Provided by: concavity_0.1+dfsg.1-4_amd64 bug


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


       concavity [options] PDBFILE OUTPUT_NAME


       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

       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


       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).


       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.

       -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:
           <>, 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.

       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.


       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


       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.


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