin/README.md +++ b/bin/README.md @@ -20,10 +20,14 @@ - after commenting out the code used for electrostatic potential calculation in the `delphipot_surface.js` script 4. Make list of output files generated in step 2 (`input_list_out`) - having suffix `*_out` + - `out_files`: run script on files in folder `potentials` 5. Make list of output files generated in step 3 (`list_surface_files`) - having suffix `*_surface` -6. Run `remove_undefined.py` on list from step 3 + - `surface_files`: run script on files in folder `potentials_surface` +6. Run `remove_undefined.py` on list from step 4, i.e. `input_list_out` + - i.e. on files in folder `potentials` 7. Make list of output files generated in step 6 (`list_clean_pot`) + - put all into a new cleaned folder 8. Run `map_atom_to_res.py` using lists from step 7 - after keeping output files and pdb files in the same folder 11. make list of csv files generated in step 8 diff --git a/bin/remove_undefined.py b/bin/remove_undefined.py index b76c2bd..55dbd95 100644 --- a/bin/remove_undefined.py +++ b/bin/remove_undefined.py @@ -5,46 +5,55 @@ Map atoms to residual. """ +from pathlib import Path +import argparse __author__ = 'Mahita Jarapu' -IFH1 = open('input_list_out', 'r') -lines1 = IFH1.readlines() -for line1 in lines1: - line1 = line1.strip("\n") - IFH2 = open(line1, 'r') - OFH2 = open(line1.split("_")[0]+'_'+'clean', 'w') - IFH2 = open(line1, 'r') - lines2 = IFH2.readlines() - print(line1) - delphi_value_dict = {} - - for i, line2 in enumerate(lines2): - if i > 0: - line2 = line2.strip("\n").split(":") - delphi_value = line2[1] - delphi_value_dict[i] = delphi_value - # print(delphi_value) - # if delphi_value == ' undefined': - # if previous.strip("\n").split(":")[1] != ' undefined': - # OFH2.write("%s\n"%(delphi_value)) - print(len(delphi_value_dict.keys())) - for line_no in delphi_value_dict.keys(): - # print(line_no) - - if delphi_value_dict[line_no] == ' undefined': - if (line_no + 1) < (len(delphi_value_dict.keys())): - if delphi_value_dict[line_no + 1] != ' undefined': - delphi_value_dict[line_no + - 1] = delphi_value_dict[line_no + 1].strip() - OFH2.write("%s\n" % (0)) - elif delphi_value_dict[line_no - 1] != ' undefined': - delphi_value_dict[line_no - - 1] = delphi_value_dict[line_no - 1].strip() - OFH2.write("%s\n" % (0)) - elif delphi_value_dict[line_no] != ' undefined': - delphi_value_dict[line_no] = delphi_value_dict[line_no].strip() - OFH2.write("%s\n" % (delphi_value_dict[line_no])) - - OFH2.close() - IFH2.close() -IFH1.close() +OUTFILE_TEMPLATE = '{}_clean' + + +def get_args(): + parser = argparse.ArgumentParser( + 'remove undefined potentials from residuals files.') + parser.add_argument( + '-f', '--folder', help='Folder with potential files to clean.') + args = parser.parse_args() + return args + + +args = get_args() + +folder = Path(args.folder) + +for _file in folder.iterdir(): + delphi_value_dict = {} + outfile = OUTFILE_TEMPLATE.format(_file) + with open(_file) as f, open(outfile, 'w') as OFH2: + header = next(f) + # add check that header is as expected? + for i, line in enumerate(f, start=1): + # example: 7CDJ_E_333 : -35.06 + line = line.strip("\n").split(":") + delphi_value = line[1] + delphi_value_dict[i] = delphi_value # change to position in AG? + + for line_no in delphi_value_dict.keys(): + # print(line_no) + """What about NaN values + + Idea is to + - replace undefined / NaN with 0 if the value before or after is defined? + """ + if delphi_value_dict[line_no] == ' undefined': + if (line_no + 1) < (len(delphi_value_dict.keys())): + if delphi_value_dict[line_no + 1] != ' undefined': + delphi_value_dict[line_no + + 1] = delphi_value_dict[line_no + 1].strip() + OFH2.write("%s\n" % (0)) # only write 0 if next is also not undef + elif delphi_value_dict[line_no - 1] != ' undefined': + delphi_value_dict[line_no - + 1] = delphi_value_dict[line_no - 1].strip() + OFH2.write("%s\n" % (0)) + elif delphi_value_dict[line_no] != ' undefined': + delphi_value_dict[line_no] = delphi_value_dict[line_no].strip() + OFH2.write("%s\n" % (delphi_value_dict[line_no])) From ab208af99927ea9e7a5255df6b20c44ede617ac9 Mon Sep 17 00:00:00 2001 From: Henry Date: Sun, 1 Aug 2021 18:55:04 +0200 Subject: [PATCH 14/16] :sparkles: update data loading and selection of AGs - create dictionary view of data - add analysis for number of regions available - select 180 AGs - enlarge plot --- compute_similarity.ipynb | 2085 +++++++++++++++++++++++--------------- 1 file changed, 1248 insertions(+), 837 deletions(-) diff --git a/compute_similarity.ipynb b/compute_similarity.ipynb index f17351f..14dd6ec 100644 --- a/compute_similarity.ipynb +++ b/compute_similarity.ipynb @@ -3,64 +3,130 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, - "outputs": [], "source": [ - "#!/usr/bin/env python\n", - "\n", - "import sys\n", - "import time\n", - "from pathlib import Path\n", - "\n", - "import numpy as np\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from sklearn.metrics import pairwise_distances\n", - "from sklearn.metrics.pairwise import pairwise_kernels\n", - "from sklearn.metrics.pairwise import cosine_similarity\n", + "#!/usr/bin/env python\r\n", + "\r\n", + "import sys\r\n", + "import time\r\n", + "from pathlib import Path\r\n", + "from collections import defaultdict\r\n", + "\r\n", + "import numpy as np\r\n", + "import matplotlib\r\n", + "import matplotlib.pyplot as plt\r\n", + "import seaborn as sns\r\n", + "from sklearn.metrics import pairwise_distances\r\n", + "from sklearn.metrics.pairwise import pairwise_kernels\r\n", + "from sklearn.metrics.pairwise import cosine_similarity\r\n", "from scipy.spatial.distance import cosine" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 2, - "metadata": {}, - "outputs": [], "source": [ - "start_time = time.time()\n", - "# Script to compute similarity matrices for subregion electrostatics of each PDB. Will need to plot them too. Hmmm -> matrix heatmap.\n", - "\n", - "## Load the data ##\n", - "\n", - "pdbs = []\n", - "potentials = {}\n", - "data_folder = Path('data')\n", - "a = open(data_folder / 'all_spike_strs_regions_pot.csv', 'r')\n", - "for line in a:\n", - " mm = line.split(',')\n", - " if len(mm) == 3 and mm[0] != 'PDB ID':\n", - " if mm[1] == 'region_1':\n", - " pdbs.append(mm[0])\n", - " temp_potential = [float(mm[2])]\n", - "\n", - " elif mm[1] != 'region_1':\n", - " temp_potential.append(float(mm[2]))\n", - "\n", - " if mm[1] == 'region_21':\n", - " potentials[mm[0]] = np.array(temp_potential)\n", - "\n", - "\n", - "a.close()" - ] + "start_time = time.time()\r\n", + "# Script to compute similarity matrices for subregion electrostatics of each PDB. Will need to plot them too. Hmmm -> matrix heatmap.\r\n", + "\r\n", + "## Load the data ##\r\n", + "\r\n", + "pdbs = []\r\n", + "potentials = {}\r\n", + "data_folder = Path('data')\r\n", + "with open(data_folder / 'all_spike_strs_regions_pot.csv', 'r') as f:\r\n", + " header = next(f).split(',')\r\n", + " print('column names:', header)\r\n", + " data = defaultdict(dict)\r\n", + " for line in f:\r\n", + " mm = line.split(',')\r\n", + "\r\n", + " if len(mm) == 3:\r\n", + " key_AG, key_region, potential = mm\r\n", + " # key_region = int(key_region.split['_'][-1]) # transform region key to int?\r\n", + " data[key_AG].update({key_region: float(potential)})\r\n", + "\r\n", + "data = dict(data)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "column names: ['PDB ID', ' Region within RBD', ' Delphi Electrostatic Potential\\n']\n" + ] + } + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "source": [ + "import pandas as pd\r\n", + "df = pd.DataFrame(data).T\r\n", + "df" + ], "outputs": [ { + "output_type": "execute_result", "data": { + "text/plain": [ + " region_1 region_2 region_3 region_4 region_5 region_6 \\\n", + "2dd8 -70.191115 39.068191 78.750559 95.703496 70.879241 -76.449713 \n", + "2ghw -106.785507 19.609688 -1.795815 -39.132425 44.017062 -105.506540 \n", + "4f2m -104.913528 40.581253 132.354564 NaN NaN NaN \n", + "4xak -170.187825 8.002645 -10.362026 32.489331 -83.017622 0.000000 \n", + "4zpt -116.596861 39.728958 -109.296683 54.022448 -47.795527 0.000000 \n", + "... ... ... ... ... ... ... \n", + "7ora 119.528058 65.582112 62.251735 -115.629936 92.654731 -24.808859 \n", + "7orb -64.065729 61.911957 77.064877 -64.943340 12.360914 -85.277850 \n", + "7r6w -67.150572 116.221509 4.690351 -129.235458 -5.786718 -54.351238 \n", + "7r6x -34.064129 -116.646085 -3.236060 -79.870917 -35.885819 29.373127 \n", + "7r7n -37.944014 21.843747 395.751540 22.940593 28.886001 74.586751 \n", + "\n", + " region_7 region_8 region_9 region_10 ... region_12 \\\n", + "2dd8 0.000000 93.516598 -55.845547 -121.090888 ... -1.083614 \n", + "2ghw 0.000000 -42.881587 11.830961 -111.973462 ... -61.256166 \n", + "4f2m NaN NaN NaN NaN ... NaN \n", + "4xak 0.000000 22.325005 -15.002110 24.087360 ... 2.597925 \n", + "4zpt 0.000000 12.374975 -6.511433 39.365383 ... 23.294556 \n", + "... ... ... ... ... ... ... \n", + "7ora 0.000000 56.913905 -14.503087 -89.224984 ... -161.690621 \n", + "7orb -59.660744 9.026050 22.382662 -104.342434 ... 128.764396 \n", + "7r6w 0.000000 64.983853 14.631888 -107.456273 ... 0.033576 \n", + "7r6x 71.473550 0.000000 28.990869 47.648078 ... -1.956275 \n", + "7r7n 0.000000 197.135961 62.370021 -100.268567 ... -3.988360 \n", + "\n", + " region_13 region_14 region_15 region_16 region_17 region_18 \\\n", + "2dd8 75.810597 131.833181 69.729486 31.898551 22.288216 -3.203085 \n", + "2ghw 64.853937 -3.509490 -7.347929 -30.154394 3.018089 5.173115 \n", + "4f2m NaN NaN NaN NaN NaN NaN \n", + "4xak -87.052227 -130.716816 -51.104001 NaN NaN NaN \n", + "4zpt -70.729916 -43.711976 13.113535 NaN NaN NaN \n", + "... ... ... ... ... ... ... \n", + "7ora 44.405440 41.702589 48.555987 -121.849770 -71.586651 -10.086030 \n", + "7orb 14.050218 1.254457 -28.472693 -33.796958 19.477023 8.134585 \n", + "7r6w 5.749872 4.985055 -43.865304 -220.855610 36.621550 50.967934 \n", + "7r6x -51.736143 127.173992 -49.092249 -56.133036 -65.851328 16.456864 \n", + "7r7n 77.950102 -21.443383 -71.517534 42.185493 43.996559 0.395534 \n", + "\n", + " region_19 region_20 region_21 \n", + "2dd8 -13.742084 NaN NaN \n", + "2ghw NaN NaN NaN \n", + "4f2m NaN NaN NaN \n", + "4xak NaN NaN NaN \n", + "4zpt NaN NaN NaN \n", + "... ... ... ... \n", + "7ora 52.540947 NaN NaN \n", + "7orb NaN NaN NaN \n", + "7r6w -24.928986 NaN NaN \n", + "7r6x -28.196962 -46.13797 NaN \n", + "7r7n NaN NaN NaN \n", + "\n", + "[221 rows x 21 columns]" + ], "text/html": [ "
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"df = pd.DataFrame(potentials).T\n", - "df" - ] + "continue with remaining complete list of antigens" + ], + "metadata": {} }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], + "execution_count": 6, "source": [ - "def lower_triangle(df):\n", - " \"\"\"Compute the correlation matrix, returning only unique values.\"\"\"\n", - " lower_triangle = pd.DataFrame(\n", - " np.tril(np.ones(df.shape), -1)).astype(bool)\n", - " lower_triangle.index, lower_triangle.columns = df.index, df.columns\n", + "def lower_triangle(df):\r\n", + " \"\"\"Compute the correlation matrix, returning only unique values.\"\"\"\r\n", + " lower_triangle = pd.DataFrame(\r\n", + " np.tril(np.ones(df.shape), -1)).astype(bool)\r\n", + " lower_triangle.index, lower_triangle.columns = df.index, df.columns\r\n", " return df.where(lower_triangle)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 7, + "source": [ + "dict_dist= {}\r\n", + "metrics = ['cosine', 'euclidean', 'l2', 'manhattan', 'l1', 'hamming', 'chebyshev'] # 'jaccard' excluded as it's for binary data\r\n", + "for _metric in metrics:\r\n", + " dict_dist[_metric] = pd.DataFrame(pairwise_distances(X=df, metric=_metric), index=df.index, columns=df.index)\r\n", + " dict_dist[_metric] = lower_triangle(dict_dist[_metric]).stack()\r\n", + "df_metrics = pd.DataFrame(dict_dist)\r\n", + "df_metrics" + ], "outputs": [ { + "output_type": "execute_result", "data": { + "text/plain": [ + " cosine euclidean l2 manhattan l1 \\\n", + "6nb3 2dd8 0.938914 342.306553 342.306553 1243.739490 1243.739490 \n", + "6nb4 2dd8 0.976647 344.053933 344.053933 1250.615352 1250.615352 \n", + " 6nb3 0.259524 125.702792 125.702792 340.787346 340.787346 \n", + "6nb7 2dd8 0.951805 313.103857 313.103857 1133.393273 1133.393273 \n", + " 6nb3 1.266582 222.754432 222.754432 736.340576 736.340576 \n", + "... ... ... ... ... ... \n", + "7r6x 7nxa 1.085743 424.789662 424.789662 1543.779197 1543.779197 \n", + " 7nxb 0.916548 384.750086 384.750086 1430.824919 1430.824919 \n", + " 7or9 0.839519 385.939161 385.939161 1326.726736 1326.726736 \n", + " 7ora 0.984735 449.786647 449.786647 1714.610068 1714.610068 \n", + " 7r6w 0.915995 431.170392 431.170392 1406.534169 1406.534169 \n", + "\n", + " hamming chebyshev \n", + "6nb3 2dd8 1.000000 160.151009 \n", + "6nb4 2dd8 1.000000 153.934798 \n", + " 6nb3 0.894737 79.543183 \n", + "6nb7 2dd8 0.947368 138.940990 \n", + " 6nb3 1.000000 115.083253 \n", + "... ... ... \n", + "7r6x 7nxa 1.000000 191.534638 \n", + " 7nxb 1.000000 176.368781 \n", + " 7or9 1.000000 194.346473 \n", + " 7ora 1.000000 182.228197 \n", + " 7r6w 1.000000 232.867594 \n", + "\n", + "[16110 rows x 7 columns]" + ], "text/html": [ "
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221 rows × 21 columns

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179 rows × 179 columns

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" - ] - }, - "metadata": {}, - "execution_count": 11 - } - ], + "outputs": [], "metadata": {} }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "source": [ "# Set up the matplotlib figure\r\n", "matplotlib.rc('xtick', labelsize=16)\r\n", @@ -1831,20 +198,7 @@ "\r\n", "ax = sns.heatmap(mean_metrics, annot=False, cmap=\"RdBu_r\", ax=ax) #annot=labels, fmt='',annot_kws={\"size\": 14}, cmap=\"RdBu_r\") #fmt=\"0.2f\", cmap=\"RdBu_r\")" ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "metadata": {} }, { From 26eeea8e3d5c77d2ff4f47c5d1beee2ffc4d1d6d Mon Sep 17 00:00:00 2001 From: enryh Date: Fri, 13 Aug 2021 12:04:22 +0200 Subject: [PATCH 16/16] :art: autoformat file --- compute_similarity.ipynb | 232 ++++++++++++++++++++------------------- 1 file changed, 121 insertions(+), 111 deletions(-) diff --git a/compute_similarity.ipynb b/compute_similarity.ipynb index 2430138..3914b50 100644 --- a/compute_similarity.ipynb +++ b/compute_similarity.ipynb @@ -3,130 +3,139 @@ { "cell_type": "code", "execution_count": null, - "source": [ - "#!/usr/bin/env python\r\n", - "\r\n", - "import sys\r\n", - "import time\r\n", - "from pathlib import Path\r\n", - "from collections import defaultdict\r\n", - "\r\n", - "import numpy as np\r\n", - "import matplotlib\r\n", - "import matplotlib.pyplot as plt\r\n", - "import seaborn as sns\r\n", - "from sklearn.metrics import pairwise_distances\r\n", - "from sklearn.metrics.pairwise import pairwise_kernels\r\n", - "from sklearn.metrics.pairwise import cosine_similarity\r\n", - "from scipy.spatial.distance import cosine" - ], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [ + "#!/usr/bin/env python\n", + "\n", + "import sys\n", + "import time\n", + "from collections import defaultdict\n", + "from pathlib import Path\n", + "\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "from scipy.spatial.distance import cosine\n", + "from sklearn.metrics import pairwise_distances\n", + "from sklearn.metrics.pairwise import cosine_similarity, pairwise_kernels" + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "start_time = time.time()\r\n", - "# Script to compute similarity matrices for subregion electrostatics of each PDB. Will need to plot them too. Hmmm -> matrix heatmap.\r\n", - "\r\n", - "## Load the data ##\r\n", - "\r\n", - "pdbs = []\r\n", - "potentials = {}\r\n", - "data_folder = Path('data')\r\n", - "with open(data_folder / 'all_spike_strs_regions_pot.csv', 'r') as f:\r\n", - " header = next(f).split(',')\r\n", - " print('column names:', header)\r\n", - " data = defaultdict(dict)\r\n", - " for line in f:\r\n", - " mm = line.split(',')\r\n", - "\r\n", - " if len(mm) == 3:\r\n", - " key_AG, key_region, potential = mm\r\n", - " # key_region = int(key_region.split['_'][-1]) # transform region key to int?\r\n", - " data[key_AG].update({key_region: float(potential)})\r\n", - "\r\n", + "start_time = time.time()\n", + "# Script to compute similarity matrices for subregion electrostatics of each PDB. Will need to plot them too. Hmmm -> matrix heatmap.\n", + "\n", + "## Load the data ##\n", + "\n", + "pdbs = []\n", + "potentials = {}\n", + "data_folder = Path(\"data\")\n", + "with open(data_folder / \"all_spike_strs_regions_pot.csv\", \"r\") as f:\n", + " header = next(f).split(\",\")\n", + " print(\"column names:\", header)\n", + " data = defaultdict(dict)\n", + " for line in f:\n", + " mm = line.split(\",\")\n", + "\n", + " if len(mm) == 3:\n", + " key_AG, key_region, potential = mm\n", + " # key_region = int(key_region.split['_'][-1]) # transform region key to int?\n", + " data[key_AG].update({key_region: float(potential)})\n", "data = dict(data)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "import pandas as pd\r\n", - "df = pd.DataFrame(data).T\r\n", + "import pandas as pd\n", + "\n", + "df = pd.DataFrame(data).T\n", "df" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Only a 98 have 21 regions defined: " - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df.notna().sum(axis=1).value_counts().sort_index(ascending=False)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "df = df.loc[:,'region_1':'region_19'].dropna()\r\n", + "df = df.loc[:, \"region_1\":\"region_19\"].dropna()\n", "df" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "continue with remaining complete list of antigens" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "def lower_triangle(df):\r\n", - " \"\"\"Compute the correlation matrix, returning only unique values.\"\"\"\r\n", - " lower_triangle = pd.DataFrame(\r\n", - " np.tril(np.ones(df.shape), -1)).astype(bool)\r\n", - " lower_triangle.index, lower_triangle.columns = df.index, df.columns\r\n", + "def lower_triangle(df):\n", + " \"\"\"Compute the correlation matrix, returning only unique values.\"\"\"\n", + " lower_triangle = pd.DataFrame(np.tril(np.ones(df.shape), -1)).astype(bool)\n", + " lower_triangle.index, lower_triangle.columns = df.index, df.columns\n", " return df.where(lower_triangle)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "dict_dist= {}\r\n", - "metrics = ['cosine', 'euclidean', 'l2', 'manhattan', 'l1', 'hamming', 'chebyshev'] # 'jaccard' excluded as it's for binary data\r\n", - "for _metric in metrics:\r\n", - " dict_dist[_metric] = pd.DataFrame(pairwise_distances(X=df, metric=_metric), index=df.index, columns=df.index)\r\n", - " dict_dist[_metric] = lower_triangle(dict_dist[_metric]).stack()\r\n", - "df_metrics = pd.DataFrame(dict_dist)\r\n", + "dict_dist = {}\n", + "metrics = [\n", + " \"cosine\",\n", + " \"euclidean\",\n", + " \"l2\",\n", + " \"manhattan\",\n", + " \"l1\",\n", + " \"hamming\",\n", + " \"chebyshev\",\n", + "] # 'jaccard' excluded as it's for binary data\n", + "for _metric in metrics:\n", + " dict_dist[_metric] = pd.DataFrame(\n", + " pairwise_distances(X=df, metric=_metric), index=df.index, columns=df.index\n", + " )\n", + " dict_dist[_metric] = lower_triangle(dict_dist[_metric]).stack()\n", + "df_metrics = pd.DataFrame(dict_dist)\n", "df_metrics" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## Normalization\n", "\n", @@ -136,84 +145,85 @@ "- $x$: a single correlation value of a metric\n", "- $X$: the set of correlations for a single metric\n", "- $z$: a singe *normalized* correlation value of a metric\n" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "stats_metrics = df_metrics.describe()\r\n", + "stats_metrics = df_metrics.describe()\n", "stats_metrics" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [ - "X_min = stats_metrics.loc['min']\r\n", - "X_max = stats_metrics.loc['max']" - ], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [ + "X_min = stats_metrics.loc[\"min\"]\n", + "X_max = stats_metrics.loc[\"max\"]" + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "df_metrics_normalized = (df_metrics - X_min) / (X_max - X_min)\r\n", + "df_metrics_normalized = (df_metrics - X_min) / (X_max - X_min)\n", "df_metrics_normalized" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## Plotting the mean metrics heatmap" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "mean_metrics = df_metrics_normalized.mean(axis=1).unstack()\r\n", + "mean_metrics = df_metrics_normalized.mean(axis=1).unstack()\n", "mean_metrics" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [ - "# Set up the matplotlib figure\r\n", - "matplotlib.rc('xtick', labelsize=16)\r\n", - "matplotlib.rc('ytick', labelsize=16)\r\n", - "\r\n", - "fig, ax = plt.subplots(figsize=(30,20)) \r\n", - "\r\n", - "ax = sns.heatmap(mean_metrics, annot=False, cmap=\"RdBu_r\", ax=ax) #annot=labels, fmt='',annot_kws={\"size\": 14}, cmap=\"RdBu_r\") #fmt=\"0.2f\", cmap=\"RdBu_r\")" - ], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [ + "# Set up the matplotlib figure\n", + "matplotlib.rc(\"xtick\", labelsize=16)\n", + "matplotlib.rc(\"ytick\", labelsize=16)\n", + "\n", + "fig, ax = plt.subplots(figsize=(30, 20))\n", + "\n", + "ax = sns.heatmap(\n", + " mean_metrics, annot=False, cmap=\"RdBu_r\", ax=ax\n", + ") # annot=labels, fmt='',annot_kws={\"size\": 14}, cmap=\"RdBu_r\") #fmt=\"0.2f\", cmap=\"RdBu_r\")" + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "isbm2021hack", + "display_name": "Python 3", "language": "python", - "name": "isbm2021hack" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -225,9 +235,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +}