Computational prediction of MHC anchor locations guides neoantigen identification and prioritization
出版年份 2023 全文链接
标题
Computational prediction of MHC anchor locations guides neoantigen identification and prioritization
作者
关键词
-
出版物
Science Immunology
Volume 8, Issue 82, Pages -
出版商
American Association for the Advancement of Science (AAAS)
发表日期
2023-04-08
DOI
10.1126/sciimmunol.abg2200
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Sources of Cancer Neoantigens beyond Single-Nucleotide Variants
- (2022) Aude-Hélène Capietto et al. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
- Characterization of the Genomic and Immunologic Diversity of Malignant Brain Tumors through Multisector Analysis
- (2021) Maximilian O. Schaettler et al. Cancer Discovery
- Mutation position is an important determinant for predicting cancer neoantigens
- (2020) Aude-Hélène Capietto et al. JOURNAL OF EXPERIMENTAL MEDICINE
- Pan-cancer analysis of whole genomes
- (2020) NATURE
- pVACtools: a computational toolkit to identify and visualize cancer neoantigens
- (2020) Jasreet Hundal et al. Cancer Immunology Research
- Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction
- (2020) Daniel K. Wells et al. CELL
- Comprehensive analysis of structural variants in breast cancer genomes using single-molecule sequencing
- (2020) Sergey Aganezov et al. GENOME RESEARCH
- Alternative Splicing in Tumors — A Path to Immunogenicity?
- (2019) Jill E. Slansky et al. NEW ENGLAND JOURNAL OF MEDICINE
- Immunogenic neoantigens derived from gene fusions stimulate T cell responses
- (2019) Wei Yang et al. NATURE MEDICINE
- Pediatric patients with acute lymphoblastic leukemia generate abundant and functional neoantigen-specific CD8+ T cell responses
- (2019) Anthony E. Zamora et al. Science Translational Medicine
- A new era of long-read sequencing for cancer genomics
- (2019) Yoshitaka Sakamoto et al. JOURNAL OF HUMAN GENETICS
- Best practices for bioinformatic characterization of neoantigens for clinical utility
- (2019) Megan M. Richters et al. Genome Medicine
- Predicting HLA class II antigen presentation through integrated deep learning
- (2019) Binbin Chen et al. NATURE BIOTECHNOLOGY
- Mutation-derived Neoantigen-specific T-cell Responses in Multiple Myeloma
- (2019) Deepak Perumal et al. CLINICAL CANCER RESEARCH
- High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets
- (2019) Xiaoshan M. Shao et al. Cancer Immunology Research
- Variations in HLA-B cell surface expression, half-life and extracellular antigen receptivity
- (2018) Brogan Yarzabek et al. eLife
- MHCflurry: Open-Source Class I MHC Binding Affinity Prediction
- (2018) Timothy J. O'Donnell et al. Cell Systems
- A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data
- (2018) Chang Xu Computational and Structural Biotechnology Journal
- RNA editing derived epitopes function as cancer antigens to elicit immune responses
- (2018) Minying Zhang et al. Nature Communications
- Actively personalized vaccination trial for newly diagnosed glioblastoma
- (2018) Norbert Hilf et al. NATURE
- Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial
- (2018) Derin B. Keskin et al. NATURE
- RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy
- (2018) Stephen K Burley et al. NUCLEIC ACIDS RESEARCH
- Noncoding regions are the main source of targetable tumor-specific antigens
- (2018) Céline M. Laumont et al. Science Translational Medicine
- NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data
- (2017) Vanessa Jurtz et al. JOURNAL OF IMMUNOLOGY
- Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis
- (2017) Samra Turajlic et al. LANCET ONCOLOGY
- An immunogenic personal neoantigen vaccine for patients with melanoma
- (2017) Patrick A. Ott et al. NATURE
- Neoantigen Vaccines Pass the Immunogenicity Test
- (2017) Gerald P. Linette et al. TRENDS IN MOLECULAR MEDICINE
- pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens
- (2016) Jasreet Hundal et al. Genome Medicine
- Gapped sequence alignment using artificial neural networks: application to the MHC class I system
- (2015) Massimo Andreatta et al. BIOINFORMATICS
- MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories
- (2015) Robert T. McGibbon et al. BIOPHYSICAL JOURNAL
- Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity
- (2014) Fei Duan et al. JOURNAL OF EXPERIMENTAL MEDICINE
- HLA-Binding Properties of Tumor Neoepitopes in Humans
- (2014) E. F. Fritsch et al. Cancer Immunology Research
- Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells
- (2013) Paul F Robbins et al. NATURE MEDICINE
- NetMHCcons: a consensus method for the major histocompatibility complex class I predictions
- (2011) Edita Karosiene et al. IMMUNOGENETICS
- Peptide binding prediction for the human class II MHC allele HLA-DP2: a molecular docking approach
- (2011) Atanas Patronov et al. BMC STRUCTURAL BIOLOGY
- Modification of MHC Anchor Residues Generates Heteroclitic Peptides That Alter TCR Binding and T Cell Recognition
- (2010) D. K. Cole et al. JOURNAL OF IMMUNOLOGY
- The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding
- (2009) Hao Zhang et al. BIOINFORMATICS
- Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior
- (2009) Yohan Kim et al. BMC BIOINFORMATICS
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