subjecting to linearity in dccm plot respect to residue number

Hello team, i had perfomed dccm for my protein-RNA complex and now ne problem i am facing is in dccm plot i want to get uniform fluctuations of residues like protein c alpha atoms first then RNA phosphate atoms later but graph which i am getting after combine selection have mixed up residues of protein rna. it is difficult to explain the graph with so much of combinations. can u please help to genrate a plot where i can have protein residues first and the rna residues.

dcd <-read.dcd("54fulldcd") NATOM = 331517 NFRAME= 1002 ISTART= 0 last = 1002 nstep = 1002 nfile = 1002 NSAVE = 1 NDEGF = 0 version 24 |======================================================================| 100% pdb <- read.pdb("pcawt.pdb") ca.inds <- atom.select(pdb, "calpha") inds <- atom.select(pdb, elety = "P") casc.local.inds <- combine.select(ca.inds,inds, operator="+") Union of selects * Selected a total of: 394 atoms * inds[["atom"]] [1] 68 130 167 197 237 289 332 380 447 488 532 562 [13] 632 662 711 767 817 861 891 942 984 1034 1095 1143 [25] 1189 1219 1271 1349 1380 1432 1485 1529 1582 1634 1674 1729 [37] 1776 1823 1878 1927 1991 2038 2090 2132 2163 2219 2278 2332 [49] 2385 2415 2479 2516 2546 2606 2636 2689 2742 2814 2844 2906 [61] 2936 3004 3045 3085 3133 3177 3219 3249 3296 3353 3396 3460 [73] 3494 3541 3596 3663 3713 3750 3780 3837 3886 3938 4003 4033 [85] 4094 4134 4188 4218 4252 4282 4316 4350 4381 4411 4445 4475 [97] 4506 4539 4570 4600 4631 4665 4699 4730 4760 4794 4825 4858 [109] 4888 4922 4953 4983 5013 5046 5080 5110 5144 5175 5208 5239 [121] 5269 5300 5333 5364 5398 5429 5462 5496 5526 5559 5589 5622 [133] 5655 5685 5715 5748 5781 5811 5844 5877 5908 5938 5971 6004 [145] 6034 6064 6097 6128 6158 6192 6222 6253 6287 6317 6347 6381 [157] 6414 6445 6478 6512 6546 6579 6610 6643 6674 6708 6741 6775 [169] 6805 6838 6871 6902 6932 6963 6997 7027 7058 7088 7121 7151 [181] 7182 7212 7242 7273 7303 7337 7368 7401 7435 7469 7500 7530 [193] 7564 7595 7625 7655 7688 7719 7753 7787 7817 7847 7877 7908 [205] 7942 7972 8003 8034 8068 8098 8132 8162 8192 8226 8257 8290 [217] 8324 8355 8386 8420 8453 8483 8514 8547 8577 8608 8641 8675 [229] 8706 8739 8770 8803 8833 8864 8894 8927 8961 8995 9025 9055 [241] 9085 9116 9150 9180 9211 9242 9276 9310 9344 9374 9408 9438 [253] 9472 9505 9536 9567 9601 9634 9667 9700 9734 9768 9798 9831 [265] 9864 9898 9931 9961 9995 10029 10062 10096 10129 10163 10194 10225 [277] 10255 10285 10319 10349 10380 10411 10442 10472 10506 10540 10570 10600 [289] 10630 10661 10694 10727 10758 10792 10825 10859 10892 10925 10958 10991 ca.inds[["atom"]] [1] 2 11 22 33 40 51 62 103 120 161 231 271 323 367 415 [16] 426 478 519 596 612 696 743 754 799 848 923 974 1016 1068 1082 [31] 1130 1178 1253 1305 1326 1414 1466 1519 1561 1614 1668 1708 1761 1808 1857 [46] 1912 1959 1973 2022 2069 2121 2198 2253 2265 2309 2364 2447 2458 2510 2581 [61] 2597 2670 2724 2776 2795 2876 2895 2971 2993 3035 3076 3117 3164 3209 3283 [76] 3330 3387 3428 3447 3528 3575 3628 3645 3694 3744 3814 3871 3920 3972 3994 [91] 4065 4076 4125 4169 casc.local.inds[["atom"]] [1] 2 11 22 33 40 51 62 68 103 120 130 161 [13] 167 197 231 237 271 289 323 332 367 380 415 426 [25] 447 478 488 519 532 562 596 612 632 662 696 711 [37] 743 754 767 799 817 848 861 891 923 942 974 984 [49] 1016 1034 1068 1082 1095 1130 1143 1178 1189 1219 1253 1271 [61] 1305 1326 1349 1380 1414 1432 1466 1485 1519 1529 1561 1582 [73] 1614 1634 1668 1674 1708 1729 1761 1776 1808 1823 1857 1878 [85] 1912 1927 1959 1973 1991 2022 2038 2069 2090 2121 2132 2163 [97] 2198 2219 2253 2265 2278 2309 2332 2364 2385 2415 2447 2458 [109] 2479 2510 2516 2546 2581 2597 2606 2636 2670 2689 2724 2742 [121] 2776 2795 2814 2844 2876 2895 2906 2936 2971 2993 3004 3035 [133] 3045 3076 3085 3117 3133 3164 3177 3209 3219 3249 3283 3296 [145] 3330 3353 3387 3396 3428 3447 3460 3494 3528 3541 3575 3596 [157] 3628 3645 3663 3694 3713 3744 3750 3780 3814 3837 3871 3886 [169] 3920 3938 3972 3994 4003 4033 4065 4076 4094 4125 4134 4169 [181] 4188 4218 4252 4282 4316 4350 4381 4411 4445 4475 4506 4539 [193] 4570 4600 4631 4665 4699 4730 4760 4794 4825 4858 4888 4922 [205] 4953 4983 5013 5046 5080 5110 5144 5175 5208 5239 5269 5300 [217] 5333 5364 5398 5429 5462 5496 5526 5559 5589 5622 5655 5685 [229] 5715 5748 5781 5811 5844 5877 5908 5938 5971 6004 6034 6064 [241] 6097 6128 6158 6192 6222 6253 6287 6317 6347 6381 6414 6445 [253] 6478 6512 6546 6579 6610 6643 6674 6708 6741 6775 6805 6838 [265] 6871 6902 6932 6963 6997 7027 7058 7088 7121 7151 7182 7212 [277] 7242 7273 7303 7337 7368 7401 7435 7469 7500 7530 7564 7595 [289] 7625 7655 7688 7719 7753 7787 7817 7847 7877 7908 7942 7972 [301] 8003 8034 8068 8098 8132 8162 8192 8226 8257 8290 8324 8355 [313] 8386 8420 8453 8483 8514 8547 8577 8608 8641 8675 8706 8739 [325] 8770 8803 8833 8864 8894 8927 8961 8995 9025 9055 9085 9116 [337] 9150 9180 9211 9242 9276 9310 9344 9374 9408 9438 9472 9505 [349] 9536 9567 9601 9634 9667 9700 9734 9768 9798 9831 9864 9898 [361] 9931 9961 9995 10029 10062 10096 10129 10163 10194 10225 10255 10285 [373] 10319 10349 10380 10411 10442 10472 10506 10540 10570 10600 10630 10661 [385] 10694 10727 10758 10792 10825 10859 10892 10925 10958 10991 xyz <- fit.xyz(fixed = pdb$xyz, mobile=dcd, fixed.inds = casc.local.inds$xyz, mobile.inds = casc.local.inds$xyz) xyz <- fit.xyz(fixed = pdb$xyz, mobile=dcd, fixed.inds = casc.local.inds$xyz, mobile.inds = casc.local.inds$xyz) xyz

Total Frames#: 1002 Total XYZs#: 994551, (Atoms#: 331517)

[1]  -28.056  -55.484  17.777  <...>  -36.307  38.148  2.751  [996540102]

attr: Matrix DIM = 1002 x 994551

jpeg("combinewt.jpg", width = 900, height = 900) cij<-dccm(xyz[,casc.local.inds$xyz]) plot(cij) dev.off()

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