Predict linear B cell epitopes of fixed length
Despite recent advances in bioinformatics, prediction of B cell eptiopes have been challenging. Here, we developed a convolutional neural network based models to accurately predict B cell binding epitopes agianst general B cell population. 63 independent models are created for representative IGHV alleles of human and mouse, which are then combined into ensemble model using linear regression.
References:
Please download this github repo.
The code can be run on Python>3.6 and Keras with tensorflow backend. Other requirements are listed on requirements.txt
The input file of DeepNeo-BCR is a single column file with query peptide list. An example data is provided within this repo.
python predict_63.py GPU_NUM MODE INPUTFILE
is the basic command line for DeepNeo-BCR.
Users can test the code using
python predict_63.py 0 all Example/example.txt
Although GPU is not necessary to run the code, it will be helpful in prompt prediction.
There are four modes available : all, human, human_reduced, mouse
'all' includes all mouse and human alleles.
'human' includes all human alleles (N=48)
'human_reduced' includes representative human alleles (N=25) and can be used if computational power is limited.
'mouse' includes mouse alleles.
We suggest using >0.3 to interpret B cell epitopes.