FusOn-pLM:一种通过聚焦概率化掩盖的融合蛋白特异性语言模型
Sophia Vincoff1, Shrey Goel2, Kseniia Kholina1
1Department of Biomedical Engineering, Duke University.
bioRxiv : the preprint server for biology
|June 19, 2024
概括
我们开发了FusOn-pLM,一种新的蛋白质语言模型,以更好地代表癌症治疗开发的融合基蛋白. 这种模型改善了对这些具有挑战性的癌症驱动蛋白质的预测.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 蛋白质工程是一种蛋白质工程.
背景情况:
- 融合蛋白是各种癌症的关键驱动因素,特别是在儿童中.
- 它们的混乱性质和缺乏可用药物的口袋使它们成为难以治疗的目标.
- 蛋白质语言模型 (pLMs) 对蛋白质表示有希望,但尚未接受融合基蛋白的训练.
研究的目的:
- 开发一个针对融合蛋白序列的优化pLM.
- 为治疗设计而生成改进的蛋白质嵌入物,以对抗融合基蛋白.
- 为了提高蛋白质乱和结合部位的预测.
主要方法:
- 微调ESM-2模型在融合蛋白序列的数据集上.
- 引入了一种新的掩盖语言建模 (MLM) 策略,使用绑定站点概率预测器.
- 专注于对关键氨基酸残留物的掩盖,以创建对coprotein感知嵌入物的融合.
主要成果:
- 与基线ESM-2相比,FusOn-pLM嵌入在融合蛋白基准指标上表现优异,与基线ESM-2相比.
- 该模型在预测蛋白质障碍方面显示出更高的准确性.
- 性能超过了手工构建的生物物理嵌入.
结论:
- FusOn-pLM提供了更有效的蛋白质表示,用于融合基蛋白.
- 这些嵌入对于下游治疗设计有价值,以聚变驱动的癌症为目标.
- 该模型是公开可用的,以促进该领域的研究.
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