人工智能 (AI) 的最新进展加速了对未知蛋白质结构的预测。然而，准确预测融合蛋白的三维（3D）结构仍然是一项艰巨的任务，因为当前基于人工智能的蛋白质结构预测主要集中在WT蛋白上，而不是自然界中新融合的蛋白质。遵循生物学的中心法则，融合蛋白是从融合转录本翻译而来的，融合转录本是通过癌症中染色体重排在两个不同基因座之间转录融合基因而产生的。准确预测融合蛋白的 3D 结构对于理解新型嵌合蛋白的功能作用和作用机制非常重要。然而，使用基于模板的模型预测其 3D 结构具有挑战性，因为已知的模板结构通常在数据库中不可用。利用多级蛋白质信息的深度学习 (DL) 模型彻底改变了蛋白质 3D 结构的预测。在这篇综述论文中，我们重点介绍了使用深度学习模型预测融合蛋白 3D 结构的最新进展和持续挑战。我们的目标是探索使用 AlphaFold2、RoseTTAFold、tr-Rosetta 和 D-I-TASSER 进行 3D 结构建模的优点和挑战。亮点：这篇综述提供了融合蛋白 3D 结构预测的总体流程和前景。本综述提供了在每个步骤中使用 AI 方法预测融合蛋白 3D 结构时应考虑的因素。本综述重点介绍了使用深度学习模型预测融合蛋白 3D 结构的最新进展和持续挑战。本综述探讨了使用 AlphaFold2、RoseTTAFold、tr-Rosetta 和 D-I-TASSER 进行 3D 结构建模的优点和挑战。© 2024 作者。约翰·威利出版的《临床与转化医学》

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action of new chimeric proteins. However, predicting their 3D structure using a template-based model is challenging because known template structures are often unavailable in databases. Deep learning (DL) models that utilize multi-level protein information have revolutionized the prediction of protein 3D structures. In this review paper, we highlighted the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using DL models. We aim to explore both the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta and D-I-TASSER for modelling the 3D structures. HIGHLIGHTS: This review provides the overall pipeline and landscape of the prediction of the 3D structure of fusion protein. This review provides the factors that should be considered in predicting the 3D structures of fusion proteins using AI approaches in each step. This review highlights the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using deep learning models. This review explores the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta, and D-I-TASSER to model 3D structures.© 2024 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.