增强基于质谱的瘤免疫的识别:机器学习波器利用HLA结合亲和力,异形指数和保留时间偏差
Feifei Wei1,2, Taku Kouro1,2, Yuko Nakamura3
1Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan.
Computational and structural biotechnology journal
|February 15, 2024
概括
识别癌症新抗原是有效疫苗的关键. 这项研究使用机器学习来改进基于质谱的免疫学,减少新抗原发现的错误阳性,以改善癌症免疫疗法.
科学领域:
- 免疫学 免疫学 免疫学
- 蛋白质组学是指蛋白质组学.
- 生物信息学是一种生物信息学.
背景情况:
- 准确的新抗原鉴定对于癌症疫苗开发和免疫治疗至关重要.
- 基于质谱的免疫皮学识别了癌细胞上的人类白细胞抗原 (HLA) ,但面临着错误阳性的挑战.
研究的目的:
- 为了提高新抗原识别的准确性,使用基于质谱的免疫组学.
- 开发和验证机器学习过器,以减少新抗原发现中的假阳性识别.
主要方法:
- 基于液体染色学-并联质谱的蛋白质组学和下一代测序被用来识别来自瘤组织的HLA呈现的新抗原.
- 机器学习模型经过训练,通过预测MS/MS光谱一致性来评估识别.
- 四个关键特征 (max Mascot 离子得分,预测的 HLA 结合亲和力,异形指数,保留时间偏差) 被选择用于过候选序列.
主要成果:
- 与传统的吉祥物方法相比,基于机器学习的rescoring显著改善了新抗原识别的过.
- 选择的物理化学特征有效地过了具有不充分碎片化一致性的识别.
- 这种方法显示了提高基于MS的免疫类药物鉴定率的潜力.
结论:
- 结合基于体物理化学特征的复合过器,可以提高基于MS的免疫类药物识别率.
- 这种优化的方法有望改善新抗原识别管道和癌症免疫治疗中的临床应用.
- 机器学习集成提供了一种强大的策略,以克服新抗原发现中的错误阳性挑战.
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