NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data

By Birkir Reynisson, Bruno Alvarez, Sinu Paul, Bjoern Peters, Morten Nielsen. Nucleic Acids Res., 2020

1,960 citations recorded by the source corpus.

Abstract Major histocompatibility complex (MHC) molecules are expressed on the cell surface, where they present peptides to T cells, which gives them a key role in the development of T-cell immune responses. MHC molecules come in two main variants: MHC Class I (MHC-I) and MHC Class II (MHC-II). MHC-I predominantly present peptides derived from intracellular proteins, whereas MHC-II predominantly presents peptides from extracellular proteins. In both cases, the binding between MHC and antigenic peptides is the most selective step in the antigen presentation pathway. Therefore, the prediction of peptide binding to MHC is a powerful utility to predict the possible specificity of a T-cell immune response. Commonly MHC binding prediction tools are trained on binding affinity or mass spectrometry-eluted ligands. Recent studies have however demonstrated how the integration of both data types can boost predictive performances. Inspired by this, we here present NetMHCpan-4.1 and NetMHCIIpan-4.0, two web servers created to predict binding between peptides and MHC-I and MHC-II, respectively. Both methods exploit tailored machine learning strategies to integrate different training data types, resulting in state-of-the-art performance and outperforming their competitors. The servers are available at http://www.cbs.dtu.dk/services/NetMHCpan-4.1/ and http://www.cbs.dtu.dk/services/NetMHCIIpan-4.0/.

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

View Paper: https://www.semanticscholar.org/paper/5a4631d5d75e3610037f87839628a4d166581e01

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Immunology

1. Alessandro Sette
2. Morten Nielsen
3. John Sidney
4. H. Ljunggren
5. Shane Crotty
6. Bjoern Peters
7. Massimo Andreatta
8. Sinu Paul

In Molecular Biology

1. Alessandro Sette
2. Morten Nielsen
3. John Sidney
4. Alba Grifoni
5. Bjoern Peters
6. Massimo Andreatta
7. Ole Lund
8. Sinu Paul

In Radiology, Nuclear Medicine and Imaging

1. Alessandro Sette
2. John Sidney
3. Morten Nielsen
4. Alba Grifoni
5. Bjoern Peters

Citations in the corpus

• SARS-CoV-2 vaccination induces immunological T cell memory able to cross-recognize variants from Alpha to Omicron (2022, positive): “Predicted HLA binding values to the relevant HLA allelic variant were calculated using the IEDB recommended NetMHCpanEL 4.1 (Reynisson et al., 2020) algorithm.”
• Efficient evolution of human antibodies from general protein language models (2023, neutral): “Although computational prediction of immunogenicity remains a challenge, especially involving recognition of discontinuous epitopes, the immunogenicity of linear peptides is better understood 43 .”
• Robust T Cell Immunity in Convalescent Individuals with Asymptomatic or Mild COVID-19 (2020, background): “Most correlative studies of immune protection against SARS-CoV-2 have focused on the induction of neutralizing antibodies (Hotez et al., 2020; Robbiani et al., 2020; Seydoux et al., 2020; Wang et al., 2020).”