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A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
2.1和1.8 一个平均C(alpha) RMSD结构预测在两个小蛋白,HP-36和s15的预测
M R Lee1, D Baker, P A Kollman
1Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California 94143-0446, USA.
Journal of the American Chemical Society
|July 18, 2001
概括
分子动力学模拟改进了最初的蛋白质结构预测. 这种方法准确地排列模型,并提高了结构准确性,从序列推进了蛋白质结构预测.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物学是结构生物学.
- 生物物理学的生物物理.
背景情况:
- 准确的蛋白质结构预测对于理解生物功能至关重要.
- 像Rosetta这样的Ab initio方法可以生成初始的结构模型.
- 为了高分辨率的准确性,这些模型的精细化是必要的.
研究的目的:
- 评估分子动力学 (MD) 模拟用于改进ab initio蛋白质结构预测的有效性.
- 评估MM/PBSA免费能源计算对这些精细模型进行排名的能力.
- 探索从序列中预测蛋白质结构的进步.
主要方法:
- 应用MD模拟来完善Rosetta预测的小头部 (HP-36) 和核糖体蛋白S15.5的结构.
- 将集群模拟轨迹分为构型家族.
- 每个家族的MM/PBSA自由能量和α碳RMSD的计算平均值.
主要成果:
- 具有较低MM/PBSA自由能量的合规家族产生了更好的C(alpha) RMSD结构.
- 实现了较低的平均C(alpha) RMSDs (1.8A对于S15,2.1A对于HP-36核心).
- 观察到MM/PBSA自由能量等级和C ((alpha) RMSDs (r ((s) = 0.77-0.83) 之间存在强烈的相关性.
结论:
- 与MM/PBSA自由能量计算相结合的MD模拟对于提炼和排名蛋白质结构预测非常强大.
- 这种综合方法促进了高分辨率的结构改进.
- 该方法在从序列数据中推进蛋白质结构预测领域方面表现有前途.
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