Provided by: norsnet_1.0.17-4_all bug

NAME

       norsnet - identifies unstructured loops from sequence

SYNOPSIS

       norsnet <FASTA_FILE> <RDBPROF_FILE> <HSSP_FILE> <OUTPUT_FILE> <PROTEIN_NAME>
       <PROFBVAL_FILE> <OUTPUT_MODE> <DEBUG>

DESCRIPTION

       NORSnet is a neural network based method that focuses on the identification of
       unstructured loops.

       NORSnet was trained to distinguish between very long contiguous segments with non-regular
       secondary structure (NORS regions) and well-folded proteins. NORSnet was trained on
       predicted information rather than on experimental data. Therefore, it was optimized on a
       large data, which is not biased by today's experimental means of capturing disorder. Thus,
       NORSnet reached into regions in sequence space that are not covered by the specialized
       disorder predictors. One disadvantage of this approach is that it is not optimal for the
       identification of the "average" disordered region.

   Conversion of PSI-BLAST alignment to HSSP format
       The most up-to-date procedure can be found at
       <https://www.rostlab.org/owiki/index.php/How_to_generate_an_HSSP_file_from_alignment#Generating_an_HSSP_profile>.

       1. Convert BLAST output to a Single Alignment Format (SAF):
            /usr/share/librg-utils-perl/blast2saf.pl fasta=<query_fasta_file> maxAli=3000 eSaf=1 \
             saf=<saf_formatted_file> <blast_output>

       2. Convert SAF format to HSSP:
            /usr/share/librg-utils-perl/copf.pl <saf_formatted_file> formatIn=saf formatOut=hssp \
             fileOut=<hssp_formatted_file> exeConvertSeq=convert_seq

       3. Filter results to 80% redundancy:
            /usr/share/librg-utils-perl/hssp_filter.pl red=80 <hssp_formatted_file> fileOut=<filtered_hssp_formatted_file>

   Output format
       Output mode 1

       Tabular output, columns:

        pos            amino acid number (1..)
        res            residue 1-letter code
        node1          output of neural network node 1
        node2          output of neural network node 2
        pred           node1 / ( node1 + node2 )
        n40            pred < 0.40 ? '-' : 'N'
        n40fil         at least 31 AA long stretches of 'N' in n40
        n59            pred < 0.59 ? '-' : 'N'
        n59fil         at least 31 AA long stretches of 'N' in n59

       'N' is for non-regular secondary structure.

REFERENCES

       Schlessinger, A., Liu, J., and Rost, B. (2007). Natively unstructured loops differ from
       other loops. PLoS Comput Biol, 3(7), e140.

OPTIONS

       FASTA_FILE
           File containing protein amino-acid sequence in fasta format.

       RDBPROF_FILE
           Secondary structure and solvent accessibility prediction by PROF in rdb format.

       HSSP_FILE
           PSI-BLAST alignment profile file converted to HSSP format.

       OUTPUT_FILE
           The name of the final NORSnet output file.

       PROFBVAL_FILE
           Flexible/rigid residues prediction by profbval(1) in rdb format (mode 5).

       OUTPUT_MODE
           NORSnet can create output files in different formats for different purposes. Valid
           modes are `1', `2' or `3'. Default mode: 1.

           -   Default mode. Use this when you do not want to give a value here but you want to
               specify debug.

           1   for metadisorder(1)

       DEBUG
           Set to 1 for debugging messages

OUTPUT

       number -
           residue number

       residue -
           residue type

       raw -
           raw value of the different between the two output nodes

EXAMPLES

        norsnet /usr/share/doc/norsnet/examples/cad23.f /usr/share/doc/norsnet/examples/cad23-fil.rdbProf /usr/share/doc/norsnet/examples/cad23-fil.hssp cad23.norsnet cad23 /usr/share/doc/norsnet/examples/cad23.profbval

ENVIRONMENT

       NORSNET_ROOT
           Overrides /usr/share/norsnet, the path to helper scripts and data files.

FILES

       *.norsnet
           default output file extension

       /usr/share/doc/norsnet/examples
           default precomputed input files directory

NOTES

       1. It is recommended to create the profiles using 3 iteration of PSI-BLAST against big
       database
       2. It is also recommended to filter the hssp files using hssp_filter.pl from the Prof
       package using the following command: perl hssp_filter.pl hssp_file red=80

AUTHOR

       A. Schlessinger <avnersch@gmail.com>

SEE ALSO

       profbval(1), prof(1).
       Main website
           <http://www.predictprotein.org/>