Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, 64023 Teramo, Italy.
Biosens Bioelectron. 2014 Feb 15;52:247-54. doi: 10.1016/j.bios.2013.08.044. Epub 2013 Sep 4.
Virtual and experimental affinity binding properties of 5 different peptides (cysteinylglycine, glutathione, Cys-Ile-His-Asn-Pro, Cys-Ile-Gln-Pro-Val, Cys-Arg-Gln-Val-Phe) vs. 14 volatile compounds belonging to relevant chemical classes were evaluated. The peptides were selected in order to have a large variability in physicochemical characteristics (including length). In virtual screening a rapid and cost-effective computational methodology for predicting binding scores of small peptide receptors vs. volatile compounds is proposed. Flexibility was considered for both ligands and peptides and each peptide conformer was treated as a possible receptor, generating a dedicated box and then running a docking process vs. all possible conformers of the 14 volatile compounds. The 5 peptides were covalently bound to gold nanoparticles and deposited onto 20 MHz quartz crystal microbalances to realize gas sensors. Gas sensing confirmed that each of the peptide conferred to the gold nanoparticles a particular selectivity pattern able to discriminate the 14 volatile compounds. The largest response was obtained for the pentapeptides Cys-Ile-His-Asn-Pro and Cys-Ile-Gln-Pro-Val while low response was achieved for the dipeptide. The comparative study, carried using a two-tailed T test, demonstrated that virtual screening was able to predict reliably the sensing ability of the pentapeptides. The dipeptide receptor exhibited 29% of virtual-experimental matching vs. 71% of glutathione and up to 93% for the pentapeptides. This virtual screening approach was proved to be a promising tool in predicting the behaviour of sensors array for gas detection.
评估了 5 种不同肽（半胱氨酸甘氨酸、谷胱甘肽、Cys-Ile-His-Asn-Pro、Cys-Ile-Gln-Pro-Val、Cys-Arg-Gln-Val-Phe）与 14 种挥发性化合物（属于相关化学类别的化合物）的虚拟和实验亲和结合特性。选择这些肽是为了使物理化学特性（包括长度）具有较大的可变性。在虚拟筛选中，提出了一种快速且经济有效的计算方法，用于预测小肽受体与挥发性化合物的结合分数。考虑到配体和肽的灵活性，并且将每个肽构象视为可能的受体，生成一个专用盒子，然后对 14 种挥发性化合物的所有可能构象运行对接过程。将 5 种肽共价结合到金纳米粒子上，并沉积在 20 MHz 石英晶体微天平上，以实现气体传感器。气体传感证实，每种肽都赋予金纳米粒子一种能够区分 14 种挥发性化合物的特定选择性模式。获得的最大响应来自五肽 Cys-Ile-His-Asn-Pro 和 Cys-Ile-Gln-Pro-Val，而二肽的响应较低。使用双尾 T 检验进行的比较研究表明，虚拟筛选能够可靠地预测五肽的传感能力。二肽受体的虚拟-实验匹配率为 29%，谷胱甘肽为 71%，五肽为 93%。该虚拟筛选方法已被证明是预测气体检测传感器阵列行为的有前途的工具。
