C-I-Tasser Standalone Installation Help Request

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Moderator: robpearc

rpearson_7
Posts: 3
Joined: Wed Nov 10, 2021 8:05 pm

C-I-Tasser Standalone Installation Help Request

Post by rpearson_7 »

Hello,

I have been able to get 3 model pdb outputs after installing the standalone package. Currently, I am trying to verify that those outputs match the server outputs by submitting the seq.fasta sequence to the C-I-Tasser webserver. Results are still pending and I will report back when I receive the results.

One known deviation from the set-up is that my pytorch version is not the 0.3.0 stipulated in the README. Also, the CUDA version I am running is newer also (11.4) As such, I have had to set the tracking_running_stats=False in InstanceNorm2d. Other unknown differences likely exist.

Please see the attached slurm output file to see if there are any red flags that I should correct. I am worried if I rely on this local version that I will get bad results and waste time and resources.
Your setting for running I-TASSER is:
-pkgdir = /home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0
-libdir = /home/rpearson/Structure_Prediction_Tools/CIT_Lib
-java_home = /usr
-python2 = /opt/intel/intelpython2/bin/python
-python3 = ~/.conda/envs/Quark_and_Itasser_Python3/bin/python3
-seqname = seq
-datadir = /home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/example
-outdir = /home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/example
-runstyle = parallel
-homoflag = benchmark
-idcut = 0.3
-cit = true
-ntemp = 20
-nmodel = 5
-light = false
-hours = 50
-LBS = true
-EC = true
-GO = true

1. make seq.txt and rmsinp
Your protein contains 143 residues:
> seq
MYQLEKEPIVGAETFYVDGAANRETKLGKAGYVTNRGRQKVVTLTDTTNQKTELQAIYLA
LQDSGLEVNIVTDSQYALGIITQWIHNWKKRGWPVKNVDLVNQIIEQLIKKEKVYLAWVP
AHKGIGGNEQVDKLVSAGIRKVL
2.0 run DeepMSA
--------- /home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/example/CITseq_MSA
hostname: gpu04.cluster
starting time: Tue Nov 9 16:26:01 PST 2021
pwd: /tmp/rpearson/CITseq
hhlib=/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/DeepMSA
Reading in 12365503 column state sequences with a total of 3308890311 residues

Iteration 1
Prefiltering database
............................................................................................................................
HMMs passed 1st prefilter (gapless profile-profile alignment) : 183097
HMMs passed 2nd prefilter (gapped profile-profile alignment) : 6423
HMMs passed 2nd prefilter and not found in previous iterations : 6423
Scoring 6423 HMMs using HMM-HMM Viterbi alignment
.................................................. 1000 HMMs searched
.................................................. 2000 HMMs searched
.................................................. 3000 HMMs searched
.................................................. 4000 HMMs searched
.................................................. 5000 HMMs searched
.................................................. 6000 HMMs searched
.....................
Realigning 997 HMMs using HMM-HMM Maximum Accuracy algorithm
.................................................. 500 HMMs aligned
.................................................. 1000 HMMs aligned

Merging hits to query profile
4780 sequences belonging to 4780 database HMMs found with an E-value < 0.001
Number of effective sequences of resulting query HMM: Neff = 7.32

Iteration 2
Prefiltering database
............................................................................................................................
HMMs passed 1st prefilter (gapless profile-profile alignment) : 256207
HMMs passed 2nd prefilter (gapped profile-profile alignment) : 10211
HMMs passed 2nd prefilter and not found in previous iterations : 10211
Scoring 10211 HMMs using HMM-HMM Viterbi alignment
.................................................. 1000 HMMs searched
.................................................. 2000 HMMs searched
.................................................. 3000 HMMs searched
.................................................. 4000 HMMs searched
.................................................. 5000 HMMs searched
.................................................. 6000 HMMs searched
.................................................. 7000 HMMs searched
.................................................. 8000 HMMs searched
.................................................. 9000 HMMs searched
.................................................. 10000 HMMs searched
..........
Realigning 999 HMMs using HMM-HMM Maximum Accuracy algorithm
.................................................. 500 HMMs aligned
.................................................
Merging hits to query profile
7515 sequences belonging to 7515 database HMMs found with an E-value < 0.001
Number of effective sequences of resulting query HMM: Neff = 9.08

Iteration 3
Prefiltering database
............................................................................................................................
HMMs passed 1st prefilter (gapless profile-profile alignment) : 294645
HMMs passed 2nd prefilter (gapped profile-profile alignment) : 12811
HMMs passed 2nd prefilter and not found in previous iterations : 12811
Scoring 12811 HMMs using HMM-HMM Viterbi alignment
.................................................. 1000 HMMs searched
.................................................. 2000 HMMs searched
.................................................. 3000 HMMs searched
.................................................. 4000 HMMs searched
.................................................. 5000 HMMs searched
.................................................. 6000 HMMs searched
.................................................. 7000 HMMs searched
.................................................. 8000 HMMs searched
.................................................. 9000 HMMs searched
.................................................. 10000 HMMs searched
.................................................. 11000 HMMs searched
.................................................. 12000 HMMs searched
........................................
Rescoring previously found HMMs with Viterbi algorithm
.................................................. 1000 HMMs searched
.................................................. 2000 HMMs searched
.................................................. 3000 HMMs searched
.................................................. 4000 HMMs searched
.................................................. 5000 HMMs searched
.................................................. 6000 HMMs searched
.................................................. 7000 HMMs searched
........................
Realigning 501 HMMs using HMM-HMM Maximum Accuracy algorithm
.................................................. 500 HMMs aligned
.................................................
Merging hits to query profile
9776 sequences belonging to 9776 database HMMs found with an E-value < 0.001
Number of effective sequences of resulting query HMM: Neff = 9.55

Query seq
Match_columns 143
No_of_seqs 13718 out of 17974
Neff 9.1
Searched_HMMs 20323
Date Tue Nov 9 16:29:37 2021
Command /home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/DeepMSA/bin/hhblits -i /tmp/rpearson/CITseq_MSA/MSAseq/seq.fasta -diff inf -d /home/rpearson/Structure_Prediction_Tools/CIT_Lib/uniclust30/uniclust30_2017_04/uniclust30_2017_04 -cpu 2 -oa3m /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.a3m -id 99 -cov 50 -o /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.log -n 3 -diff inf

No Hit Prob E-value P-value Score SS Cols Query HMM Template HMM
1 tr|A0A0Q8D4D4|A0A0Q8D4D4_9GAMM 100.0 1.9E-52 7.7E-58 291.4 0.0 132 10-142 20-162 (167)
2 tr|A0A0Q8D4D4|A0A0Q8D4D4_9GAMM 100.0 1.9E-52 7.7E-58 291.4 0.0 132 10-142 20-162 (167)
3 tr|X6C4L2|X6C4L2_9RHIZ Ribonuc 100.0 7.6E-52 3E-57 286.4 0.0 130 11-141 3-143 (181)
4 tr|X6C4L2|X6C4L2_9RHIZ Ribonuc 100.0 7.6E-52 3E-57 286.4 0.0 130 11-141 3-143 (181)
5 tr|A0A0Q7V974|A0A0Q7V974_9RHIZ 100.0 1.6E-51 6.4E-57 284.5 0.0 131 10-141 7-148 (181)
6 tr|A0A0Q7V974|A0A0Q7V974_9RHIZ 100.0 1.6E-51 6.4E-57 284.5 0.0 131 10-141 7-148 (181)
7 tr|E6PMY8|E6PMY8_9ZZZZ Ribonuc 100.0 1.6E-51 6.6E-57 286.0 0.0 133 9-142 25-168 (172)
8 tr|E6PMY8|E6PMY8_9ZZZZ Ribonuc 100.0 1.6E-51 6.6E-57 286.0 0.0 133 9-142 25-168 (172)
9 tr|V9M0C8|V9M0C8_9CAUD Ribonuc 100.0 2.4E-51 9.6E-57 285.9 0.0 128 13-141 2-142 (174)
10 tr|V9M0C8|V9M0C8_9CAUD Ribonuc 100.0 2.4E-51 9.6E-57 285.9 0.0 128 13-141 2-142 (174)
11 tr|G8QUY9|G8QUY9_SPHPG Ribonuc 100.0 4.9E-51 2E-56 280.4 0.0 131 11-142 4-148 (151)
12 tr|G8QUY9|G8QUY9_SPHPG Ribonuc 100.0 4.9E-51 2E-56 280.4 0.0 131 11-142 4-148 (151)
13 tr|A0A0F9W3V9|A0A0F9W3V9_9ZZZZ 100.0 7E-50 3E-55 274.2 0.0 130 11-141 3-144 (145)
14 tr|A0A0F9W3V9|A0A0F9W3V9_9ZZZZ 100.0 7E-50 3E-55 274.2 0.0 130 11-141 3-144 (145)
15 tr|A0A124FTJ9|A0A124FTJ9_9FIRM 100.0 1.1E-49 4.2E-55 276.9 0.0 131 10-141 26-168 (184)
16 tr|A0A124FTJ9|A0A124FTJ9_9FIRM 100.0 1.1E-49 4.2E-55 276.9 0.0 131 10-141 26-168 (184)
17 tr|R6XG80|R6XG80_9BACT Ribonuc 100.0 1.4E-49 5.4E-55 275.5 0.0 131 10-141 7-145 (173)
18 tr|R6XG80|R6XG80_9BACT Ribonuc 100.0 1.4E-49 5.4E-55 275.5 0.0 131 10-141 7-145 (173)
19 tr|D5SZ45|D5SZ45_PLAL2 Ribonuc 100.0 4.1E-49 1.6E-54 272.8 0.0 134 8-142 21-170 (176)
20 tr|D5SZ45|D5SZ45_PLAL2 Ribonuc 100.0 4.1E-49 1.6E-54 272.8 0.0 134 8-142 21-170 (176)
21 tr|A0A0F9U2V7|A0A0F9U2V7_9ZZZZ 100.0 5.2E-49 2E-54 271.5 0.0 130 11-141 24-164 (190)
22 tr|A0A0F9U2V7|A0A0F9U2V7_9ZZZZ 100.0 5.2E-49 2E-54 271.5 0.0 130 11-141 24-164 (190)
23 tr|S6CD20|S6CD20_9ACTN Ribonuc 100.0 1.7E-48 6.5E-54 270.0 0.0 132 9-141 27-175 (189)
24 tr|S6CD20|S6CD20_9ACTN Ribonuc 100.0 1.7E-48 6.5E-54 270.0 0.0 132 9-141 27-175 (189)
25 tr|E0WR63|E0WR63_9ENTR Ribonuc 100.0 3.5E-48 1.3E-53 266.9 0.0 135 7-142 23-168 (185)
26 tr|E0WR63|E0WR63_9ENTR Ribonuc 100.0 3.5E-48 1.3E-53 266.9 0.0 135 7-142 23-168 (185)
27 tr|H5Y409|H5Y409_9FIRM Ribonuc 100.0 8.2E-48 3.2E-53 266.8 0.0 131 10-141 20-163 (186)
28 tr|H5Y409|H5Y409_9FIRM Ribonuc 100.0 8.2E-48 3.2E-53 266.8 0.0 131 10-141 20-163 (186)
29 tr|Q1NWE1|Q1NWE1_9DELT Ribonuc 100.0 3.5E-47 1.4E-52 272.8 0.0 134 6-141 84-231 (243)
30 tr|Q1NWE1|Q1NWE1_9DELT Ribonuc 100.0 3.5E-47 1.4E-52 272.8 0.0 134 6-141 84-231 (243)
31 tr|A0A1A9BBW0|A0A1A9BBW0_9ACTN 100.0 5.1E-47 1.9E-52 261.3 0.0 133 8-141 5-149 (189)
32 tr|A0A1A9BBW0|A0A1A9BBW0_9ACTN 100.0 5.1E-47 1.9E-52 261.3 0.0 133 8-141 5-149 (189)
33 sp|A7HB50|RNH_ANADF Ribonuclea 100.0 2.6E-46 9.8E-52 256.7 0.0 130 11-141 7-155 (175)
34 sp|A7HB50|RNH_ANADF Ribonuclea 100.0 2.6E-46 9.8E-52 256.7 0.0 130 11-141 7-155 (175)
35 tr|X1NJS0|X1NJS0_9ZZZZ Unchara 100.0 4.5E-46 1.8E-51 268.7 0.0 130 11-140 6-151 (235)
36 tr|X1NJS0|X1NJS0_9ZZZZ Unchara 100.0 4.5E-46 1.8E-51 268.7 0.0 130 11-140 6-151 (235)
37 tr|A0A0H2YVV2|A0A0H2YVV2_ECOK1 100.0 4.9E-46 1.8E-51 253.6 0.0 131 11-142 40-181 (192)
38 tr|A0A0H2YVV2|A0A0H2YVV2_ECOK1 100.0 4.9E-46 1.8E-51 253.6 0.0 131 11-142 40-181 (192)
39 tr|F0RK40|F0RK40_DEIPM Ribonuc 100.0 5.1E-46 1.9E-51 255.5 0.0 132 10-142 36-179 (180)
40 tr|F0RK40|F0RK40_DEIPM Ribonuc 100.0 5.1E-46 1.9E-51 255.5 0.0 132 10-142 36-179 (180)
41 tr|A7NII6|A7NII6_ROSCS Ribonuc 100.0 6.7E-46 2.5E-51 264.2 0.0 133 8-142 91-236 (245)
42 tr|A7NII6|A7NII6_ROSCS Ribonuc 100.0 6.7E-46 2.5E-51 264.2 0.0 133 8-142 91-236 (245)
43 tr|B8GBH3|B8GBH3_CHLAD Ribonuc 100.0 6.9E-45 2.6E-50 257.3 0.0 135 7-142 79-225 (241)
44 tr|B8GBH3|B8GBH3_CHLAD Ribonuc 100.0 6.9E-45 2.6E-50 257.3 0.0 135 7-142 79-225 (241)
45 tr|K9PAW4|K9PAW4_CYAGP Ribonuc 100.0 9.7E-45 3.8E-50 265.6 0.0 127 12-140 8-149 (269)
46 tr|K9PAW4|K9PAW4_CYAGP Ribonuc 100.0 9.7E-45 3.8E-50 265.6 0.0 127 12-140 8-149 (269)
47 tr|A0A0J8TX39|A0A0J8TX39_9MYCO 100.0 1.2E-44 4.5E-50 246.6 0.0 131 9-140 11-152 (186)
48 tr|A0A0J8TX39|A0A0J8TX39_9MYCO 100.0 1.2E-44 4.5E-50 246.6 0.0 131 9-140 11-152 (186)
49 tr|K0NR62|K0NR62_DESTT Ribonuc 100.0 1.9E-44 6.9E-50 250.8 0.0 137 6-142 63-212 (224)
50 tr|K0NR62|K0NR62_DESTT Ribonuc 100.0 1.9E-44 6.9E-50 250.8 0.0 137 6-142 63-212 (224)
51 tr|C9XWE3|C9XWE3_CROTZ Ribonuc 100.0 2.1E-44 7.8E-50 245.1 0.0 131 11-142 41-182 (196)
52 tr|C9XWE3|C9XWE3_CROTZ Ribonuc 100.0 2.1E-44 7.8E-50 245.1 0.0 131 11-142 41-182 (196)
53 tr|E1X212|E1X212_HALMS Ribonuc 100.0 3.3E-44 1.2E-49 249.6 0.0 132 11-142 61-211 (213)
54 tr|E1X212|E1X212_HALMS Ribonuc 100.0 3.3E-44 1.2E-49 249.6 0.0 132 11-142 61-211 (213)
55 tr|A0A0M3JRL4|A0A0M3JRL4_ANISI 100.0 6.5E-44 2.7E-49 264.6 0.0 134 8-142 108-258 (260)
56 tr|A0A0M3JRL4|A0A0M3JRL4_ANISI 100.0 6.5E-44 2.7E-49 264.6 0.0 134 8-142 108-258 (260)
57 tr|A5US20|A5US20_ROSS1 Ribonuc 100.0 7.5E-44 2.8E-49 251.0 0.0 126 13-141 2-139 (214)
58 tr|A5US20|A5US20_ROSS1 Ribonuc 100.0 7.5E-44 2.8E-49 251.0 0.0 126 13-141 2-139 (214)
59 tr|A0A1A7R7W3|A0A1A7R7W3_9GAMM 100.0 7.4E-44 2.9E-49 277.6 0.0 131 11-142 3-139 (487)
60 tr|A0A1A7R7W3|A0A1A7R7W3_9GAMM 100.0 7.4E-44 2.9E-49 277.6 0.0 131 11-142 3-139 (487)
61 tr|A0A0M2YGQ0|A0A0M2YGQ0_PANAN 100.0 1.1E-43 4.2E-49 238.0 0.0 115 26-141 1-126 (138)
62 tr|A0A0M2YGQ0|A0A0M2YGQ0_PANAN 100.0 1.1E-43 4.2E-49 238.0 0.0 115 26-141 1-126 (138)
63 tr|A0A160SYM8|A0A160SYM8_9CHLR 100.0 2.4E-43 9E-49 255.3 0.0 131 10-141 2-143 (294)
64 tr|A0A160SYM8|A0A160SYM8_9CHLR 100.0 2.4E-43 9E-49 255.3 0.0 131 10-141 2-143 (294)
65 tr|A0A0G1VAA6|A0A0G1VAA6_9BACT 100.0 3.9E-43 1.5E-48 231.6 0.0 129 13-142 2-137 (139)
66 tr|A0A0G1VAA6|A0A0G1VAA6_9BACT 100.0 3.9E-43 1.5E-48 231.6 0.0 129 13-142 2-137 (139)
67 tr|K4ZF22|K4ZF22_PAEAL Ribonuc 100.0 2.9E-42 1.1E-47 235.7 0.0 132 11-142 3-166 (174)
68 tr|K4ZF22|K4ZF22_PAEAL Ribonuc 100.0 2.9E-42 1.1E-47 235.7 0.0 132 11-142 3-166 (174)
69 tr|A9B1F3|A9B1F3_HERA2 Ribonuc 100.0 3.6E-42 1.3E-47 240.9 0.0 132 9-141 83-225 (236)
70 tr|A9B1F3|A9B1F3_HERA2 Ribonuc 100.0 3.6E-42 1.3E-47 240.9 0.0 132 9-141 83-225 (236)
71 tr|M4MZ79|M4MZ79_9HIV1 Pol pro 100.0 2.2E-41 7.9E-47 256.3 0.0 135 1-143 525-659 (659)
72 tr|C3Y169|C3Y169_BRAFL Putativ 100.0 2.7E-41 1E-46 235.0 0.0 132 10-142 47-194 (203)
73 tr|C3Y169|C3Y169_BRAFL Putativ 100.0 2.7E-41 1E-46 235.0 0.0 132 10-142 47-194 (203)
74 tr|A0A1A6GLB6|A0A1A6GLB6_NEOLE 100.0 4.8E-41 1.9E-46 244.0 0.0 132 10-142 96-243 (245)
75 tr|A0A1A6GLB6|A0A1A6GLB6_NEOLE 100.0 4.8E-41 1.9E-46 244.0 0.0 132 10-142 96-243 (245)
76 tr|A0A0N5A652|A0A0N5A652_PARTI 100.0 5.2E-41 2E-46 230.4 0.0 130 11-141 16-162 (163)
77 tr|A0A0N5A652|A0A0N5A652_PARTI 100.0 5.2E-41 2E-46 230.4 0.0 130 11-141 16-162 (163)
78 tr|A0A0C9N1A2|A0A0C9N1A2_9FUNG 100.0 5.7E-41 2.3E-46 250.8 0.0 136 7-142 131-283 (284)
79 tr|A0A0C9N1A2|A0A0C9N1A2_9FUNG 100.0 5.7E-41 2.3E-46 250.8 0.0 136 7-142 131-283 (284)
80 tr|A0A182S4S9|A0A182S4S9_ANOFN 100.0 6.8E-41 2.7E-46 230.8 0.0 133 9-141 13-162 (168)
81 tr|A0A182S4S9|A0A182S4S9_ANOFN 100.0 6.8E-41 2.7E-46 230.8 0.0 133 9-141 13-162 (168)
82 tr|A0A165CPQ4|A0A165CPQ4_9BASI 100.0 6.8E-41 2.7E-46 250.1 0.0 135 7-141 107-255 (303)
83 tr|A0A165CPQ4|A0A165CPQ4_9BASI 100.0 6.8E-41 2.7E-46 250.1 0.0 135 7-141 107-255 (303)
84 tr|A0A177I2N9|A0A177I2N9_9RHIZ 100.0 7.9E-41 2.9E-46 230.6 0.0 130 13-143 3-139 (215)
85 tr|A0A177I2N9|A0A177I2N9_9RHIZ 100.0 7.9E-41 2.9E-46 230.6 0.0 130 13-143 3-139 (215)
86 tr|U2EWP2|U2EWP2_CLOS4 Ribonuc 100.0 8.4E-41 3.1E-46 227.4 0.0 135 6-141 45-191 (197)
87 tr|U2EWP2|U2EWP2_CLOS4 Ribonuc 100.0 8.4E-41 3.1E-46 227.4 0.0 135 6-141 45-191 (197)
88 tr|A0A0K9JWM0|A0A0K9JWM0_9BURK 100.0 1.3E-40 4.8E-46 226.7 0.0 132 9-141 6-148 (196)
89 tr|A0A0K9JWM0|A0A0K9JWM0_9BURK 100.0 1.3E-40 4.8E-46 226.7 0.0 132 9-141 6-148 (196)
90 tr|A0A0G1TJ95|A0A0G1TJ95_9BACT 100.0 1.8E-40 7E-46 245.7 0.0 130 11-140 4-217 (308)
91 tr|A0A0G1TJ95|A0A0G1TJ95_9BACT 100.0 1.8E-40 7E-46 245.7 0.0 130 11-140 4-217 (308)
92 tr|E4UCC9|E4UCC9_LIBSC Ribonuc 100.0 2E-40 7.4E-46 218.0 0.0 117 12-141 6-133 (138)
93 tr|E4UCC9|E4UCC9_LIBSC Ribonuc 100.0 2E-40 7.4E-46 218.0 0.0 117 12-141 6-133 (138)
94 tr|A0A0P8C5W0|A0A0P8C5W0_9CYAN 100.0 3.6E-40 1.3E-45 246.2 0.0 127 13-139 48-194 (376)
95 tr|A0A0P8C5W0|A0A0P8C5W0_9CYAN 100.0 3.6E-40 1.3E-45 246.2 0.0 127 13-139 48-194 (376)
96 tr|K9HYZ0|K9HYZ0_AGABB Unchara 100.0 3.7E-40 1.4E-45 220.2 0.0 124 15-138 2-144 (144)
97 tr|K9HYZ0|K9HYZ0_AGABB Unchara 100.0 3.7E-40 1.4E-45 220.2 0.0 124 15-138 2-144 (144)
98 tr|A0A143QEC5|A0A143QEC5_9NOCA 100.0 4.2E-40 1.5E-45 222.5 0.0 132 11-142 10-151 (177)
99 tr|A0A143QEC5|A0A143QEC5_9NOCA 100.0 4.2E-40 1.5E-45 222.5 0.0 132 11-142 10-151 (177)
100 tr|A6FRK2|A6FRK2_9RHOB Ribonuc 100.0 4.2E-40 1.6E-45 225.2 0.0 131 11-142 28-174 (186)

Writing A3M alignment to /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.a3m
Done
hhlib=/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/DeepMSA
Input file = /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.a3m
Output file = /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.60.a3m
Read /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.a3m with 17238 sequences
Alignment in /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.a3m contains 143 match states
15442 out of 17238 sequences passed filter (60% min coverage, 99% max pairwise sequence identity)
Writing A3M alignment to /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.60.a3m
Done
created folder /tmp/rpearson/CITseq_MSA/MSAseq
/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/DeepMSA/bin/hhblits -i /tmp/rpearson/CITseq_MSA/MSAseq/seq.fasta -diff inf -d /home/rpearson/Structure_Prediction_Tools/CIT_Lib/uniclust30/uniclust30_2017_04/uniclust30_2017_04 -cpu 2 -oa3m /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.a3m -id 99 -cov 50 -o /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.log -n 3 -diff inf; grep -v '^>' /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.a3m|sed 's/[a-z]//g' > /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.aln
/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/DeepMSA/bin/hhfilter -i /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.a3m -o /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.60.a3m -id 99 -cov 60; grep -v '^>' /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.60.a3m|sed 's/[a-z]//g' > /tmp/rpearson/CITseq_MSA/MSAseq/hhblits.60.aln
HHblits Neff: 128.04
Final MSA by hhblits with Nf >=128.0
/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/DeepMSA/bin/rmRedundantSeq 99 60 /tmp/rpearson/CITseq_MSA/MSAseq/final.aln > /tmp/rpearson/CITseq_MSA/MSAseq/final.60.aln
Re-filter final MSA to Nf >= 128.0
ending time: Tue Nov 9 16:30:08 PST 2021
2.1 run Psi-blast
2.2 Predict secondary structure with PSSpred...
2.3 Predict solvent accessibility...
2.4 run pairmod
2.4.1 removing homology templates based on benchmark and 0.3
submit threading jobs first and run pair during threading
3.0 do contact prediction
run restriplet for contact prediction...
hostname: gpu04.cluster
starting time: Tue Nov 9 18:44:09 PST 2021
pwd: /tmp/rpearson/CITseq

----------- calculate neff ---------------------

-------------- run ResTriplet -------------------
_____ _____ _____ _
| | | |___ ___ ___ _| |
| --| --| | | | . | _| -_| . |
|_____|_____|_|_|_| _|_| |___|___|
|_|

using CPU (1 thread(s))

Reweighted 15442 sequences with threshold 0.8 to Beff=9340.13 weight mean=0.604852, min=0.00094518, max=1

Will optimize 9020869 32-bit variables

iter eval f(x) ║x║ ║g║ step
1 1 2.74171e+06 26445.9 1.9651707e+10 6e-06
2 1 2.66688e+06 26444.2 1.4071797e+10 4.65e-06
3 1 2.59172e+06 26444.3 1.0189366e+10 4.79e-06
4 1 2.51755e+06 26447.9 7.2947651e+09 5.52e-06
5 1 2.44376e+06 26458.8 5.1634458e+09 6.92e-06
6 1 2.3711e+06 26480.2 3.8186977e+09 9.23e-06
7 1 2.2983e+06 26519.5 2.961836e+09 1.22e-05
8 1 2.22563e+06 26585.1 2.2918126e+09 1.55e-05
9 1 2.1535e+06 26689.6 1.8561375e+09 2e-05
10 1 2.08191e+06 26852.8 1.523963e+09 2.5e-05
11 1 2.01145e+06 27096.2 1.2674463e+09 3.1e-05
12 1 1.94226e+06 27456.2 1.0703459e+09 3.88e-05
13 1 1.87559e+06 27987.8 1.0107605e+09 4.86e-05
14 1 1.81325e+06 28784.8 1.2384842e+09 5.71e-05
15 1 1.76091e+06 30050.6 1.6939524e+09 5.57e-05
16 2 1.73126e+06 31191.6 8.9849824e+08 2.97e-05
17 2 1.71449e+06 31818.6 9.0250637e+08 2.77e-05
18 1 1.69614e+06 32378.2 7.247609e+08 2.33e-05
19 1 1.67812e+06 32848.2 6.2222272e+08 2.35e-05
20 1 1.65936e+06 33280.3 5.9538502e+08 2.48e-05
21 1 1.64069e+06 33672.7 4.7474304e+08 2.27e-05
22 1 1.62189e+06 34041 5.0973389e+08 2.63e-05
23 1 1.60295e+06 34392.2 4.0652278e+08 2.28e-05
24 1 1.58424e+06 34738.6 4.1765302e+08 2.68e-05
25 1 1.56534e+06 35076.9 3.5719744e+08 2.53e-05
26 1 1.54682e+06 35412.3 3.7575318e+08 2.83e-05
27 1 1.52821e+06 35778.1 3.1224144e+08 2.72e-05
28 1 1.51036e+06 36160.6 3.5041062e+08 3.25e-05
29 1 1.49252e+06 36620 2.9692826e+08 3.11e-05
30 1 1.47616e+06 37156.1 4.3898154e+08 3.86e-05
31 1 1.46036e+06 37885.7 3.7396989e+08 3.18e-05
32 2 1.45246e+06 38388 3.8250035e+08 2.31e-05
33 1 1.44445e+06 39010.8 4.3559168e+08 2.54e-05
34 1 1.43736e+06 39724.6 4.3547027e+08 2.35e-05
35 2 1.43314e+06 40201.3 4.1700224e+08 1.45e-05
36 1 1.42887e+06 40618.5 3.3866038e+08 1.29e-05
37 1 1.42447e+06 41009.6 3.6071498e+08 1.44e-05
38 1 1.41976e+06 41375.3 3.2717456e+08 1.21e-05
39 1 1.4152e+06 41700.9 2.6854003e+08 1.16e-05
40 1 1.41063e+06 42006.1 2.7940493e+08 1.29e-05
41 1 1.40586e+06 42296.5 2.5620978e+08 1.15e-05
42 1 1.40109e+06 42558.8 2.0468267e+08 1.13e-05
43 1 1.39636e+06 42813.5 2.0829584e+08 1.33e-05
44 1 1.39155e+06 43059.5 2.0130517e+08 1.25e-05
45 1 1.38683e+06 43296.4 1.5436029e+08 1.21e-05
46 1 1.38212e+06 43524.3 1.5306966e+08 1.52e-05
47 1 1.37738e+06 43760.3 1.5305258e+08 1.55e-05
48 1 1.3727e+06 43996.2 1.1182708e+08 1.52e-05
49 1 1.36816e+06 44240.5 1.328651e+08 2.13e-05
50 1 1.36362e+06 44507.5 1.1042094e+08 1.91e-05
51 1 1.35942e+06 44792.3 1.160173e+08 2.41e-05
52 1 1.3553e+06 45156.8 1.2725809e+08 2.86e-05
53 2 1.35322e+06 45380.6 86657488 1.58e-05
54 1 1.35127e+06 45627 1.2519568e+08 2.55e-05
55 1 1.34932e+06 45918.7 1.1294399e+08 2.05e-05

Done with status code 0 - Success!

Final fx = 1348246.500000

Writing raw output to /tmp/rpearson/CITseq_restriplet/protein.out.raw
xnorm = 103.19
Output can be found in /tmp/rpearson/CITseq_restriplet/protein.out.del
/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/ResTriplet/aaweights.py:189: NumbaWarning:
Compilation is falling back to object mode WITH looplifting enabled because Function "cal_large_matrix1" failed type inference due to: No implementation of function Function(<built-in function zeros>) found for signature:

>>> zeros(list(int64)<iv=None>)

There are 2 candidate implementations:
- Of which 2 did not match due to:
Overload of function 'zeros': File: numba/core/typing/npydecl.py: Line 511.
With argument(s): '(list(int64)<iv=None>)':
No match.

During: resolving callee type: Function(<built-in function zeros>)
During: typing of call at /home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/ResTriplet/aaweights.py (198)


File "aaweights.py", line 198:
def cal_large_matrix1(msa,weight):
<source elided>
pa=np.zeros((N,ALPHA))
cov=np.zeros([N*ALPHA,N*ALPHA ])
^

@jit
/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/ResTriplet/aaweights.py:189: NumbaWarning:
Compilation is falling back to object mode WITHOUT looplifting enabled because Function "cal_large_matrix1" failed type inference due to: Cannot determine Numba type of <class 'numba.core.dispatcher.LiftedLoop'>

File "aaweights.py", line 199:
def cal_large_matrix1(msa,weight):
<source elided>
cov=np.zeros([N*ALPHA,N*ALPHA ])
for i in range(N):
^

@jit
/home/rpearson/.conda/envs/Quark_and_Itasser_Python3/lib/python3.6/site-packages/numba/core/object_mode_passes.py:152: NumbaWarning: Function "cal_large_matrix1" was compiled in object mode without forceobj=True, but has lifted loops.

File "aaweights.py", line 192:
def cal_large_matrix1(msa,weight):
<source elided>
#output:21*l*21*l
ALPHA=21
^

state.func_ir.loc))
/home/rpearson/.conda/envs/Quark_and_Itasser_Python3/lib/python3.6/site-packages/numba/core/object_mode_passes.py:162: NumbaDeprecationWarning:
Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.

For more information visit https://numba.pydata.org/numba-doc/late ... -using-jit

File "aaweights.py", line 192:
def cal_large_matrix1(msa,weight):
<source elided>
#output:21*l*21*l
ALPHA=21
^

state.func_ir.loc))
deeppre.py:62: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.
x=Variable(torch.FloatTensor(plm),volatile=True)
cuda is ready? : True
/tmp/rpearson/CITseq_restriplet/protein.aln
143

done.
------------- sort output of ResTriplet.dat -----------

----------- copy results back --------------
ResTriplet is complete now

ending time: Tue Nov 9 18:53:21 PST 2021
run tripletres for contact prediction...
hostname: gpu04.cluster
starting time: Tue Nov 9 18:53:22 PST 2021
pwd: /tmp/rpearson/CITseq

----------- calculate neff ---------------------

-------------- run TripletRes -------------------
_____ _____ _____ _
| | | |___ ___ ___ _| |
| --| --| | | | . | _| -_| . |
|_____|_____|_|_|_| _|_| |___|___|
|_|

using CPU (1 thread(s))

Reweighted 15442 sequences with threshold 0.8 to Beff=9340.13 weight mean=0.604852, min=0.00094518, max=1

Will optimize 9020869 32-bit variables

iter eval f(x) ║x║ ║g║ step
1 1 2.74171e+06 26445.9 1.9651707e+10 6e-06
2 1 2.66688e+06 26444.2 1.4071797e+10 4.65e-06
3 1 2.59172e+06 26444.3 1.0189366e+10 4.79e-06
4 1 2.51755e+06 26447.9 7.2947651e+09 5.52e-06
5 1 2.44376e+06 26458.8 5.1634458e+09 6.92e-06
6 1 2.3711e+06 26480.2 3.8186977e+09 9.23e-06
7 1 2.2983e+06 26519.5 2.961836e+09 1.22e-05
8 1 2.22563e+06 26585.1 2.2918126e+09 1.55e-05
9 1 2.1535e+06 26689.6 1.8561375e+09 2e-05
10 1 2.08191e+06 26852.8 1.523963e+09 2.5e-05
11 1 2.01145e+06 27096.2 1.2674463e+09 3.1e-05
12 1 1.94226e+06 27456.2 1.0703459e+09 3.88e-05
13 1 1.87559e+06 27987.8 1.0107605e+09 4.86e-05
14 1 1.81325e+06 28784.8 1.2384842e+09 5.71e-05
15 1 1.76091e+06 30050.6 1.6939524e+09 5.57e-05
16 2 1.73126e+06 31191.6 8.9849824e+08 2.97e-05
17 2 1.71449e+06 31818.6 9.0250637e+08 2.77e-05
18 1 1.69614e+06 32378.2 7.247609e+08 2.33e-05
19 1 1.67812e+06 32848.2 6.2222272e+08 2.35e-05
20 1 1.65936e+06 33280.3 5.9538502e+08 2.48e-05
21 1 1.64069e+06 33672.7 4.7474304e+08 2.27e-05
22 1 1.62189e+06 34041 5.0973389e+08 2.63e-05
23 1 1.60295e+06 34392.2 4.0652278e+08 2.28e-05
24 1 1.58424e+06 34738.6 4.1765302e+08 2.68e-05
25 1 1.56534e+06 35076.9 3.5719744e+08 2.53e-05
26 1 1.54682e+06 35412.3 3.7575318e+08 2.83e-05
27 1 1.52821e+06 35778.1 3.1224144e+08 2.72e-05
28 1 1.51036e+06 36160.6 3.5041062e+08 3.25e-05
29 1 1.49252e+06 36620 2.9692826e+08 3.11e-05
30 1 1.47616e+06 37156.1 4.3898154e+08 3.86e-05
31 1 1.46036e+06 37885.7 3.7396989e+08 3.18e-05
32 2 1.45246e+06 38388 3.8250035e+08 2.31e-05
33 1 1.44445e+06 39010.8 4.3559168e+08 2.54e-05
34 1 1.43736e+06 39724.6 4.3547027e+08 2.35e-05
35 2 1.43314e+06 40201.3 4.1700224e+08 1.45e-05
36 1 1.42887e+06 40618.5 3.3866038e+08 1.29e-05
37 1 1.42447e+06 41009.6 3.6071498e+08 1.44e-05
38 1 1.41976e+06 41375.3 3.2717456e+08 1.21e-05
39 1 1.4152e+06 41700.9 2.6854003e+08 1.16e-05
40 1 1.41063e+06 42006.1 2.7940493e+08 1.29e-05
41 1 1.40586e+06 42296.5 2.5620978e+08 1.15e-05
42 1 1.40109e+06 42558.8 2.0468267e+08 1.13e-05
43 1 1.39636e+06 42813.5 2.0829584e+08 1.33e-05
44 1 1.39155e+06 43059.5 2.0130517e+08 1.25e-05
45 1 1.38683e+06 43296.4 1.5436029e+08 1.21e-05
46 1 1.38212e+06 43524.3 1.5306966e+08 1.52e-05
47 1 1.37738e+06 43760.3 1.5305258e+08 1.55e-05
48 1 1.3727e+06 43996.2 1.1182708e+08 1.52e-05
49 1 1.36816e+06 44240.5 1.328651e+08 2.13e-05
50 1 1.36362e+06 44507.5 1.1042094e+08 1.91e-05
51 1 1.35942e+06 44792.3 1.160173e+08 2.41e-05
52 1 1.3553e+06 45156.8 1.2725809e+08 2.86e-05
53 2 1.35322e+06 45380.6 86657488 1.58e-05
54 1 1.35127e+06 45627 1.2519568e+08 2.55e-05
55 1 1.34932e+06 45918.7 1.1294399e+08 2.05e-05

Done with status code 0 - Success!

Final fx = 1348246.500000

Writing raw output to /tmp/rpearson/CITseq_tripletres/protein.out.raw
xnorm = 103.19
Output can be found in /tmp/rpearson/CITseq_tripletres/protein.out.del
/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/TripletRes/aaweights.py:189: NumbaWarning:
Compilation is falling back to object mode WITH looplifting enabled because Function "cal_large_matrix1" failed type inference due to: No implementation of function Function(<built-in function zeros>) found for signature:

>>> zeros(list(int64)<iv=None>)

There are 2 candidate implementations:
- Of which 2 did not match due to:
Overload of function 'zeros': File: numba/core/typing/npydecl.py: Line 511.
With argument(s): '(list(int64)<iv=None>)':
No match.

During: resolving callee type: Function(<built-in function zeros>)
During: typing of call at /home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/TripletRes/aaweights.py (198)


File "aaweights.py", line 198:
def cal_large_matrix1(msa,weight):
<source elided>
pa=np.zeros((N,ALPHA))
cov=np.zeros([N*ALPHA,N*ALPHA ])
^

@jit
/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/TripletRes/aaweights.py:189: NumbaWarning:
Compilation is falling back to object mode WITHOUT looplifting enabled because Function "cal_large_matrix1" failed type inference due to: Cannot determine Numba type of <class 'numba.core.dispatcher.LiftedLoop'>

File "aaweights.py", line 199:
def cal_large_matrix1(msa,weight):
<source elided>
cov=np.zeros([N*ALPHA,N*ALPHA ])
for i in range(N):
^

@jit
/home/rpearson/.conda/envs/Quark_and_Itasser_Python3/lib/python3.6/site-packages/numba/core/object_mode_passes.py:152: NumbaWarning: Function "cal_large_matrix1" was compiled in object mode without forceobj=True, but has lifted loops.

File "aaweights.py", line 192:
def cal_large_matrix1(msa,weight):
<source elided>
#output:21*l*21*l
ALPHA=21
^

state.func_ir.loc))
/home/rpearson/.conda/envs/Quark_and_Itasser_Python3/lib/python3.6/site-packages/numba/core/object_mode_passes.py:162: NumbaDeprecationWarning:
Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.

For more information visit https://numba.pydata.org/numba-doc/late ... -using-jit

File "aaweights.py", line 192:
def cal_large_matrix1(msa,weight):
<source elided>
#output:21*l*21*l
ALPHA=21
^

state.func_ir.loc))
deeppre.py:51: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.
x,x1,x2=Variable(x,volatile=True),Variable(torch.FloatTensor(plm),volatile=True),Variable(torch.FloatTensor(cov),volatile=True)
cuda is ready? : True
/tmp/rpearson/CITseq_tripletres/protein.aln
143

done.
------------- sort output of TripletRes.dat -----------

----------- copy results back --------------
TripletRes is complete now

ending time: Tue Nov 9 19:02:02 PST 2021
run respre for contact prediction...
hostname: gpu04.cluster
starting time: Tue Nov 9 19:02:04 PST 2021
pwd: /tmp/rpearson/CITseq

----------- calculate neff ---------------------

-------------- run ResPRE -------------------
cuda is ready? : True

[10%]
>>>>> [20%]
>>>>>>>>>> [30%]
>>>>>>>>>>>>>>> [40%]
>>>>>>>>>>>>>>>>>>>> [50%]
>>>>>>>>>>>>>>>>>>>>>>>>> [60%]
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> [70%]
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> [80%]
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> [90%]
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> [100%]/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/ResPre/aaweights.py:189: NumbaWarning:
Compilation is falling back to object mode WITH looplifting enabled because Function "cal_large_matrix1" failed type inference due to: No implementation of function Function(<built-in function zeros>) found for signature:

>>> zeros(list(int64)<iv=None>)

There are 2 candidate implementations:
- Of which 2 did not match due to:
Overload of function 'zeros': File: numba/core/typing/npydecl.py: Line 511.
With argument(s): '(list(int64)<iv=None>)':
No match.

During: resolving callee type: Function(<built-in function zeros>)
During: typing of call at /home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/ResPre/aaweights.py (198)


File "aaweights.py", line 198:
def cal_large_matrix1(msa,weight):
<source elided>
pa=np.zeros((N,ALPHA))
cov=np.zeros([N*ALPHA,N*ALPHA ])
^

@jit
/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/contact/ResPre/aaweights.py:189: NumbaWarning:
Compilation is falling back to object mode WITHOUT looplifting enabled because Function "cal_large_matrix1" failed type inference due to: Cannot determine Numba type of <class 'numba.core.dispatcher.LiftedLoop'>

File "aaweights.py", line 199:
def cal_large_matrix1(msa,weight):
<source elided>
cov=np.zeros([N*ALPHA,N*ALPHA ])
for i in range(N):
^

@jit
/home/rpearson/.conda/envs/Quark_and_Itasser_Python3/lib/python3.6/site-packages/numba/core/object_mode_passes.py:152: NumbaWarning: Function "cal_large_matrix1" was compiled in object mode without forceobj=True, but has lifted loops.

File "aaweights.py", line 192:
def cal_large_matrix1(msa,weight):
<source elided>
#output:21*l*21*l
ALPHA=21
^

state.func_ir.loc))
/home/rpearson/.conda/envs/Quark_and_Itasser_Python3/lib/python3.6/site-packages/numba/core/object_mode_passes.py:162: NumbaDeprecationWarning:
Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.

For more information visit https://numba.pydata.org/numba-doc/late ... -using-jit

File "aaweights.py", line 192:
def cal_large_matrix1(msa,weight):
<source elided>
#output:21*l*21*l
ALPHA=21
^

state.func_ir.loc))
respre.py:52: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.
x=Variable(x,volatile=True)

done.
------------- sort output of respre.dat -----------

----------- copy results back --------------
ResPRE is complete now

ending time: Tue Nov 9 19:03:02 PST 2021
run resplm for contact prediction...
hostname: gpu04.cluster
starting time: Tue Nov 9 19:03:03 PST 2021
pwd: /tmp/rpearson/CITseq

----------- calculate neff ---------------------

-------------- run ResPLM -------------------
_____ _____ _____ _
| | | |___ ___ ___ _| |
| --| --| | | | . | _| -_| . |
|_____|_____|_|_|_| _|_| |___|___|
|_|

using CPU (1 thread(s))

Reweighted 15442 sequences with threshold 0.8 to Beff=9340.13 weight mean=0.604852, min=0.00094518, max=1

Will optimize 9020869 32-bit variables

iter eval f(x) ║x║ ║g║ step
1 1 2.74171e+06 26445.9 1.9651707e+10 6e-06
2 1 2.66688e+06 26444.2 1.4071797e+10 4.65e-06
3 1 2.59172e+06 26444.3 1.0189366e+10 4.79e-06
4 1 2.51755e+06 26447.9 7.2947651e+09 5.52e-06
5 1 2.44376e+06 26458.8 5.1634458e+09 6.92e-06
6 1 2.3711e+06 26480.2 3.8186977e+09 9.23e-06
7 1 2.2983e+06 26519.5 2.961836e+09 1.22e-05
8 1 2.22563e+06 26585.1 2.2918126e+09 1.55e-05
9 1 2.1535e+06 26689.6 1.8561375e+09 2e-05
10 1 2.08191e+06 26852.8 1.523963e+09 2.5e-05
11 1 2.01145e+06 27096.2 1.2674463e+09 3.1e-05
12 1 1.94226e+06 27456.2 1.0703459e+09 3.88e-05
13 1 1.87559e+06 27987.8 1.0107605e+09 4.86e-05
14 1 1.81325e+06 28784.8 1.2384842e+09 5.71e-05
15 1 1.76091e+06 30050.6 1.6939524e+09 5.57e-05
16 2 1.73126e+06 31191.6 8.9849824e+08 2.97e-05
17 2 1.71449e+06 31818.6 9.0250637e+08 2.77e-05
18 1 1.69614e+06 32378.2 7.247609e+08 2.33e-05
19 1 1.67812e+06 32848.2 6.2222272e+08 2.35e-05
20 1 1.65936e+06 33280.3 5.9538502e+08 2.48e-05
21 1 1.64069e+06 33672.7 4.7474304e+08 2.27e-05
22 1 1.62189e+06 34041 5.0973389e+08 2.63e-05
23 1 1.60295e+06 34392.2 4.0652278e+08 2.28e-05
24 1 1.58424e+06 34738.6 4.1765302e+08 2.68e-05
25 1 1.56534e+06 35076.9 3.5719744e+08 2.53e-05
26 1 1.54682e+06 35412.3 3.7575318e+08 2.83e-05
27 1 1.52821e+06 35778.1 3.1224144e+08 2.72e-05
28 1 1.51036e+06 36160.6 3.5041062e+08 3.25e-05
29 1 1.49252e+06 36620 2.9692826e+08 3.11e-05
30 1 1.47616e+06 37156.1 4.3898154e+08 3.86e-05
31 1 1.46036e+06 37885.7 3.7396989e+08 3.18e-05
32 2 1.45246e+06 38388 3.8250035e+08 2.31e-05
33 1 1.44445e+06 39010.8 4.3559168e+08 2.54e-05
34 1 1.43736e+06 39724.6 4.3547027e+08 2.35e-05
35 2 1.43314e+06 40201.3 4.1700224e+08 1.45e-05
36 1 1.42887e+06 40618.5 3.3866038e+08 1.29e-05
37 1 1.42447e+06 41009.6 3.6071498e+08 1.44e-05
38 1 1.41976e+06 41375.3 3.2717456e+08 1.21e-05
39 1 1.4152e+06 41700.9 2.6854003e+08 1.16e-05
40 1 1.41063e+06 42006.1 2.7940493e+08 1.29e-05
41 1 1.40586e+06 42296.5 2.5620978e+08 1.15e-05
42 1 1.40109e+06 42558.8 2.0468267e+08 1.13e-05
43 1 1.39636e+06 42813.5 2.0829584e+08 1.33e-05
44 1 1.39155e+06 43059.5 2.0130517e+08 1.25e-05
45 1 1.38683e+06 43296.4 1.5436029e+08 1.21e-05
46 1 1.38212e+06 43524.3 1.5306966e+08 1.52e-05
47 1 1.37738e+06 43760.3 1.5305258e+08 1.55e-05
48 1 1.3727e+06 43996.2 1.1182708e+08 1.52e-05
49 1 1.36816e+06 44240.5 1.328651e+08 2.13e-05
50 1 1.36362e+06 44507.5 1.1042094e+08 1.91e-05
51 1 1.35942e+06 44792.3 1.160173e+08 2.41e-05
52 1 1.3553e+06 45156.8 1.2725809e+08 2.86e-05
53 2 1.35322e+06 45380.6 86657488 1.58e-05
54 1 1.35127e+06 45627 1.2519568e+08 2.55e-05
55 1 1.34932e+06 45918.7 1.1294399e+08 2.05e-05

Done with status code 0 - Success!

Final fx = 1348246.500000

Writing raw output to /tmp/rpearson/CITseq_resplm/protein.out.raw
xnorm = 103.19
Output can be found in /tmp/rpearson/CITseq_resplm/protein.out.del
resplm.py:62: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.
x=Variable(torch.FloatTensor(plm),volatile=True)
cuda is ready? : True
/tmp/rpearson/CITseq_resplm/protein.aln
143

done.
------------- sort output of resplm.dat -----------

----------- copy results back --------------
ResPLM is complete now

ending time: Tue Nov 9 19:11:18 PST 2021
run deepplm for contact prediction...
hostname: gpu04.cluster
starting time: Tue Nov 9 19:11:19 PST 2021
pwd: /tmp/rpearson/CITseq

----------- calculate neff ---------------------

-------------- run deepplm -------------------
_____ _____ _____ _
| | | |___ ___ ___ _| |
| --| --| | | | . | _| -_| . |
|_____|_____|_|_|_| _|_| |___|___|
|_|

using CPU (1 thread(s))

Reweighted 15442 sequences with threshold 0.8 to Beff=9340.13 weight mean=0.604852, min=0.00094518, max=1

Will optimize 9020869 32-bit variables

iter eval f(x) ║x║ ║g║ step
1 1 2.74171e+06 26445.9 1.9651707e+10 6e-06
2 1 2.66688e+06 26444.2 1.4071797e+10 4.65e-06
3 1 2.59172e+06 26444.3 1.0189366e+10 4.79e-06
4 1 2.51755e+06 26447.9 7.2947651e+09 5.52e-06
5 1 2.44376e+06 26458.8 5.1634458e+09 6.92e-06
6 1 2.3711e+06 26480.2 3.8186977e+09 9.23e-06
7 1 2.2983e+06 26519.5 2.961836e+09 1.22e-05
8 1 2.22563e+06 26585.1 2.2918126e+09 1.55e-05
9 1 2.1535e+06 26689.6 1.8561375e+09 2e-05
10 1 2.08191e+06 26852.8 1.523963e+09 2.5e-05
11 1 2.01145e+06 27096.2 1.2674463e+09 3.1e-05
12 1 1.94226e+06 27456.2 1.0703459e+09 3.88e-05
13 1 1.87559e+06 27987.8 1.0107605e+09 4.86e-05
14 1 1.81325e+06 28784.8 1.2384842e+09 5.71e-05
15 1 1.76091e+06 30050.6 1.6939524e+09 5.57e-05
16 2 1.73126e+06 31191.6 8.9849824e+08 2.97e-05
17 2 1.71449e+06 31818.6 9.0250637e+08 2.77e-05
18 1 1.69614e+06 32378.2 7.247609e+08 2.33e-05
19 1 1.67812e+06 32848.2 6.2222272e+08 2.35e-05
20 1 1.65936e+06 33280.3 5.9538502e+08 2.48e-05
21 1 1.64069e+06 33672.7 4.7474304e+08 2.27e-05
22 1 1.62189e+06 34041 5.0973389e+08 2.63e-05
23 1 1.60295e+06 34392.2 4.0652278e+08 2.28e-05
24 1 1.58424e+06 34738.6 4.1765302e+08 2.68e-05
25 1 1.56534e+06 35076.9 3.5719744e+08 2.53e-05
26 1 1.54682e+06 35412.3 3.7575318e+08 2.83e-05
27 1 1.52821e+06 35778.1 3.1224144e+08 2.72e-05
28 1 1.51036e+06 36160.6 3.5041062e+08 3.25e-05
29 1 1.49252e+06 36620 2.9692826e+08 3.11e-05
30 1 1.47616e+06 37156.1 4.3898154e+08 3.86e-05
31 1 1.46036e+06 37885.7 3.7396989e+08 3.18e-05
32 2 1.45246e+06 38388 3.8250035e+08 2.31e-05
33 1 1.44445e+06 39010.8 4.3559168e+08 2.54e-05
34 1 1.43736e+06 39724.6 4.3547027e+08 2.35e-05
35 2 1.43314e+06 40201.3 4.1700224e+08 1.45e-05
36 1 1.42887e+06 40618.5 3.3866038e+08 1.29e-05
37 1 1.42447e+06 41009.6 3.6071498e+08 1.44e-05
38 1 1.41976e+06 41375.3 3.2717456e+08 1.21e-05
39 1 1.4152e+06 41700.9 2.6854003e+08 1.16e-05
40 1 1.41063e+06 42006.1 2.7940493e+08 1.29e-05
41 1 1.40586e+06 42296.5 2.5620978e+08 1.15e-05
42 1 1.40109e+06 42558.8 2.0468267e+08 1.13e-05
43 1 1.39636e+06 42813.5 2.0829584e+08 1.33e-05
44 1 1.39155e+06 43059.5 2.0130517e+08 1.25e-05
45 1 1.38683e+06 43296.4 1.5436029e+08 1.21e-05
46 1 1.38212e+06 43524.3 1.5306966e+08 1.52e-05
47 1 1.37738e+06 43760.3 1.5305258e+08 1.55e-05
48 1 1.3727e+06 43996.2 1.1182708e+08 1.52e-05
49 1 1.36816e+06 44240.5 1.328651e+08 2.13e-05
50 1 1.36362e+06 44507.5 1.1042094e+08 1.91e-05
51 1 1.35942e+06 44792.3 1.160173e+08 2.41e-05
52 1 1.3553e+06 45156.8 1.2725809e+08 2.86e-05
53 2 1.35322e+06 45380.6 86657488 1.58e-05
54 1 1.35127e+06 45627 1.2519568e+08 2.55e-05
55 1 1.34932e+06 45918.7 1.1294399e+08 2.05e-05

Done with status code 0 - Success!

Final fx = 1348246.500000

Writing raw output to /tmp/rpearson/CITseq_deepplm/protein.out.raw
xnorm = 103.19
Output can be found in /tmp/rpearson/CITseq_deepplm/protein.out.del
deepplm.py:52: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.
x=Variable(x,volatile=True)
cuda is ready? : True
/tmp/rpearson/CITseq_deepplm/protein.aln
143
model will train? : False

------------- sort output of deepplm.dat -----------

----------- copy results back --------------
DeepPLM is complete now

ending time: Tue Nov 9 19:19:08 PST 2021
3.1 do threading
start parallel threading CEthreader
start parallel threading mCEthreader
start parallel threading eCEthreader
start parallel threading PPAS
start parallel threading dPPAS
start parallel threading dPPAS2
start parallel threading Env-PPAS
start parallel threading MUSTER
start parallel threading wPPAS
start parallel threading wdPPAS
start parallel threading wMUSTER
running pair now ................
FORTRAN STOP
30000 5127160 total lib str & residues
number of observations 21.31391 1307545.
pair done
target type = very
exclude homologous templates...
3.2 make restraints
n_gooda=2, n_good=2.98288288288288, n=11, type_tmp=easy-medm
/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/example without init: /home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/example/init.NOT
/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/example without init: /home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/example/init.NOT
ori: NOT NOT MUST dPPA wdPP wMUS wPPA dPPA PPAS Env- CEth
0 0 20 20 20 20 20 20 20 20 20
0 0 9 9 10 10 10 9 10 10 7
n_good=45, M0=9, M_eff=9, M_eff_good=9, M_cut1=6.5, M_cut2=3.5
easy: NOT NOT MUST dPPA wdPP wMUS wPPA dPPA PPAS Env- CEth
0 0 5 5 5 5 5 5 5 5 5
seq: type= easy, n_good=45, M0= 9, M1_for_medm=1.8, n_sg= 9 rule=SEQ
#_of_strong_hit=18, n_full=18, n_gap=0
M0/M2/M3=9/4.5/3 - 18 - 18
domain2=no

seq easy----n_temp=20
nt=1 133 15.312 1 1rilA MUSTER
1 n_ros=0 --- 133 15.312 1 1rilA MUSTER
nt=2 133 32.893 2 1rilA dPPAS
2 n_ros=0 --- 133 32.893 2 1rilA dPPAS
nt=3 136 40.072 3 7o0gA2 wdPPAS
3 n_ros=0 --- 136 40.072 3 7o0gA2 wdPPAS
nt=4 132 19.958 4 1rilA wMUSTER
4 n_ros=0 --- 132 19.958 4 1rilA wMUSTER
nt=5 136 34.338 5 7o0gA2 wPPAS
5 n_ros=0 --- 136 34.338 5 7o0gA2 wPPAS
nt=6 133 71.149 6 1rilA dPPAS2
6 n_ros=0 --- 133 71.149 6 1rilA dPPAS2
nt=7 133 27.760 7 1rilA PPAS
7 n_ros=0 --- 133 27.760 7 1rilA PPAS
nt=8 136 33.133 8 7o0gA2 Env-PPAS
8 n_ros=0 --- 136 33.133 8 7o0gA2 Env-PPAS
nt=9 125 8.059 9 3hstB CEthreader
9 n_ros=0 --- 125 8.059 9 3hstB CEthreader
nt=10 137 13.746 10 2qkkA MUSTER
10 n_ros=0 --- 137 13.746 10 2qkkA MUSTER
nt=11 137 29.831 11 2qkkA dPPAS
11 n_ros=0 --- 137 29.831 11 2qkkA dPPAS
nt=12 133 39.405 12 1rilA wdPPAS
12 n_ros=0 --- 133 39.405 12 1rilA wdPPAS
nt=13 135 19.470 13 7o0gA2 wMUSTER
13 n_ros=0 --- 135 19.470 13 7o0gA2 wMUSTER
nt=14 133 34.065 14 1rilA wPPAS
14 n_ros=0 --- 133 34.065 14 1rilA wPPAS
nt=15 137 62.492 15 2qkkA dPPAS2
15 n_ros=0 --- 137 62.492 15 2qkkA dPPAS2
nt=16 136 27.745 16 7o0gA2 PPAS
16 n_ros=0 --- 136 27.745 16 7o0gA2 PPAS
nt=17 133 31.643 17 1rilA Env-PPAS
17 n_ros=0 --- 133 31.643 17 1rilA Env-PPAS
nt=18 137 8.059 18 2qkkA CEthreader
18 n_ros=0 --- 137 8.059 18 2qkkA CEthreader
nt=19 126 11.994 19 3u3gD MUSTER
19 n_ros=0 --- 126 11.994 19 3u3gD MUSTER
nt=20 126 26.534 20 3u3gD dPPAS
20 n_ros=0 --- 126 26.534 20 3u3gD dPPAS
nt=21 137 36.266 21 2qkkA wdPPAS
nt=22 136 18.019 22 2qkkA wMUSTER
nt=23 136 31.051 23 2qkkA wPPAS
nt=24 126 55.607 24 3u3gD dPPAS2
nt=25 136 25.052 25 2qkkA PPAS
nt=26 137 29.011 26 2qkkA Env-PPAS
nt=27 133 8.024 27 1rilA CEthreader
nt=28 124 11.676 28 4e19A MUSTER
nt=29 124 26.004 29 4e19A dPPAS
nt=30 137 30.788 30 2hb5A wdPPAS
nt=31 137 15.823 31 2hb5A wMUSTER
nt=32 136 25.898 32 2hb5A wPPAS
nt=33 124 54.069 33 4e19A dPPAS2
nt=34 126 22.586 34 3u3gD PPAS
nt=35 124 28.542 35 4e19A Env-PPAS
nt=36 124 7.917 36 4e19A CEthreader
nt=37 136 11.574 37 2hb5A MUSTER
nt=38 137 24.670 38 2hb5A dPPAS
nt=39 126 28.389 39 3u3gD wdPPAS
nt=40 125 14.775 40 3u3gD wMUSTER
nt=41 126 25.557 41 3u3gD wPPAS
nt=42 137 48.087 42 2hb5A dPPAS2
nt=43 119 21.294 43 4e19A PPAS
nt=44 126 25.993 44 3u3gD Env-PPAS
nt=45 122 7.598 45 2ehgA CEthreader
nt=46 125 9.869 46 2kq2A MUSTER
nt=47 125 19.371 47 2kq2A dPPAS
nt=48 124 28.146 48 4e19A wdPPAS
nt=49 123 14.497 49 4e19A wMUSTER
nt=50 124 25.140 50 4e19A wPPAS
nt=51 123 40.901 51 2kq2A dPPAS2
nt=52 137 20.770 52 2hb5A PPAS
nt=53 137 24.742 53 2hb5A Env-PPAS
nt=54 125 7.527 54 3u3gD CEthreader
nt=55 122 8.119 55 3hstB MUSTER
nt=56 122 15.878 56 3hstB dPPAS
nt=57 121 23.699 57 2kq2A wdPPAS
nt=58 124 12.979 58 2kq2A wMUSTER
nt=59 124 22.953 59 2kq2A wPPAS
nt=60 122 30.105 60 3hstB dPPAS2
nt=61 124 19.040 61 2kq2A PPAS
nt=62 125 22.663 62 2kq2A Env-PPAS
nt=63 137 7.350 63 2hb5A CEthreader
nt=64 124 8.023 64 3ey1A MUSTER
nt=65 121 15.211 65 3ey1A dPPAS
nt=66 124 22.657 66 3ey1A wdPPAS
nt=67 123 10.870 67 3ey1A wMUSTER
nt=68 124 20.158 68 3ey1A wPPAS
nt=69 118 29.905 69 3ey1A dPPAS2
nt=70 125 15.361 70 3ey1A PPAS
nt=71 128 19.893 71 3ey1A Env-PPAS
nt=72 127 4.796 72 3ey1A CEthreader
nt=73 117 6.042 73 2ehgA MUSTER
nt=74 120 10.187 74 2ehgA dPPAS
nt=75 121 20.669 75 3hstB wdPPAS
nt=76 121 10.782 76 3hstB wMUSTER
nt=77 122 17.663 77 3hstB wPPAS
nt=78 120 17.872 78 2ehgA dPPAS2
nt=79 121 13.905 79 3hstB PPAS
nt=80 122 17.924 80 3hstB Env-PPAS
nt=81 139 4.583 81 5awhA3 CEthreader
nt=82 135 3.539 82 6m8nA MUSTER
nt=83 98 7.263 83 4mlcA2 dPPAS
nt=84 118 16.233 84 2ehgA wdPPAS
nt=85 116 8.279 85 2ehgA wMUSTER
nt=86 118 13.707 86 2ehgA wPPAS
nt=87 56 8.581 87 2e19A dPPAS2
nt=88 120 10.147 88 2ehgA PPAS
nt=89 120 13.490 89 2ehgA Env-PPAS
nt=90 118 4.405 90 1c0mC CEthreader
nt=91 138 3.530 91 5ctqA MUSTER
nt=92 97 6.086 92 6du6A3 dPPAS
nt=93 92 5.096 93 4mlcA2 wdPPAS
nt=94 115 4.022 94 2kw7A wMUSTER
nt=95 104 4.249 95 5anpA wPPAS
nt=96 68 7.511 96 5ytpA dPPAS2
nt=97 115 4.464 97 2kw7A PPAS
nt=98 111 5.402 98 1s4dE1 Env-PPAS
nt=99 143 3.944 99 2yhaA CEthreader
nt=100 115 3.527 100 2kw7A MUSTER
nt=101 68 5.713 101 5ytpA dPPAS
nt=102 78 4.950 102 3u9lA1 wdPPAS
nt=103 131 3.937 103 1q7tA wMUSTER
nt=104 106 4.208 104 5nnyA2 wPPAS
nt=105 61 7.444 105 3shgB dPPAS2
nt=106 111 4.417 106 5anpA PPAS
nt=107 120 5.117 107 1gg4A2 Env-PPAS
nt=108 143 3.909 108 2yhaA2 CEthreader
nt=109 134 3.469 109 1q7tA MUSTER
nt=110 61 5.595 110 2e19A dPPAS
nt=111 97 4.938 111 4j29A wdPPAS
nt=112 132 3.824 112 6faoA wMUSTER
nt=113 109 4.174 113 2kw7A wPPAS
nt=114 66 7.378 114 3zpjA3 dPPAS2
nt=115 141 4.214 115 5x4zD PPAS
nt=116 102 5.044 116 2yboA1 Env-PPAS
nt=117 143 3.235 117 4nspA1 CEthreader
nt=118 141 3.463 118 3wh9A MUSTER
nt=119 65 5.517 119 2mqkA dPPAS
nt=120 79 4.841 120 2ltaA wdPPAS
nt=121 141 3.702 121 3wflA wMUSTER
nt=122 97 4.057 122 1xfcA3 wPPAS
nt=123 64 7.277 123 5ejoA dPPAS2
nt=124 120 4.182 124 2mfzA PPAS
nt=125 132 4.998 125 2e1mC Env-PPAS
nt=126 143 3.022 126 4ioxA CEthreader
nt=127 141 3.349 127 3zizA MUSTER
nt=128 67 5.458 128 5m97B dPPAS
nt=129 54 4.599 129 7cq8B1 wdPPAS
nt=130 141 3.623 130 3zizA wMUSTER
nt=131 111 4.037 131 6ltpA4 wPPAS
nt=132 58 7.277 132 3rg2A2 dPPAS2
nt=133 101 3.916 133 1xfcA3 PPAS
nt=134 101 4.795 134 4e16A1 Env-PPAS
nt=135 143 2.490 135 6voyA CEthreader
nt=136 132 3.335 136 6faoA MUSTER
nt=137 79 5.458 137 3urmA1 dPPAS
nt=138 90 4.551 138 4q2tA1 wdPPAS
nt=139 140 3.604 139 3wh9A wMUSTER
nt=140 124 3.969 140 1ivnA wPPAS
nt=141 63 7.144 141 2mqkA dPPAS2
nt=142 110 3.885 142 5nnyA2 PPAS
nt=143 115 4.740 143 1o5lA Env-PPAS
nt=144 129 2.348 144 2kq2A CEthreader
nt=145 131 3.319 145 4kzsA MUSTER
nt=146 94 5.419 146 6echA2 dPPAS
nt=147 80 4.502 147 3kvoA1 wdPPAS
nt=148 136 3.480 148 4i2nA wMUSTER
nt=149 128 3.962 149 3hp4A wPPAS
nt=150 60 7.110 150 2g1uA2 dPPAS2
nt=151 128 3.854 151 1xe3D2 PPAS
nt=152 124 4.731 152 2g0tB1 Env-PPAS
nt=153 143 1.745 153 6m5rA2 CEthreader
nt=154 141 3.289 154 3wflA MUSTER
nt=155 80 5.419 155 3ek2A1 dPPAS
nt=156 91 4.454 156 2lndA wdPPAS
nt=157 129 3.394 157 5xr2A wMUSTER
nt=158 98 3.887 158 5ejqA2 wPPAS
nt=159 82 7.043 159 2ltaA dPPAS2
nt=160 132 3.807 160 6tb4K PPAS
nt=161 115 4.685 161 3iwzD1 Env-PPAS
nt=162 120 1.568 162 3e74B2 CEthreader
nt=163 130 3.216 163 5kc9A MUSTER
nt=164 67 5.360 164 5heaA2 dPPAS
nt=165 103 4.441 165 5anpA wdPPAS
nt=166 122 3.372 166 5xcyA wMUSTER
nt=167 100 3.832 167 7ltcA1 wPPAS
nt=168 54 7.010 168 1xvhA2 dPPAS2
nt=169 137 3.799 169 6v98A PPAS
nt=170 91 4.593 170 4k2hA1 Env-PPAS
nt=171 143 1.461 171 1t47A CEthreader
nt=172 138 3.211 172 4i2nA MUSTER
nt=173 61 5.360 173 6gygB dPPAS
nt=174 109 4.332 174 2kw7A wdPPAS
nt=175 129 3.362 175 7k010 wMUSTER
nt=176 102 3.818 176 1s4dE1 wPPAS
nt=177 71 6.943 177 3ixqA1 dPPAS2
nt=178 109 3.783 178 7by5A4 PPAS
nt=179 81 4.547 179 2zatA1 Env-PPAS
nt=180 104 1.426 180 4xb6A CEthreader
end of reading of init_all.dat
n_use=20, ndist0=4
4.1 run simulation
Congradulations! All your input files are correct. You can run TASSER simulations now!

run 5 parallel simulations
run the first simulation job CITseqsim_1A
4.2 check finished simulations
5.1 do clustering
No. of trajectory files: 8
8.0000000 3.5000000 12.000000
5.2 build full-atomic model
6 Estimate global accuracy, local accuracy of models and B-factor
7 run COACH to predict function: Ligand-binding site,EC number,GO terms...
/home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/I-TASSERmod/runCOACH.pl -pkgdir /home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0 -libdir /home/rpearson/Structure_Prediction_Tools/CIT_Lib -runstyle parallel -protname seq -model model1.pdb -datadir /home/rpearson/Structure_Prediction_Tools/C-I-TASSER-1.0/example -homoflag benchmark -idcut 0.3 -LBS true -EC true -GO true
rpearson_7
Posts: 3
Joined: Wed Nov 10, 2021 8:05 pm

Re: C-I-Tasser Standalone Installation Help Request

Post by rpearson_7 »

The output I got for my C-ITASSER does not match but is close to the webserver's results. Is this okay? What is happening here? Any help would be great!
xiaogenz
Posts: 139
Joined: Sun Apr 25, 2021 12:02 am

Re: C-I-Tasser Standalone Installation Help Request

Post by xiaogenz »

It's normal because the standalone package uses some different LOMETS threading methods due to the copyright.
jlspzw
Posts: 97
Joined: Tue May 04, 2021 5:04 pm

Re: C-I-Tasser Standalone Installation Help Request

Post by jlspzw »

Hi

Could you paste some top lines (like top 20 lines) from restriplet.dat tripletres.dat, respre.dat resplm.dat deepplm.dat init.mCEthreader, init.eCEthreader

and a file list of *.bz2 in the folder

It seems most procedures are correctly running, except the warring of PyTorch.

The performance of our server is slightly better (the gap is very small) than the standalone version due to the LOMETS and contact predictors version different as you mentioned.

Best
Wei
rpearson_7
Posts: 3
Joined: Wed Nov 10, 2021 8:05 pm

Re: C-I-Tasser Standalone Installation Help Request

Post by rpearson_7 »

Hi Wei,

I am so sorry for the late reply. I had all but given up on this problem but, sure enough, it is still important to solve for our team.

Here is all of the information you have requested. Thanks so much!

restriplet.dat
9453 781.0828
70 116 0.992
71 117 0.992
69 115 0.991
72 118 0.991
14 69 0.990
16 33 0.990
15 70 0.990
16 32 0.990
17 56 0.989
15 33 0.989
17 31 0.989
18 31 0.988
17 32 0.987
16 71 0.987
14 34 0.987
15 34 0.986
19 30 0.986
68 114 0.986
18 30 0.986


tripletres.dat
9453 781.0828
17 56 1.000
70 116 1.000
16 71 1.000
71 117 1.000
30 56 1.000
17 72 1.000
69 115 1.000
31 135 0.999
15 70 0.999
19 52 0.999
31 134 0.999
70 115 0.999
20 135 0.999
68 114 0.999
70 117 0.999
72 118 0.999
14 69 0.999
28 52 0.999
33 131 0.999


respre.dat
9453 781.0828
30 56 0.997
70 116 0.996
15 70 0.996
17 72 0.995
20 135 0.995
16 71 0.994
72 118 0.994
70 117 0.994
71 117 0.994
28 52 0.993
17 32 0.993
68 114 0.993
70 115 0.992
15 69 0.992
54 100 0.992
69 115 0.991
14 69 0.991
16 33 0.991
15 71 0.990


resplm.dat
9453 781.0828
16 71 0.999
70 116 0.998
15 70 0.998
17 72 0.997
71 117 0.997
30 56 0.996
17 56 0.996
14 69 0.996
69 115 0.995
20 135 0.995
15 69 0.994
13 68 0.994
72 118 0.993
70 117 0.993
15 71 0.993
68 114 0.993
70 115 0.992
17 32 0.992
31 134 0.992


deepplm.dat
9453 781.0828
15 70 1.000
16 71 1.000
15 69 1.000
70 116 1.000
71 117 1.000
17 72 1.000
14 69 1.000
70 115 1.000
15 68 1.000
20 135 1.000
69 115 1.000
70 117 1.000
15 71 1.000
13 68 1.000
13 69 1.000
15 60 1.000
17 32 1.000
30 56 1.000
68 114 1.000


init.mCEthreader
20 143 (20, Lch)
125 5.878 1 3hstB 0.160 0.874(=125/143) (L_ali,Z,i,pdb,id,cov)
ATOM 10 CA VAL 10 -27.176 5.231 -4.382 1 SER
ATOM 11 CA GLY 11 -26.030 3.563 -7.565 2 VAL
ATOM 12 CA ALA 12 -26.882 0.068 -8.770 3 LYS
ATOM 13 CA GLU 13 -24.416 -1.741 -11.093 4 VAL
ATOM 14 CA THR 14 -24.004 -5.168 -12.763 5 VAL
ATOM 15 CA PHE 15 -20.581 -6.830 -12.393 6 ILE
ATOM 16 CA TYR 16 -19.481 -9.511 -14.733 7 GLU
ATOM 17 CA VAL 17 -16.262 -11.314 -14.041 8 ALA
ATOM 18 CA ASP 18 -14.295 -14.200 -15.481 9 ASP
ATOM 19 CA GLY 19 -10.853 -15.770 -15.505 10 GLY
ATOM 20 CA ALA 20 -8.977 -18.899 -16.575 11 GLY
ATOM 21 CA ALA 21 -5.547 -20.419 -17.318 12 SER
ATOM 22 CA ASN 22 -3.923 -22.487 -20.013 13 ARG
ATOM 23 CA ARG 23 -2.847 -25.422 -17.880 14 GLY
ATOM 24 CA GLU 24 -4.515 -24.842 -14.514 15 ASN
ATOM 25 CA THR 25 -2.295 -23.775 -13.492 16 PRO
ATOM 26 CA LYS 26 -0.563 -22.054 -16.362 17 GLY
ATOM 27 CA LEU 27 -0.389 -18.654 -18.150 18 PRO


init.eCEthreader
20 143 (20, Lch)
125 11.911 1 3hstB 0.160 0.874(=125/143) (L_ali,Z,i,pdb,id,cov)
ATOM 10 CA VAL 10 -27.176 5.231 -4.382 1 SER
ATOM 11 CA GLY 11 -26.030 3.563 -7.565 2 VAL
ATOM 12 CA ALA 12 -26.882 0.068 -8.770 3 LYS
ATOM 13 CA GLU 13 -24.416 -1.741 -11.093 4 VAL
ATOM 14 CA THR 14 -24.004 -5.168 -12.763 5 VAL
ATOM 15 CA PHE 15 -20.581 -6.830 -12.393 6 ILE
ATOM 16 CA TYR 16 -19.481 -9.511 -14.733 7 GLU
ATOM 17 CA VAL 17 -16.262 -11.314 -14.041 8 ALA
ATOM 18 CA ASP 18 -14.295 -14.200 -15.481 9 ASP
ATOM 19 CA GLY 19 -10.853 -15.770 -15.505 10 GLY
ATOM 20 CA ALA 20 -8.977 -18.899 -16.575 11 GLY
ATOM 21 CA ALA 21 -5.547 -20.419 -17.318 12 SER
ATOM 22 CA ASN 22 -3.923 -22.487 -20.013 13 ARG
ATOM 23 CA ARG 23 -2.847 -25.422 -17.880 14 GLY
ATOM 24 CA GLU 24 -4.515 -24.842 -14.514 15 ASN
ATOM 25 CA THR 25 -2.295 -23.775 -13.492 16 PRO
ATOM 26 CA LYS 26 -0.563 -22.054 -16.362 17 GLY
ATOM 27 CA LEU 27 -0.389 -18.654 -18.150 18 PRO


file list of *.bz2 in the folder
rep1.tra1A.bz2 rep2.tra1A.bz2 rep3.tra1A.bz2 rep4.tra1A.bz2 rep5.tra1A.bz2 rep6.tra1A.bz2 rep7.tra1A.bz2 rep8.tra1A.bz2

I hope all of this helps. I will check back in soon.

Best,

Rich
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