Agwora Derrick, Gufu Bonaya, Marik Tamás, Papp Tamás, Vágvölgyi Csaba, Kredics László, Tyagi Chetna
Department of Biotechnology and Microbiology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary.
HUN-REN-SZTE Fungal Pathomechanisms Research Group, University of Szeged, Szeged, Hungary.
Comput Struct Biotechnol J. 2025 Mar 12;27:1067-1080. doi: 10.1016/j.csbj.2025.03.015. eCollection 2025.
Hydrophobins (HFB) find application in various industries including biotechnology and medical devices; therefore, it is imperative to elucidate and learn more about their folded structures. Few fungal HFB protein structures are available in the Protein Data Bank (PDB), and fewer have been elucidated using homology modeling or short molecular dynamics (MD) simulations in the literature. Moreover, many homology modeling algorithms will only model the region with sequence identity. Therefore, we turned towards the state-of-the-art, artificial intelligence powered AlphaFold. It performed well in predicting the core β-barrel, a characteristic of HFBs, except for HFB9A which was unfolded with low confidence scores. These initial structures were then prepared for accelerated MD simulation in the hope of observing higher protein folding. With 500 ns long aMD simulations, we were able to obtain folded and energetically stable conformations for all the proteins except HFB9A, which exhibited much higher disorder, connected with higher atomic fluctuation, lowest hydrophobicity and overall compactness, and lesser secondary structure formation as visualized during the aMD simulation. The underlying intrinsic disorder in the HFBs was found to be the basis of harder-to-reach folding by AF2 which can be compensated by enhanced sampling MD simulations like the aMD technique. The characteristic two disordered loops of class I HFBs were obtained for SC3, while HFB9A showed that not all class I HFBs contain them. Class II HFBs were more stable, folded and compact with secondary structure motifs conserved throughout the trajectory which can be correlated with their comparatively much lower intrinsic disorder.
疏水蛋白(HFB)在包括生物技术和医疗设备在内的各种行业中都有应用;因此,阐明并更多地了解它们的折叠结构势在必行。蛋白质数据库(PDB)中可用的真菌HFB蛋白质结构很少,并且在文献中使用同源建模或短分子动力学(MD)模拟阐明的更少。此外,许多同源建模算法仅对具有序列同一性的区域进行建模。因此,我们转向了最先进的、由人工智能驱动的AlphaFold。它在预测核心β桶(HFB的一个特征)方面表现良好,除了HFB9A以低置信度得分展开。然后准备这些初始结构以进行加速MD模拟,希望观察到更高的蛋白质折叠。通过500纳秒长的aMD模拟,我们能够获得除HFB9A之外所有蛋白质的折叠且能量稳定的构象,HFB9A表现出更高的无序性,与更高的原子波动、最低的疏水性和整体紧凑性相关,并且在aMD模拟过程中观察到二级结构形成较少。发现HFB中潜在的内在无序是AF2难以实现折叠的基础,这可以通过增强采样MD模拟(如aMD技术)来补偿。对于SC3获得了I类HFB的特征性两个无序环,而HFB9A表明并非所有I类HFB都包含它们。II类HFB更稳定、折叠且紧凑,在整个轨迹中二级结构基序保守,这可以与其相对低得多的内在无序性相关联。