The web-server AcalPred was developed to discriminate between acidic and alkaline enzymes based on the sequence information. The analysis of variance was used to seek optimized g-gap dipeptide. The anticipated overall success rates are 96.7% by using jackknife cross-validation. 96.3% acidic enzymes and 97.1% alkaline enzymes can be correctly identified. All data can be downloaded from the Data window of this web-server.
(1) For each submission, the number of protein 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.