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FusOn-pLM:一种通过调整速率掩盖的融合对蛋白特异性语言模型

Sophia Vincoff¹, Shrey Goel², Kseniia Kholina¹

¹Department of Biomedical Engineering, Duke University, Durham, NC, USA.

Nature communications

|February 7, 2025

概括

我们开发了FusOn-pLM,这是一种新型的蛋白质语言模型,其训练基于推动儿科癌症的融合基蛋白. 该工具增强了对这些具有挑战性的目标的理解,并有助于设计未来的癌症疗法.

科学领域:

计算生物学是一种计算生物学.
癌症基因组学癌症基因组学
蛋白质信息学是指蛋白质信息学.

背景情况:

由染色体转位产生的融合基蛋白是儿科癌症的关键驱动因素.
这些蛋白质本质上是无序的,缺乏可用药物的口袋,这给治疗带来了重大挑战.
现有的蛋白质语言模型 (pLMs) 尚未专门训练融合基蛋白序列.

研究的目的:

开发一个专门的pLM,FusOn-pLM,用于分析融合基蛋白.
改进对蛋白质融合特征和功能的预测.
为促进融合驱动癌症的治疗发现提供工具.

主要方法:

策划了融合coprotein序列 (FusOn-DB) 的一个全面的数据集.
微调了一个pLM,使用一个带坐标排序的掩盖语言建模策略,具有动态掩盖率 (15% - 40%).
评估了FusOn-pLM的融合特定任务,包括局部化,点形成,疾病预测和药物耐药性突变预测.

主要成果:

与基线嵌入相比,FusOn-pLM在聚变特定预测任务中表现优越.
该模型成功预测了耐药性突变,为治疗耐药性机制提供了洞察力.
FusOn-pLM生成了与生物相关的蛋白质表示.

结论:

FusOn-pLM代表了在应用pLMs来对coproteins进行融合的重大进步.
该模型为设计针对融合驱动的儿科癌症的向治疗提供了宝贵的见解.
这种方法有助于制定克服治疗耐药性的策略.

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