PIPLOM: prediction of exogenous peptide loading on major histocompatibility complex class I molecules.

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Bioinformatics advances Pub Date : 2025-03-03 eCollection Date: 2025-01-01 DOI:10.1093/bioadv/vbaf037

Florian Schmidt, Kanxing Wu, Lorenz Gerber, Florence Chioh Wen Jing, Daniel Carbajo, Glenn Wong Choon Lim, Melissa Wirawan, Christine Eng, Katja Fink, Daniel T MacLeod, Michael Fehlings, Andreas Wilm

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Abstract

Summary: The exogenous, i.e. in vitro, loading of peptides onto major histocompatibility complex (MHC) class I molecules is a key step in many immunology-related experimental workflows. Here, we provide a machine learning solution, PIPLOM, which is specifically tailored to predict whether peptides can be loaded exogenously onto an MHC class I molecule. Benchmarking on 38 unseen epitopes with in-house ELISA (enzyme-linked immunosorbent assay) experiments showed that PIPLOM is outperforming well-established methods such as NETMHCpan-4.0 or MHCflurry, which are commonly used for the related task of predicting epitope HLA (human leukocyte antigen) haplotype specificity.

Availability and implementation: Source code and data are available as Zenodo package 10.5281/zenodo.13771214. PIPLOM is available as a web service at https://piplom.immunoscape.com/.

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PIPLOM：预测外源肽在主要组织相容性复合体I类分子上的负载。

摘要：外源性，即在体外，将肽加载到主要组织相容性复合体（MHC） I类分子上是许多免疫学相关实验流程中的关键步骤。在这里，我们提供了一个机器学习解决方案，PIPLOM，专门用于预测肽是否可以外源性加载到MHC I类分子上。用ELISA（酶联免疫吸附试验）对38个未见的表位进行基准测试表明，PIPLOM优于NETMHCpan-4.0或MHCflurry等成熟的方法，这些方法通常用于预测表位HLA（人类白细胞抗原）单倍型特异性的相关任务。可用性和实现：源代码和数据可以在Zenodo软件包10.5281/ Zenodo .13771214中获得。PIPLOM作为web服务可在https://piplom.immunoscape.com/上获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics advances

CiteScore

1.60

自引率

0.00%

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