{"id":25,"date":"2012-04-17T15:11:27","date_gmt":"2012-04-17T15:11:27","guid":{"rendered":"https:\/\/pbpred-us2b.univ-nantes.fr\/pbe\/?page_id=25"},"modified":"2015-06-27T20:57:17","modified_gmt":"2015-06-27T20:57:17","slug":"pb-pentapept-home","status":"publish","type":"page","link":"https:\/\/pbpred-us2b.univ-nantes.fr\/pbe\/?page_id=25","title":{"rendered":"PB-PENTAdb home"},"content":{"rendered":"

PB-PENTAdb : database of pentapeptides<\/em><\/strong>
\nA database of pentapeptides was derived from SCOP 1.75 culled at 100% sequence identity (astral100 dataset<\/a>) but also at lower sequence identity filtering. A sliding window of 5 residues is used to extract the coordinates of every pentapeptide contained in all domains from SCOP. The dihedral phi and psi angles are then calculated and mapped to a trained neural network of 16 cells each representing a local structure prototype (the protein blocks) named a, b, c, … , n, o, p<\/em> according to the methodology developed by de Brevern et al (2000)<\/a>. Each pentapeptide hence extracted is assigned to the PB with which it has the lowest rmsda value (angular root mean square deviation<\/em>). Information about the sequence, the PB, the dihedral angles, the secondary structure (from DSSP<\/a>), solvent accessibility (from NACCESS<\/a>) and backbone B-factors are stored in PB-PENTAdb database.
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Basic search<\/h2>\n

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\nThe whole process for building the database is illustrated below :
\n\"pentapeptide<\/a>\n<\/td>\n		\nThe program takes as input a pentapeptide amino acid sequence in the input text box below.
\nHint : use the underscore sign “_” to designate any amino acid. Example<\/em> DI_RG.<\/p>\n
Enter your pentapeptide amino acid sequence here :<\/strong>
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