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Theory:
• Secondary structure prediction is a set of techniques in bioinformatics that aim to predict the secondary structures of proteins and nucleic acid sequences based only on knowledge of their primary structure.
• For proteins, this means predicting the formation of protein structures such as alpha helices and beta strands, while for nucleic acids it means predicting the formation of nucleic acid structures like helixes and stem-loop structures through base pairing and base stacking interactions.
• Protein structure prediction is the prediction of the three-dimensional structure of a protein from its amino acid sequence that is the prediction of its secondary, tertiary, and quaternary structure from its primary structure. Structure prediction is fundamentally different from the inverse problem of protein design.
• Protein structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry, it is highly important in medicine and biotechnology.
• SOPMA (Self-Optimized Prediction Method with Alignment) is an improvement of SOPM method. These methods are based on the homologue method of Levin et al..
• 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 RBTR_KLEAE (270 aa) and 4'33 minutes for MDR3_HUMAN (1270 aa)). So, be careful when using it in alignment (the total computing time can't be above 2 hours).
Procedure:
• Respective protein sequences of human cox2 (NP_000954.1) in Fasta format was retrieved from NCBI (ncbi.nlm.nih.gov).
• Log in to http://npsa-pbil.ibcp.fr/cgi-bin/npsa_au...sopma.html
• Paste the respective sequences in the given input boxes.
• Click run.
• Results were displayed as soon as the given job completed.