A Brief Introduction of LnSignal

Protein signal peptides play a vital role in the targeting and translocation of most secreted proteins and many integral membrane proteins in both prokaryotes and eukaryotes. Consequently, accurate prediction of signal peptides and their cleavage sites is an important task in molecular biology. In the present study, firstly, we develop a novel discriminative scoring method for classifying proteins with or without signal peptides. Secondly, we consider the prediction task of signal peptide cleavage sites as a sequence labeling problem and apply Conditional Random Fields (CRFs) algorithm to solve it. The web server LnSignal (Labelling N-terminal Signal petide cleavage site) was developed by integrating position-specific amino acid propensities based on the highest average positions and conditional random fields. For a query protein sequence, it will discriminate whether is is secretory protein or not, and further identify the cleavage site of secretory protein. For more information, refer to the original paper that has documented the predictor.

Caveat

To obtain the predicted result with the expected success rate, the query proteins should be in Fasta format as input, please reference the example. The following points are important when using LnSignal:

1. LnSignal is focused on the prediction of N-terminal signal peptides only.     2. Your input sequence length should be at least more than 50aa and no more than 6000aa counted from the N-terminal. The number of input sequences should be no more than 10 at a time.     3. LnSignal is designed for "eukaryotic","Gram-positive" and "Gram-negative" proteins. Please select the correct organism type before you start computation.