通过蛋白质语言模型发现用于癌症免疫治疗的高活性kynureninases
Hyunuk Eom1, Sukhwan Park2, Kye Soo Cho3
1Department of Chemistry, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.
Nucleic acids research
|January 8, 2025
概括
这项研究引入了一种使用蛋白质语言模型来发现和优先考虑治疗应用酶的新方法. 开发的方法成功地识别出一种强大的 kynureninase,在临床前模型中显著降低瘤重量.
科学领域:
- 生物技术是生物技术.
- 酶工程是什么? 酶工程是什么?
- 计算生物学 计算生物学
背景情况:
- 针对特定应用的酶发现具有挑战性,因为需要高通量选.
- kynureninases 水解L-kynurenine,这是一个涉及瘤免疫抑制的代谢物.
- 向免疫抑制瘤微环境是抗癌疗法的关键策略.
研究的目的:
- 开发和验证一种基于动力参数的计算方法来发现和优先考虑酶.
- 为了确定在克服瘤免疫抑制方面潜在的治疗用途的新型kynureninases.
- 在体外和体内实验评估计算选择的kynureninases的疗效.
主要方法:
- 采用同类学搜索和蛋白质语言模型来发现和优先考虑酶.
- 专注于鉴定kynureninases,因为它们具有解L-kynurenine的潜力.
- 实验验证了排名第一的 kynureninase 候选物的催化活性和体内疗效.
主要成果:
- 与现有的酶相比,确定了一种kinureninase,与现有的酶相比,其营业额几乎增加了两倍.
- 在接受选择酶治疗的小鼠模型中,瘤重量显著减少了3.4倍.
- 验证了计算工作流程在预测和选择有效酶方面的准确性.
结论:
- 开发的计算方法使得有针对性的,定量化的酶发现和选择具有高精度.
- 这种方法显著提高了酶利用范围,用于包括癌症治疗在内的各种应用.
- 这些发现为克服免疫抑制瘤微环境提供了一个有希望的策略.
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