Prediction of 3D Structure of Drug Target GPCR Using Evolutionary Computation

G蛋白偶联受体（GPCR）是当今最重要的药物靶蛋白之一。然而，除了少数蛋白质外，GPCR的3D结构尚未通过X射线晶体学确定，因为GPCR难以结晶。此外，由于GPCR的结构起伏很大，计算预测其构象存在一定的局限性，本研究提出了一种新的GPCR热起伏模型，将多个时间尺度上的热起伏结合起来模拟蛋白质的构象变化。本研究提出了一种新的方法，预测一个最好的3D结构的GPCR从各种波动的候选结构，通过使用进化计算，包括受体-配体对接模拟。使用人白三烯B4受体BLT 1作为样品GPCR对该方法进行了验证。

G-Protein coupled receptors (GPCRs) are one of the most important drug target proteins today. However, the 3D structures of GPCRs have not yet been determined by X-ray crystallography, except a few proteins, because GPCRs are difficult to crystallize. In addition, due to the huge structural fluctuation of GPCRs, there is a limitation to computationally predict GPCR's accurate conformation.This study developed a new method for modeling GPCR thermal fluctuations, where conformation changes of the proteins are modeled by combining fluctuations on multiple time scales. This study proposed a novel method which predicts a best 3D structure of a GPCR from a variety of fluctuating candidate structures, by using the evolutionary computation including receptor-ligand docking simulations. The method was validated using human leukotriene B4 receptor BLT1 as a sample GPCR.