Gapped sequence alignment using artificial neural networks: application to the MHC class I system

Abstract

Motivation: Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8-11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods trained on peptides of single lengths. Also, we illustrate how the location of deletions can aid the interpretation of the modes of binding of the peptide-MHC, as in the case of long peptides bulging out of the MHC groove or protruding at either terminus. Finally, we demonstrate that the method can learn the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm. Availability and implementation: The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped sequence alignment is publicly available at:

A Targeted LC‐MS Strategy for Low‐Abundant HLA Class‐I‐Presented Peptide Detection Identifies Novel Human Papillomavirus T‐Cell Epitopes

“…The predictions were performed for 8–11‐mer peptides with NetMHC 4.0,18 NetMHC 3.4,19 NetMHCpan 3.0,20 NetMHCpan 2.8,20 NetMHCcons 1.1,21 Consensus,22 PickPocket 1.1,23 SMM,24 SMMPMBEC,25 BIMAS,26 and SYFPEITHI 27. Results of all prediction algorithms were combined.…”

Systematic identification of cancer-specific MHC-binding peptides with RAVEN

“…To identify peptides encoded by CSGs suitable for a targeted immunotherapy, we implemented the artificial neural network (ANN) algorithm 30,31 provided by the immune epitope database IEDB 3.0. 32 RAVEN can apply this ANN algorithm to predict peptide-affinities for different peptide lengths and the most common human and murine MHC-subtypes.…”

“…In our list of 806 CSGs, RAVEN predicted potential highly affine peptides for 9-mers, which usually show optimal binding to most MHC class I molecules, 30,33 and for HLA-A02:01, which is the most common MHC-I in Caucasians 34 with an allele frequency of 0.2755. 35 RAVEN automatically crosschecked these peptides by a text search algorithm with ApacheLucene 36,37 against the human reference-proteome (UniProt release 2015_06) to exclude sequence identity with non-specifically expressed proteins.…”

“…Therefore, RAVEN uses artificial neural networks (ANN) and a prediction algorithm developed by NetMHC. 30,31 The peptide search service 36 of UniProt is queried via a RESTful web service which API is provided and integrated by Protein Information Resource (PIR) using ApacheLucene for peptide text searches. 36,37 In RAVEN, this approach is available for the most common alleles in human and mouse.…”

Peptide-Binding Groove Contraction Linked to the Lack of T Cell Response: Using Complex Structure and Energy To Identify Neoantigens

“…One category comprises residue-based (or sequence-based) methods (such as those reported in Refs. [9][10][11][12][13][14][15][16][17][18]. These methods predict the HLA-complexation affinity or stability of a peptide with coarse granularity at the residue level.…”