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Intracellular Signaling Cascades01:24

Intracellular Signaling Cascades

53.6K
Once a ligand binds to a receptor, the signal is transmitted through the membrane and into the cytoplasm. The continuation of a signal in this manner is called signal transduction. Signal transduction only occurs with cell-surface receptors, which cannot interact with most components of the cell, such as DNA. Only internal receptors can interact directly with DNA in the nucleus to initiate protein synthesis. When a ligand binds to its receptor, conformational changes occur that affect the...
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Rab Cascades01:25

Rab Cascades

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Rab GTPases act in a regulated cascade during membrane fusion, helping the lipid bilayers mix. The Rab family of proteins are active when bound to GTP, and inactive when bound to GDP. Hence, they act as guanine nucleotide-dependent molecular switches. Rab-GTP recognizes and binds to long or short-range tethering proteins to capture the target vesicle. These tethers coordinate with SNAREs on the vesicle and the target membrane to assemble the trans SNARE complex that locks the mixing bilayers.
3.6K
Amplifying Signals via Enzymatic Cascade01:22

Amplifying Signals via Enzymatic Cascade

18.6K
When a ligand binds to a cell-surface receptor, the receptor's intracellular domain changes shape, which may either activate its enzyme function or allow its binding to other molecules. The initial signal is amplified by most signal transduction pathways. This means that a single ligand molecule can activate multiple molecules of a downstream target. Proteins that relay a signal are most commonly phosphorylated at one or more sites, activating or inactivating the protein. Kinases catalyze...
18.6K
MAPK Signaling Cascades01:07

MAPK Signaling Cascades

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Mitogen-activated protein kinase, or MAPK pathway, activates three sequential kinases to regulate cellular responses such as proliferation, differentiation, survival, and apoptosis. The canonical MAPK pathway starts with a mitogen or growth factor binding to an RTK. The activated RTKs stimulate Ras, which recruits Raf or MAP3 Kinase (MAPKKK), the first kinase of the MAPK signaling cascade. Raf further phosphorylates and activates MEK or MAP2 Kinases (MAPKK), which in turn phosphorylates MAP...
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Cascaded Op Amps01:16

Cascaded Op Amps

1.1K
Operational amplifiers (op-amps) are versatile electronic components that can be interconnected in a cascade - one after another in a linear sequence. This cascading is possible due to their infinite input resistance and zero output resistance, allowing them to maintain their input-output relationships even when connected in series.
In a cascaded system, each op-amp is referred to as a stage. The output of one stage drives the input of the subsequent stage. As the input signal passes through...
1.1K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.0K
VSEPR Theory for Determination of Electron Pair Geometries
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相关实验视频

Updated: Feb 7, 2026

Green Fluorescent Protein-based Expression Screening of Membrane Proteins in Escherichia coli
08:46

Green Fluorescent Protein-based Expression Screening of Membrane Proteins in Escherichia coli

Published on: January 6, 2015

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从H&E图像中预测蛋白级联表达.

Alejandro Leyva, Abdul Rehman Akbar, M Khalid Khan Niazi

    medRxiv : the preprint server for health sciences
    |February 6, 2026
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    概括
    此摘要是机器生成的。

    预测癌症下游蛋白质表达是至关重要的. 一个新的细胞级人工智能模型CellViT成功地从病理图像中预测了亡级联蛋白质,优于传统的补丁级方法.

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    A Protocol for Computer-Based Protein Structure and Function Prediction
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    Green Fluorescent Protein-based Expression Screening of Membrane Proteins in Escherichia coli
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    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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    A Protocol for Computer-Based Protein Structure and Function Prediction
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    A Protocol for Computer-Based Protein Structure and Function Prediction

    Published on: November 3, 2011

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    科学领域:

    • 计算病理学计算病理学
    • 人工智能在瘤学中的应用
    • 生物医学数据分析

    背景情况:

    • 在瘤性途径中蛋白质表达是癌症发展的关键.
    • 预测下游蛋白质信号对于了解癌症进展至关重要.
    • 目前的AI模型通常预测单个蛋白质,缺乏对信号传播的洞察力.

    研究的目的:

    • 开发和评估一种人工智能模型,用于预测乳腺癌下游蛋白质表达.
    • 为了比较细胞级视觉变压器 (ViT) 与补丁级视觉变压器 (ViT) 在此任务中的性能.
    • 评估细胞亡和DNA损伤/修复 (DDR) 级联对预测蛋白质表达的有用性.

    主要方法:

    • 利用了TCGA-BRCA数据集中的反相蛋白阵列 (RPPA) 和整片图像 (WSI).
    • 开发了一个细胞级ViT模型 (CellViT),并将其与补丁级ViT模型进行比较.
    • 专注于预测亡级联中的五个关键蛋白质,使用DDR级联作为控制.

    主要成果:

    • 补丁级ViT模型未能获得统计学上显著的预测结果 (R平方值<0.1).
    • 细胞ViT显示了预测能力,在五个测试折叠中实现R平方值>0.1.
    • 由于亡级联在形态上具有指示性,其预测性能明显高于DDR级联.

    结论:

    • 像CellViT这样的细胞级人工智能模型在预测WSI下游蛋白质表达方面比补丁级模型更有效.
    • 形态相关的生物通路,如亡,是人工智能驱动的蛋白质表达预测的更好的目标.
    • 这种方法提供了一种新的方法来推断癌症中的功能性蛋白质信号传递.