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通过AlphaFold预测折叠蛋白质中的侧链形状:前景和挑战

Gia G Maisuradze¹, Abhishek Thakur², Kisan Khatri³

¹Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania; Department of Chemistry, Temple University, Philadelphia, Pennsylvania; Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, New York.

Biophysical journal

|September 22, 2025

概括

这项研究评估了AlphaFold2和AlphaFold3预测蛋白质侧链形状的能力. 虽然通常是准确的,特别是对于常见的状态,但罕见的构造构成了一个挑战,AlphaFold3比ColabFold略有改善.

科学领域:

* 计算生物学 * 计算生物学
* 结构生物学 * 结构生物学
* 生物信息学是一门学科.

背景情况:

* AlphaFold从氨基酸序列中彻底改变了蛋白质结构的预测.
* 预测折叠蛋白质内单个氨基酸侧链结构仍然是一个关键的挑战.
*了解侧链形状对于分子建模和药物设计至关重要.

研究的目的:

* 评估ColabFold (AlphaFold2实现) 和AlphaFold3在预测蛋白质侧链形状方面的准确性.
* 调查影响预测准确性的因素,例如侧链类型和结构模板的使用.
* 探索AlphaFold与突变预测模型的集成,以分析合作效应.

主要方法:

*对基准蛋白质组 (A组和最近发布的结构) 的ColabFold和AlphaFold3的评估.
* 计算各种二面角 (χ1~χ3) 的预测误差.
* 应用波茨模型进行大规模的突变扫描和随后使用ColabFold进行结构分析.

主要成果:

* ColabFold在基准蛋白质上显示了 χ1 的预测误差为 ~14%, χ3 二面角的预测误差为 ~48%.
*对于非极性侧链来说,预测准确性更高,对于结构模板来说略有改善.
* ColabFold对常见的旋转器状态有偏见,这可能会限制对罕见形状的预测.
*AlphaFold3的侧链预测准确度略高于ColabFold.
* ColabFold对于最近释放的未在AlphaFold2训练中使用的蛋白质结构保持了类似的准确性.

结论:

* ColabFold和AlphaFold3可以预测蛋白质侧链形状,准确度显著,特别是对于常见的状态.
*由于训练数据中的偏差,模型可能会遇到罕见的侧链形状.
*将基于序列的模型与AlphaFold集成,为研究突变诱导的结构变化及其对健康的影响提供了一种新的方法.

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