ProCyon:一种用于蛋白质表型的多式基础模型
Owen Queen1,2, Yepeng Huang1, Robert Calef1,3
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
一个新的多式模式模型ProCyon,使用序列,结构和文本来预测人类蛋白质的功能. 它为人们不太了解的蛋白质产生了洞察力,并有助于疾病研究,比如在多发性硬化症中识别新型基因.
科学领域:
- 生物化学和分子生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 很大一部分人体蛋白质缺乏经过实验验证的功能或特定背景的表型见解.
- 准确的蛋白质表征对于理解生物过程和疾病机制至关重要.
研究的目的:
- 开发一种通用方法,在各种知识领域中建模蛋白质表型.
- 介绍ProCyon,一种用于生成和预测蛋白质表型的多式基础模型.
主要方法:
- ProCyon集成了蛋白质序列,结构和自然语言处理.
- 该模型在一个新的数据集ProCyon-Instruct上进行训练,该数据集包含3300万个蛋白质表型指令.
- 基准测试涉及对现有的单模式和多模式模型进行数十项任务的ProCyon评估.
主要成果:
- 在各种基准测试任务中,ProCyon展示了竞争性表现.
- 该模型成功地根据药物机制和疾病背景检索出蛋白质.
- ProCyon生成未表征的蛋白质的表型描述,包括与帕金森病相关的蛋白质.
- 多发性硬化症的实验验证确定了新的基因和阐明了途径机制.
结论:
- ProCyon提供了一种强大的,可通用的方法,用于产生对人类蛋白质组的功能性见解.
- 该模型的多式联络方法增强了对蛋白质表型的预测和理解.
- "ProCyon"有可能加速生物发现和疾病研究.
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