G-protein-coupled receptors (GPCRs) are a large and functionally diverse protein superfamily, which form a seven transmembrane (TM) helices bundle with alternating extra-cellular and intracellular loops. GPCRs are considered to be one of the most important groups of drug targets because they are involved in a broad range of body functions and processes and are related to major diseases. In this paper we present a new technology, named PREDICT, for modeling the 3D structure of any GPCR from its amino acid sequence. This approach takes into account both internal protein properties (i.e., the amino acid sequence) and the properties of the membrane environment. Unlike competing approaches, the new technology does not rely on the single known structure of rhodopsin, and is thus capable of predicting novel GPCR conformations. We demonstrate the capabilities of PREDICT in reproducing the known experimental structure of rhodopsin. In principle, PREDICT-generated models offer new opportunities for structure-based drug discovery towards GPCR targets.

中文翻译：

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从序列建模GPCR的3D结构。

G蛋白偶联受体（GPCR）是一个功能强大的大型蛋白超家族，它形成七个跨膜（TM）螺旋束，并带有交替的细胞外和细胞内环。GPCR被认为是最重要的药物靶标组之一，因为它们涉及广泛的身体功能和过程，并且与主要疾病有关。在本文中，我们提出了一种名为PREDICT的新技术，用于根据其氨基酸序列对任何GPCR的3D结构进行建模。该方法考虑了内部蛋白质特性（即氨基酸序列）和膜环境的特性。与竞争方法不同，新技术不依赖视紫红质的单一已知结构，因此能够预测新的GPCR构象。我们展示了PREDICT在复制视紫红质的已知实验结构中的功能。原则上，PREDICT生成的模型为针对GPCR目标的基于结构的药物发现提供了新的机会。