T Prates Erica, Demerdash Omar, Shah Manesh, Rush Tomás A, Kalluri Udaya C, Jacobson Daniel A
Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
Genome Science and Technology, The University of Tennessee-Knoxville, Knoxville, TN 37996, USA.
Comput Struct Biotechnol J. 2025 Jun 18;27:2782-2795. doi: 10.1016/j.csbj.2025.06.029. eCollection 2025.
Microbiome assembly, structure, and dynamics significantly influence plant health. Secreted microbial signaling molecules initiate and mediate symbiosis by binding to structurally compatible plant receptors. For example, lipo-chitooligosaccharides (LCOs), produced by nitrogen-fixing rhizobial bacteria and various fungi, are recognized by plant lysin motif receptor-like kinases (LysM-RLKs), which activate the common symbiotic pathway. Accurately predicting these molecular interactions could reveal complementary signatures underlying the initial stages of endosymbiosis. Despite the breakthrough in protein-ligand structure prediction with deep learning-based tools, such as AlphaFold3, the large size and highly flexible nature of signaling compounds like LCOs present major challenges for detailed structural characterization and binding-affinity prediction. Typical structure-/physics-based methods of ligand virtual screening are designed for small, drug-like molecules, often rely on high-resolution, experimentally determined structures of the protein receptors, and rarely achieve sufficient sampling to obtain converged thermodynamic quantities with large ligands. In this study, we developed a hybrid molecular dynamics/machine learning (MD/ML) approach capable of predicting binding affinity rankings with high accuracy in systems involving large, flexible ligands, despite limited experimental structural information. Using coarse initial structural models, the predictions using the MD/ML workflow achieved strong alignment with experimental trends, particularly in the top-affinity tier for four legume LysM-RLKs (LYR3) binding to LCOs and a chitooligosaccharide. Furthermore, the MD-based conformation selection protocol provided critical structural insights into substrate specificity and binding mechanisms. This study demonstrates a powerful method to screen for challenging cognate ligand-receptors and advance our understanding of the molecular basis of microbial colonization in plants.
微生物群落的组装、结构和动态对植物健康有显著影响。分泌的微生物信号分子通过与结构兼容的植物受体结合来启动和介导共生关系。例如，固氮根瘤菌和各种真菌产生的脂壳寡糖（LCOs）可被植物溶素基序受体样激酶（LysM-RLKs）识别，从而激活共同的共生途径。准确预测这些分子相互作用可以揭示内共生初始阶段潜在的互补特征。尽管基于深度学习的工具（如AlphaFold3）在蛋白质-配体结构预测方面取得了突破，但像LCOs这样的信号化合物体积大且具有高度灵活性，这给详细的结构表征和结合亲和力预测带来了重大挑战。典型的基于结构/物理的配体虚拟筛选方法是为小分子、类药物分子设计的，通常依赖于蛋白质受体的高分辨率实验测定结构，并且很少能实现足够的采样以获得与大配体收敛的热力学量。在本研究中，我们开发了一种混合分子动力学/机器学习（MD/ML）方法，尽管实验结构信息有限，但该方法能够在涉及大的、灵活配体的系统中高精度地预测结合亲和力排名。使用粗略的初始结构模型，MD/ML工作流程的预测与实验趋势高度一致，特别是在四种豆科植物LysM-RLKs（LYR3）与LCOs和一种壳寡糖结合的高亲和力层级中。此外，基于MD的构象选择协议为底物特异性和结合机制提供了关键的结构见解。这项研究展示了一种强大的方法，用于筛选具有挑战性的同源配体-受体，并加深我们对植物中微生物定殖分子基础的理解。
