Welcome to the Home Page of AtbPpred

AtbPpred is a web based two-layer prediction server for anti-tubercular peptides. In the first layer, we applied a two-step feature selection procedure and identified the optimal feature set individually for nine different feature encodings, whose corresponding models were developed using extremely randomized tree (ERT). In the second-layer, the predicted probability of AtbPs from the above nine models were considered as input features to ERT and developed the final predictor. For a given peptide, AtbPpred predicts its class and probability values.

Please select one of the following model for the prediction


We used the same positve dataset while constructing the two prediction models (AntiTB_MD and AntiTB_RD), but the negative dataset is different. AntitB_MD and AntiTB_RD respectively utilized anti-bacterial and random peptides.


AtbPpred: A robust sequence-based prediction of anti-tubercular peptides using extremely randomized trees (Computational and Structural Biotechnology Journal ) 
[Please cite this paper if you find AtbPpred useful in your research

Contact: bala@ajou.ac.kr