由AlphaFold生成一个高可信度的域-域接口类型集,以指导蛋白质复杂结构预测
Johanna Lena Geist1, Chop Yan Lee1, Joelle Morgan Strom1
1Institute of Molecular Biology (IMB) gGmbH, Mainz 55128, Germany.
Bioinformatics (Oxford, England)
|August 22, 2024
概括
改善蛋白质复合体建模需要准确的域-域接口 (DDI) 类型. 这项研究生成了一个DDI参考集,确定了高可信度类型,并使用它们来指导AlphaFold (AF) 预测,提高结构建模成功率.
科学领域:
- 结构生物学是结构生物学.
- 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.
背景情况:
- AlphaFold (AF) 具有先进的蛋白质复杂结构预测,但其对全长序列的准确性是有限的.
- 精确预测蛋白质-蛋白质接口,特别是域-域接口 (DDI),对于改进复杂建模至关重要.
- 像3did这样的现有计算资源推断了DDI类型,但缺乏用于验证的精心策划的参考集.
研究的目的:
- 开发一个可靠的域-域接口 (DDI) 类型的参考集合.
- 使用AlphaFold (AF) 提高蛋白质复杂结构预测的准确性.
- 确定高可信度DDI用于指导蛋白相互作用的AF建模.
主要方法:
- 从3did数据库中手动整理了80个DDI类型,以创建一个参考集合.
- 利用后勤回归来从3did资源中预测高可信度DDI.
- 应用预测的高保证度DDI来指导AF建模,用于53,000个人类蛋白相互作用.
主要成果:
- 确定了40%的最初评估的DDI类型由于不准确的域映射或晶体接触等问题而未获得批准.
- 在考虑的5724个中,成功预测了2411个高置信度DDI类型.
- 在1129个预测的DDI中,为604个生成了高度自信的AF模型,其中47%仍然缺乏使用全长序列的自信模型.
结论:
- 手动策划的DDI类型参考集对于改进计算预测至关重要.
- 识别和利用高可信度DDI显著提高了AF引导蛋白质复合体建模的成功率.
- 需要进一步精细化,以改善AF建模,以便在全长序列仍然具有挑战的情况下进行AF建模.
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