Evidence map›Paper›PMID 35289353›Full record

ArticleBriefings in bioinformatics2022

HLA3D: an integrated structure-based computational toolkit for immunotherapy.

Xingyu Li, Xue Lin, Xueyin Mei, Pin Chen, Anna Liu, Weicheng Liang, Shan Chang, Jian Li

Open access · hybridAbstract read
In one paragraph

Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 46% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Computational model for drug research.Briefings in bioinformatics · 2024
    Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Xingyu LiKey Laboratory of DGHD, MOE, School of Life Science and Technology, Southeast University, Nanjing, China.
Xue LinDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Xueyin MeiKey Laboratory of DGHD, MOE, School of Life Science and Technology, Southeast University, Nanjing, China.
Pin ChenKey Laboratory of DGHD, MOE, School of Life Science and Technology, Southeast University, Nanjing, China.
Anna LiuKey Laboratory of DGHD, MOE, School of Life Science and Technology, Southeast University, Nanjing, China.
Weicheng LiangKey Laboratory of DGHD, MOE, School of Life Science and Technology, Southeast University, Nanjing, China.ORCID 0000-0002-8708-1143
Shan ChangInstitute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China.ORCID 0000-0001-7169-9398
Jian LiKey Laboratory of DGHD, MOE, School of Life Science and Technology, Southeast University, Nanjing, China.ORCID 0000-0001-8706-6421
Southeast University · CNJiangsu University of Technology · CNNanjing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationThe human major histocompatibility complex (MHC), also known as human leukocyte antigen (HLA), plays an important role in the adaptive immune system by presenting non-self-peptides to T cell receptors. The MHC region has been shown to be associated with a variety of diseases, including autoimmune diseases, organ transplantation and tumours. However, structural analytic tools of HLA are still sparse compared to the number of identified HLA alleles, which hinders the disclosure of its pathogenic mechanism.

resultTo provide an integrative analysis of HLA, we first collected 1296 amino acid sequences, 256 protein data bank structures, 120 000 frequency data of HLA alleles in different populations, 73 000 publications and 39 000 disease-associated single nucleotide polymorphism sites, as well as 212 modelled HLA heterodimer structures. Then, we put forward two new strategies for building up a toolkit for transplantation and tumour immunotherapy, designing risk alignment pipeline and antigenic peptide prediction pipeline by integrating different resources and bioinformatic tools. By integrating 100 000 calculated HLA conformation difference and online tools, risk alignment pipeline provides users with the functions of structural alignment, sequence alignment, residue visualization and risk report generation of mismatched HLA molecules. For tumour antigen prediction, we first predicted 370 000 immunogenic peptides based on the affinity between peptides and MHC to generate the neoantigen catalogue for 11 common tumours. We then designed an antigenic peptide prediction pipeline to provide the functions of mutation prediction, peptide prediction, immunogenicity assessment and docking simulation. We also present a case study of hepatitis B virus mutations associated with liver cancer that demonstrates the high legitimacy of our antigenic peptide prediction process. HLA3D, including different HLA analytic tools and the prediction pipelines, is available at http://www.hla3d.cn/.

Indexed as

Histocompatibility Antigens Class INeoplasmsComputational BiologyHLA AntigensHumansImmunotherapyPeptidesProtein BindingHistocompatibility Antigens Class IHLA AntigensPeptidesHLA structuremutationneoantigen predictionorgan transplantation

Identifiers

PMID35289353
PMCPMC9116210
OpenAlexW4220782560

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.