Computational design of recognition peptide based plasmonic biosensor for small molecules detection

从气味结合蛋白（OBPs）衍生的一系列肽被战略性地设计用于识别与SARS-CoV-2相关的挥发性有机化合物。选择乙醇、壬醛、苯甲醛、乙酸和丙酮作为COVID-19感染期间呼出的代表性挥发性有机化合物。利用计算对接和预测工具对OBP肽进行表征和分析。考察了对接模型、结合亲和力、序列特异性和结构折叠等因素。此外，DNA适体等生物受体和APTES等有机受体已被证实并用于高灵敏度的小分子检测，如皮质醇和芳香族化合物。使用计算对接方法演示了相互作用机制。

A series of peptides, derived from odorant-binding proteins (OBPs), were strategically designed to recognize VOCs associated with SARS-CoV-2. Ethanol, nonanal, benzaldehyde, acetic acid, and acetone were chosen as representative VOCs found in exhaled breath during COVID-19 infection. Computational docking and prediction tools were employed to characterize and analyze the OBP peptides. Various factors, such as docking models, binding affinity, sequence specificity, and structural folding, were examined.Additionally, bioreceptors such as DNA aptamers and organic receptors like APTES have been confirmed and utilized for highly sensitive detection of small molecules, such as cortisol and aromatic compounds. The interaction mechanisms were demonstrated using computational docking approaches.