PDF(Pubmed)
Abstract：
Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of \"wet-lab\" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.
摘要：
癌症仍然是世界范围内死亡的主要原因，需要新的治疗靶点。膜蛋白是各种癌症类型的关键参与者，但与可溶性蛋白相比，它们面临着独特的挑战。计算药物发现工具的出现为解决这些挑战提供了一种有前途的方法，允许优先考虑“湿实验室”实验。在这次审查中,我们探索计算方法在膜蛋白肿瘤表征中的应用，特别关注三个突出的膜蛋白家族：受体酪氨酸激酶（RTK），G蛋白偶联受体（GPCRs），和溶质载体蛋白（SLC）。我们选择这些家庭是因为他们的理解水平和研究数据可用性不同，这给计算分析带来了不同的挑战和机遇。我们讨论了多组数据的利用，机器学习,和基于结构的方法来研究与每个家族中癌症进展相关的异常蛋白质功能。此外，我们强调了考虑更广泛的细胞环境的重要性，特别是,蛋白质之间的交叉对话。尽管存在挑战,计算工具有望在癌症中解剖膜蛋白失调。随着计算能力和数据资源的不断发展，这些工具有望在识别和优先考虑膜蛋白作为个性化抗癌靶标方面发挥关键作用。
