Ubuntu Manpage: concavity - predictor of protein ligand binding sites from structure and conservation

Provided by: concavity_0.1+dfsg.1-6_amd64

NAME

       concavity - predictor of protein ligand binding sites from structure and conservation

SYNOPSIS

       concavity [options] PDBFILE OUTPUT_NAME

DESCRIPTION

ConCavity predicts protein ligand binding sites by combining evolutionary sequence conservation and 3D
structure.

ConCavity takes as input a PDB format protein structure PDBFILE and optionally files that characterize
the evolutionary sequence conservation of the chains in the structure file.

The following result files are produced by default:

• Residue ligand binding predictions for each chain (*.scores).

• Residue ligand binding predictions in a PDB format file (residue scores placed in the temp. factor
field, *_residue.pdb).

• Pocket prediction locations in a DX format file (*.dx).

• PyMOL script to visualize the predictions (*.pml).

To visualize the predictions in PyMol (it if is installed on your system), load the script by typing
"pymol 1G6C_test1.pml" at the prompt or by loading it through the pymol interface.

The PDB and DX files can be input into other molecular viewers if preferred. Several additional output
formats are available; see below. Note that the residue numbering in the .scores files may not match that
of the PDB file.

The ConCavity approach proceeds in three conceptual steps: grid creation, pocket extraction, and residue
mapping (see Methods in paper). First, the structural and evolutionary properties of the protein are used
to create a regular 3D grid surrounding the protein in which the score associated with each grid point
represents an estimated likelihood that it overlaps a bound ligand atom. Second, groups of contiguous,
high-scoring grid points are clustered to extract pockets that adhere to given shape and size
constraints. Finally, every protein residue is scored with an estimate of how likely it is to bind to a
ligand based on its proximity to extracted pockets.

Each of the algorithms described for these steps is implemented in concavity. See the examples.

REFERENCES

       Capra JA, Laskowski RA, Thornton JM, Singh M, and Funkhouser TA(2009) Predicting Protein Ligand Binding
       Sites by Combining Evolutionary Sequence Conservation and 3D Structure. PLoS Comput Biol, 5(12).

OPTIONS

PDBFILE is a protein structure file in PDB format. OUTPUT_NAME becomes part of the output file names and
may not contain "/". Output is written to the current directory.

Input
-conservation PATH
If the "-conservation" option is not given, then conservation information is not considered. Note
that there are separate conservation files for each protein chain in the structure, and the input to
the -conservation option is the prefix of these files. Pre-computed conservation files available for
almost the entire PQS on the ConCavity web site. If you'd like to compute sequence conservation
values for your own alignments, we recommend the JSD algorithm:
<http://compbio.cs.princeton.edu/conservation/>, available as score_conservation(1) from the
conservation-code package.

Grid Creation
-grid_method ligsite|surfnet|pocketfinder|custom
-resolution int int int
Set the grid resolution.

-spacing float
Set the grid spacing.

Pocket Extraction
-extraction_method search|topn|custom
-extraction_threshold_range_cutoff FLOAT
Stop the iterative search method when the diameter of the binary search window is less than
-extraction_threshold_range_cutoff * upper_threshold. Recommended value: 1e-6. Default: 0.

Residue Mapping
-res_map_method blur|dist|dist-thresh|custom

Each of these algorithms is described in the text, and each has a number of additional parameters that
change their behavior. The "custom" option allows you to set the values of all parameters for each step
yourself. The presets (e.g. ligsite, search, blur) may override values you set on the command line, so
use "custom" to have complete control.

Output
There are also several output format options. Pocket prediction grid values can be output in the
following formats:

-print_grid_dx 0|1
DX format. This is 1 by default.

-print_grid_pdb 0|1
PDB format. The residue predictions are output as a PDB file with the residue scores mapped to the
temp. factor field and pocket numbers to the residue sequence field.

-print_grid_txt 0|1
Raw text.

-v Verbose mode.

EXAMPLES

       Note: you may have to copy and uncompress the example data files before running the following examples.

       1.  This will run concavity with default values (equivalent to ConCavity^L in the paper) on the structure
           1G6C.pdb and consider the conservation values found in conservation_data/.  This set  of  predictions
           will be called "test1".  This produces the following default result files in the current directory:

            concavity -conservation /usr/share/doc/concavity/examples/conservation_data/1G6C /usr/share/doc/concavity/examples/1G6C.pdb test1

       2.  For  example  to  score  the  structure 1G6C.pdb with ConCavity_Pocketfinder, Search, and Blur, you'd
           type:

            concavity -conservation /usr/share/doc/concavity/examples/conservation_data/1G6C -grid_method pocketfinder -extraction_method search -res_map_method blur /usr/share/doc/concavity/examples/1G6C.pdb cc-pocketfinder_search_blur

NOTES

       The authors primarily use PyMol and Chimera for visualization, but the range of output formats means  you
       should  be able to import the data into most structural analysis program.  Let us know if there are other
       output formats you'd like to see.