Turkish-German University, Molecular Biotechnology, Sahinkaya Caddesi, No. 106, Beykoz, Istanbul 34820, Turkey.
Int J Biol Macromol. 2024 Sep;276(Pt 1):133813. doi: 10.1016/j.ijbiomac.2024.133813. Epub 2024 Jul 11.
In recent years, a variety of three-dimensional structure prediction tools, including AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold, have been employed in the investigation of intrinsically disordered proteins. However, a comprehensive validation of these tools specifically for intrinsically disordered proteins has yet to be conducted. In this study, we utilize AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold to predict the structure of a model intrinsically disordered α-synuclein protein. Additionally, extensive replica exchange molecular dynamics simulations of the intrinsically disordered protein are conducted. The resulting structures from both structure prediction tools and replica exchange molecular dynamics simulations are analyzed for radius of gyration, secondary and tertiary structure properties, as well as Cα and Hα chemical shift values. A comparison of the obtained results with experimental data reveals that replica exchange molecular dynamics simulations provide results in excellent agreement with experimental observations. However, none of the structure prediction tools utilized in this study can fully capture the structural characteristics of the model intrinsically disordered protein. This study shows that a cluster of ensembles are required for intrinsically disordered proteins. Artificial-intelligence based structure prediction tools such as AlphaFold3 and C-I-TASSER could benefit from stochastic sampling or Monte Carlo simulations for generating an ensemble of structures for intrinsically disordered proteins.
近年来,多种三维结构预测工具,包括 AlphaFold2、AlphaFold3、I-TASSER、C-I-TASSER、Phyre2、ESMFold 和 RoseTTAFold,已被用于研究无规卷曲蛋白质。然而,这些工具专门用于无规卷曲蛋白质的全面验证尚未进行。在这项研究中,我们使用 AlphaFold2、AlphaFold3、I-TASSER、C-I-TASSER、Phyre2、ESMFold 和 RoseTTAFold 来预测模型无规卷曲 α-突触核蛋白的结构。此外,还对无规卷曲蛋白质进行了广泛的 replica 交换分子动力学模拟。从结构预测工具和 replica 交换分子动力学模拟得到的结构,分析其旋转半径、二级和三级结构特性以及 Cα 和 Hα 化学位移值。将获得的结果与实验数据进行比较表明, replica 交换分子动力学模拟提供的结果与实验观察非常吻合。然而,本研究中使用的任何结构预测工具都无法完全捕捉到模型无规卷曲蛋白质的结构特征。这项研究表明,无规卷曲蛋白质需要一组构象。基于人工智能的结构预测工具,如 AlphaFold3 和 C-I-TASSER,可以从随机采样或蒙特卡罗模拟中受益,以生成无规卷曲蛋白质的结构集合。