The 3DFI pipeline automates 3D structure prediction, structural homology searches and data visualization at the genome scale. Protein structures predicted in PDB format are searched against a local copy of the RSCB PDB database with GESAMT (General Efficient Structural Alignment of Macromolecular Targets) from the CCP4 package. Known PDB structures can also be searched against sets of predicted structures to identify potential structural homologs in predicted datasets.
- Introduction
- Requirements
- Installation
- Howto
- Miscellaneous
- Funding and acknowledgments
- References
Inferring the function of proteins using computational approaches usually involves performing some sort of homology search based on sequences or structures. In sequence-based searches, nucleotide or amino acid sequences are queried against known proteins or motifs using tools such as BLAST, DIAMOND, CDD, or Pfam, but those searches may fail if the proteins investigated are highly divergent. In structure-based searches, proteins are searched instead at the 3D level for structural homologs.
Because structure often confers function in biology, structural homologs often share similar functions, even if the building blocks are not the same (i.e. a wheel made of wood or steel is still a wheel regardless of its composition). Using this approach, we might be able to infer putative functions for proteins that share little to no similarity at the sequence level with known proteins, assuming that a structural match can be found.
To perform structure-based predictions we need 3D structures — either determined experimentally or predicted computationally — that we can query against other structures, such as those from the RCSB PDB. We also need tools that can search for homology at the structural level. Several tools are now available to predict protein structures, many of which are implemented as web servers for ease of use. A listing can be found at CAMEO, a website that evaluates their accuracy and reliability. Fewer tools are available to perform searches at the 3D levels (e.g. SSM and GESAMT). SSM is implemented in PDBeFold while GESAMT is part of the CCP4 package.
Although predicting the structure of a protein and searching for structural homologs can be done online, for example by using SWISS-MODEL and PDBeFold, genomes often code for thousands of proteins and applying this approach on a genome scale would be time consuming and error prone. We implemented the 3DFI pipeline to enable the use of structure-based homology searches at a genome-wide level.
Requirements to perform 3D structure prediction, structural homology searches and data visualization locally with 3DFI are as follows:
- At least one of the following protein structure prediction tools:
- RaptorX (Template-based predictions)
- trRosetta (Deep-learning-based predictions)
- AlphaFold2 (Deep-learning-based predictions)
- RoseTTAFold (Deep-learning-based predictions)
- Their dependencies, e.g.:
- GESAMT from the CCP4 package to perform structural homology searches
- UCSF ChimeraX to align and visualize structural homologs
- Perl modules (most of which should be bundled with Perl 5)
Alternatively, any set of PDB files can be fed as input for structural homology searches/visualization with GESAMT/ChimeraX. For example, protein structures predicted using web-based platforms such as SWISS-MODEL, predicted locally with pipelines like I-TASSER, or downloaded from the new EMBL-EBI/Deepmind AlphaFold Protein Structure Database can be used as queries for structural homology searches.
The 3DFI pipeline can be downloaded directly from GitHub with git clone. For ease of use, the 3DFI directories can be also be set as environment variables. The script setup_3DFI.pl can be used to facilitate this process.
## To download 3DFI from GitHub:
git clone https://github.com/PombertLab/3DFI.git
## To set 3DFI directories as environment variables:
cd 3DFI/
export TDFI=$(pwd)
export RX_3DFI=$TDFI/Prediction/RaptorX
export TR_3DFI=$TDFI/Prediction/trRosetta
export AF_3DFI=$TDFI/Prediction/AlphaFold2
export RF_3DFI=$TDFI/Prediction/RoseTTAFold
export HS_3DFI=$TDFI/Homology_search
export VZ_3DFI=$TDFI/Visualization
## To set 3DFI directories as environment variables with setup_3DFI.pl:
setup_3DFI.pl \
-p ~/GitHub/3DFI/ \
-c ~/.bashrcOptions for setup_3DFI.pl are:
-p (--path) Path to 3DFI installation directory [Default: ./]
-c (--config) Configuration file to edit
To perform template-based 3D structure predictions locally with RaptorX, the standalone programs should be downloaded and installed according to the authors’ instructions. Using RaptorX also requires MODELLER. To help with their installation, the raptorx_installation_notes.sh is provided, with source and installation directories to be edited according to user preferences.
To run RaptorX from anywhere with raptorx.pl, the environment variable RAPTORX_HOME should be set first:
## Setting up the RaptorX installation directory as an environment variable:
export RAPTORX_HOME=/opt/RaptorX
## Creating a working directory for RaptorX:
export RESULTS=~/Results_3DFI
export RX=$RESULTS/RAPTORX_3D
mkdir -p $RESULTS $RXTo predict 3D structures with RaptorX using raptorx.pl:
## Running RaptorX on provided examples:
$RX_3DFI/raptorx.pl \
-t 10 \
-k 2 \
-i $TDFI/Examples/FASTA \
-o $RXOptions for raptorx.pl are:
-t (--threads) Number of threads to use [Default: 10]
-i (--input) Folder containing fasta files
-o (--output) Output folder
-k (--TopK) Number of top template(s) to use per protein for model building [Default: 1]
-m (--modeller) MODELLER binary name [Default: mod10.1] ## Use absolute or relative path if not set in \$PATH
NOTES:
-
If segmentation faults occur on AMD ryzen CPUs with the blastpgp version provided with the RaptorX CNFsearch1.66_complete.zip package (under util/BLAST), replace it with the latest BLAST legacy version (2.2.26) from NCBI.
-
The following warning message about 6f45D can be safely ignored; it refers to a problematic file in the RaptorX datasets but does not impede folding. To silence this error message, see how to remove references to 6f45D in raptorx_installation_notes.sh.
.....CONTENT BAD AT TEMPLATE FILE /path/to/RaptorX_databases/TPL_BC100//6f45D.tpl -> [FEAT line 115 CA_contact 21]
template file 6f45D format bad or missing
- RaptorX expects a PYTHONHOME environment variable but runs fine without it. The following warning message can be safely ignored, and silenced by setting up a PYTHONHOME environment variable. Note that setting PYTHONHOME can create issues with other applications.
Could not find platform dependent libraries <exec_prefix>
Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>]
- The import site warning message below can also be safely ignored:
'import site' failed; use -v for traceback
To perform 3D structure predictions locally with trRosetta, HH-suite3, tensorflow version 1.15 and PyRosetta must be installed. A database for HHsuite3's hhblits, such as Uniclust, should also be installed. Note that hhblits databases should be located on a solid state disk (ideally NVME) to reduce i/o bottlenecks during homology searches.
For ease of use, tensorflow 1.15 and PyRosetta can be installed in a conda environment. For more detail, see trRosetta_installation_notes.sh.
To install tensorflow with GPU in conda:
## The files can eat through GPU VRAM very quickly. 8 Gb is usually insufficient.
conda create -n tfgpu python=3.7
conda activate tfgpu
pip install tensorflow-gpu==1.15
pip install numpy==1.19.5
conda install cudatoolkit==10.0.130
conda install cudnn==7.6.5To install tensorflow with CPU in conda:
conda create -n tfcpu python=3.7
conda activate tfcpu
pip install tensorflow-cpu==1.15
pip install numpy==1.19.5Running trRosetta involves 3 main steps: 1) searches with HHsuite3's hhblits to generate alignments (.a3m); 2) prediction of protein inter-residue geometries (.npz) with trRosetta's predict.py; and 3) prediction of 3D structures (.pdb) with trRosetta.py and PyRosetta. Performing these predictions on several proteins can be automated with 3DFI scripts.
## Setting up trRosetta installation directories as environment variables:
export TRROSETTA_HOME=/opt/trRosetta
export TRROSETTA_SCRIPTS=$TRROSETTA_HOME/trRosetta_scripts
## Creating a working directory for trRosetta:
export RESULTS=~/Results_3DFI
export TR=$RESULTS/TRROSETTA_3D
mkdir -p $RESULTS $TR- To convert FASTA sequences to single string FASTA sequences with fasta_oneliner.pl, type:
$TR_3DFI/fasta_oneliner.pl \
-f $TDFI/Examples/FASTA/*.fasta \
-o $TR/FASTA_OLOptions for fasta_oneliner.pl are:
-f (--fasta) FASTA files to convert
-o (--output) Output folder
- To run hhblits searches with run_hhblits.pl, type:
## Setting Uniclust database (https://uniclust.mmseqs.com/) location:
export UNICLUST=/media/FatCat/databases/UniRef30_2020_06
## Running hhblits on multiple evalues independently:
$TR_3DFI/run_hhblits.pl \
-t 10 \
-f $TR/FASTA_OL/ \
-o $TR/HHBLITS/ \
-d $UNICLUST/UniRef30_2020_06 \
-e 1e-40 1e-10 1e-03 1e+01
## Running hhblits on evalues sequentially, from stricter to more permissive:
$TR_3DFI/run_hhblits.pl \
-t 10 \
-f $TR/FASTA_OL/ \
-o $TR/HHBLITS/ \
-d $UNICLUST/UniRef30_2020_06 \
-s \
-se 1e-70 1e-50 1e-30 1e-10 1e-06 1e-04 1e+01Options for run_hhblits.pl are:
-t (--threads) Number of threads to use [Default: 10]
-f (--fasta) Folder containing fasta files
-o (--output) Output folder
-d (--database) Uniclust database to query
-v (--verbosity) hhblits verbosity; 0, 1 or 2 [Default: 2]
## E-value options
-e (--evalues) Desired evalue(s) to query independently
-s (--seq_it) Iterates sequentially through evalues
-se (--seq_ev) Evalues to iterate through sequentially [Default:
1e-70 1e-60 1e-50 1e-40 1e-30 1e-20 1e-10 1e-08 1e-06 1e-04 1e+01 ]
-ne (--num_it) # of hhblits iteration per evalue (-e) [Default: 3]
-ns (--num_sq) # of hhblits iteration per sequential evalue (-s) [Default: 1]
- To create .npz files containing inter-residue geometries with create_npz.pl, type:
## activate conda environment tfcpu or tfgpu
conda activate tfcpu
## Creating npz files:
$TR_3DFI/create_npz.pl \
-a $TR/HHBLITS/*.a3m \
-o $TR/NPZ/Options for create_npz.pl are:
-a (--a3m) .a3m files generated by hhblits
-o (--output) Output folder [Default: ./]
-t (--trrosetta) trRosetta installation directory (TRROSETTA_HOME)
-m (--model) trRosetta model directory [Default: model2019_07]
- To generate .pdb files containing 3D models from the .npz files with create_pdb.pl, type:
## activate conda environment tfcpu or tfgpu
conda activate tfcpu
## Creating PDB files
$TR_3DFI/create_pdb.pl \
-c 10 \
-n $TR/NPZ/ \
-o $TR/PDB/ \
-f $TR/FASTA_OL/Options for create_pdb.pl are:
-c (--cpu) Number of cpu threads to use [Default: 10] ## i.e. runs n processes in parallel
-m (--memory) Memory available (in Gb) to threads [Default: 16]
-n (--npz) Folder containing .npz files
-o (--output) Output folder [Default: ./]
-f (--fasta) Folder containing the oneliner fasta files
-t (--trrosetta) trRosetta installation directory (TRROSETTA_HOME)
-p (--python) Preferred Python interpreter [Default: python]
- The .pdb files thus generated contain lines that are not standard and that can prevent applications such as PDBeFOLD to run on the corresponding files. We can clean up the PDB files with sanitize_pdb.pl as follows:
$TR_3DFI/sanitize_pdb.pl \
-p $TR/PDB/*.pdb \
-o $TR/PDB_cleanOptions for sanitize_pdb.pl are:
-p (--pdb) .pdb files generated by trRosetta
-o (--output) Output folder
How to set up AlphaFold2 to run as a docker image is described on its GitHub page. Notes about how to install it on Fedora 33/34 with CUDA 11.1+ (for GPUs with compute capability 8.6) are available in af2_installation_notes.sh.
The alphafold.pl script is a Perl wrapper that enables running AlphaFold2 in batch mode. To simplify its use, the ALPHA_HOME and ALPHA_OUT environment variables can be set in the shell.
## Setting up AlphaFold2 installation directory and output folder as environment variables:
export ALPHA_HOME=/opt/alphafold
export ALPHA_OUT=/media/Data/alphafold_results
## Creating a working directory for AlphaFold2:
export RESULTS=~/Results_3DFI
export AF=$RESULTS/ALPHAFOLD_3D
mkdir -p $RESULTS $AFTo run alphafold.pl on multiple fasta files, type:
$AF_3DFI/alphafold.pl \
-f $TDFI/Examples/FASTA/*.fasta \
-o $AF/ResultsOptions for alphafold.pl are:
-f (--fasta) FASTA files to fold
-o (--outdir) Output directory
-m (--max_date) --max_template_date option (YYYY-MM-DD) from AlphaFold2 [Default: current date]
-c (--casp14) casp14 preset (--preset=casp14)
-d (--full_dbs) full_dbs preset (--preset=full_dbs)
-n (--no_gpu) Turns off GPU acceleration
-ah (--alpha_home) AlphaFold2 installation directory
-ao (--alpha_out) AlphaFold2 output directory
Folding results per protein will be located in corresponding subdirectories. Results with AlphaFold will contain PDB files for unrelaxed models (i.e. predicted as is before relaxation), relaxed models, and ranked models from best (0) to worst (4). Each subdirectory should look like this:
ls -l $AF/Results/sequence_1/
total 4
total 47404
-rw-r--r-- 1 jpombert jpombert 1085545 Jul 23 08:27 features.pkl
drwxr-xr-x 1 jpombert jpombert 106 Jul 23 08:27 msas
-rw-r--r-- 1 jpombert jpombert 149210 Jul 23 08:27 ranked_0.pdb
-rw-r--r-- 1 jpombert jpombert 149210 Jul 23 08:27 ranked_1.pdb
-rw-r--r-- 1 jpombert jpombert 149210 Jul 23 08:27 ranked_2.pdb
-rw-r--r-- 1 jpombert jpombert 149210 Jul 23 08:27 ranked_3.pdb
-rw-r--r-- 1 jpombert jpombert 149210 Jul 23 08:27 ranked_4.pdb
-rw-r--r-- 1 jpombert jpombert 327 Jul 23 08:27 ranking_debug.json
-rw-r--r-- 1 jpombert jpombert 149210 Jul 23 08:27 relaxed_model_1.pdb
-rw-r--r-- 1 jpombert jpombert 149210 Jul 23 08:27 relaxed_model_2.pdb
-rw-r--r-- 1 jpombert jpombert 149210 Jul 23 08:27 relaxed_model_3.pdb
-rw-r--r-- 1 jpombert jpombert 149210 Jul 23 08:27 relaxed_model_4.pdb
-rw-r--r-- 1 jpombert jpombert 149210 Jul 23 08:27 relaxed_model_5.pdb
-rw-r--r-- 1 jpombert jpombert 9084306 Jul 23 08:27 result_model_1.pkl
-rw-r--r-- 1 jpombert jpombert 9084306 Jul 23 08:27 result_model_2.pkl
-rw-r--r-- 1 jpombert jpombert 9127362 Jul 23 08:27 result_model_3.pkl
-rw-r--r-- 1 jpombert jpombert 9127362 Jul 23 08:27 result_model_4.pkl
-rw-r--r-- 1 jpombert jpombert 9127362 Jul 23 08:27 result_model_5.pkl
-rw-r--r-- 1 jpombert jpombert 766 Jul 23 08:27 timings.json
-rw-r--r-- 1 jpombert jpombert 73112 Jul 23 08:27 unrelaxed_model_1.pdb
-rw-r--r-- 1 jpombert jpombert 73112 Jul 23 08:27 unrelaxed_model_2.pdb
-rw-r--r-- 1 jpombert jpombert 73112 Jul 23 08:27 unrelaxed_model_3.pdb
-rw-r--r-- 1 jpombert jpombert 73112 Jul 23 08:27 unrelaxed_model_4.pdb
-rw-r--r-- 1 jpombert jpombert 73112 Jul 23 08:27 unrelaxed_model_5.pdbThe script parse_af_results.pl can be used to recurse through the subdirectories and copy the PDB model(s) with more descriptive names including the prefixes of the FASTA files to a selected location. To use parse_af_results.pl, type:
$AF_3DFI/parse_af_results.pl \
-a $AF/Results \
-o $AF/Parsed_PDBs \
-p k \
-t 5Options for parse_af_results.pl are:
-a (--afdir) AlphaFold output directory
-o (--outdir) Parsed output directory
-p (--pdbtype) ranked (k), relaxed (r), unrelaxed (u), all (a) [Default: k]
-t (--top) Top X number of pdb files to keep, from best to worst (max 5) [Default: 1]
-v (--verbose) Adds verbosity
How to set up RoseTTAFold to run using conda is described on its GitHub page. Notes about how to install it on Fedora 33/34 are available in rfold_installation_notes.sh.
The rosettafold.pl script is a Perl wrapper that enables running the RoseTTAFold run_e2e_ver.sh / run_pyrosetta_ver.sh scripts in batch mode. To simplify its use, the ROSETTAFOLD_HOME environment variable can be set in the shell.
## Setting up RoseTTAFold installation directory as an environment variable:
export ROSETTAFOLD_HOME=/opt/RoseTTAFold
## Creating a working directory for RoseTTAFold:
export RESULTS=~/Results_3DFI
export RF=$RESULTS/ROSETTAFOLD_3D
mkdir -p $RESULTS $RFTo run rosettafold.pl on multiple fasta files, type:
$RF_3DFI/rosettafold.pl \
-f $TDFI/Examples/FASTA/*.fasta \
-o $RF/e2e/Options for rosettafold.pl are:
-f (--fasta) FASTA files to fold
-o (--outdir) Output directory
-t (--type) Folding type: pyrosetta (py) or end-to-end (e2e) [Default: e2e]
-r (--rosetta) RoseTTAFold installation directory
Note that the e2e folding option is constrained by video RAM and requires a CUDA-enabled GPU with more than 8 Gb of RAM to tackle large proteins (a video card with at least 24 Gb of RAM is recommended). If out of memory, the 'RuntimeError: CUDA out of memory' will appear in the log/network.stderr file and the .pdb file will not be generated. The pyrosetta folding option is slower (CPU-bound) but not constrained by video RAM.
Folding results per protein will be located in corresponding subdirectories. Results with the e2e option should look like below, with the model generated named t000_.e2e.pdb:
ls -l $RF/e2e/sequence_1/
total 4248
drwxrwxr-x 1 jpombert jpombert 508 Jul 22 14:45 hhblits
drwxrwxr-x 1 jpombert jpombert 232 Jul 22 14:45 log
-rw-rw-r-- 1 jpombert jpombert 914410 Jul 22 14:45 t000_.atab
-rw-rw-r-- 1 jpombert jpombert 23517 Jul 22 14:46 t000_.e2e_init.pdb
-rw-rw-r-- 1 jpombert jpombert 2873473 Jul 22 14:46 t000_.e2e.npz
-rw-rw-r-- 1 jpombert jpombert 31356 Jul 22 14:46 t000_.e2e.pdb
-rw-rw-r-- 1 jpombert jpombert 481742 Jul 22 14:45 t000_.hhr
-rw-rw-r-- 1 jpombert jpombert 3941 Jul 22 14:45 t000_.msa0.a3m
-rw-rw-r-- 1 jpombert jpombert 4195 Jul 22 14:45 t000_.msa0.ss2.a3m
-rw-rw-r-- 1 jpombert jpombert 254 Jul 22 14:45 t000_.ss2Results with the pyrosetta option should look like below, with the models generated (5 in total) located in the model/ subfolder:
ls -l $RF/py/sequence_1/
total 4284
drwxrwxr-x 1 jpombert jpombert 508 Jul 22 15:28 hhblits
drwxrwxr-x 1 jpombert jpombert 388 Jul 22 15:45 log
drwxrwxr-x 1 jpombert jpombert 310 Jul 22 15:45 model
-rw-rw-r-- 1 jpombert jpombert 4125 Jul 22 15:29 parallel.fold.list
drwxrwxr-x 1 jpombert jpombert 1020 Jul 22 15:45 pdb-3track
-rw-rw-r-- 1 jpombert jpombert 2959088 Jul 22 15:29 t000_.3track.npz
-rw-rw-r-- 1 jpombert jpombert 914410 Jul 22 15:29 t000_.atab
-rw-rw-r-- 1 jpombert jpombert 481760 Jul 22 15:29 t000_.hhr
-rw-rw-r-- 1 jpombert jpombert 3941 Jul 22 15:28 t000_.msa0.a3m
-rw-rw-r-- 1 jpombert jpombert 4195 Jul 22 15:28 t000_.msa0.ss2.a3m
-rw-rw-r-- 1 jpombert jpombert 254 Jul 22 15:28 t000_.ss2The script parse_rf_results.pl can be used to recurse through the subdirectories and copy the PDB model(s) with more descriptive names including the prefixes of the FASTA files to a selected location. To use parse_rf_results.pl, type:
$RF_3DFI/parse_rf_results.pl \
-r $RF/e2e \
-o $RF/e2e_parsed \
-p e2eOptions for parse_rf_results.pl are:
-r (--rfdir) RoseTTAFold output directory
-o (--outdir) Parsed output directory
-p (--pdbtype) PDB type: pyrosetta (py) or end-to-end (e2e) [Default: e2e]
-t (--top) Top X number of pdb files to keep for pyrosetta PDBs, from best to worst (max 5) [Default: 1]
-v (--verbose) Adds verbosity
PDB files from the Protein Data Bank can be downloaded directly from its website. Detailed instructions are provided here. Because of the large size of this dataset, downloading it using rsync is recommended. This can be done with update_PDB.pl as follows:
## Setting up RCSB PDB database location:
export RCSB_PDB=/media/FatCat/databases/RCSB_PDB/
## Downloading the RCSB PDB database:
$HS_3DFI/update_PDB.pl \
-o $RCSB_PDB \
-n 15 \
-vOptions for update_PDB.pl are:
-o (--outdir) PDB output directory [Default: PDB]
-n (--nice) Defines niceness (adjusts scheduling priority)
-v (--verbose) Adds verbosity
To create a tab-delimited list of PDB entries and their titles and chains from the downloaded PDB gzipped files (pdb*.ent.gz), we can use PDB_headers.pl (requires PerlIO::gzip):
## Setting up 3DFI results location:
export RESULTS=~/Results_3DFI
## Running a list of titles and chains from PDB files
$HS_3DFI/PDB_headers.pl \
-p $RCSB_PDB \
-o $RESULTS/PDB_titles.tsvOptions for PDB_headers.pl are:
-p (--pdb) Directory containing PDB files downloaded from RCSB PDB/PDBe (gzipped)
-o (--output) Output file in tsv format
-v (--verbose) Prints progess every X file [Default: 1000]
The list created should look like this:
5tzz TITLE CRYSTAL STRUCTURE OF HUMAN PHOSPHODIESTERASE 2A IN COMPLEX WITH 1-[(3-BROMO-4-FLUOROPHENYL)CARBONYL]-3,3-DIFLUORO-5-{5-METHYL-[1,2, 4]TRIAZOLO[1,5-A]PYRIMIDIN-7-YL}PIPERIDINE
5tzz A CGMP-DEPENDENT 3',5'-CYCLIC PHOSPHODIESTERASE
5tzz B CGMP-DEPENDENT 3',5'-CYCLIC PHOSPHODIESTERASE
5tzz C CGMP-DEPENDENT 3',5'-CYCLIC PHOSPHODIESTERASE
5tzz D CGMP-DEPENDENT 3',5'-CYCLIC PHOSPHODIESTERASE
4tza TITLE TGP, AN EXTREMELY THERMOSTABLE GREEN FLUORESCENT PROTEIN CREATED BY STRUCTURE-GUIDED SURFACE ENGINEERING
4tza C FLUORESCENT PROTEIN
4tza A FLUORESCENT PROTEIN
4tza B FLUORESCENT PROTEIN
4tza D FLUORESCENT PROTEIN
4tz3 TITLE ENSEMBLE REFINEMENT OF THE E502A VARIANT OF SACTELAM55A FROM STREPTOMYCES SP. SIREXAA-E IN COMPLEX WITH LAMINARITETRAOSE
4tz3 A PUTATIVE SECRETED PROTEIN
5tzw TITLE CRYSTAL STRUCTURE OF HUMAN PHOSPHODIESTERASE 2A IN COMPLEX WITH 1-[(3,4-DIFLUOROPHENYL)CARBONYL]-3,3-DIFLUORO-5-{5-METHYL-[1,2, 4]TRIAZOLO[1,5-A]PYRIMIDIN-7-YL}PIPERIDINE
5tzw A CGMP-DEPENDENT 3',5'-CYCLIC PHOSPHODIESTERASE
5tzw B CGMP-DEPENDENT 3',5'-CYCLIC PHOSPHODIESTERASE
5tzw C CGMP-DEPENDENT 3',5'-CYCLIC PHOSPHODIESTERASE
5tzw D CGMP-DEPENDENT 3',5'-CYCLIC PHOSPHODIESTERASE
Before performing structural homology searches with GESAMT (from the CCP4 package), we should first create an archive to speed up the searches. We can also update the archive later as sequences are added (for example after the RCSB PDB files are updated with rsync). GESAMT archives can be created/updated with run_GESAMT.pl:
## Creating environment variables pointing to our GESAMT archive:
export GESAMT_ARCHIVE=/media/FatCat/databases/GESAMT_ARCHIVE/
## To create a GESAMT archive:
$HS_3DFI/run_GESAMT.pl \
-cpu 10 \
-make \
-arch $GESAMT_ARCHIVE \
-pdb $RCSB_PDB
## To update a GESAMT archive:
$HS_3DFI/run_GESAMT.pl \
-cpu 10 \
-update \
-arch $GESAMT_ARCHIVE \
-pdb $RCSB_PDBOptions to create/update a GESAMT archive with run_GESAMT.pl are:
-c (--cpu) CPU threads [Default: 10]
-a (--arch) GESAMT archive location [Default: ./]
-m (--make) Create a GESAMT archive
-u (--update) Update existing archive
-p (--pdb) Folder containing RCSB PDB files to archive
Structural homology searches with GESAMT can also be performed with run_GESAMT.pl:
## Creating a working directory for GESAMT:
export RESULTS=~/Results_3DFI
export GSMT=$RESULTS/GESAMT_RESULTS
mkdir -p $RESULTS $GSMT
## Performing structural homology searches with GESAMT:
$HS_3DFI/run_GESAMT.pl \
-cpu 10 \
-query \
-arch $GESAMT_ARCHIVE \
-input $TDFI/Examples/PDB/*.pdb \
-o $GSMT \
-mode normalOptions to query a GESAMT archive with run_GESAMT.pl are:
-c (--cpu) CPU threads [Default: 10]
-a (--arch) GESAMT archive location [Default: ./]
-q (--query) Query a GESAMT archive
-i (--input) PDF files to query
-o (--outdir) Output directory [Default: ./]
-d (--mode) Query mode: normal of high [Default: normal]
Results of GESAMT homology searches will be found in the *.gesamt files generated. Content of these files should look like:
# Hit PDB Chain Q-score r.m.s.d Seq. Nalign nRes File
# No. code Id Id. name
1 5V7K A 0.5610 1.9652 0.1262 214 255 pdb5v7k.ent.gz
2 5T9D C 0.5607 2.0399 0.1268 213 247 pdb5t9d.ent.gz
3 6CX4 A 0.5590 1.9153 0.1226 212 255 pdb6cx4.ent.gz
4 1PLR A 0.5551 1.9919 0.1302 215 258 pdb1plr.ent.gz
To add definitions/products to the PDB matches found with GESAMT, we can use the list generated by PDB_headers.pl together with descriptive_GESAMT_matches.pl:
$HS_3DFI/descriptive_GESAMT_matches.pl \
-r $RESULTS/PDB_titles.tsv \
-m $GSMT/*.gesamt \
-q 0.3 \
-b 5 \
-o $RESULTS/GESAMT.matchesOptions for descriptive_GESAMT_matches.pl are:
-r (--rcsb) Tab-delimited list of RCSB structures and their titles ## see PDB_headers.pl
-p (--pfam) Tab-delimited list of PFAM structures and their titles (http://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.clans.tsv.gz)
-m (--matches) Results from GESAMT searches ## see run_GESAMT.pl
-q (--qscore) Q-score cut-off [Default: 0.3]
-b (--best) Keep the best match(es) only (top X hits)
-o (--output) Output name [Default: Gesamt.matches]
The concatenated list generated should look like:
### sequence_1
sequence_1 1 3KDF D 0.6587 1.6953 0.1545 110 119 pdb3kdf.ent.gz REPLICATION PROTEIN A 32 KDA SUBUNIT
sequence_1 2 1QUQ C 0.6466 1.7595 0.1545 110 119 pdb1quq.ent.gz PROTEIN (REPLICATION PROTEIN A 32 KD SUBUNIT)
sequence_1 3 2PQA A 0.6421 1.7176 0.1504 113 128 pdb2pqa.ent.gz REPLICATION PROTEIN A 32 KDA SUBUNIT
sequence_1 4 3KDF B 0.6415 1.8156 0.1545 110 118 pdb3kdf.ent.gz REPLICATION PROTEIN A 32 KDA SUBUNIT
sequence_1 5 4GNX B 0.6381 1.7193 0.1182 110 122 pdb4gnx.ent.gz PUTATIVE UNCHARACTERIZED PROTEIN
### sequence_2
sequence_2 1 3K0X A 0.6349 1.5848 0.1163 86 99 pdb3k0x.ent.gz PROTEIN TEN1
sequence_2 2 3KF6 B 0.6186 1.4624 0.1163 86 105 pdb3kf6.ent.gz PROTEIN TEN1
sequence_2 3 5DOI F 0.6057 1.8040 0.1310 84 93 pdb5doi.ent.gz TELOMERASE ASSOCIATED PROTEIN P45
sequence_2 4 5DOI H 0.5974 1.8506 0.1310 84 93 pdb5doi.ent.gz TELOMERASE ASSOCIATED PROTEIN P45
sequence_2 5 5DOI G 0.5958 1.8593 0.1310 84 93 pdb5doi.ent.gz TELOMERASE ASSOCIATED PROTEIN P45
### sequence_3
sequence_3 1 5V7K A 0.5610 1.9652 0.1262 214 255 pdb5v7k.ent.gz PROLIFERATING CELL NUCLEAR ANTIGEN
sequence_3 2 5T9D C 0.5607 2.0399 0.1268 213 247 pdb5t9d.ent.gz PROLIFERATING CELL NUCLEAR ANTIGEN
sequence_3 3 6CX4 A 0.5590 1.9153 0.1226 212 255 pdb6cx4.ent.gz PROLIFERATING CELL NUCLEAR ANTIGEN
sequence_3 4 1PLR A 0.5551 1.9919 0.1302 215 258 pdb1plr.ent.gz PROLIFERATING CELL NUCLEAR ANTIGEN (PCNA)
sequence_3 5 3IFV C 0.5527 2.2070 0.1075 214 240 pdb3ifv.ent.gz PCNA
Visually inspecting the predicted 3D structure of a protein is an important step in determing the validity of any identified structural homolog. Though a .pdb file may be obtained from a protein structure prediction tool, the quality of the fold may be low. Alternatively, though GESAMT may return a structural homolog with a reasonable Q-score, the quality of the alignment may be low. A low fold/alignment-quality can result in both false-positives (finding a structural homolog when one doesn't exist) and false-negatives (not finding a structural homolog when one exists). Visually inspecting protein structures and structural homolog alignments is an easy way to prevent these outcomes. This can be done with the excellent ChimeraX molecular visualization program.
Examples:
- A good result, in which both the folding and the alignment are good.
- A false-negative, where the quality of the protein folding is low, resulting in a failure to find a structural homolog.
- A false-positive, where the quality of the fold is high, but the alignment-quality is low and a pseudo-structural homolog is found.
To prepare visualizations for inspection, we can use prepare_visualizations.pl to automatically align predicted proteins with their GESAMT-determined structural homologs. These alignments are performed with ChimeraX via its API.
## Creating shortcut to results directory
export RESULTS=~/Results_3DFI
export RCSB_PDB=/media/FatCat/databases/RCSB_PDB/
## Preparing data for visualization:
$VZ_3DFI/prepare_visualizations.pl \
-g $TDFI/Examples/GESAMT.matches \
-p $TDFI/Examples/PDB/ \
-r $RCSB_PDB \
-o $RESULTS/VisualizationOptions for prepare_visualizations.pl are:
-g (--gesamt) GESAMT descriptive matches ## generated by descriptive_matches.pl
-p (--pred) Absolute path to predicted .pdb files
-r (--rcsb) Absolute path to RCSB .ent.gz files
-k (--keep) Keep unzipped RCSB .ent files
-o (--outdir) Output directory for ChimeraX sessions [Default: ./3D_Visualizations]
To inspect the 3D structures, we can run inspect_3D_structures.pl:
$VZ_3DFI/inspect_3D_structures.pl \
-v $RESULTS/VisualizationThe output should result in something similar to the following:
Available 3D visualizations for sequence_1:
1. sequence_1.pdb
2. sequence_1_1quq.cxs
3. sequence_1_2pqa.cxs
4. sequence_1_3kdf.cxs
5. sequence_1_4gnx.cxs
Options:
[1-5] open corresponding file
[a] advance to next locus tag
[p] return to previous locus tag
[n] to create a note for locus tag
[x] to exit 3D inspection
Selection:
In this example, selecting [1] will open the visualization of the predicted 3D structure with ChimeraX:
The structure can then be interacted with using ChimeraX commands. For example, the structure can be colored with a rainbow scheme to better distinguish between structural domains:
Alternatively, selecting [2-6] will open the visualization of the alignment of the predicted 3D structure with its selected structural homolog:
Single FASTA files for protein structure prediction can be created with split_Fasta.pl:
split_Fasta.pl \
-f file.fasta \
-o output_folder \
-e fasta
If desired, single sequences can further be subdivided into smaller segments using sliding windows. This can be useful for very large proteins, which can be difficult to fold computationally. Options for split_Fasta.pl are:
-f (--fasta) FASTA input file (supports gzipped files)
-o (--output) Output directory [Default: Split_Fasta]
-e (--ext) Desired file extension [Default: fasta]
-w (--window) Split individual fasta sequences into fragments using sliding windows [Default: off]
-s (--size) Size of the the sliding window [Default: 250 (aa)]
-l (--overlap) Sliding window overlap [Default: 100 (aa)]
RCSB PDB files can be split per chain with split_PDB.pl:
split_PDB.pl \
-p files.pdb \
-o output_folder \
-e pdb
Options for split_PDB.pl are:
-p (--pdb) PDB input file (supports gzipped files)
-o (--output) Output directory. If blank, will create one folder per PDB file based on file prefix
-e (--ext) Desired file extension [Default: pdb]
Files can be renamed using regular expressions with rename_files.pl:
rename_files.pl \
-o 'i{0,1}-t26_1-p1' \
-n '' \
-f *.fasta
Options for rename_files.pl are:
-o (--old) Old pattern/regular expression to replace with new pattern
-n (--new) New pattern to replace with; defaults to blank [Default: '']
-f (--files) Files to rename
This work was supported in part by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (award number R15AI128627) to Jean-Francois Pombert. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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