具有反向折叠和蛋白质结构预测的结体设计
Patrick Bryant1,2, Arne Elofsson3,4
1Science for Life Laboratory, 172 21, Solna, Sweden.
Communications chemistry
|October 25, 2023
概括
我们开发了一种结合Foldseek,ESM-IF1和AlphaFold2 (AF) 的计算方法来设计结合剂. 这个框架准确地预测了结合亲和力,使得新型诊断和治疗药物的发现成为可能.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质工程是一种蛋白质工程.
- 结构生物信息学 结构生物信息学
背景情况:
- 结体的计算设计对于开发新的诊断和治疗方法至关重要.
- 现有的方法需要优化,以针对特定的蛋白质接口.
研究的目的:
- 开发和验证用于设计针对蛋白质接口的酸结合剂的计算框架.
- 评估AlphaFold2 (AF) 在评估设计的结剂时的准确性.
- 确定成功结剂设计的关键特征.
主要方法:
- 一个共同的框架,整合了Foldseek用于骨干播种,修改了ESM-IF1用于序列生成,以及AlphaFold2 (AF)用于绑定器评估.
- AF使用MSA表示用于受体结构和单一序列用于绑定器.
- 使用接收器操作特征 (ROC) 曲线下的面积 (AUC) 评估的绑定得分性能.
主要成果:
- AF准确地评估结体,在异构体病例中达到0.96的ROC AUC.
- 具有较高残留接触量的种子的设计更成功,并且通常更短.
- 在接口位置的序列恢复和预测局部距离差异 (pLDDT) 分数之间存在强烈的相关性.
- 设计向预期受体的pLDDT中位数比非预期受体高了60%.
- ESM-IF1成功设计了185个异构体和42个同构体接口的结合剂,其性能优于ProteinMPNN.
结论:
- 集成的计算框架有效地为特定的蛋白质接口设计结体.
- 开发的结合得分在选择成功的结合剂方面表现出很高的准确性.
- 设计特征,如接触密度和序列恢复,可以预测粘合剂的成功.
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