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相关概念视频

Multi-pass Transmembrane Proteins and β-barrels01:09

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In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
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Integral membrane proteins are tightly associated with the cell membrane and play a crucial role in cell communication, signaling, adhesion, and transport of the molecules. Some integral membrane proteins are present only in the membrane monolayer. For example, the enzyme fatty acid amide hydrolase is present in the cytoplasmic side of the membrane monolayer. In contrast, another type of integral membrane protein, also known as a transmembrane protein, spans across the membrane. Transmembrane...
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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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The rough ER membrane synthesizes, assembles, and embeds transmembrane proteins in diverse topologies. These proteins function as transporters or channels and can remain in the ER membrane or are sent to the Golgi complex, lysosome, and cell membrane.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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MEMO-Stab2:基于多视图序列的深度学习框架,用于预测突变诱导的跨膜蛋白的稳定性变化.

Yihang Bao1,2, Zhe Liu3, Hui Jin1,2

  • 1Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.

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概括
此摘要是机器生成的。

预测突变如何影响跨膜蛋白的稳定性至关重要. 新的深度学习工具MEMO-Stab2仅使用氨基酸序列准确预测这些变化,其性能优于蛋白质工程的现有方法.

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科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 结构生物学 结构生物学

背景情况:

  • 准确预测因点突变而导致的蛋白质热力学稳定性变化对于理解蛋白质功能和工程蛋白质至关重要.
  • 跨膜蛋白 (TMP) 对于细胞过程和药物开发至关重要,但由于结构数据有限,研究它们具有挑战性.
  • 当前的预测工具通常需要3D结构或多个序列对齐,这对于TMP来说通常是不可用的或质量差的.

研究的目的:

  • 开发一个快速,结构独立的深度学习框架,用于预测TMP中突变诱导的稳定性变化.
  • 通过不需要实验性的3D结构或明确的多个序列对齐来克服现有的预测器的局限性.

主要方法:

  • 介绍了MEMO-Stab2,一个深度学习框架,将突变稳定性预测重新定义为二进制分类任务.
  • 集成的多视图功能使用变压器架构,结合了来自多个预训练的蛋白质语言模型 (PLM) 和基于PLM的结构预测的嵌入.
  • 利用PLM隐式捕获直接从氨基酸序列中的进化和结构信息.

主要成果:

  • MEMO-Stab2实现了高性能,在内部基准上F1得分为0.92,超过了现有的专业和一般预测工具.
  • 在多样化的TMP家族中表现出强大的泛化,具有低序列特征和在超膜核心等具有挑战性的区域中表现出卓越的性能.
  • 验证了计算效率,在几分钟内实现了大规模的突变选.

结论:

  • MEMO-Stab2为预测突变对TMP稳定性的影响提供了一种实用,强大和高效的解决方案.
  • 结构独立的方法显著扩大了稳定性预测对TMP的适用性.
  • 该工具有助于加强跨膜蛋白变体评估和工程工作.