使用ReFOLD精炼方法和AlphaFold2循环过程,改善蛋白质三级和四级结构预测
Recep Adiyaman1, Nicholas S Edmunds1, Ahmet G Genc1
1School of Biological Sciences, University of Reading, Reading RG6 6EX, UK.
Bioinformatics advances
|June 26, 2023
概括
该 ReFOLD 管道改进了 AlphaFold2 (AF2) 预测,大大提高了 3D 模型的准确性,以适度的计算成本. AF2回收进一步增强单体和多体模型的三级和四级结构预测.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物信息学 结构生物信息学
- 蛋白质结构预测 蛋白质结构预测
背景情况:
- AlphaFold2 (AF2) 大大减少了蛋白质结构预测的准确性差距.
- 然而,AF2模型仍然需要改进以获得最佳准确性.
- 像分子动力学 (MD) 模拟这样的传统方法是计算密集的.
研究的目的:
- 开发一个计算效率高的管道 (ReFOLD) 来改进AF2预测的蛋白质结构.
- 利用AF2回收来提高三级和四级结构的预测.
- 为了提高单质和多质蛋白模型的准确性.
主要方法:
- 调整 ReFOLD 管道以改进 AF2 预测.
- 利用AF2回收利用自定义模板输入来进行结构预测.
- 使用Molprobity分数和lDDT进行准确性评估.
主要成果:
- 根据Molprobity得分,ReFOLD改善了94%的基于Molprobity得分的生成3D模型.
- 对于单体和多体模型,AF2回收显示了高的改善率,高达100% (MSA) 和97.8% (单序) 的非AF2单体模型.
- 对非AF2M模型而言,回收的多重模型显示了高达94%的改善.
结论:
- ReFOLD管道提供了一种具有成本效益的方法,用于增强AF2蛋白质结构预测.
- AF2回收是一种强大的策略,可以进一步提高三级和四级结构的精度.
- 开发的工具可以通过MultiFOLD docker包和ReFOLD web服务器访问.
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