DeepMM implements fully automated de novo structure modeling method, MAINMAST, which builds three-dimensional models of a protein from a near-atomic resolution EM map. The method directly traces the protein’s main-chain and identifies Cα positions as tree-graph structures in the EM map.
DeepMM uses MAINMAST to trace the main chain paths on main chain probability maps.Before running an interactive session, install Jackal software locally in your account:
1) following the link:
https://honiglab.c2b2.columbia.edu/software/cgi-bin/software.pl?input=Jackal
manually download the file jackal_64bit.tar.gz to your local computer
2) sftp/transfer the file to Biowulf
3) ungzip/untar it:
tar -zxf jackal_64bit.tar.gz
and
4) move the resulting folder jackal_64bit
to the directory: /data/$USER
Allocate an interactive session and run the program. Sample session:
[user@biowulf]$ sinteractive --mem=8g --gres=gpu:k80:1,lscratch:10 -c4 [user@cn4213 ~]$ module load DeepMM [+] Loading CUDA Toolkit 10.2.89 ... [+] Loading cuDNN/7.6.5/CUDA-10.2 libraries... [+] Loading blast 2.11.0+ ... [+] Loading DeepMM 20210722Set the environment variables:
[user@cn4213 ~]$ export JACKALDIR=/data/$USER/jackal_64bit [user@cn4213 ~]$ PATH=/data/$USER/jackal_64bit/bin:$PATH [user@cn4213 ~]$ git clone https://github.com/JiahuaHe/DeepMMRun DeepMM on the sample data:
[user@cn4213 ~]$ cd DeepMM/5185 [user@cn4213 ~]$ preprocess.py -m 5185_zoned.mrc -n map.npz -t 3.21 [INFO] 10733 voxels with density value greater than 3.21000 are retained. [INFO] 10733 voxels are saved to map.npz [user@cn4213 ~]$ pred_MCCA.py -n map.npz -m predMC.mrc -c predCA.mrc LOAD] loading data... [PREDICT] predicting... 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 84/84 [00:04<00:00, 17.43it/s] [user@cn4213 ~]$ mrc2situs.py -m 5185_zoned.mrc -s map.situs [user@cn4213 ~]$ mrc2situs.py -m predMC.mrc -s predMC.situs [user@cn4213 ~]$ mrc2situs.py -m predCA.mrc -s predCA.situs [user@cn4213 ~]$ getldp predMC.situs predCA.situs > LDP.pdb [user@cn4213 ~]$ getvox map.situs LDP.pdb > LDP.mcv [user@cn4213 ~]$ pred_AASS.py -m LDP.mcv -l LDP.pdb > LDP_AASS.pdb 100%|█████████████████████████████████████████████████████████████████████| 7/7 [00:03<00:00, 2.18it/s] [user@cn4213 ~]$ trace LDP_AASS.pdb -nrd 100 > paths.pdb [user@cn4213 ~]$ align paths.pdb seq.fasta.spd3 # ALIGN.F # Align sequence to main-chain paths # Parameter setting: # wca = 1.600000 # whelix = 1.000000 # wsheet = 0.7000000 # wcoil = 0.8000000 # waa = 1.000000 # wss = 0.5000000 # rmsd = 5.000000 # nout = 10 # prefix = model # Reading LDP paths from file paths.pdb # Path 1 consists of 565 LDPs. # Path 2 consists of 560 LDPs. # Path 3 consists of 534 LDPs. # Path 4 consists of 538 LDPs. # Path 5 consists of 511 LDPs. # Path 6 consists of 520 LDPs. # Path 7 consists of 543 LDPs. # Path 8 consists of 484 LDPs. # Path 9 consists of 551 LDPs. # Path 10 consists of 475 LDPs. # Number of paths: 10 # Reading SS prediction from file seq.fasta.spd3 # Reading predicted SS from .SPD3 file seq.fasta.spd3 # Number of residues: 155 # Start alignment! # Model 1 62.156 # Model 2 57.567 # Model 3 78.881 # Model 4 76.093 # Model 5 78.612 # Model 6 90.198 # Model 7 87.193 # Model 8 73.144 # Model 9 71.957 # Model 10 100.461 # Model 11 112.469 # Model 12 108.363 # Model 13 139.715 # Model 14 130.736 # Model 15 123.928 # Model 16 116.492 # Model 17 34.216 # Model 18 35.854 # Model 19 27.489 # Model 20 44.658 # Model 21 45.993 # Model 22 52.989 # Model 23 42.962 # Model 24 34.723 # Model 25 110.321 ... # Model 158 59.206 # Model 159 40.211 # Model 160 27.201 # write models into one .pdb fileEnd the interactive session:
[user@cn4213 ~]$ exit [user@biowulf ~]$