NPS@SOPMA

BioCodeKb - Bioinformatics Knowledgebase

SOPMA (Self-Optimized Prediction Method with Alignment) is an improvement of SOPM method. These methods are based on the homologue methods for sequencing. The improvement takes place in the fact that SOPMA takes into account information from an alignment of sequences belonging to the same family. If there are no homologous sequences the SOPMA prediction is the SOPM one.

It can take up to 5 minutes to compute SOPMA for a sequence (45 seconds for 270 aa) and 4'33 minutes for almost 1270 aa).


Recently a new method called the self-optimized prediction method (SOPM) has been described to improve the success rate in the prediction of the secondary structure of proteins. This improved SOPM method (SOPMA) correctly predicts 69.5% of amino acids for a three-state description of the secondary structure (alpha-helix, beta-sheet and coil) in a whole database containing 126 chains of non-homologous (less than 25% identity) proteins.


Uses

  • Sequence similarity search with FASTA, BLAST, PSI-BLAST, and SSEARCH on protein databases such as SWISS-PROT, SP-TrEMBL or NRL_3D.

  • Sites and signatures detection with PATTINPROT or PROSCAN. PATTINPROT allow a search of one or several pattern on a protein database or on an individual sequence. PROSCAN scan a protein sequence against PROSITE.

  • Multiple alignments with CLUSTALW or MULTALIN.

  • Secondary structure prediction with 12 different methods and a consensus prediction of those methods. Available methods are SOPM, SOPMA, HNN, MLRC, DPM, DSC, GORI, GORII, GORIV, PHD, PREDATOR and SIMPA96.

  • Primary structure analysis such as: physico-chemical profiles, coil-coiled detection, helix-turn-helix DNA-binding motifs prediction, amino-acids composition and sequence coloring.


Characteristics

  • All methods proposed by NPS@ are piped.
    The output of one method could be the input of another one.

  • We can insert secondary structure prediction in multiple alignments.

  • We can upload our own database and apply NPS@’s methods on it.

  • We can download NPS@’s data in protein sequence analysis software on our local computer for further analysis, to save them or insert them in an article.

  • The NPSA link allows us to apply NPS@’s methods on a sequence. Even more, when the sequence comes from a 3D database (NRL-3D), we have some useful links to retrieve and work with 3D data.

  • NPS@ offers links on international databases (SWISSPROT, PROSITE, CATH and SCOP).

  • NPS@ works with data of an ACNUC query.


We can set the number of conformational states to predict the structures: 3 or 4.

The similarity threshold parameter is the threshold below which a subject peptide is rejected when it's compared with a query peptide of the sequence.

The window size parameter sets the length of the peptides to use.

We can see the results as following terms:

  • MPSA/ANTHEPROT link to view the prediction in these local protein sequence analysis software.

  • The color coded prediction (a sequence line and below the corresponding predicted states).

  • The sequence length.

  • The percentage of each secondary element.

  • Two graphics. The first to better visualize the prediction. In the second, there are the score curves for each predicted state.

  • The parameters used.

  • Links on the prediction result text file and on intermediate result text files.

  • Links on intermediate result files to view them in MPSA/ANTHEPROT.

Need to learn more about NPS@SOPMA and much more?

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