Prediction of MHC class I binding peptides using probability distribution functions |
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Authors: | Sudhir Singh Soam Feroz Khan Bharat Bhasker Bhartendu Nath Mishra |
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Institution: | 1.Institute of Engineering & Technology, (A Constituent College of Uttar Pradesh Technical University, Lucknow) Lucknow, India;2.Bioinformatics & In Silico Biology Division, Central Institute of Medicinal & Aromatic Plants (CSIR), Lucknow, India;3.Indian Institute of Management, Prabandh Nagar, Lucknow India |
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Abstract: | Binding of peptides to specific Major Histo-compatibility Complex (MHC) molecule is important for understanding
immunity and has applications to vaccine discovery and design of immunotherapy. Artificial neural networks (ANN) are
widely used by predictions tools to classify the peptides as binders or nonbinders (BNB). However, the number of known
binders to a specific MHC molecule is limited in many cases, which poses a computational challenge for prediction of BNB
and hence, needs improvement in learning of ANN. Here, we describe, the application of probability distribution functions to
initialize 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 AROC for 90% of test
cases for Weibull, Uniform and Rayleigh distributions is in the range 0.90-1.0. Further, the standard deviation for AROC was
minimum for Weibull distribution, and may be used to train the artificial neural network for HLAA*0201 MHC ClassI
binders and nonbinders prediction. |
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Keywords: | T cell Epitope ANN Probability distribution MHC binder/non binder |
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