小基规模的同质性搜索结构预测
Sewon Lee1, Gyuri Kim1, Eli Levy Karin2
1School of Biological Sciences, Seoul National University, Gwanak-gu, Seoul 08826, South Korea.
Cold Spring Harbor perspectives in biology
|February 5, 2024
概括
利用序列读取档案 (SRA) 来预测蛋白质结构显著提高了准确性. 纳入SRA数据改善了66%的目标的预测,超过了标准方法.
科学领域:
- 计算生物学 计算生物学
- 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:
- 多个序列对齐 (MSA) 对于蛋白质结构预测至关重要,正如AlphaFold2在CASP15竞赛中的成功所证明的那样.
- 有效利用MSA仍然是提高预测准确性的关键领域.
研究的目的:
- 调查大规模序列读取档案 (SRA) 数据对蛋白质结构预测准确性的影响.
- 评估SRA衍生的同类和先进的ColabFold特征对预测性能的贡献.
主要方法:
- 对SRA进行了petabase规模的搜索,以生成CASP15目标的广泛对齐的同类.
- 来自SRA的MSA与默认的ColabFold搜索MSA合并,并作为ColabFold预测的输入.
- 系统测试了深度同质性搜索和 ColabFold 的高级功能 (例如,增加回收) 的效果.
主要成果:
- 使用SRA数据提高了高精度预测 (GDT_TS>70) 不易目标的66%,而默认的ColabFold搜索MSAs是52%.
- 包含SRA同类物是改善ColabFold的CASP15排名从第11位到第3位的主要因素.
- 先进的 ColabFold 功能也为预测准确性的整体改进做出了贡献.
结论:
- 通过像SRA这样的大规模数据源扩大MSA的范围是增强蛋白质结构预测的强大策略.
- 将SRA数据与可乐Fold等优化计算方法相结合,可以在预测蛋白质结构方面显著改进.
- 进一步分析深层同质性和先进预测特征是有必要的,以充分利用MSA数据.
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