PMID- 37068304
OWN - NLM
STAT- MEDLINE
DCOM- 20230522
LR  - 20230523
IS  - 1477-4054 (Electronic)
IS  - 1467-5463 (Linking)
VI  - 24
IP  - 3
DP  - 2023 May 19
TI  - A comprehensive assessment and comparison of tools for HLA class I 
      peptide-binding prediction.
LID - bbad150 [pii]
LID - 10.1093/bib/bbad150 [doi]
AB  - Human leukocyte antigen class I (HLA-I) molecules bind intracellular peptides 
      produced by protein hydrolysis and present them to the T cells for immune 
      recognition and response. Prediction of peptides that bind HLA-I molecules is 
      very important in immunotherapy. A growing number of computational predictors 
      have been developed in recent years. We survey a comprehensive collection of 27 
      tools focusing on their input and output data characteristics, key aspects of the 
      underlying predictive models and their availability. Moreover, we evaluate 
      predictive performance for eight representative predictors. We consider a wide 
      spectrum of relevant aspects including allele-specific analysis, influence of 
      negative to positive data ratios and runtime. We also curate high-quality 
      benchmark datasets based on analysis of the consistency of the data labels. 
      Results reveal that each considered method provides accurate results, which can 
      be explained by our analysis that finds that their predictive models capture 
      meaningful binding motifs. Although some methods are overall more accurate than 
      others, we find that none of them is universally superior. We provide a 
      comprehensive comparison of the convenience as well as the accuracy of the 
      methods under specific prediction scenarios, such as for specific alleles, 
      metrics of predictive performance and constraints on runtime. Our systematic and 
      broad analysis provides informative clues to the users to identify the most 
      suitable tools for a given prediction scenario and for the developers to design 
      future methods.
CI  - (c) The Author(s) 2023. Published by Oxford University Press. All rights reserved. 
      For Permissions, please email: journals.permissions@oup.com.
FAU - Wang, Meng
AU  - Wang M
AD  - School of Computer Science and engineering, Central South University, Changsha 
      410083, China.
FAU - Kurgan, Lukasz
AU  - Kurgan L
AD  - Department of Computer Science, Virginia Commonwealth University, Richmond, VA 
      23284, USA.
FAU - Li, Min
AU  - Li M
AD  - School of Computer Science and engineering, Central South University, Changsha 
      410083, China.
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 (Peptides)
RN  - 0 (Histocompatibility Antigens Class I)
SB  - IM
MH  - Humans
MH  - Protein Binding
MH  - *Peptides/chemistry
MH  - *Histocompatibility Antigens Class I
OTO - NOTNLM
OT  - HLA-peptide
OT  - binding prediction
OT  - tools comparison
EDAT- 2023/04/18 06:00
MHDA- 2023/05/22 06:42
CRDT- 2023/04/17 17:32
PHST- 2022/12/28 00:00 [received]
PHST- 2023/03/27 00:00 [revised]
PHST- 2023/03/29 00:00 [accepted]
PHST- 2023/05/22 06:42 [medline]
PHST- 2023/04/18 06:00 [pubmed]
PHST- 2023/04/17 17:32 [entrez]
AID - 7126340 [pii]
AID - 10.1093/bib/bbad150 [doi]
PST - ppublish
SO  - Brief Bioinform. 2023 May 19;24(3):bbad150. doi: 10.1093/bib/bbad150.