融合蛋白三维结构预测中的人工智能:回顾和视角
1Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Clinical and translational medicine
|August 1, 2024
概括
使用人工智能 (AI) 预测融合蛋白质的三维 (3D) 结构具有挑战性. 本综述探讨了像AlphaFold2这样的深度学习 (DL) 模型用于融合蛋白结构预测,突出了进步和障碍.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物学是结构生物学.
- 生物信息学是一种生物信息学.
背景情况:
- 人工智能 (AI) 已经推进了蛋白质结构预测,但融合蛋白质带来了独特的挑战.
- 融合蛋白是由癌症中的基因融合产生的,它们的3D结构对于理解功能至关重要.
- 基于模板的建模是困难的,因为缺乏新型融合蛋白的已知结构.
研究的目的:
- 通过使用深度学习 (DL) 模型,审查融合蛋白的3D结构预测的最新进展.
- 探索用于融合蛋白模型的特定DL工具 (AlphaFold2,RoseTTAFold,tr-Rosetta,D-I-TASSER) 的优点和挑战.
- 概述人工智能驱动的融合蛋白结构预测的管道和因素.
主要方法:
- 关于AI和DL应用在蛋白质结构预测中的当前文献的综述.
- 分析了包括AlphaFold2,RoseTTAFold,tr-Rosetta和D-I-TASSER在内的深度学习模型.
- 在DL模型中讨论多层次蛋白质信息的利用.
主要成果:
- 深度学习模型显示了对预测融合蛋白3D结构的重大前景.
- 特定的DL工具提供不同程度的成功,并面临着不同的挑战.
- 确定了影响不同人工智能方法预测准确性的关键因素.
结论:
- 使用DL精确预测融合蛋白的3D结构是一个不断发展的领域,面临着持续的挑战.
- 了解这些挑战对于推进癌症和其他领域中嵌合蛋白的功能分析至关重要.
- 需要进一步开发针对融合蛋白调整的AI模型.
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