NORSNET(1) | User Commands | NORSNET(1) |
norsnet - identifies unstructured loops from sequence
norsnet <FASTA_FILE> <RDBPROF_FILE> <HSSP_FILE> <OUTPUT_FILE> <PROTEIN_NAME> <PROFBVAL_FILE> <OUTPUT_MODE> <DEBUG>
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.
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>.
/usr/share/librg-utils-perl/blast2saf.pl fasta=<query_fasta_file> maxAli=3000 eSaf=1 \ saf=<saf_formatted_file> <blast_output>
/usr/share/librg-utils-perl/copf.pl <saf_formatted_file> formatIn=saf formatOut=hssp \ fileOut=<hssp_formatted_file> exeConvertSeq=convert_seq
/usr/share/librg-utils-perl/hssp_filter.pl red=80 <hssp_formatted_file> fileOut=<filtered_hssp_formatted_file>
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.
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
A. Schlessinger <avnersch@gmail.com>
2022-01-18 | 1.0.17 |