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The web-server iATP was developed to identify the anti-tubercular peptides based on the sequence information. The feature selection technique was used to seek optimized g-gap dipeptide. The Model 1 is trained based on benchmark dataset S1, and Model 2 is trained based on the benchmark dataset S2. More details could be found in the paper .
Caveat (1) For each submission, the number of peptide sequences is limited at 100 or less; (2) The input sequences must be in FASTA format; i.e., each protein sequence should start with a greater-than symbol (" > ") in the first column. The words right after the " > " symbol in the single initial line are optional and only used for the purpose of identification and description. (3) If a query sequence contains any illegal character, the prediction will be stopped. |