Curtin Medical School, Curtin Health Innovation Research Institute, GPO Box U1987, Perth, WA 6845, Australia.
Curtin Institute for Data Science, Curtin University, GPO Box U1987, Perth, WA 6845, Australia.
Int J Mol Sci. 2023 Oct 16;24(20):15239. doi: 10.3390/ijms242015239.
Fungal effector proteins are important in mediating disease infections in agriculturally important crops. These secreted small proteins are known to interact with their respective host receptor binding partners in the host, either inside the cells or in the apoplastic space, depending on the localisation of the effector proteins. Consequently, it is important to understand the interactions between fungal effector proteins and their target host receptor binding partners, particularly since this can be used for the selection of potential plant resistance or susceptibility-related proteins that can be applied to the breeding of new cultivars with disease resistance. In this study, molecular docking simulations were used to characterise protein-protein interactions between effector and plant receptors. Benchmarking was undertaken using available experimental structures of effector-host receptor complexes to optimise simulation parameters, which were then used to predict the structures and mediating interactions of effector proteins with host receptor binding partners that have not yet been characterised experimentally. Rigid docking was applied for both the so-called bound and unbound docking of MAX effectors with plant HMA domain protein partners. All bound complexes used for benchmarking were correctly predicted, with 84% being ranked as the top docking pose using the ZDOCK scoring function. In the case of unbound complexes, a minimum of 95% of known residues were predicted to be part of the interacting interface on the host receptor binding partner, and at least 87% of known residues were predicted to be part of the interacting interface on the effector protein. Hydrophobic interactions were found to dominate the formation of effector-plant protein complexes. An optimised set of docking parameters based on the use of ZDOCK and ZRANK scoring functions were established to enable the prediction of near-native docking poses involving different binding interfaces on plant HMA domain proteins. Whilst this study was limited by the availability of the experimentally determined complexed structures of effectors and host receptor binding partners, we demonstrated the potential of molecular docking simulations to predict the likely interactions between effectors and their respective host receptor binding partners. This computational approach may accelerate the process of the discovery of putative interacting plant partners of effector proteins and contribute to effector-assisted marker discovery, thereby supporting the breeding of disease-resistant crops.
真菌效应蛋白在介导农业重要作物的疾病感染中起着重要作用。这些分泌的小蛋白已知与它们各自的宿主受体结合伙伴在宿主中相互作用，无论是在细胞内还是在质外体空间，这取决于效应蛋白的定位。因此，了解真菌效应蛋白与其靶宿主受体结合伙伴之间的相互作用非常重要，特别是因为这可以用于选择潜在的与植物抗性或易感性相关的蛋白质，这些蛋白质可以应用于新的抗病品种的选育。在这项研究中，使用分子对接模拟来描述效应蛋白和植物受体之间的蛋白质-蛋白质相互作用。使用现有的效应子-宿主受体复合物的实验结构进行基准测试，以优化模拟参数，然后使用这些参数来预测尚未通过实验表征的效应蛋白与宿主受体结合伙伴的结构和介导相互作用。刚性对接用于 MAX 效应蛋白与植物 HMA 结构域蛋白伙伴的所谓绑定和非绑定对接。所有用于基准测试的绑定复合物都被正确预测，使用 ZDOCK 评分函数，84%的复合物被评为最佳对接构象。在非绑定复合物的情况下，预测至少 95%的已知残基是宿主受体结合伙伴相互作用界面的一部分，并且预测至少 87%的已知残基是效应蛋白相互作用界面的一部分。发现疏水相互作用主导效应蛋白-植物蛋白复合物的形成。基于 ZDOCK 和 ZRANK 评分函数的优化对接参数集被建立，以实现涉及植物 HMA 结构域蛋白不同结合界面的近天然对接构象的预测。虽然这项研究受到效应蛋白和宿主受体结合伙伴的实验确定的复合物结构的可用性的限制，但我们证明了分子对接模拟预测效应蛋白与其各自的宿主受体结合伙伴之间可能相互作用的潜力。这种计算方法可以加速发现效应蛋白的假定相互作用的植物伙伴的过程，并有助于效应蛋白辅助的标记发现，从而支持抗病作物的选育。
