The accurate prediction of peptide-MHC class I binding probabilities is a critical endeavor in
immunoinformatics, with broad implications for vaccine development and immunotherapies …

Aims: Generation of the human anti-MUC1 peptide through neural network training and
monomeric design method. Analyzing 9-mer peptide potential computationally for treatment …

Summary MHC class I‐restricted CD8 T‐lymphocyte epitopes comprise anchor motifs, T‐cell
receptor (TCR) contact residues and the peptide backbone. Serial variant epitopes with …

Throughout time functional immunology has accumulated vast amounts of quantitative and
qualitative data relevant to the design and discovery of vaccines. Such data includes, but is …

The major histocompatibility complex (MHC) plays important roles in the workings of the
human immune system. Specificity of MHC binding to peptide fragments from cellular and …

Antigenic peptides presented to T cells by MHC molecules are essential for T or B cells to
proliferate and eventually differentiate into effector cells or memory cells. MHC binding …

Introduction Class I HLA's polymorphism has hampered CTL epitope mapping with
laborious experiments. Objectives are 1) to evaluate the novel in silico model in predicting …

Shift invariant predictors are described herein. By way of example, a system for predicting
binding information relating to a binding of a protein and a ligand can include a trained …

Quantitative structure-activity relationships (QSAR) is a well established ligand-based
approach to drug design. It correlates changes in the chemical structure of a series of …

Recent advances in next generation sequencing expanded the availability of tumor
mutanome data that list the mutations present in cancer cells. Mutated proteins are an …