NetMHCpan 4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data

Jurtz, Vanessa Isabell; Paul, Sinu; Andreatta, Massimo; Marcatili, Paolo; Peters, Bjoern; Nielsen, Morten

doi:10.1101/149518

Cited by 311 publications

(503 citation statements)

References 31 publications

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“…In this way, the models are presented with cases that are neither physically nor biologically meaningful, such as residues divided by a disordered region, that are far apart in primary and tertiary structure but presented to the model as consecutive. In contrast, by using a recent training procedure strategy, we can train the model on all residues, including the disordered ones, thus increasing the accuracy of annotated features in the data and reduce the frustration during training.…”

Section: Discussionmentioning

confidence: 99%

NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning

et al. 2019

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The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed. Here, we present NetSurfP‐2.0, a novel tool that can predict the most important local structural features with unprecedented accuracy and runtime. NetSurfP‐2.0 is sequence‐based and uses an architecture composed of convolutional and long short‐term memory neural networks trained on solved protein structures. Using a single integrated model, NetSurfP‐2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences. We assessed the accuracy of NetSurfP‐2.0 on several independent test datasets and found it to consistently produce state‐of‐the‐art predictions for each of its output features. We observe a correlation of 80% between predictions and experimental data for solvent accessibility, and a precision of 85% on secondary structure 3‐class predictions. In addition to improved accuracy, the processing time has been optimized to allow predicting more than 1000 proteins in less than 2 hours, and complete proteomes in less than 1 day.

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Section: Discussionmentioning

confidence: 99%

NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning

et al. 2019

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“…The three highest‐scoring epitopes for each supertype were selected for analyses of binding affinity to their corresponding representative supertypes in NetMHCpan 4.0 server which predicted epitope‐MHC‐I binding using ANN trained on quantitative binding data and mass spectroscopy‐derived MHC eluted ligands (http://www.cbs.dtu.dk/services/NetMHCpan). 9 Default threshold % ranks of 0.5 for strong binders, and 2 for weak binders were used.…”

Section: Methodsmentioning

confidence: 99%

Immunoinformatics‐aided identification of T cell and B cell epitopes in the surface glycoprotein of 2019‐nCoV

Baruah

Bose

2020

Journal of Medical Virology

283

273

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The 2019 novel coronavirus (2019-nCoV) outbreak has caused a large number of deaths with thousands of confirmed cases worldwide, especially in East Asia.This study took an immunoinformatics approach to identify significant cytotoxic T lymphocyte (CTL) and B cell epitopes in the 2019-nCoV surface glycoprotein. Also, interactions between identified CTL epitopes and their corresponding major histocompatibility complex (MHC) class I supertype representatives prevalent in China were studied by molecular dynamics simulations. We identified five CTL epitopes, three sequential B cell epitopes and five discontinuous B cell epitopes in the viral surface glycoprotein. Also, during simulations, the CTL epitopes were observed to be binding MHC class I peptide-binding grooves via multiple contacts, with continuous hydrogen bonds and salt bridge anchors, indicating their potential in generating immune responses. Some of these identified epitopes can be potential candidates for the development of 2019-nCoV vaccines.

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“…This concept was implemented into MixMHCpred and led to the refinement of known HLA motifs. However, a strong bias against infrequent alleles and HLA‐C alleles with less stringent motifs became evident . To complement this impediment, HLA‐C motifs from mono‐allelic transfectants were recently published …”

Section: Bioinformatic Analysis Of Ms Datamentioning

confidence: 99%

“…Furthermore, binding assays show that peptides can bind to HLA molecules in vitro , but do not take into account any intracellular processing preferences. However, netMHCpan‐4·0 integrates both publicly available HLA ligandomics data and binding affinity data, thus increasing the sensitivity and specificity of their binding prediction …”

Section: Bioinformatic Analysis Of Ms Datamentioning

confidence: 99%

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Mapping the tumour human leukocyte antigen (HLA) ligandome by mass spectrometry

2018

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The entirety of human leukocyte antigen (HLA)-presented peptides is referred to as the HLA ligandome of a cell or tissue, in tumours often termed immunopeptidome. Mapping the tumour immunopeptidome by mass spectrometry (MS) comprehensively views the pathophysiologically relevant antigenic signature of human malignancies. MS is an unbiased approach stringently filtering the candidates to be tested as opposed to epitope prediction algorithms. In the setting of peptide-specific immunotherapies, MS-based strategies significantly diminish the risk of lacking clinical benefit, as they yield highly enriched amounts of truly presented peptides. Early immunopeptidomic efforts were severely limited by technical sensitivity and manual spectra interpretation. The technological progress with development of orbitrap mass analysers and enhanced chromatographic performance led to vast improvements in mass accuracy, sensitivity, resolution, and speed. Concomitantly, bioinformatic tools were developed to process MS data, integrate sequencing results, and deconvolute multi-allelic datasets. This enabled the immense advancement of tumour immunopeptidomics. Studying the HLA-presented peptide repertoire bears high potential for both answering basic scientific questions and translational application. Mapping the tumour HLA ligandome has started to significantly contribute to target identification for the design of peptide-specific cancer immunotherapies in clinical trials and compassionate need treatments. In contrast to prediction algorithms, rare HLA allotypes and HLA class II can be adequately addressed when choosing MS-guided target identification platforms. Herein, we review the identification of tumour HLA ligands focusing on sources, methods, bioinformatic data analysis, translational application, and provide an outlook on future developments.

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NetMHCpan 4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data

Cited by 311 publications

References 31 publications

NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning

NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning

Immunoinformatics‐aided identification of T cell and B cell epitopes in the surface glycoprotein of 2019‐nCoV

Mapping the tumour human leukocyte antigen (HLA) ligandome by mass spectrometry

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