EvoRator2:使用深度学习基于蛋白质结构信息预测特定站点的氨基酸替代.
Natan Nagar1, Jérôme Tubiana2, Gil Loewenthal1
1The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
Journal of molecular biology
|June 25, 2023
概括
EvoRator2使用蛋白质结构预测耐受性氨基酸,有助于对蛋白质的研究,其序列数据有限. 这种深度学习工具可以改进突变效应的预测,特别是对于孤儿和de novo蛋白质.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物学是结构生物学.
- 分子进化是分子进化的过程.
背景情况:
- 多个序列对齐 (MSAs) 推断耐受性氨基酸,但对于少数同类蛋白质而言是有限的.
- 孤儿和de novo设计的蛋白质缺乏足够的同源序列用于传统的基于MSA的分析.
研究的目的:
- 开发一种深度学习算法 (EvoRator2),只使用蛋白质结构信息来预测耐受性氨基酸.
- 为了应对具有有限或没有同源序列的蛋白质分析的挑战.
主要方法:
- 埃沃拉托2在超过15000个蛋白质结构上受过训练.
- 该算法根据来自原子坐标文件的结构信息预测耐受性氨基酸.
- 使用位置加权评分矩阵 (PSSM) 和深度突变扫描 (DMS) 实验来评估性能.
主要成果:
- 在预测PSSM方面,EvoRator2显示了令人满意的结果.
- 它在DMS实验中预测突变效应方面取得了近乎最先进的性能,在某些目标上表现优于现有的方法.
- 将EvoRator2与基于MSA的方法结合起来,提高了DMS实验的预测准确性和稳定性.
结论:
- 通过单独使用蛋白质结构,EvoRator2有效地预测耐受性氨基酸替代.
- 该工具对于研究孤儿蛋白和新设计的蛋白质非常有价值.
- 埃沃拉托网络服务器可用于更广泛的应用.
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