基于HP模型的大麻素受体CB2折叠研究

Cannabinoid receptor Type 2(简称CB2)是大麻素受体的一种亚型,因为其无中枢神经副作用,不会产生成瘾性及耐受性,显示出了非常好的开发前景和潜在的应用价值。其作为免疫调节剂、神经保护剂和抗癌药等将具有巨大市场价值。目前,CB2蛋白的空间结构还未被测定出来,对于CB2的折叠问题研究也开展的较少,为了研究大麻素受体亚型蛋白CB2的折叠问题以及方便更多的研究人员对CB2空间结构和相关药理特性的研究,本文提出了一种基于HP模型的折叠求解方法。通过使用回溯机制和蒙特卡罗方法对此优化问题进行求解,算法可有效的在全局范围内进行寻找最优解,避免了掉入局部最优问题。实验结果表明,本文方法获取的CB2蛋白空间构象具有较低的能量值,折叠情况较好。

Research on the folding of cannabinoid receptor type 2 based on HP model

Cannabinoid receptor Type 2(CB2) is a subtype of cannabinoid receptor. It shows a very good development prospect and potential application value because it has no central nervous side effect and there is no addiction or tolerance on it. As an immunomodulator, a neuroprotective agent, an anticancer drug and so on, it will be of great market value. At present, the space structure of CB2 protein has not been determined and there is less research on the problem of CB2 folding. In order to investigate the folding problem of cannabinoid receptor subtype protein CB2 and make it convenient for more researchers to study the space structure and related pharmacological properties of CB2, this paper proposes an HP based solution. By using backtracking mechanism and Monte Carlo method to solve the optimizing problem, our algorithm can effectively find the optimal solution in the global scope and avoid falling into the local optimum trap. The experimental results show that the spatial conformation of CB2 protein obtained by this method has a low energy value, and the folding situation is good.