Prediction of MHC class I binding peptides using profile motifs.

By Pedro A. Reche, John-Paul Glutting, Ellis L. Reinherz. Human Immunology, 2002

400 citations recorded by the source corpus.

Subfields: Molecular Biology, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e9ffbef45a5ca47ae332b3e661748b95b8b963b3

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In Molecular Biology

In Oncology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence (2007, methodology): “Once such information has been obtained, the next step has been to generate peptide-binding predictions using either simple motif searches strategies [8] or complete statistical matrices representing the frequency of each amino acid in each position [9–13].”
Novel Machine Learning Methods for MHC Class I Binding Prediction (2010, methodology): “Many computational methods for the classification of peptides into MHC-I binders or non-binders have been proposed: ranging from matrices [14, 17] to machine learning [3, 1].”
A Systematic Assessment of MHC Class II Peptide Binding Predictions and Evaluation of a Consensus Approach (2008, background): “Others base similar scoring matrices on multiple peptide alignments (RANKPEP [27,28]) or domain expert knowledge (SYFPEITHI method [15]).”
pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens (2016, methodology): “For example, to optimize identification and selection of vaccine neoantigens, several in silico epitope binding prediction methods have been developed [11 – 15].”
Improved methods for predicting peptide binding affinity to MHC class II molecules (2018, methodology): “Given this, many different methods have been developed, including NetMHCII (15), NetMHCIIpan (16), TEPITOPE (17), TEPITOPEpan (18),PROPRED (19), RANKPEP (20)(21) and SVRMHC (22).”
A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction (2020, methodology): “RANKPEP[23]predictstheMHCclassI-bindingpeptidesusing profile motifs by calculating the PSSM of ligands bound to a given HLA allotype.”
Prediction of CTL epitopes using QM, SVM and ANN techniques. (2004, methodology): “To predict T cell epitopes with fair accuracy, a large number of MHC binders prediction methods were developed during last decade [23,24,32,33] .”
Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles (2004, methodology): “The figure was prepared using GRASP (Nicholls et al. 1991) structural alignment of diverse peptides is best guaranteed if the peptides are of the same length (Reche et al. 2002).”