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用人工智能蛋白质分析平台照亮可使用药物的人类蛋白质组
Guy W Dayhoff1, Daniel Kortzak1, Ruibin Liu1
1Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD 21201, U.S.A.
bioRxiv : the preprint server for biology
|September 18, 2025
概括
一个新的AI平台,AiPP,从序列中预测蛋白质连接体相互作用位点,创建一个全蛋白质组的地图,以加快对不可抗药性标的药物发现.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 蛋白质组方法在覆盖范围和数据异质性方面存在局限性.
- 现有的机器学习 (ML) 模型在结构依赖性和各种实验标签方面扎.
研究的目的:
- 开发一个多式人工智能平台 (AiPP) 来直接从氨基酸序列预测和表征蛋白质配体相互作用位点.
- 创建一个全面的,全蛋白质组的可结合部位图谱,以帮助治疗发现.
主要方法:
- 利用进化规模的蛋白质大语言模型 (LLM) 来为AiPP平台提供动力.
- 从基于活动的蛋白质分析 (ABPP) 和共晶结构数据库开发了协调的ML培训套件.
- 实施了基于LLM表示的集群框架,以协调和增强实验数据.
主要成果:
- AiPP准确地预测了氨酸连接事件,仅在ABPP数据上训练时,可以从共晶体结构中恢复80%.
- 在"不可抗药"的转录因子中发现了可结合的口袋,以及在蛋白质氨酸酸酶中以前未被检测到的部位.
- 创建了一个全蛋白质体共价结合能力图谱,识别治疗点中的新位,如MC3R.
结论:
- AiPP推进了可链接蛋白质组图谱的创建,这对于了解蛋白质功能和加速药物开发至关重要.
- 该平台有效地识别了以前通过实验方法错过的蛋白质中的可结合位点,包括全位点.
- 基于LLM的方法提供了一个广泛适用的方法,用于在蛋白质组学和ML模型开发中查询大规模的异构数据.
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