File size: 72,140 Bytes
56b0328 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 | import os
import numpy as np
import pandas as pd
import torch
import json
import sys
# Add project root to path
project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, project_root)
import pdbutils
import cifutils
from na_data_utils import PDBDataset
# Load the parameters file.
params_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "preprocess_dataset.json")
params = json.load(open(params_path))
# Load constants from the parameters file.
na_side_chain_atoms_len = len(['N9', 'C8', 'C7', 'N7', 'C6', 'N6', 'O6', 'C5', 'C4', 'N4', 'O4', 'N3', 'C2', 'N2', 'O2', 'N1'])
residue_cutoff = params["BATCH_TOKENS"]
num_neighbors = params["NUM_NEIGHBORS"]
interface_distance_cutoff = 5.0 # distance for interface in angstroms
if params["ATOMS_TO_LOAD"] == "backbone":
atom_list_to_save = ['N', 'CA', 'C', 'O', #protein atoms
'OP1', 'OP2', 'P', "O5'", "C5'", "C4'", "O4'", "C3'", "O3'", "C2'", "O2'", "C1'" #nucleic acid atoms
]
elif params["ATOMS_TO_LOAD"] == "all":
atom_list_to_save = ['N', 'CA', 'C', 'CB', 'O', 'CG', 'CG1', 'CG2', 'OG', 'OG1', 'SG', 'CD', 'CD1', 'CD2', 'ND1', 'ND2', 'OD1', 'OD2', 'SD', 'CE', 'CE1', 'CE2', 'CE3', 'NE', 'NE1', 'NE2', 'OE1', 'OE2', 'CH2', 'NH1', 'NH2', 'OH', 'CZ', 'CZ2', 'CZ3', 'NZ', 'OXT', #protein atoms
'OP1', 'OP2', 'P', "O5'", "C5'", "C4'", "O4'", "C3'", "O3'", "C2'", "O2'", "C1'", 'N9', 'C8', 'C7', 'N7', 'C6', 'N6', 'O6', 'C5', 'C4', 'N4', 'O4', 'N3', 'C2', 'N2', 'O2', 'N1' #nucleic acid atoms
]
# Create the parsers and dataset.
cif_parser = cifutils.CIFParser(skip_res=params["EXCLUDE_RES"],
randomize_nmr_model=params["RANDOMIZE_NMR_MODEL"])
pdb_parser = pdbutils.PDBParser()
pdb_dataset = PDBDataset(cif_parser=cif_parser,
pdb_parser=pdb_parser,
atom_list_to_save=atom_list_to_save,
parse_protein=params["PARSE_PROTEIN"],
parse_dna=params["PARSE_DNA"],
parse_rna=params["PARSE_RNA"],
parse_rna_as_dna=params["PARSE_RNA_AS_DNA"],
na_shared_tokens=params["NA_SHARED_TOKENS"],
protein_backbone_occ_cutoff=params["PROTEIN_BACKBONE_OCC_CUTOFF"],
protein_side_chain_occ_cutoff=params["PROTEIN_SIDE_CHAIN_OCC_CUTOFF"],
dna_backbone_occ_cutoff=params["DNA_BACKBONE_OCC_CUTOFF"],
dna_side_chain_occ_cutoff=params["DNA_SIDE_CHAIN_OCC_CUTOFF"],
rna_backbone_occ_cutoff=params["RNA_BACKBONE_OCC_CUTOFF"],
rna_side_chain_occ_cutoff=params["RNA_SIDE_CHAIN_OCC_CUTOFF"],
crop_large_structures=params["CROP_LARGE_STRUCTURES"],
batch_tokens=params["BATCH_TOKENS"],
na_ref_atom=params["NA_REF_ATOM"]
)
# Create a mask for the side chain atoms.
side_chain_mask = np.zeros(len(pdb_dataset.atom_dict), dtype = np.int32) # [N]
for atom_name in pdb_dataset.atom_dict:
if (atom_name not in pdb_dataset.protein_backbone_list) and \
(atom_name not in pdb_dataset.dna_backbone_list) and \
(atom_name not in pdb_dataset.rna_backbone_list):
side_chain_mask[pdb_dataset.atom_dict[atom_name]] = 1
side_chain_pairwise_mask = side_chain_mask[:, None] * side_chain_mask[None, :] # [N, N]
def write_text_file(path, contents):
with open(path, mode = "wt") as f:
f.write(contents)
class HB_data:
# Class modified from Andrew Favor.
# amino acid type to integer
num2aa=[
'ALA','ARG','ASN','ASP','CYS',
'GLN','GLU','GLY','HIS','ILE',
'LEU','LYS','MET','PHE','PRO',
'SER','THR','TRP','TYR','VAL',
'UNK','MAS',
' DA',' DC',' DG',' DT', ' DX',
' RA',' RC',' RG',' RU', ' RX',
'HIS_D', # only used for cart_bonded
'Al', 'As', 'Au', 'B',
'Be', 'Br', 'C', 'Ca', 'Cl',
'Co', 'Cr', 'Cu', 'F', 'Fe',
'Hg', 'I', 'Ir', 'K', 'Li', 'Mg',
'Mn', 'Mo', 'N', 'Ni', 'O',
'Os', 'P', 'Pb', 'Pd', 'Pr',
'Pt', 'Re', 'Rh', 'Ru', 'S',
'Sb', 'Se', 'Si', 'Sn', 'Tb',
'Te', 'U', 'W', 'V', 'Y', 'Zn',
'ATM'
]
aa2num= {x:i for i,x in enumerate(num2aa)}
aa2num['MEN'] = 20
aa2num_stripped = {x.strip():i for i,x in enumerate(num2aa)}
aa2num_stripped['MEN'] = 20
# full sc atom representation
NTOTAL = 36
aa2long=[
(" N "," CA "," C "," O "," CB ", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","3HB ", None, None, None, None, None, None, None, None), #0 ala
(" N "," CA "," C "," O "," CB "," CG "," CD "," NE "," CZ "," NH1"," NH2", None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","1HG ","2HG ","1HD ","2HD "," HE ","1HH1","2HH1","1HH2","2HH2"), #1 arg
(" N "," CA "," C "," O "," CB "," CG "," OD1"," ND2", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","1HD2","2HD2", None, None, None, None, None, None, None), #2 asn
(" N "," CA "," C "," O "," CB "," CG "," OD1"," OD2", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ", None, None, None, None, None, None, None, None, None), #3 asp
(" N "," CA "," C "," O "," CB "," SG ", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB "," HG ", None, None, None, None, None, None, None, None), #4 cys
(" N "," CA "," C "," O "," CB "," CG "," CD "," OE1"," NE2", None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","1HG ","2HG ","1HE2","2HE2", None, None, None, None, None), #5 gln
(" N "," CA "," C "," O "," CB "," CG "," CD "," OE1"," OE2", None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","1HG ","2HG ", None, None, None, None, None, None, None), #6 glu
(" N "," CA "," C "," O ", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," H ","1HA ","2HA ", None, None, None, None, None, None, None, None, None, None), #7 gly
(" N "," CA "," C "," O "," CB "," CG "," ND1"," CD2"," CE1"," NE2", None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","2HD ","1HE ","2HE ", None, None, None, None, None, None), #8 his
(" N "," CA "," C "," O "," CB "," CG1"," CG2"," CD1", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA "," HB ","1HG2","2HG2","3HG2","1HG1","2HG1","1HD1","2HD1","3HD1", None, None), #9 ile
(" N "," CA "," C "," O "," CB "," CG "," CD1"," CD2", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB "," HG ","1HD1","2HD1","3HD1","1HD2","2HD2","3HD2", None, None), #10 leu
(" N "," CA "," C "," O "," CB "," CG "," CD "," CE "," NZ ", None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","1HG ","2HG ","1HD ","2HD ","1HE ","2HE ","1HZ ","2HZ ","3HZ "), #11 lys
(" N "," CA "," C "," O "," CB "," CG "," SD "," CE ", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","1HG ","2HG ","1HE ","2HE ","3HE ", None, None, None, None), #12 met
(" N "," CA "," C "," O "," CB "," CG "," CD1"," CD2"," CE1"," CE2"," CZ ", None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","1HD ","2HD ","1HE ","2HE "," HZ ", None, None, None, None), #13 phe
(" N "," CA "," C "," O "," CB "," CG "," CD ", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," HA ","1HB ","2HB ","1HG ","2HG ","1HD ","2HD ", None, None, None, None, None, None), #14 pro
(" N "," CA "," C "," O "," CB "," OG ", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HG "," HA ","1HB ","2HB ", None, None, None, None, None, None, None, None), #15 ser
(" N "," CA "," C "," O "," CB "," OG1"," CG2", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HG1"," HA "," HB ","1HG2","2HG2","3HG2", None, None, None, None, None, None), #16 thr
(" N "," CA "," C "," O "," CB "," CG "," CD1"," CD2"," NE1"," CE2"," CE3"," CZ2"," CZ3"," CH2", None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","1HD ","1HE "," HZ2"," HH2"," HZ3"," HE3", None, None, None), #17 trp
(" N "," CA "," C "," O "," CB "," CG "," CD1"," CD2"," CE1"," CE2"," CZ "," OH ", None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","1HD ","1HE ","2HE ","2HD "," HH ", None, None, None, None), #18 tyr
(" N "," CA "," C "," O "," CB "," CG1"," CG2", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA "," HB ","1HG1","2HG1","3HG1","1HG2","2HG2","3HG2", None, None, None, None), #19 val
(" N "," CA "," C "," O "," CB ", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","3HB ", None, None, None, None, None, None, None, None), #20 unk
(" N "," CA "," C "," O "," CB ", None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","3HB ", None, None, None, None, None, None, None, None), #21 mask
(" O4'"," C1'"," C2'"," OP1"," P "," OP2"," O5'"," C5'"," C4'"," C3'"," O3'"," N9 "," C4 "," N3 "," C2 "," N1 "," C6 "," C5 "," N7 "," C8 "," N6 ", None, None,"H5''"," H5'"," H4'"," H3'","H2''"," H2'"," H1'"," H2 "," H61"," H62"," H8 ", None, None), #22 DA
(" O4'"," C1'"," C2'"," OP1"," P "," OP2"," O5'"," C5'"," C4'"," C3'"," O3'"," N1 "," C2 "," O2 "," N3 "," C4 "," N4 "," C5 "," C6 ", None, None, None, None,"H5''"," H5'"," H4'"," H3'","H2''"," H2'"," H1'"," H42"," H41"," H5 "," H6 ", None, None), #23 DC
(" O4'"," C1'"," C2'"," OP1"," P "," OP2"," O5'"," C5'"," C4'"," C3'"," O3'"," N9 "," C4 "," N3 "," C2 "," N1 "," C6 "," C5 "," N7 "," C8 "," N2 "," O6 ", None,"H5''"," H5'"," H4'"," H3'","H2''"," H2'"," H1'"," H1 "," H22"," H21"," H8 ", None, None), #24 DG
(" O4'"," C1'"," C2'"," OP1"," P "," OP2"," O5'"," C5'"," C4'"," C3'"," O3'"," N1 "," C2 "," O2 "," N3 "," C4 "," O4 "," C5 "," C7 "," C6 ", None, None, None,"H5''"," H5'"," H4'"," H3'","H2''"," H2'"," H1'"," H3 "," H71"," H72"," H73"," H6 ", None), #25 DT
(" O4'"," C1'"," C2'"," OP1"," P "," OP2"," O5'"," C5'"," C4'"," C3'"," O3'", None, None, None, None, None, None, None, None, None, None, None, None,"H5''"," H5'"," H4'"," H3'","H2''"," H2'"," H1'", None, None, None, None, None, None), #26 DX (unk DNA)
(" O4'"," C1'"," C2'"," OP1"," P "," OP2"," O5'"," C5'"," C4'"," C3'"," O3'"," O2'"," N1 "," C2 "," N3 "," C4 "," C5 "," C6 "," N6 "," N7 "," C8 "," N9 ", None," H5'","H5''"," H4'"," H3'"," H2'","HO2'"," H1'"," H2 "," H61"," H62"," H8 ", None, None), #27 A
(" O4'"," C1'"," C2'"," OP1"," P "," OP2"," O5'"," C5'"," C4'"," C3'"," O3'"," O2'"," N1 "," C2 "," O2 "," N3 "," C4 "," N4 "," C5 "," C6 ", None, None, None," H5'","H5''"," H4'"," H3'"," H2'","HO2'"," H1'"," H42"," H41"," H5 "," H6 ", None, None), #28 C
(" O4'"," C1'"," C2'"," OP1"," P "," OP2"," O5'"," C5'"," C4'"," C3'"," O3'"," O2'"," N1 "," C2 "," N2 "," N3 "," C4 "," C5 "," C6 "," O6 "," N7 "," C8 "," N9 "," H5'","H5''"," H4'"," H3'"," H2'","HO2'"," H1'"," H1 "," H22"," H21"," H8 ", None, None), #29 G
(" O4'"," C1'"," C2'"," OP1"," P "," OP2"," O5'"," C5'"," C4'"," C3'"," O3'"," O2'"," N1 "," C2 "," O2 "," N3 "," C4 "," O4 "," C5 "," C6 ", None, None, None," H5'","H5''"," H4'"," H3'"," H2'","HO2'"," H1'"," H3 "," H5 "," H6 ", None, None, None), #30 U
(" O4'"," C1'"," C2'"," OP1"," P "," OP2"," O5'"," C5'"," C4'"," C3'"," O3'"," O2'", None, None, None, None, None, None, None, None, None, None, None," H5'","H5''"," H4'"," H3'"," H2'","HO2'"," H1'", None, None, None, None, None, None), #31 RX (unk RNA)
(" N "," CA "," C "," O "," CB "," CG "," NE2"," CD2"," CE1"," ND1", None, None, None, None, None, None, None, None, None, None, None, None, None," H "," HA ","1HB ","2HB ","2HD ","1HE ","1HD ", None, None, None, None, None, None), #-1 his_d
]
aa2long_stripped = []
for aa_tuple in aa2long:
aa_tuple_stripped = tuple(map(lambda atom_name: atom_name.strip() if atom_name is not None else atom_name, aa_tuple))
aa2long_stripped.append(aa_tuple_stripped)
def __init__(self, seq, xyz, idx=None, **kwargs):
# Required parameters
self.seq = seq
self.xyz = xyz
if not idx:
self.idx = torch.arange(len(seq))
# Optional parameters with default values
self.incl_protein = kwargs.get('incl_protein', True)
self.eps = kwargs.get('eps', 1e-8)
# self.use_eigennormals = kwargs.get('use_eigennormals', True)
# self.use_all_base_atoms_for_MBD = kwargs.get('use_all_base_atoms_for_MBD', False)
self.edges_to_compute = kwargs.get('edges_to_compute', ['S']) # list base edges to compute, if we want to analyze WC/Hoog/etc
self.perp_base_edge = kwargs.get('perp_base_edge', 'S') # edge orthogonal to x- and z-directions in base frames (which is generally the sugar edge)
self.hbond_da_upper = kwargs.get('hbond_da_upper', 3.9)
self.hbond_ha_upper = kwargs.get('hbond_ha_upper', 2.5)
self.seq_cutoff = kwargs.get('seq_cutoff', 2)
compute_local_base_params = kwargs.get('compute_local_base_params', False)
compute_pairwise_base_params = kwargs.get('compute_pairwise_base_params', False)
compute_paired_bases = kwargs.get('compute_paired_bases', False)
compute_helical_params = kwargs.get('compute_helical_params', False)
self.base_geometry_limits = {}
self.base_geometry_limits['D_ij'] = kwargs.get('D_ij_limit', 20.0)
self.base_geometry_limits['H_ij'] = kwargs.get('H_ij_limit', 1.5)
self.base_geometry_limits['P_ij'] = kwargs.get('P_ij_limit', np.pi/5)
self.base_geometry_limits['B_ij'] = kwargs.get('B_ij_limit', np.pi/5)
# self.base_geometry_limits['O_ij'] = kwargs.get('O_ij_limit', 1.5) # Not used right now, currently allow all values of opening
self.bp_val_cutoff= kwargs.get('bp_val_cutoff', 0.5) # minimum basepairing score for using a pair when computing helical params
self.hbond_same_coeff = kwargs.get('hbond_same_coeff', 0.0)
self.hbond_diff_coeff = kwargs.get('hbond_diff_coeff', 1.0)
self.min_hbonds_for_bp = kwargs.get('min_hbonds_for_bp', 2.0)
self.bp_hbond_coeff = kwargs.get('bp_hbond_coeff', 8.0)
self.clamp_pairwise_params = kwargs.get('clamp_pairwise_params', False)
# Initialize computed attributes
self._init_hb_chemdata()
self._compute_initial_values()
self._compute_hbnets(store_hb_data_dict=kwargs.get('store_hb_data_dict', False))
# For now only doing for NA:
if self.is_na.sum() > 0: # Only compute nucleic params when there are nucleics in the structure
self._init_nuc_chemdata()
self._edges_to_compute = list(set(self.edges_to_compute) | {self.perp_base_edge}) # Must compute this base-edge
self._compute_local_base_params() # Define canonical base-frames for the specified edges
if compute_pairwise_base_params or compute_paired_bases:
self._compute_pairwise_base_params() # Compute pairwise geometric parameters between bases
self._compute_paired_bases() # Classify bases using H-bond count and pairwise base geometry filters
if compute_helical_params:
self._compute_helical_params() # In progress...
def _compute_initial_values(self):
self.len_s = int(self.seq.shape[0])
self.sel = torch.arange(self.len_s)
self.seq_neighbors = torch.le(torch.abs(self.sel[:, None] - self.sel[None, :]), self.seq_cutoff)
self.is_protein = torch.logical_and((0 <= self.seq), (self.seq <= 21))
self.is_dna = torch.logical_and((22 <= self.seq), (self.seq <= 25))
self.is_rna = torch.logical_and((27 <= self.seq), (self.seq <= 30))
self.is_na = torch.logical_or(self.is_dna, self.is_rna)
self.na_inds = [i for i,is_na_i in enumerate(self.is_na) if is_na_i]
self.na_tensor_inds = {na_i:i for i,na_i in enumerate(self.na_inds)}
frame_xyz = self.xyz[:,1,:]
padded_centers = torch.cat([frame_xyz[:1], frame_xyz[:], frame_xyz[-1:]])
self.D_ij_vec = frame_xyz.unsqueeze(0) - frame_xyz.unsqueeze(1) # pairwise displacement vector between frame centers
self.D_ij = self.D_ij_vec.norm(dim=-1)
self.M_i = ((padded_centers[1:-1] - padded_centers[:-2]) + (padded_centers[2:] - padded_centers[1:-1])) / 2 # average direction vector from consecutive frames in backbone
self.M_i_doublet = padded_centers[1:] - padded_centers[:-1]
def _compute_hbnets(self, store_hb_data_dict=False):
# Distance between frames is between lower and upper bounds:
D_ij_filter = (self.D_ij <= self.base_geometry_limits['D_ij'])
# neighbor filter for all polymer types:
neighbor_inds = torch.triu(D_ij_filter.bool(),diagonal=1).nonzero(as_tuple=True)
pairwise_indices = list(zip(neighbor_inds[0].tolist(), neighbor_inds[1].tolist()))
bp_pred_summation = torch.zeros_like(self.D_ij)
# self.hb_data_dict = {i:{j:[] for j in range(self.len_s)} for i in range(self.len_s) }
hb_data_dict = {i:{j:{} for j in range(self.len_s)} for i in range(self.len_s) }
self.hbond_summation = torch.zeros_like(D_ij_filter, dtype=torch.float)
for i,j in pairwise_indices:
for a_i, is_donor_i in zip(self.hbond_atoms[HB_data.num2aa[self.seq[i]]]['names'],self.hbond_atoms[HB_data.num2aa[self.seq[i]]]['donor']):
for a_j, is_donor_j in zip(self.hbond_atoms[HB_data.num2aa[self.seq[j]]]['names'],self.hbond_atoms[HB_data.num2aa[self.seq[j]]]['donor']):
atom_pair = f"{a_i}-{a_j}" # avoid duplicate counting for atom pairs
if (is_donor_i+is_donor_j)==1 and (atom_pair not in hb_data_dict[i][j].keys()):
a_i_ind = HB_data.aa2long[self.seq[i]].index(a_i)
a_j_ind = HB_data.aa2long[self.seq[j]].index(a_j)
# Create vector between donor and acceptor atoms
d_ijk_vec = self.xyz[i,a_i_ind] - self.xyz[j,a_j_ind]
d_ijk_vec_norm = d_ijk_vec/d_ijk_vec.norm(dim=-1)
# Create vector giving direction to donor and acceptor along sidechain covalent bond:
a_i_vec = torch.cat(
[(self.xyz[i,a_i_ind]-self.xyz[i,HB_data.aa2long[self.seq[i]].index(r_i)])[:,None] for r_i in self.rear_atoms[HB_data.num2aa[self.seq[i]]][a_i]],
dim=1).mean(dim=1)
a_i_vec_norm = a_i_vec/(a_i_vec.norm(dim=-1) + self.eps)
a_j_vec = torch.cat(
[(self.xyz[j,a_j_ind]-self.xyz[j,HB_data.aa2long[self.seq[j]].index(r_j)])[:,None] for r_j in self.rear_atoms[HB_data.num2aa[self.seq[j]]][a_j]],
dim=1).mean(dim=1)
a_j_vec_norm = a_j_vec/(a_j_vec.norm(dim=-1) + self.eps)
num_rear_i = len(self.rear_atoms[HB_data.num2aa[self.seq[i]]][a_i])
element_i = ''.join([_ for _ in a_i if _.isalpha()])[0]
ideal_angle_i = self.ideal_angle_dict[element_i][num_rear_i]
num_rear_j = len(self.rear_atoms[HB_data.num2aa[self.seq[j]]][a_j])
element_j = ''.join([_ for _ in a_j if _.isalpha()])[0]
ideal_angle_j = self.ideal_angle_dict[element_j][num_rear_j]
ideal_angle_h = torch.tensor((is_donor_i*ideal_angle_i) + (is_donor_j*ideal_angle_j))
xyz_d_ijk = ( is_donor_i * self.xyz[i,a_i_ind] ) + ( is_donor_j * self.xyz[j,a_j_ind] )
xyz_a_ijk = ((1-is_donor_i) * self.xyz[i,a_i_ind] ) + ((1-is_donor_j) * self.xyz[j,a_j_ind] )
# (1, rd): vector pointing to donor atom from rear atom(s):
rd_ijk_vec = (is_donor_i * a_i_vec_norm) + (is_donor_j * a_j_vec_norm)
rd_ijk_vec_norm = rd_ijk_vec/(rd_ijk_vec.norm(dim=-1) + self.eps)
# (2, da): vector pointing from donor atom to acceptor atom, approximately in direction of the hydrogen:
da_ijk_vec = xyz_a_ijk - xyz_d_ijk
da_ijk_vec_norm = da_ijk_vec/(da_ijk_vec.norm(dim=-1) + self.eps)
# (3, ar): vector pointing to acceptor atom from rear atom(s):
ar_ijk_vec = ((is_donor_i-1)*a_i_vec_norm) + ((is_donor_j-1)*a_j_vec_norm)
ar_ijk_vec_norm = ar_ijk_vec/(ar_ijk_vec.norm(dim=-1) + self.eps)
norm_vec = torch.cross(-rd_ijk_vec_norm, da_ijk_vec_norm, dim=-1)
norm_unit = norm_vec / (norm_vec.norm() + self.eps) # Avoid divide-by-zero
perp_vec = torch.cross(norm_unit, -rd_ijk_vec_norm, dim=-1)
perp_unit = perp_vec / (perp_vec.norm() + self.eps)
# (4, dh): predicted ideal angle pointing from donor atom to hydrogen atom:
dh_ijk_vec = (torch.sin(ideal_angle_h) * perp_unit) - (torch.cos(ideal_angle_h) * rd_ijk_vec_norm)
dh_ijk_vec_norm = dh_ijk_vec / (dh_ijk_vec.norm() + self.eps) # norm actually matters here, because Donor -> H distance is exactly 1A.
ideal_xyz_h_ijk = xyz_d_ijk + dh_ijk_vec_norm # Compute ideal hydrogen placement
# (5, ha): vector pointing from ideal hydrogen to acceptor atom
ha_ijk_vec = xyz_a_ijk - ideal_xyz_h_ijk
ha_ijk_vec_norm = ha_ijk_vec / (ha_ijk_vec.norm() + self.eps)
t_rdh = torch.acos( ( -rd_ijk_vec_norm * dh_ijk_vec_norm ).sum(dim=-1) )
t_rda = torch.acos( ( -rd_ijk_vec_norm * da_ijk_vec_norm ).sum(dim=-1) )
t_dha = torch.acos( ( -dh_ijk_vec_norm * ha_ijk_vec_norm ).sum(dim=-1) )
t_dar = torch.acos( ( -da_ijk_vec_norm * ar_ijk_vec_norm ).sum(dim=-1) )
t_har = torch.acos( ( -ha_ijk_vec_norm * ar_ijk_vec_norm ).sum(dim=-1) )
da_ijk = da_ijk_vec.norm(dim=-1)
ha_ijk = ha_ijk_vec.norm(dim=-1)
hbond_da_filter = ( da_ijk <= self.hbond_da_upper )
hbond_ha_filter = ( ha_ijk <= self.hbond_ha_upper ) # SHOULD BE MOST IMPORTANT
hbond_t_rda_filter = ( t_rda >= 5*np.pi/9 ) # cutoff (100 degrees) proposed by: https://pmc.ncbi.nlm.nih.gov/articles/PMC8261469/
hbond_t_dar_filter = ( t_dar >= 5*np.pi/9 ) # similar logic to above
hbond_t_dha_filter = ( t_dha >= np.pi/2 ) # could also increase this one maybe
bond_prob_ij = (hbond_ha_filter * hbond_da_filter * hbond_t_rda_filter * hbond_t_dar_filter).float()
self.hbond_summation[i,j] += bond_prob_ij
self.hbond_summation[j,i] += bond_prob_ij
hb_data_dict[i][j][atom_pair] = {'d': da_ijk, 'l': ha_ijk, "t_rdh": t_rdh, "t_rda": t_rda, "t_dha": t_dha, "t_dar": t_dar, "t_har": t_har, 'atoms': atom_pair, "bonded": bond_prob_ij, }
hb_data_dict[j][i][atom_pair] = {'d': da_ijk, 'l': ha_ijk, "t_rdh": t_rdh, "t_rda": t_rda, "t_dha": t_dha, "t_dar": t_dar, "t_har": t_har, 'atoms': atom_pair, "bonded": bond_prob_ij, }
if store_hb_data_dict:
self.hb_data_dict = hb_data_dict
def _compute_local_base_params(self):
"""
local base params , based on interaction-edges
"""
xyz_na = self.xyz[self.is_na]
seq_na = self.seq[self.is_na]
"""
(1). Compute base normals and correct orientation based on backbone direction.
"""
base_atom_xyz = torch.stack([xyz_na[i,self.ring_atom_inds[HB_data.num2aa[s_i]],:] for i,s_i in enumerate(seq_na)] )
base_atom_centers = torch.mean(base_atom_xyz, dim=1)
centered_points = base_atom_xyz - base_atom_centers.unsqueeze(1)
cov_matrix = torch.einsum('bij,bik->bjk', centered_points, centered_points) / (centered_points.shape[1] - 1)
eigenvalues, eigenvectors = torch.linalg.eigh(cov_matrix)
# Keep N_i local, since we will only need Z_i after this function
N_i = eigenvectors[:, :, 0] / eigenvectors[:, :, 0].norm(dim=1, keepdim=True)
# Correct base normals to point in direction of backbone 5' -> 3' by projecting backbone vec M_i onto this unit Z_i vector to flip direction if necessary
self.Z_i = N_i * torch.sum(self.M_i[self.is_na] * N_i, dim=-1, keepdim=True)
self.Z_i = self.Z_i / (torch.norm(self.Z_i, dim=-1, keepdim=True) + self.eps)
"""
(2). Compute the desired edge-vectors for the bases (watson-crick, hoogstein, sugar, etc)
* W edge: N1 of purine, N3 of pyrimidine
* H edge: N7 of purine, C5 of pyrimidine
* S edge: N3 of purine, C1' of pyrimidine
* B (pseudo)-edge: connects C1' to first base-atom (N1 or N3?)
"""
# Compute X and Y vectors for edges of interest:
self.edge_X_vecs, self.edge_Y_vecs = {}, {}
for edge in self.edges_to_compute:
self.edge_X_vecs[edge] = torch.stack([xyz_na[i,self.vec_atom_inds[HB_data.num2aa[s_i]][f'{edge}_stop'],:] - xyz_na[i,self.vec_atom_inds[HB_data.num2aa[s_i]][f'{edge}_start'],:] for i, s_i in enumerate(seq_na)])
self.edge_X_vecs[edge] = self.edge_X_vecs[edge] / (torch.norm(self.edge_X_vecs[edge], dim=-1, keepdim=True) + self.eps)
# self.edge_Y_vecs[edge] = torch.cross(self.edge_X_vecs[edge], N_i, dim=-1)
self.edge_Y_vecs[edge] = torch.cross(self.edge_X_vecs[edge], self.Z_i, dim=-1)
self.edge_Y_vecs[edge] = self.edge_Y_vecs[edge] / (torch.norm(self.edge_Y_vecs[edge], dim=-1, keepdim=True) + self.eps)
"""
(3). Define canonical base frames in terms of one specific edge.
The sugar edge generally works best here, as it most often points towards binding partner
(orthogonal to both major groove and helical axis)
"""
self.X_i = torch.cross(self.Z_i, self.edge_X_vecs[self.perp_base_edge], dim=-1)
self.X_i = self.X_i / (torch.norm(self.X_i, dim=-1, keepdim=True) + self.eps)
self.Y_i = torch.cross(self.X_i, self.Z_i, dim=-1)
self.Y_i = self.Y_i / (torch.norm(self.Y_i, dim=-1, keepdim=True) + self.eps)
self.base_atom_centers = base_atom_centers
def _compute_pairwise_base_params(self):
D_ij_vec_na = self.D_ij_vec[torch.arange(self.is_na.sum()).unsqueeze(1), torch.arange(self.is_na.sum())]
base_D_ij_vec = self.base_atom_centers.unsqueeze(0) - self.base_atom_centers.unsqueeze(1)
# stack mean Z-direction vectors for parallel (0) and antiparallel (1) orientations in zeroth-axis:
Z_ij_oris = 0.5*torch.stack((self.Z_i.unsqueeze(1) + self.Z_i.unsqueeze(0), self.Z_i.unsqueeze(1) - self.Z_i.unsqueeze(0) ), dim=0)
# Check which are parallel or antiparallel:
bases_are_antiparallel = (Z_ij_oris[1].norm(dim=-1) > Z_ij_oris[0].norm(dim=-1)).long()
# Extract mean Z-direction based on maximum shared direction between planes of base i and j:
Z_ij = Z_ij_oris[bases_are_antiparallel, torch.arange(self.is_na.sum()).unsqueeze(1), torch.arange(self.is_na.sum())]
Z_ij = Z_ij / (torch.norm(Z_ij, dim=-1, keepdim=True) + self.eps)
Y_ij = D_ij_vec_na / (torch.norm(D_ij_vec_na, dim=-1, keepdim=True) + self.eps)
X_ij = torch.cross(Z_ij, Y_ij, dim=-1)
X_ij = X_ij / (torch.norm(X_ij, dim=-1, keepdim=True) + self.eps)
self.H_ij = torch.sum(base_D_ij_vec * Z_ij, dim=-1)
self.H_ij_vec = self.H_ij[...,None] * Z_ij
# Opening: angle between local x_i and x_j within global X_ij-Y_ij plane:
proj_X_i_XY = ((self.X_i[:, None, :] * X_ij).sum(dim=-1, keepdim=True) * X_ij) + ((self.X_i[:, None, :] * Y_ij).sum(dim=-1, keepdim=True) * Y_ij)
proj_X_i_XY_norm = proj_X_i_XY / (torch.norm(proj_X_i_XY, dim=-1, keepdim=True) + self.eps)
cos_opening = (proj_X_i_XY_norm * proj_X_i_XY_norm.transpose(1,0)).sum(dim=-1)
if self.clamp_pairwise_params:
cos_opening = torch.clamp(cos_opening, -1.0, 1.0)
O_ij = torch.acos(cos_opening)
# Buckle: angle between local z_i and z_j within global Y_ij-Z_ij plane:
proj_Z_i_YZ = ((self.Z_i[:, None, :] * Y_ij).sum(dim=-1, keepdim=True) * Y_ij) + ((self.Z_i[:, None, :] * Z_ij).sum(dim=-1, keepdim=True) * Z_ij)
proj_Z_i_YZ_norm = proj_Z_i_YZ / (torch.norm(proj_Z_i_YZ, dim=-1, keepdim=True) + self.eps)
cos_buckle = (proj_Z_i_YZ_norm * -proj_Z_i_YZ_norm.transpose(1,0)).sum(dim=-1)
if self.clamp_pairwise_params:
cos_buckle = torch.clamp(cos_buckle, -1.0, 1.0)
cos_buckle = torch.clamp(cos_buckle, -1.0, 1.0)
B_ij = torch.acos(cos_buckle)
# Propeller: angle between local z_i and z_j within global Z_ij-X_ij plane:
proj_Z_i_ZX = ((self.Z_i[:, None, :] * Z_ij).sum(dim=-1, keepdim=True) * Z_ij) + ((self.Z_i[:, None, :] * X_ij).sum(dim=-1, keepdim=True) * X_ij)
proj_Z_i_ZX_norm = proj_Z_i_ZX / (torch.norm(proj_Z_i_ZX, dim=-1, keepdim=True) + self.eps)
cos_propeller = (proj_Z_i_ZX_norm * -proj_Z_i_ZX_norm.transpose(1,0)).sum(dim=-1)
if self.clamp_pairwise_params:
cos_propeller = torch.clamp(cos_propeller, -1.0, 1.0)
P_ij = torch.acos(cos_propeller)
# Local frame components for sidechains:
self.X_ij = X_ij
self.Y_ij = Y_ij
self.Z_ij = Z_ij
# pairwise base parameters:
self.O_ij = O_ij
self.B_ij = B_ij
self.P_ij = P_ij
self.bases_are_antiparallel = bases_are_antiparallel
def _compute_paired_bases(self):
# Compute baseline bp probability based on hydrogen bond count
bp_preds = torch.sigmoid(self.bp_hbond_coeff * (self.hbond_summation - (self.min_hbonds_for_bp - 1))) # offset by 1 for midpoint
# basepair-specific filters:
both_nucleic_filter = self.is_na[:,None] * self.is_na[None,:]
# Frame distance filter, already taken care of
# D_ij_filter = (self.D_ij_vec.norm(dim=-1) < self.base_geometry_limits['D_ij'])
# Rise between base-planes is within lower and upper bounds
# H_ij_filter = (self.H_ij.norm(dim=-1) > -self.base_geometry_limits['H_ij']) & (self.H_ij.norm(dim=-1) < self.base_geometry_limits['H_ij'])
H_ij_filter = (self.H_ij >= -self.base_geometry_limits['H_ij']) & (self.H_ij <= self.base_geometry_limits['H_ij'])
# H_ij_filter = (self.H_ij.T >= -self.base_geometry_limits['H_ij']) & (self.H_ij.T <= self.base_geometry_limits['H_ij'])
# Buckle between bases is either lower than lower bound or higher than upper bound (stay close to 0 or pi):
B_ij_filter = (self.B_ij <= (np.pi - self.base_geometry_limits['B_ij'])) | (self.B_ij >= self.base_geometry_limits['B_ij'])
# Propeller between bases is either lower than lower bound or higher than upper bound (stay close to 0 or pi):
P_ij_filter = (self.P_ij <= (np.pi - self.base_geometry_limits['P_ij'])) | (self.P_ij >= self.base_geometry_limits['P_ij'])
# combine for full basepair filter:
bp_geom_filter = torch.zeros(self.len_s, self.len_s, dtype=torch.bool)
bp_geom_filter[torch.outer(self.is_na, self.is_na)] = (H_ij_filter * B_ij_filter * P_ij_filter).flatten()
# bp_geom_filter[torch.outer(self.is_na, self.is_na)] = ( B_ij_filter * P_ij_filter).flatten()
# bp_geom_filter[torch.outer(self.is_na, self.is_na)] = (H_ij_filter * P_ij_filter).flatten()
# bp_geom_filter[torch.outer(self.is_na, self.is_na)] = (H_ij_filter * B_ij_filter ).flatten()
self.basepairs_ij = both_nucleic_filter * bp_geom_filter * bp_preds
def _compute_helical_params(self):
len_na = self.Z_i.shape[0] # Do I need this?
nucleic_frames = self.xyz[self.is_na, 1, :]
doublet_inds = [(i,j) for (i,j) in zip(range(0,len_na-1),range(1,len_na))]
Zm_i = torch.zeros_like(self.Z_i)
Zh_i = torch.zeros_like(self.Z_i)
# Local doublet step params
tilt_i = torch.zeros(len_na)
roll_i = torch.zeros(len_na)
twist_i = torch.zeros(len_na)
shift_i = torch.zeros(len_na)
slide_i = torch.zeros(len_na)
rise_i = torch.zeros(len_na)
# Local helical parameters
inclination_i = torch.zeros(len_na)
tip_i = torch.zeros(len_na)
helical_twist_i = torch.zeros(len_na)
x_disp_i = torch.zeros(len_na)
y_disp_i = torch.zeros(len_na)
helical_rise_i = torch.zeros(len_na)
# avg_factor = torch.zeros_like(self.Z_i[:,0])
avg_factor = torch.zeros(len_na)
for i,j in doublet_inds:
avg_factor[i] += 1.0
avg_factor[j] += 1.0
basepaired_inds = (self.basepairs_ij >= self.bp_val_cutoff).bool().nonzero(as_tuple=True)
pairwise_indices = list(zip(basepaired_inds[0].tolist(), basepaired_inds[1].tolist()))
# partner_info_dict = {i:{'partner_ind':[], 'orientation':[], 'num_hbonds':[], 'bp_score': []} for i in range(len_na)}
partner_info_dict = {i:{'partner_ind':[], 'orientation':[], 'num_hbonds':[], 'bp_score': []} for i in self.na_inds}
for i, j in pairwise_indices:
_i,_j = self.na_tensor_inds[i], self.na_tensor_inds[j]
partner_info_dict[i]['partner_ind'].append(j)
partner_info_dict[i]['orientation'].append(self.bases_are_antiparallel[_i,_j])
partner_info_dict[i]['num_hbonds'].append(self.hbond_summation[_i,_j])
partner_info_dict[i]['bp_score'].append(self.basepairs_ij[_i,_j])
# TO DO: sort partner_info_dict[i]['partner_ind'] list by: first by orientation, then by num_hbonds
# If we don't do the sorting, no need to compile these lists in the dict. Can just index directly from precomputed values.
# for i in partner_info_dict.keys():
for i_1, i_2 in doublet_inds:
partners_i_1 = [self.na_tensor_inds[_] for _ in partner_info_dict[self.na_inds[i_1]]['partner_ind']]
partners_i_2 = [self.na_tensor_inds[_] for _ in partner_info_dict[self.na_inds[i_2]]['partner_ind']]
# set_trace()
# j_1 = partners_i_1[0] # index-[0] is just a placeholder for later iteration
# j_2 = partners_i_2[0] # index-[0] is just a placeholder for later iteration
num_partners_i_1 = len(partners_i_1) # later change to be length of iterable
num_partners_i_2 = len(partners_i_2) # later change to be length of iterable
for j_1 in partners_i_1:
# _j_1 = self.na_tensor_inds[j_1]
for j_2 in partners_i_2:
X_1 = self.X_ij[i_1,j_1]
Y_1 = self.Y_ij[i_1,j_1]
X_2 = self.X_ij[i_2,j_2]
Y_2 = self.Y_ij[i_2,j_2]
Xp = X_2 + X_1
Xn = X_2 - X_1
Yp = Y_2 + Y_1
Yn = Y_2 - Y_1
M_12 = 0.5*((nucleic_frames[i_2]+nucleic_frames[j_2]) - (nucleic_frames[i_1]+nucleic_frames[j_1]))
Zm = torch.cross(Xp, Yp, dim=-1) / ((Xp.norm(dim=-1) * Yp.norm(dim=-1)) + self.eps)
Zh = torch.cross(Xn, Yn, dim=-1) / ((Xn.norm(dim=-1) * Yn.norm(dim=-1)) + self.eps)
Zm_i[i_1] += Zm / (avg_factor[i_1]+self.eps)
Zh_i[i_1] += Zh / (avg_factor[i_1]+self.eps)
Zm_i[i_2] += Zm / (avg_factor[i_2]+self.eps)
Zh_i[i_2] += Zh / (avg_factor[i_2]+self.eps)
tilt_ij = -torch.arcsin(torch.sum(Zm * X_1 , dim=-1))
roll_ij = torch.arcsin(torch.sum(Zm * Y_1 , dim=-1))
twist_ij = torch.arccos(torch.sum(torch.cross(X_1 , Zm, dim=-1) * torch.cross(X_2 , Zm, dim=-1), dim=-1))
shift_ij = torch.sum(M_12 * (Xp / (torch.norm(Xp, dim=-1)+self.eps)), dim=-1)
slide_ij = torch.sum(M_12 * (Yp / (torch.norm(Yp, dim=-1)+self.eps)), dim=-1)
rise_ij = torch.sum(M_12 * Zm , dim=-1)
inclination_ij = torch.arcsin(torch.sum(Zh * X_1 , dim=-1))
tip_ij = -torch.arcsin(torch.sum(Zh * Y_1 , dim=-1))
helical_twist_ij = -torch.arccos(torch.sum(torch.cross(X_1 , Zh, dim=-1) * torch.cross(X_2 , Zh, dim=-1), dim=-1))
x_disp_ij = torch.sum(M_12 * Xn / (torch.norm(Xn, dim=-1)+self.eps), dim=-1)
y_disp_ij = torch.sum(M_12 * Yn / (torch.norm(Yn, dim=-1)+self.eps), dim=-1)
helical_rise_ij = -torch.sum(M_12 * Zh, dim=-1)
# NEXT JUST ADD THESE PARAMS TO SOME PRE-INITIALIZED TENSOR AND DIVIDE BY AVG_FACTOR TO AVERAGE:
# For doublet position-1:
avg_factor[i_1] += 1.0
tilt_i[i_1] += tilt_ij
roll_i[i_1] += roll_ij
twist_i[i_1] += twist_ij
shift_i[i_1] += shift_ij
slide_i[i_1] += slide_ij
rise_i[i_1] += rise_ij
inclination_i[i_1] += inclination_ij
tip_i[i_1] += tip_ij
helical_twist_i[i_1] += helical_twist_ij
x_disp_i[i_1] += x_disp_ij
y_disp_i[i_1] += y_disp_ij
helical_rise_i[i_1] += helical_rise_ij
# For doublet position-2:
avg_factor[i_2] += 1.0
tilt_i[i_2] += tilt_ij
roll_i[i_2] += roll_ij
twist_i[i_2] += twist_ij
shift_i[i_2] += shift_ij
slide_i[i_2] += slide_ij
rise_i[i_2] += rise_ij
inclination_i[i_2] += inclination_ij
tip_i[i_2] += tip_ij
helical_twist_i[i_2] += helical_twist_ij
x_disp_i[i_2] += x_disp_ij
y_disp_i[i_2] += y_disp_ij
helical_rise_i[i_2] += helical_rise_ij
self.tilt_i = tilt_i / (avg_factor + self.eps)
self.roll_i = roll_i / (avg_factor + self.eps)
self.twist_i = twist_i / (avg_factor + self.eps)
self.shift_i = shift_i / (avg_factor + self.eps)
self.slide_i = slide_i / (avg_factor + self.eps)
self.rise_i = rise_i / (avg_factor + self.eps)
self.inclination_i = inclination_i / (avg_factor + self.eps)
self.tip_i = tip_i / (avg_factor + self.eps)
self.helical_twist_i = helical_twist_i / (avg_factor + self.eps)
self.x_disp_i = x_disp_i / (avg_factor + self.eps)
self.y_disp_i = y_disp_i / (avg_factor + self.eps)
self.helical_rise_i = helical_rise_i / (avg_factor + self.eps)
def _init_hb_chemdata(self):
# RESIDUE | DONORS | ACCEPTORS
self.hbond_atoms = {
"ALA": {"names":[ ],
"donor":[ ]},
"ARG": {"names":[" NH1"," NH2" ],
"donor":[ 1 , 1 ]},
"ASN": {"names":[" ND2", " OD1" ],
"donor":[ 1 , 0 ]},
"ASP": {"names":[" OD2", " OD1"," OD2" ],
"donor":[ 1 , 0 , 0 ]},
"CYS": {"names":[" SG " ],
"donor":[ 1 ]},
"GLN": {"names":[" NE2", " OE1" ],
"donor":[ 1 , 0 ]},
"GLU": {"names":[" OE2", " OE1"," OE2" ],
"donor":[ 1 , 0 , 0 ]},
"GLY": {"names":[ ],
"donor":[ ]},
"HIS": {"names":[" ND1"," NE2", " ND1"," NE2" ],
"donor":[ 1 , 1 , 0 , 0 ]},
"ILE": {"names":[ ],
"donor":[ ]},
"LEU": {"names":[ ],
"donor":[ ]},
"LYS": {"names":[" NZ " ],
"donor":[ 1 ]},
"MET": {"names":[ " SD " ],
"donor":[ 0 ]},
"PHE": {"names":[ ],
"donor":[ ]},
"PRO": {"names":[ ],
"donor":[ ]},
"SER": {"names":[" OG " ],
"donor":[ 1 ]},
"THR": {"names":[" OG1" ],
"donor":[ 1 ]},
"TRP": {"names":[ " NE1" ],
"donor":[ 0 ]},
"TYR": {"names":[" OH " ],
"donor":[ 1 ]},
"VAL": {"names":[ ],
"donor":[ ]},
"UNK": {"names":[ ],
"donor":[ ]},
"MAS": {"names":[ ],
"donor":[ ]},
" DA": {"names":[" N6 ", " N1 "," N3 "," N7 " ],
"donor":[ 1 , 0 , 0 , 0 ]},
" DG": {"names":[" N1 "," N2 "," N7 ", " O6 "," N1 "," N3 "," N7 " ],
"donor":[ 1 , 1 , 1 , 0 , 0 , 0 , 0 ]},
" DC": {"names":[" N4 "," N3 ", " O2 "," N3 " ],
"donor":[ 1 , 1 , 0 , 0 ]},
" DT": {"names":[" N3 ", " O2 "," O4 " ],
"donor":[ 1 , 0 , 0 ]},
" DX": {"names":[ ],
"donor":[ ]},
" RA": {"names":[" O2'"," N6 ", " N1 "," N3 "," N7 " ],
"donor":[ 1 , 1 , 0 , 0 , 0 ]},
" RG": {"names":[" O2'"," N1 "," N2 "," N7 ", " O6 "," N1 "," N3 "," N7 "],
"donor":[ 1 , 1 , 1 , 1 , 0 , 0 , 0 , 0 ]},
" RC": {"names":[" O2'"," N4 "," N3 ", " O2 "," N3 " ],
"donor":[ 1 , 1 , 1 , 0 , 0 ]},
" RU": {"names":[" O2'"," N3 ", " O2 "," O4 " ],
"donor":[ 1 , 1 , 0 , 0 ]},
" RX": {"names":[" O2'", ],
"donor":[ 1 , ]},
}
# define atoms behind all donors/acceptors/tip-atoms so that we can use them to draw a vector giving the direction of [rear-atoms] -> [tip-atoms]
self.rear_atoms = {
"ALA": {},
"ARG": {" NH1":[" CZ "], " NH2":[" CZ "],},
"ASN": {" OD1":[" CG "], " ND2":[" CG "],},
"ASP": {" OD1":[" CG "], " OD2":[" CG "],},
"CYS": {" SG ":[" CB "],},
"GLN": {" OE1":[" CD "], " NE2":[" CD "],},
"GLU": {" OE1":[" CD "], " OE2":[" CD "],},
"GLY": {},
"HIS": {" ND1":[" CG "," CE1"], " NE2":[" CD2"," CE1"],},
"ILE": {},
"LEU": {},
"LYS": {" NZ ":[" CE "],},
"MET": {" SD ":[" CG "," CE "],},
"PHE": {},
"PRO": {},
"SER": {" OG ":[" CB "],},
"THR": {" OG1":[" CB "],},
"TRP": {" NE1":[" CD1"," CE2"],},
"TYR": {" OH ":[" CZ "],},
"VAL": {},
"UNK": {},
"MAS": {},
" DA": {" N6 ":[" C6 ",], " N1 ":[" C2 "," C6 "], " N3 ":[" C2 "," C4 "], " N7 ":[" C5 "," C8 "],},
" DG": {" N1 ":[" C2 "," C6 "], " N2 ":[" C2 ",], " N7 ":[" C5 "," C8 "], " O6 ":[" C6 ",], " N3 ":[" C2 "," C4 "], " N7 ":[" C5 "," C8 "],},
" DC": {" N4 ":[" C4 ",], " N3 ":[" C2 "," C5 "], " O2 ":[" C2 ",],},
" DT": {" N3 ":[" C2 "," C4 "], " O2 ":[" C2 ",], " O4 ":[" C4 ",],},
" DX": {},
" RA": {" O2'":[" C2'",], " N6 ":[" C6 ",], " N1 ":[" C2 "," C6 "], " N3 ":[" C2 "," C4 "], " N7 ":[" C5 "," C8 "],},
" RG": {" O2'":[" C2'",], " N1 ":[" C2 "," C6 "], " N2 ":[" C2 ",], " N7 ":[" C5 "," C8 "], " O6 ":[" C6 ",], " N3 ":[" C2 "," C4 "], " N7 ":[" C5 "," C8 "],},
" RC": {" O2'":[" C2'",], " N4 ":[" C4 ",], " N3 ":[" C2 "," C5 "], " O2 ":[" C2 ",],},
" RU": {" O2'":[" C2'",], " N3 ":[" C2 "," C4 "], " O2 ":[" C2 ",], " O4 ":[" C4 ",],},
" RX": {" O2'":[" C2'",], },
}
self.ideal_angle_dict = {
'O': {
1: 109.5*(np.pi/180),
2: 180.0*(np.pi/180)},
'N': {
1: 120.0*(np.pi/180),
2: 180.0*(np.pi/180)},
'S': { # TO DO: CHECK IF BOND ANGLES ARE CORRECT!
1: 109.5*(np.pi/180),
2: 180.0*(np.pi/180)},
'P': { # TO DO: CHECK IF BOND ANGLES ARE CORRECT!
1: 120.0*(np.pi/180),
2: 180.0*(np.pi/180)},
}
def _init_nuc_chemdata(self):
self.nuc_resi_3letter = [" DA"," DG"," DC"," DT"," RA"," RG"," RC"," RU"]
# Vectors between atom pairs that define each interaction edge of each base
self.vec_atom_dict = {
" DA": {"W_start":" N1 ","W_stop":" N6 ", "H_start":" N7 ","H_stop":" N6 ", "S_start":" C1'","S_stop":" N3 ", "B_start":" C1'","B_stop":" N9 " },
" DG": {"W_start":" N1 ","W_stop":" O6 ", "H_start":" N7 ","H_stop":" O6 ", "S_start":" C1'","S_stop":" N3 ", "B_start":" C1'","B_stop":" N9 " },
" DC": {"W_start":" N3 ","W_stop":" N4 ", "H_start":" C5 ","H_stop":" N4 ", "S_start":" C1'","S_stop":" O2 ", "B_start":" C1'","B_stop":" N1 " },
" DT": {"W_start":" N3 ","W_stop":" O4 ", "H_start":" C5 ","H_stop":" O4 ", "S_start":" C1'","S_stop":" O2 ", "B_start":" C1'","B_stop":" N1 " },
" RA": {"W_start":" N1 ","W_stop":" N6 ", "H_start":" N7 ","H_stop":" N6 ", "S_start":" C1'","S_stop":" N3 ", "B_start":" C1'","B_stop":" N9 " },
" RG": {"W_start":" N1 ","W_stop":" O6 ", "H_start":" N7 ","H_stop":" O6 ", "S_start":" C1'","S_stop":" N3 ", "B_start":" C1'","B_stop":" N9 " },
" RC": {"W_start":" N3 ","W_stop":" N4 ", "H_start":" C5 ","H_stop":" N4 ", "S_start":" C1'","S_stop":" O2 ", "B_start":" C1'","B_stop":" N1 " },
" RU": {"W_start":" N3 ","W_stop":" O4 ", "H_start":" C5 ","H_stop":" O4 ", "S_start":" C1'","S_stop":" O2 ", "B_start":" C1'","B_stop":" N1 " },
}
self.vec_atom_inds = {s_i: {k_ij: HB_data.aa2long[HB_data.aa2num[s_i]].index(a_ij) for k_ij, a_ij in self.vec_atom_dict[s_i].items() } for s_i in self.nuc_resi_3letter}
self.edge_to_ind = {'W':0 , 'H':1 , 'S':2 ,'B':3}
self.ring_atom_list = [" N1 "," C2 "," N3 "," C4 "," C6 "," C5 "]
self.ring_atom_inds = {s_i: [HB_data.aa2long[HB_data.aa2num[s_i]].index(a_ij) for a_ij in self.ring_atom_list ] for s_i in self.nuc_resi_3letter}
def convert_mpnn_representation(S, X, X_m, rna_mask):
"""
Given a sequence, atom coordinates, and atom mask in the NA-MPNN format,
output the sequence, atom coordinates, and atom mask in an RFaa-like format.
Arguments:
S (np.int32 np.ndarray): an L length array representing the sequence of
the biomolecular assembly.
X (np.float32 np.ndarray): an L x num_atom_types x 3 array representing
the coordinates of each atom for each residue in the biomolecular
assembly.
X_m (np.int32 np.ndarray): an L x num_atom_types x 3 array mask that is
1 if the corresponding atom in the specified residue was loaded
and 0 otherwise.
rna_mask (np.int32 np.ndarray): an L length array mask representing
whether the residue is an RNA residue.
Returns:
S_rfaa (np.int32 np.ndarray): an L length array representing the
sequence of the biomolecular assembly in the RFaa format.
X_rfaa (np.float32 np.ndarray): an L x num_atom_types x 3 array
representing the coordinates of each atom for each residue in the
biomolecular assembly in the RFaa format.
"""
atom_idx_to_name = {atom_idx:atom_name for (atom_name, atom_idx) in pdb_dataset.atom_dict.items()}
# Convert the sequence to the RFaa format, being aware of the possible
# shared token representation of NA-MPNN.
S_rfaa = []
for i in range(S.shape[0]):
restype_int = S[i]
restype = pdb_dataset.int_to_restype[restype_int]
if rna_mask[i]:
# Handle the case when shared nucleic acid tokens are used. If
# shared tokens are not used, the RNA tokens still need to be
# converted to the RFaa notation.
if restype == "DA" or restype == "A":
restype_rfaa = "RA"
elif restype == "DC" or restype == "C":
restype_rfaa = "RC"
elif restype == "DG" or restype == "G":
restype_rfaa = "RG"
elif restype == "DT" or restype == "U":
restype_rfaa = "RU"
elif restype == "DX" or restype == "RX":
restype_rfaa = "RX"
else:
raise Exception("RNA restype not recognized.")
else:
restype_rfaa = restype
restype_int_rfaa = HB_data.aa2num_stripped[restype_rfaa]
S_rfaa.append(restype_int_rfaa)
S_rfaa = np.array(S_rfaa, dtype = np.int64)
# Convert the atom coordinates to the RFaa format.
X_rfaa = np.zeros((X.shape[0], HB_data.NTOTAL, 3), dtype = np.float32)
for i in range(X.shape[0]):
restype_int_rfaa = S_rfaa[i]
for atom_idx in range(X.shape[1]):
if X_m[i, atom_idx] == 1:
atom_type = atom_idx_to_name[atom_idx]
# Don't load any atoms beyond backbone for UNK, DX, RX.
if (HB_data.num2aa[restype_int_rfaa] in ["UNK", " DX", " RX"]) and \
(atom_type not in HB_data.aa2long_stripped[restype_int_rfaa]):
continue
# There are rare cases in the PDB where a DNA/RNA hybrid chain
# is mislabeled as DNA. In these cases, the data processing
# pipeline labels RNA residues as DNA residues, and there is
# an error with transfering the O2' atom into the RFaa format.
if (HB_data.num2aa[restype_int_rfaa] in [" DA", " DC", " DG", " DT"]) and \
(atom_type == "O2'"):
continue
# RFaa does not represent the OXT atom type.
if atom_type == "OXT":
continue
# Write the atom coordinates to the RFaa format.
atom_idx_rfaa = HB_data.aa2long_stripped[restype_int_rfaa].index(atom_type)
X_rfaa[i, atom_idx_rfaa] = X[i, atom_idx]
return S_rfaa, X_rfaa
def get_base_pair_mask_and_index(S, X, X_m, rna_mask):
"""
Given a sequence, atom coordinates, and atom mask, compute the base pairing
residues and the canonical base pairing residues (represented as a mask
and index of the base pairing partner).
Arguments:
S (np.int32 np.ndarray): an L length array representing the sequence of
the biomolecular assembly.
X (np.float32 np.ndarray): an L x num_atom_types x 3 array representing
the coordinates of each atom for each residue in the biomolecular
assembly.
X_m (np.int32 np.ndarray): an L x num_atom_types x 3 array mask that is
1 if the corresponding atom in the specified residue was loaded
and 0 otherwise.
rna_mask (np.int32 np.ndarray): an L length array mask representing
whether the residue is an RNA residue.
Returns:
base_pair_mask (np.int32 np.ndarray): an L length array mask that is
1 if the corresponding residue is involved in a base pair
interaction and 0 otherwise.
base_pair_index (np.int64 np.ndarray): an L length array that
represents the index of the partner residue in a base pairing
interaction. For residues not in a base pairing interaction,
defined as 0, but it is necessary to the base_pair_mask in
conjunction.
canonical_base_pair_mask (np.int32 np.ndarray): similar to
base_pair_mask, but limited to positions that make canonical base
pairing interactions.
canonical_base_pair_index (np.int64 np.ndarray): similar to
base_pair_index, but limited to positions that make canonical base
pairing interactions.
"""
# Convert to the representation needed for the HB_data object.
S_rfaa, X_rfaa = convert_mpnn_representation(S, X, X_m, rna_mask)
hb_data = HB_data(torch.tensor(S_rfaa),
torch.tensor(X_rfaa),
compute_paired_bases=True,
compute_helical_params=True
)
# basepairs_ij is only created if there is non-DX/RX nucleic acids in the
# structure.
if hb_data.is_na.sum() > 0:
base_pairs_prob = hb_data.basepairs_ij.detach().cpu().numpy()
base_pairs_binary = (base_pairs_prob > 0.5).astype(np.int32)
# Only consider base pairing interactions that have one partner.
base_pair_mask = (np.sum(base_pairs_binary, axis = -1) == 1).astype(np.int32)
base_pair_index = np.argmax(base_pairs_binary, axis = -1).astype(np.int64)
else:
base_pair_mask = np.zeros(S_rfaa.shape[0], dtype = np.int32)
base_pair_index = np.zeros(S_rfaa.shape[0], dtype = np.int64)
# Base pair mask needs to be updated so that the base pairing partner
# also exists.
base_pair_mask = base_pair_mask * base_pair_mask[base_pair_index]
# Make sure to update the base pair index using the base pair mask.
base_pair_index = base_pair_index * base_pair_mask
# Create the canonical base pair mask and index, removing any base pairing
# interactions with non-canonical sequences.
canonical_base_pair_mask = np.copy(base_pair_mask)
canonical_base_pair_index = np.copy(base_pair_index)
for i in range(len(S)):
if base_pair_mask[i] == 1:
restype_i = S[i]
restype_j = S[base_pair_index[i]]
if (restype_i, restype_j) not in pdb_dataset.na_canonical_base_pair_ints:
canonical_base_pair_mask[i] = 0
canonical_base_pair_mask[base_pair_index[i]] = 0
# Make sure to update the canonical base pair index using the canonical base
# pair mask.
canonical_base_pair_index = canonical_base_pair_index * canonical_base_pair_mask
return base_pair_mask, base_pair_index, canonical_base_pair_mask, canonical_base_pair_index
# Get nearest neighbors
def get_nearest_interface_neighbors_to_res_i(X, protein_mask, na_mask, i, eps = 1E-6):
if protein_mask[i] == 1:
mask = na_mask
elif na_mask[i] == 1:
mask = protein_mask
dX = X - X[i]
D = mask * torch.sqrt(torch.sum(dX ** 2, 1) + eps)
D_max, _ = torch.max(D, -1, keepdim=True)
D_adjust = D + (1. - mask) * (D_max + eps)
D_neighbors, E_idx = torch.topk(D_adjust, np.minimum(num_neighbors, X.shape[0]), dim=-1, largest=False)
return E_idx
def get_interface_masks(X, X_m, protein_mask, dna_mask, rna_mask):
L = X.shape[0]
na_mask = dna_mask + rna_mask #[L]
interface_mask = np.zeros(L, dtype = np.int32)
Ca = X[:,pdb_dataset.atom_dict["CA"],:]
na_ref_atom = X[:,pdb_dataset.atom_dict[params["NA_REF_ATOM"]],:]
side_chain_interface_mask = np.zeros(L, dtype = np.int32)
nearest_protein_side_chain_index = np.zeros(L, dtype = np.int64)
for i in range(L):
nearest_neighbor_idx = get_nearest_interface_neighbors_to_res_i(torch.tensor(Ca + na_ref_atom), torch.tensor(protein_mask), torch.tensor(na_mask), i)
nearest_protein_side_chain_distance = None
for j in nearest_neighbor_idx:
if not (na_mask[i] == 1 or na_mask[j] == 1):
continue
res_i_X = X[i] #[N,3]
res_i_X_m = X_m[i] #[N]
res_j_X = X[j] #[N,3]
res_j_X_m = X_m[j] #[N]
# Compute the per-atom pairwise distance.
dX = res_i_X[:,None,:] - res_j_X[None,:,:] #[N,N,3]
pairwise_atom_distances = np.sqrt(np.sum(dX ** 2, axis = -1)) #[N,N]
# Mask out pairwise distances for atoms that do not exist.
X_m_pairwise = res_i_X_m[:,None] * res_j_X_m[None,:] #[N,N]
min_distance = np.min(pairwise_atom_distances[(X_m_pairwise == 1)])
if min_distance < interface_distance_cutoff:
if (protein_mask[i] == 1 and na_mask[j] == 1) or (protein_mask[j] == 1 and na_mask[i] == 1):
interface_mask[i] = 1
interface_mask[j] = 1
X_m_side_chain_pairwise = X_m_pairwise * side_chain_pairwise_mask
if np.count_nonzero(X_m_side_chain_pairwise) > 0:
min_side_chain_distance = np.min(pairwise_atom_distances[(X_m_side_chain_pairwise == 1)])
if min_side_chain_distance < interface_distance_cutoff:
if (protein_mask[i] == 1 and na_mask[j] == 1) or (protein_mask[j] == 1 and na_mask[i] == 1):
side_chain_interface_mask[i] = 1
side_chain_interface_mask[j] = 1
if protein_mask[j] == 1 and \
(nearest_protein_side_chain_distance == None or \
min_side_chain_distance < nearest_protein_side_chain_distance):
nearest_protein_side_chain_index[i] = j
nearest_protein_side_chain_distance = min_side_chain_distance
return interface_mask, side_chain_interface_mask, nearest_protein_side_chain_index
if __name__ == "__main__":
# Load the command line arguments.
input_csv_path = sys.argv[1]
output_directory = sys.argv[2]
modulo = int(sys.argv[3])
remainder = int(sys.argv[4])
# Load the csv, containing the structure path and pwm paths.
df = pd.read_csv(input_csv_path)
# Output directory file paths.
sequences_directory = os.path.join(output_directory, "sequences")
asmb_lengths_directory = os.path.join(output_directory, "asmb_lengths")
asmb_interface_masks_directory = os.path.join(output_directory, "asmb_interface_masks")
asmb_side_chain_interface_masks_directory = os.path.join(output_directory, "asmb_side_chain_interface_masks")
asmb_nearest_protein_side_chain_index_directory = os.path.join(output_directory, "asmb_nearest_protein_side_chain_index")
asmb_base_pair_masks_directory = os.path.join(output_directory, "asmb_base_pair_masks")
asmb_base_pair_index_directory = os.path.join(output_directory, "asmb_base_pair_index")
asmb_canonical_base_pair_masks_directory = os.path.join(output_directory, "asmb_canonical_base_pair_masks")
asmb_canonical_base_pair_index_directory = os.path.join(output_directory, "asmb_canonical_base_pair_index")
bad_directory = os.path.join(output_directory, "bad")
# Make output directories.
os.makedirs(sequences_directory, exist_ok = True)
os.makedirs(asmb_lengths_directory, exist_ok = True)
os.makedirs(asmb_interface_masks_directory, exist_ok = True)
os.makedirs(asmb_side_chain_interface_masks_directory, exist_ok = True)
os.makedirs(asmb_nearest_protein_side_chain_index_directory, exist_ok = True)
os.makedirs(asmb_base_pair_masks_directory, exist_ok = True)
os.makedirs(asmb_base_pair_index_directory, exist_ok = True)
os.makedirs(asmb_canonical_base_pair_masks_directory, exist_ok = True)
os.makedirs(asmb_canonical_base_pair_index_directory, exist_ok = True)
os.makedirs(bad_directory, exist_ok = True)
# Preprocess data.
for iii in range(len(df)):
if (iii + 1) % modulo != remainder:
continue
example_dict = df.iloc[iii].to_dict()
structure_file_name = os.path.basename(example_dict["structure_path"])
# Handle GZipped files.
if structure_file_name.endswith(".gz"):
structure_name = os.path.splitext(os.path.splitext(structure_file_name)[0])[0]
else:
structure_name = os.path.splitext(structure_file_name)[0]
sequences_path = os.path.join(sequences_directory, structure_name + ".csv")
asmb_lengths_path = os.path.join(asmb_lengths_directory, structure_name + ".npy")
asmb_interface_masks_path = os.path.join(asmb_interface_masks_directory, structure_name + ".npy")
asmb_side_chain_interface_masks_path = os.path.join(asmb_side_chain_interface_masks_directory, structure_name + ".npy")
asmb_nearest_protein_side_chain_index_path = os.path.join(asmb_nearest_protein_side_chain_index_directory, structure_name + ".npy")
asmb_base_pair_masks_path = os.path.join(asmb_base_pair_masks_directory, structure_name + ".npy")
asmb_base_pair_index_path = os.path.join(asmb_base_pair_index_directory, structure_name + ".npy")
asmb_canonical_base_pair_masks_path = os.path.join(asmb_canonical_base_pair_masks_directory, structure_name + ".npy")
asmb_canonical_base_pair_index_path = os.path.join(asmb_canonical_base_pair_index_directory, structure_name + ".npy")
bad_path = os.path.join(bad_directory, structure_name + ".txt")
try:
assemblies, chain_sequences = pdb_dataset.load_for_structure_preprocessing(example_dict)
except Exception as e:
write_text_file(bad_path, str(e))
continue
if assemblies == "pass" or (len(assemblies) == 0):
write_text_file(bad_path, "cifutils_failed_to_load_assemblies")
continue
asmb_lengths_dict = {}
asmb_interface_masks_dict = {}
asmb_side_chain_interface_masks_dict = {}
asmb_nearest_protein_side_chain_index_dict = {}
asmb_base_pair_masks_dict = {}
asmb_base_pair_index_dict = {}
asmb_canonical_base_pair_masks_dict = {}
asmb_canonical_base_pair_index_dict = {}
missing_na_count = 0
for (assembly_id, out_dict) in assemblies:
# Filter out assemblies with no resolved/occupied nucleic acids.
if (out_dict["dna_L"] == 0) and (out_dict["rna_L"] == 0):
missing_na_count += 1
continue
# Get the base pair mask and index. If the sequence longer than the
# normal batch size for MPNN, then the base pair mask and index will
# be empty.
if out_dict["S"].shape[0] > residue_cutoff:
base_pair_mask = np.zeros(out_dict["S"].shape, dtype = np.int32)
base_pair_index = np.zeros(out_dict["S"].shape, dtype = np.int64)
canonical_base_pair_mask = np.zeros(out_dict["S"].shape, dtype = np.int32)
canonical_base_pair_index = np.zeros(out_dict["S"].shape, dtype = np.int64)
else:
base_pair_mask, base_pair_index, canonical_base_pair_mask, canonical_base_pair_index = \
get_base_pair_mask_and_index(out_dict["S"],
out_dict["X"],
out_dict["X_m"],
out_dict["rna_mask"])
# Get the interface masks.
interface_mask, side_chain_interface_mask, nearest_protein_side_chain_index = \
get_interface_masks(out_dict["X"],
out_dict["X_m"],
out_dict["protein_mask"],
out_dict["dna_mask"],
out_dict["rna_mask"])
# Save the per-assembly data.
asmb_lengths_dict[assembly_id] = (out_dict["macromolecule_L"], out_dict["protein_L"], out_dict["dna_L"], out_dict["rna_L"])
asmb_interface_masks_dict[assembly_id] = interface_mask
asmb_side_chain_interface_masks_dict[assembly_id] = side_chain_interface_mask
asmb_nearest_protein_side_chain_index_dict[assembly_id] = nearest_protein_side_chain_index
asmb_base_pair_masks_dict[assembly_id] = base_pair_mask
asmb_base_pair_index_dict[assembly_id] = base_pair_index
asmb_canonical_base_pair_masks_dict[assembly_id] = canonical_base_pair_mask
asmb_canonical_base_pair_index_dict[assembly_id] = canonical_base_pair_index
if len(list(asmb_lengths_dict)) > 0:
chain_sequences_lines = ["chain_id,chain_type,sequence"]
for chain_sequence_line in chain_sequences:
chain_sequence_line = tuple(map(lambda x: "" if x is None else x, chain_sequence_line))
chain_sequences_lines.append(",".join(chain_sequence_line))
chain_sequences_str = "\n".join(chain_sequences_lines)
write_text_file(sequences_path, chain_sequences_str)
np.save(asmb_lengths_path, asmb_lengths_dict)
np.save(asmb_interface_masks_path, asmb_interface_masks_dict)
np.save(asmb_side_chain_interface_masks_path, asmb_side_chain_interface_masks_dict)
np.save(asmb_nearest_protein_side_chain_index_path, asmb_nearest_protein_side_chain_index_dict)
np.save(asmb_base_pair_masks_path, asmb_base_pair_masks_dict)
np.save(asmb_base_pair_index_path, asmb_base_pair_index_dict)
np.save(asmb_canonical_base_pair_masks_path, asmb_canonical_base_pair_masks_dict)
np.save(asmb_canonical_base_pair_index_path, asmb_canonical_base_pair_index_dict)
elif missing_na_count == len(assemblies):
write_text_file(bad_path, "all_assemblies_no_resolved_and_occupied_nucleic_acids")
continue
else:
write_text_file(bad_path, "all_assemblies_failed")
continue |