Prediction of MHC class I binding peptides using probability distribution functions

Binding of peptides to specific Major Histo-compatibility Complex (MHC) molecule is important for understandingimmunity and has applications to vaccine discovery and design of immunotherapy. Artificial neural networks (ANN) arewidely used by predictions tools to classify the peptides as binders or non­binders (BNB). However, the number of knownbinders to a specific MHC molecule is limited in many cases, which poses a computational challenge for prediction of BNBand hence, needs improvement in learning of ANN. Here, we describe, the application of probability distribution functions toinitialize the weights and biases of the artificial neural network in order to predict HLA­A*0201 binders and non­binders.The 10­fold cross validation has been used to validate the results. It is evident from the results that the A_ROC for 90% of testcases for Weibull, Uniform and Rayleigh distributions is in the range 0.90-1.0. Further, the standard deviation for AROC wasminimum for Weibull distribution, and may be used to train the artificial neural network for HLA­A*0201 MHC Class­Ibinders and non­binders prediction.