3DBMPP: 3D Beta-barrel Membrane Protein Predictor

Overview:

Pipeline for 3D structure prediction for transmembrane beta barrel proteins (TMBs) or outer membrane proteins (OMPs). The method was described in High-resolution structure prediction of β-barrel membrane proteins. W Tian, M Lin, K Tang, J Liang, H Naveed. Proceedings of the National Academy of Sciences 115 (7), 1511-1516 Please cite the paper if you use 3DBMPP in your research.

    @article {Tian201716817,
        author = {Tian, Wei and Lin, Meishan and Tang, Ke and Liang, Jie and Naveed, Hammad},
        title = {High-resolution structure prediction of β-barrel membrane proteins},
        year = {2018},
        doi = {10.1073/pnas.1716817115},
        publisher = {National Academy of Sciences},
        issn = {0027-8424},
        URL = {http://www.pnas.org/content/early/2018/01/25/1716817115},
        eprint = {http://www.pnas.org/content/early/2018/01/25/1716817115.full.pdf},
        journal = {Proceedings of the National Academy of Sciences}
    }

Note:

This software depends on rough information of secondary structure of a TMB. This information can be obtained through 3rd-party software such as the ones listed in http://www.ompdb.org/links.php.

Due to the computation complexity, this package only provides the structure prediction for the barrel domains of the TMBs. For the loop sampling mentioned in Tian et. al, please refer to https://github.com/uic-lianglab/ompg-public.

You may also check https://github.com/nerrull/BetaBarrelRefactor, which is a refactored version by Etienne Richan of 3DBMPP with loop modeling via MODELLER.

Dependency:

This software depends the following python packages

• numpy
• biopython

and following software

• BBQ algorithm (BBQ algorithm is a part of BioShell)
• Scwrl4

Installation:

Linux:

git clone https://github.com/jksr/3dbmpp-pipe.git

cd bin/src

make

cd ../..

Scwrl4 should also be installed. Please refer to http://dunbrack.fccc.edu/scwrl4.

Usage:

1. Fisrt, a folder needs to be created to store all the input files and results.

2. Put the fasta file into the folder created.

3. Put a file with rough information of the starting and ending points of beta strands into the folder. Please see example/1bxw.strands for an example. The seqid of the starting and ending points shall be consistent with the fasta file. The staring and ending points of beta strands can be estimated via the secondary structure prediction tools listed in http://www.ompdb.org/links.php.

4. Information from the PSICOV sequence convariation analysis may help the structure prediction. Please refer to https://github.com/psipred/psicov for the installation and usage. This input is not obligatory. One can create a empty file with filename ending with .psicov in the folder to skip this step. However, the accuray of the prediction may be affected.

5. In our method, we classified TMBs into five groups. One need to determine which group the pretoin is in before the predition. The five groups are listed below:

Groups	Description	Example PBD ids
1	Small TMBs (strand#<16) w/o inplugs or outclamps	1bxw, 1qj8, 1p4t, 2f1t, 1thq, 2erv, 2lhf, 3dzm, 1qd6, 2f1c, 1k24, 1i78, 2wjr, 4pr7
2	Small TMBs (strand#<16) w/ inplugs or outclamps	1t16, 1uyn, 1tly, 3aeh, 3bs0, 3dwo, 3fid, 3kvn, 4e1s
3	Medium oligomeric TMBs (16≤strand#<20)	2mpr, 1a0s, 2omf, 2por, 1prn, 1e54, 2o4v, 3vzt, 4n75
4	Medium monomeric TMBs (16≤strand#<20)	2qdz, 2ynk, 3emn, 3rbh, 3syb, 3szv, 4c00, 4gey
5	Large TMBs (strand#≥20)	1fep, 2fcp, 1kmo, 1nqe, 1xkw, 2vqi, 3csl, 3rfz, 3v8x, 4q35

6. Run the follow command to predict the 3D structure of the transmembrane beta barrel protein.

python 3dbmpp.py --group *group_id* --folder *input_folder* --scwrlpath "*path_to_scwrl4_executable*"

For example,

python 3dbmpp.py --group 1 --folder example --scwrlpath "*path_to_scwrl4_executable*"