Prediction of MHC class I binding peptides using probability distribution functions

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 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 non-binders (BNB). However, the number of known binders to a specific MHC molecule is limited in...

Prediction of MHC class I binding peptides using profile motifs.

Peptides that bind to a given major histocompatibility complex (MHC) molecule share sequence similarity. Therefore, a position specific scoring matrix (PSSM) or profile derived from a set of peptides known to bind to a specific MHC molecule would be a suitable predictor of whether other peptides might bind, thus anticipating possible T-cell epitopes within a protein. In this approach, the bindi...

Prediction of MHC Class I Binding Peptides Using an Ensemble Learning Approach

To be able to predict the binding affinity of peptides to major histocompatibility complex (MHC) molecules is an important issue in the field of immunology. A standard inductive learning algorithm called C4.5 [3] which generates a classifier in the form of a decision tree is used to predict the binding propensity of peptides to a MHC class I molecule. Only the primary structure of the peptides ...

Improving Prediction of MHC Class I Binding Peptides with Additional Binding Data

The ability to predict MHC-binding peptides is of great immunological importance in understanding immune response regulation and designing vaccine. Various computational peptide sequence based methods, such as HMM, ANN, CART and SVM, have been proposed to tackle this problem by learning a predictive model based on known binding data of the MHC molecule. We present a novel computational approach...

MHC Class I-Binding Peptides Experiment Design Method for Prediction of Empirical Evaluation of a Dynamic