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相关概念视频

Protein Folding Quality Check in the RER01:29

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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Using Caenorhabditis elegans as a Model System to Study Protein Homeostasis in a Multicellular Organism
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使用RoseTTAFold对蛋白质稳定性和功能进行零射击突变效应预测.

Sanaa Mansoor1,2,3, Minkyung Baek1,2,4, David Juergens1,2,3

  • 1Department of Biochemistry, University of Washington, Seattle, Washington, WA, USA.

Protein science : a publication of the Protein Society
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概括
此摘要是机器生成的。

蛋白质语言模型RFjoint可以在没有特殊训练的情况下准确预测突变效应. 这种RoseTTAFold的变体展示了对蛋白质序列结构景观的广泛理解,与其他先进模型相提并论.

关键词:
深度学习是一种深度学习.语言模型语言模型突变效应的预测和预测蛋白质设计 蛋白质设计零射击预测的预测.

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科学领域:

  • 计算生物学 计算生物学
  • 蛋白质工程是指蛋白质的工程.
  • 生物信息学是一种生物信息学.

背景情况:

  • 预测突变如何影响蛋白质的功能和稳定性是分子生物学中的一个重大挑战.
  • 目前的方法通常需要对特定蛋白质家族进行广泛的培训.

研究的目的:

  • 为了评估RFjoint的性能,RoseTTAFold变体训练了序列和结构恢复,预测突变效应.
  • 为了比较RFjoint的零射击预测准确度与其他已建立的模型.

主要方法:

  • 评估了RFjoint在预测不同蛋白质家族突变效应方面的准确性.
  • 将RFjoint的性能与MSA变压器 (零射击) 和DeepSequence (家庭特定培训) 进行了比较.

主要成果:

  • 在没有特定任务的微调的情况下,RFjoint在预测突变效应方面实现了与MSA变压器和DeepSequence相似的准确性.
  • 该模型展示了对蛋白质突变景观的强有力的理解,相当于大型蛋白质语言模型.

结论:

  • RFjoint对蛋白质序列结构关系有着广泛的了解,使其成为蛋白质建模的多功能工具.
  • RFjoint的联合序列结构推理能力有望通过进一步的监督训练来提高突变效应预测.