预测:一个由分类器策划的AlphaFold建模的蛋白质-蛋白质相互作用数据库
Ernst W Schmid1, Johannes C Walter1,2
1Department of Biological Chemistry & Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA.
bioRxiv : the preprint server for biology
|April 22, 2024
概括
一个新的机器学习分类器,SPOC,准确地区分了由AlphaFold-Multimer预测的真实蛋白质-蛋白质相互作用 (PPI) 和假阳性. 这一进步使得可靠的全蛋白质组结构互动组映射成为可能.
科学领域:
- 分子生物学分子生物学
- 结构生物学 结构生物学
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对生物过程至关重要,但全面的结构数据有限.
- 像AlphaFold-Multimer (AF-M) 这样的现有工具可以预测PPI,但他们的信心指标往往会产生许多错误的阳性.
- 需要一种可靠的方法来从生物发现的大规模预测中过准确的PPI.
研究的目的:
- 开发一种计算工具,以准确地分类由AlphaFold-Multimer预测的真假蛋白质相互作用.
- 改进从AF-M获得的大规模结构互动组数据的解释.
- 促进新型PPI的发现,特别是在人类基因组维护的背景下.
主要方法:
- 训练了一个机器学习模型,结构预测和Omics告知分类器 (SPOC),使用精选的数据集.
- 评估了SPOC在区分真实PPI与虚假阳性的表现,在各种生物环境中.
- 将SPOC应用于大约300个人类基因组维护蛋白的全方位相互作用矩阵.
主要成果:
- SPOC在分离真假PPI预测方面表现出很高的准确性,表现优于标准AF-M信心指标.
- 为人类基因组维护蛋白生成了大约4万个PPI预测,可在 predictomes.org.org 获取.
- 确定了高可信度的PPI,在基因组维护途径中提出了新的假设.
结论:
- SPOC提供了一个强大的框架来解释大规模的AF-M预测和过可靠的PPI.
- 确定了高可信度的PPI,为基因组维护研究提供了新的假设.
- 这项工作为构建一个全面的蛋白质组范围的结构互动组奠定了基础.
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