Chakraborty Chiranjib, Hsu Minna J, Agoramoorthy Govindasamy
School of Computer and Information Sciences, Galgotias University, Greater Noida, India.
Cell Biochem Biophys. 2014 Nov;70(2):907-22. doi: 10.1007/s12013-014-9998-0.
The occurrence of type 2 diabetes (T2D) accounts for 90-95 % of all diabetes. Intestine hormone glucagon-like peptide-1 (GLP-1) has an antidiabetic role that enhances insulin secretion and pancreatic β-cell proliferation. GLP-1 is degraded by the enzyme dipeptidyl peptidase-4 (DPP-4) rapidly. Hence, the DPP-4 inhibition has been preferred not only for the treatment but also as a major drug target. Sitagliptin and Diprotin-A are antihyperglycemic agents for the treatment of T2D. However, little is known on the molecular dynamics of DPP-4 and the interaction properties with its ligands, namely Sitagliptin and Diprotin-A. This study has used the latest bioinformatic tools to understand the molecular dynamics and its interaction properties of DPP-4. This study has explored the number of α helices, β strands, β hairpins, Ψ loop, β bulges, β turns, and ϒ turns and they were 19, 46, 25, 1, 14, 70, and 4, respectively. The highest number of H-bonds was recorded in α helix of domain-1, and the lowest number H-bonds were noted in α helix of domain-2. During interaction between residues, in A- and B-chain, 47 and 48 residues are involved for interaction, and interaction interface area was more in A-Chain (2176 Å(2)). From DPP-4 and Sitagliptin interaction, three residues in active sites such as Try226, Glu205, and Glu206 were involved in three H-bond formation, while 10 other amino acids (Try547, Try667, Asn710, Val711, His740, Ser630, Ser209, Arg358, Phe357, and Val207) were involved in hydrophobic interactions. In this review, we have shown the importance of bioinformatics as an excellent tool for a rapid method to assess the molecular dynamics and its interaction properties of DPP-4. Our predictions highlighted in this review will help researchers to understand the interaction properties and recognition of interactive sites to design more DPP-4 inhibitors for the treatment of T2D and drug discovery.
2型糖尿病（T2D）的发病率占所有糖尿病的90 - 95%。肠道激素胰高血糖素样肽-1（GLP-1）具有抗糖尿病作用，可增强胰岛素分泌和胰腺β细胞增殖。GLP-1会迅速被二肽基肽酶-4（DPP-4）酶降解。因此，抑制DPP-4不仅是治疗的首选方法，也是一个主要的药物靶点。西他列汀和二肽基肽酶抑制剂A（Diprotin-A）是用于治疗T2D的降血糖药物。然而，关于DPP-4的分子动力学及其与配体（即西他列汀和二肽基肽酶抑制剂A）的相互作用特性知之甚少。本研究使用了最新的生物信息学工具来了解DPP-4的分子动力学及其相互作用特性。本研究探究了α螺旋、β链、β发夹、Ψ环、β凸起（β bulges）、β转角和ϒ转角的数量，它们分别为19、46、25、1、14、70和4。在结构域1的α螺旋中记录到的氢键数量最多，而在结构域2的α螺旋中氢键数量最少。在A链和B链的残基相互作用过程中，有47和48个残基参与相互作用，且A链中的相互作用界面面积更大（2176 Ų）。从DPP-4与西他列汀的相互作用来看，活性位点的三个残基，如色氨酸226（Try226）、谷氨酸205（Glu205）和谷氨酸206（Glu206）参与形成了三个氢键，而其他10个氨基酸（色氨酸547、色氨酸667、天冬酰胺710、缬氨酸711、组氨酸740、丝氨酸630、丝氨酸209、精氨酸358、苯丙氨酸357和缬氨酸207）参与了疏水相互作用。在这篇综述中，我们展示了生物信息学作为一种快速评估DPP-4分子动力学及其相互作用特性的优秀工具的重要性。我们在本综述中强调的预测将有助于研究人员了解相互作用特性和识别相互作用位点，从而设计出更多用于治疗T2D的DPP-4抑制剂并进行药物研发。
