气味受体功能表达和配体识别的调控机制及机器学习模型预测的研究

气味受体功能表达是鉴定其特异性配体（气味分子）的前提，受体结合气味分子是嗅觉发生的首要步骤。气味信号的识别是通过不同气味受体的组合进行编码，但是这种编码机制依然不十分清楚。课题组前期研究工作表明受体对气味分子的反应及受体的功能表达受特定的氨基酸残基调控。基于以上研究结果，本项目拟用突变受体构建、荧光素酶分析和分子动力学模拟揭示调控气味受体调谐宽度的所有氨基酸残基并构建结合腔模型。在气味受体上明确氨基酸残基的协同作用对受体功能表达的调控。利用机器学习模型预测受体与气味分子结合和受体功能表达并通过荧光素酶分析验证模型预测结果。因此，我们将从氨基酸和三维结构层面揭示气味受体功能表达和配体结合的调控机制，为加快气味受体去孤儿化进程提供大量实验数据，从而为理解嗅觉编码机制提供崭新的理论基础。

Functional expression of odorant receptor is the prerequisite to identity its specific ligand (odorant molecule), while the binding between odor and receptor is the primary step in odor perception. Recognition of odor signals is via the combinatorial coding by various odorant receptors. However, the mechanism underlying the olfactory coding is still elusive. Our previous work showed specific amino acid residues in odorant receptors regulated the odor responsiveness and functional expression. Based on these data, this proposal will identify the residues responsible for the tuning breadth of odorant receptor and establish the three-dimensional binding cavity model through mutated receptor construction, luciferase assay and molecular dynamic simulation. Meanwhile, we will elucidate the synergic role of various amino acid residues in functional expression of odorant receptors. Through establishing machine learning model, we will predict the ligand binding and functional expression of odorant receptors. All the predictive results will be verified by cell-based functional assay in vitro. Thus, we will disclose the mechanism underlying the ligand binding and functional expression of odorant receptors at amino acid residue and three-dimensional structural levels. This study will provide numerous data for the deorphanization of odorant receptors and accordingly put forward new theoretical basis for understanding the olfactory coding.