解开共同进化的约束,以建模蛋白质构成的异质性
Shimian Li1,2, Chengwei Zhang3,4, Lupeng Kong2
1New Cornerstone Science Laboratory, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.
Communications chemistry
|February 26, 2026
概括
EvoSplit解开蛋白质共同进化的信号,以预测不同的蛋白质构造,改善折叠切换蛋白质的结构预测准确度,并识别与癌症相关的新型蛋白质结构.
科学领域:
- 结构生物学 结构生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 描述多态蛋白质构造对于理解蛋白质功能和开发向疗法至关重要.
- 来自同源序列的共同进化约束为蛋白质结构和功能提供了洞察力.
- 通过注意力机制,MSA变压器有效地捕获共同进化的信号.
研究的目的:
- 开发一种方法,EvoSplit,用于解开与不同蛋白质构造相关的共同进化信号.
- 为了利用这些信号来改善蛋白质结构预测.
- 识别具有潜在构造多样性的蛋白质,特别是那些与癌症相关的蛋白质.
主要方法:
- 使用MSA变压器捕获的多形态共进化的信号.
- 应用EvoSplit来解开特定于形状的共同进化信息.
- 评估EvoSplit的性能与现有方法 (如AF-Cluster) 相比.
- 超出结构预测工具训练集的蛋白质构造的预测.
- 分析蛋白质-蛋白质相互作用并执行分子动力学模拟.
主要成果:
- 在85种折叠切换蛋白质上,EvoSplit的性能优于AF-Cluster.
- 该方法成功地模拟了AlphaFold2的训练数据中不包括的蛋白质的结构.
- 确定了54种候选人类蛋白质,具有与癌症相关的潜在构造多样性.
- 埃沃斯普利特始终预测了五个GTPases的两个构造,其中一个是新的发现.
- 通过蛋白质-蛋白质相互作用分析揭示了与HRAS功能相关的新型形状.
结论:
- EvoSplit有效地解开共同进化的信号,以预测不同的蛋白质构造.
- 这种方法提高了蛋白质结构预测的准确性,特别是对于动态蛋白质.
- 在与癌症相关的蛋白质中识别新型结构为新的治疗策略开辟了道路.
- 通过进化分析和分子动力学模拟的进一步验证支持了这些发现.
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