使用蛋白质语言模型和图形神经网络预测裂部位
Paula Cifuentes1,2, Ramon Adàlia3,4, Ismael Zamora3
1Universitat Pompeu Fabra, 08003, Barcelona, Spain. paula.cifuentes01@estudiant.upf.edu.
Scientific reports
|October 31, 2025
概括
预测裂部位对于药物开发至关重要. 使用蛋白质语言模型和图形神经网络的新in silico方法提高了准确性,特别是在含有非天然氨基酸的复杂循环上.
科学领域:
- 制药科学 制药科学
- 计算生物学 计算生物学
- 生物技术是生物技术.
背景情况:
- 类治疗药物具有很高的选择性和有效性,但由于其低生物可用性和蛋白酶敏感性,在口服时面临挑战.
- 目前用于预测裂部位的in silico方法依赖于手动特征工程,限制了它们分析复杂结构的能力,例如循环或非天然氨基酸含.
研究的目的:
- 开发新的in silico方法来准确预测裂部位,克服现有方法的局限性.
- 通过改进in silico分析工具来加强基于的治疗药物的开发.
主要方法:
- 微调蛋白质语言模型 (ESM-2) 以利用已学习的结构嵌入来预测裂变部位,而无需手动特征工程.
- 使用图形神经网络 (GNN) 与等级原子和氨基酸水平图表来表示和分析复杂的结构,包括含非天然氨基酸的循环.
主要成果:
- 蛋白质语言模型方法通过利用预训练的结构嵌入来有效地预测裂变部位.
- 该GNN方法证明了处理复杂的循环结构的能力,通过一项案例研究验证,将in silico预测与非天然氨基酸含有循环的实验数据进行比较.
结论:
- 新的in silico方法,包括蛋白质语言模型和图形神经网络,在裂部位预测方面取得了重大进展.
- 这些改进的计算工具对于优化类药物开发至关重要,特别是对于复杂的类模式.
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