Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.
Methods Mol Biol. 2024;2809:263-274. doi: 10.1007/978-1-0716-3874-3_17.
The availability of extensive MHC-peptide binding data has boosted machine learning-based approaches for predicting binding affinity and identifying binding motifs. These computational tools leverage the wealth of binding data to extract essential features and generate a multitude of potential peptides, thereby significantly reducing the cost and time required for experimental procedures. MAM is one such tool for predicting the MHC-I-peptide binding affinity, extracting binding motifs, and generating new peptides with high affinity. This manuscript provides step-by-step guidance on installing, configuring, and executing MAM while also discussing the best practices when using this tool.
大量 MHC-肽结合数据的可用性推动了基于机器学习的方法，用于预测结合亲和力和识别结合基序。这些计算工具利用丰富的结合数据来提取必要的特征，并生成大量潜在的肽，从而大大降低了实验程序所需的成本和时间。MAM 是一种用于预测 MHC-I-肽结合亲和力、提取结合基序和生成具有高亲和力的新肽的工具。本文档提供了安装、配置和执行 MAM 的逐步指南，同时还讨论了使用此工具的最佳实践。
