PMID- 33454737
OWN - NLM
STAT- MEDLINE
DCOM- 20211122
LR  - 20211122
IS  - 1477-4054 (Electronic)
IS  - 1467-5463 (Linking)
VI  - 22
IP  - 5
DP  - 2021 Sep 2
TI  - Anthem: a user customised tool for fast and accurate prediction of binding 
      between peptides and HLA class I molecules.
LID - bbaa415 [pii]
LID - 10.1093/bib/bbaa415 [doi]
AB  - Neopeptide-based immunotherapy has been recognised as a promising approach for 
      the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and 
      induce an immune response, their binding to human leukocyte antigen class I 
      (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus 
      rely on the prediction of such binding. With the use of mass spectrometry, the 
      scale of naturally presented HLA ligands that could be used to develop such 
      predictors has been expanded. However, there are rarely efforts that focus on the 
      integration of these experimental data with computational algorithms to 
      efficiently develop up-to-date predictors. Here, we present Anthem for accurate 
      HLA-I binding prediction. In particular, we have developed a user-friendly 
      framework to support the development of customisable HLA-I binding prediction 
      models to meet challenges associated with the rapidly increasing availability of 
      large amounts of immunopeptidomic data. Our extensive evaluation, using both 
      independent and experimental datasets shows that Anthem achieves an overall 
      similar or higher area under curve value compared with other contemporary tools. 
      It is anticipated that Anthem will provide a unique opportunity for the 
      non-expert user to analyse and interpret their own in-house or publicly deposited 
      datasets.
CI  - (c) The Author(s) 2021. Published by Oxford University Press. All rights reserved. 
      For Permissions, please email: journals.permissions@oup.com.
FAU - Mei, Shutao
AU  - Mei S
AUID- ORCID: 0000-0002-0604-7745
AD  - Biomedicine Discovery Institute and Department of Biochemistry and Molecular 
      Biology, Monash University, Australia.
FAU - Li, Fuyi
AU  - Li F
AUID- ORCID: 0000-0001-5216-3213
AD  - Department of Microbiology and Immunology, The Peter Doherty Institute for 
      Infection and Immunity, The University of Melbourne, Australia.
FAU - Xiang, Dongxu
AU  - Xiang D
AD  - Biomedicine Discovery Institute and Department of Biochemistry and Molecular 
      Biology, Monash University, Australia.
FAU - Ayala, Rochelle
AU  - Ayala R
AD  - Biomedicine Discovery Institute and Department of Biochemistry and Molecular 
      Biology, Monash University, Australia.
FAU - Faridi, Pouya
AU  - Faridi P
AD  - Biomedicine Discovery Institute and Department of Biochemistry and Molecular 
      Biology, Monash University, Australia.
FAU - Webb, Geoffrey I
AU  - Webb GI
AD  - Information Technology at Monash University, Australia.
FAU - Illing, Patricia T
AU  - Illing PT
AD  - Biomedicine Discovery Institute and Department of Biochemistry and Molecular 
      Biology, Monash University, Australia.
FAU - Rossjohn, Jamie
AU  - Rossjohn J
AD  - Biomedicine Discovery Institute and Department of Biochemistry and Molecular 
      Biology, Monash University, Australia.
FAU - Akutsu, Tatsuya
AU  - Akutsu T
AD  - Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan.
FAU - Croft, Nathan P
AU  - Croft NP
AD  - Biomedicine Discovery Institute and Department of Biochemistry and Molecular 
      Biology, Monash University, Australia.
FAU - Purcell, Anthony W
AU  - Purcell AW
AD  - Monash Biomedicine Discovery Institute and Department of Biochemistry and 
      Molecular Biology, Monash University, Australia.
FAU - Song, Jiangning
AU  - Song J
AUID- ORCID: 0000-0001-8031-9086
AD  - Monash Biomedicine Discovery Institute and Biochemistry and Molecular Biology, 
      Monash University, Australia.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - Brief Bioinform
JT  - Briefings in bioinformatics
JID - 100912837
RN  - 0 (Epitopes)
RN  - 0 (Histocompatibility Antigens Class I)
RN  - 0 (Peptides)
SB  - IM
MH  - *Algorithms
MH  - *Databases, Protein
MH  - *Epitopes/chemistry/immunology
MH  - *Histocompatibility Antigens Class I/chemistry/immunology
MH  - Humans
MH  - Immunotherapy
MH  - Neoplasms/immunology/therapy
MH  - *Peptides/chemistry/immunology
MH  - *Software
OTO - NOTNLM
OT  - HLA-I peptide binding prediction
OT  - machine learning
OT  - model customisation
OT  - scoring function
OT  - web server
EDAT- 2021/01/18 06:00
MHDA- 2021/11/23 06:00
CRDT- 2021/01/17 20:33
PHST- 2020/09/30 00:00 [received]
PHST- 2020/11/29 00:00 [revised]
PHST- 2020/12/16 00:00 [accepted]
PHST- 2021/01/18 06:00 [pubmed]
PHST- 2021/11/23 06:00 [medline]
PHST- 2021/01/17 20:33 [entrez]
AID - 6102669 [pii]
AID - 10.1093/bib/bbaa415 [doi]
PST - ppublish
SO  - Brief Bioinform. 2021 Sep 2;22(5):bbaa415. doi: 10.1093/bib/bbaa415.