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Regulated mRNA Transport02:22

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In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
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Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
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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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序列到位置:由深度预训练的语言模型驱动的蛋白质亚细胞定位.

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    这项研究介绍了SubLoc,这是一种用于预测蛋白质细胞下定位的深度学习算法. SubLoc准确地识别了蛋白质的位置,改进了疾病研究的传统方法.

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    科学领域:

    • 计算生物学是一种计算生物学.
    • 生物信息学是一种生物信息学.
    • 分子生物学分子生物学

    背景情况:

    • 蛋白质亚细胞局部化对细胞功能至关重要,错位化与各种疾病有关.
    • 确定蛋白质定位的传统方法是艰苦,耗时和复杂的.
    • 需要有效和准确的计算方法来预测蛋白质亚细胞局部化.

    研究的目的:

    • 开发一种基于深度学习的新算法SubLoc,用于预测蛋白质细胞下定位.
    • 整合蛋白质序列和结构信息,以提高预测准确度.
    • 为传统实验方法提供更有效,更准确的替代方案.

    主要方法:

    • 利用ProtT5蛋白语言模型生成蛋白质嵌入矢量.
    • 使用氨基酸联系信息构建了一个3D蛋白质结构图,并用图形卷积网络进行处理.
    • 采用双向封闭的循环单元与多头注意力机制来分析序列特征和整合数据.

    主要成果:

    • SubLoc在预测10个亚细胞区的蛋白质定位方面表现出色.
    • 该算法在精度,回忆和MCC值方面表现优于现有的比较方法.
    • 在识别细胞质和线粒体位置方面,SubLoc显示出特别高的准确性.

    结论:

    • SubLoc提供了一种强大而准确的深度学习方法,用于蛋白质细胞下定位预测.
    • 序列和结构数据的整合显著提高了预测能力.
    • SubLoc有可能加速细胞生物学和疾病机制的研究.