NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction
By Morten Nielsen, Ole Lund. BMC Bioinformatics, 2009
524 citations recorded by the source corpus.
BackgroundThe major histocompatibility complex (MHC) molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event.ResultsHere, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods.ConclusionThe NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.
Subfields: Immunology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/93f722e0e9929bb1df1e39c92c56d076ffcf4a63
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Immunology
- Bjoern Peters
- Morten Nielsen
- Massimo Andreatta
- Sinu Paul
- Alessandro Sette
- Swapnil Mahajan
- Sandeep Kumar Dhanda
- Paolo Marcatili
In Molecular Biology
- Bjoern Peters
- Morten Nielsen
- Alessandro Sette
- Massimo Andreatta
- Sinu Paul
- Paolo Marcatili
- J. Greenbaum
- Swapnil Mahajan
Citations in the corpus
- The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design (2017, methodology): “Several different prediction methods are available including IEDB recommended, Consensus (41, 46), netMHCIIpan (47, 48), SMM-align (49), Sturniolo (50), and NN-align (51).”
- Fundamentals and Methods for T- and B-Cell Epitope Prediction (2017, methodology): “Likewise, ANNs were also applied first to the prediction of peptide binding to MHC I [48, 49] and later to MHC II molecules [50] (Table 1).”
- A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction (2020, methodology): “It uses a BP algorithm to update the weights between units [81].”
- NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets (2016, methodology): “We have previously demonstrated how a simple neural network-based machine learning algorithm, NNAlign, can be effectively applied to identify binding motifs in quantitative receptor-ligands datasets [22, 23], and have with great success applied this method to learn the binding motifs and develop acc”
- Improved methods for predicting peptide binding affinity to MHC class II molecules (2018, methodology): “Using the data set from 2016, we retrained NetMHCII (15) and NetMHCIIpan (11) using a five-fold crossvalidation setup to generate two new versions of these methods, named NetMHCII-2.”
- Gapped sequence alignment using artificial neural networks: application to the MHC class I system (2016, methodology): “2 Neural network architecture The method was implemented as a feed-forward artificial neural network ensemble with a single hidden layer as previously described (Nielsen and Lund, 2009).”
- An introduction to deep learning on biological sequence data: examples and solutions (2017, methodology): “0 (Karosiene et al. , 2013), which relies on an ensemble of feed forward neural networks trained by an algorithm tailored to the problem (Andreatta et al. , 2011; Nielsen and Lund, 2009).”