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相关概念视频

Introduction to Enzymes01:22

Introduction to Enzymes

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The use of enzymes by humans dates to 7000 BCE. Humans first used enzymes to ferment sugars and produce alcohol without knowing that this was an enzyme-catalyzed reaction. Wilhelm Kuhne coined the term 'enzyme' in 1877 from the Greek words ‘en’ meaning ‘in’ or ‘within’ and ‘zyme’ meaning ‘yeast.’
Most enzymes are proteins that speed up biochemical reactions without being consumed. Enzymes contain one or more active sites that...
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Catalytically Perfect Enzymes01:07

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The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
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Enzymes02:34

Enzymes

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Inside living organisms, enzymes act as catalysts for many biochemical reactions involved in cellular metabolism. The role of enzymes is to reduce the activation energies of biochemical reactions by forming complexes with its substrates. The lowering of activation energies favor an increase in the rates of biochemical reactions.
Enzyme deficiencies can often translate into life-threatening diseases. For example, a genetic abnormality resulting in the deficiency of the enzyme G6PD...
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Enzyme-Linked Immunosorbent Assay01:33

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In 1971, Peter Perlman and Eva Engvall developed an Enzyme-linked immunosorbent assay (ELISA or EIA). ELISA differs from western blot in that the assays are conducted in microtiter plates or in vivo rather than on an absorbent membrane.
There are many different types of ELISAs, but they all involve an antibody molecule whose constant region binds an enzyme, leaving the variable region free to bind its specific antigen.  Enzyme-substrate reaction allows the antigen to be visualized or...
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A Protocol for Computer-Based Protein Structure and Function Prediction
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平行卷积对比学习方法用于酶功能的预测.

Xindi Yu, Shusen Zhou, Mujun Zang

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    一种新的并行卷积对比学习 (PCCL) 方法提高了酶功能预测的准确性. 这种利用ESM-2和卷积神经网络 (CNN) 的方法改善了预测,特别是复杂的多功能酶.

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    科学领域:

    • 生物化学和生物信息学
    • 计算生物学 计算生物学
    • 酵素工程是什么意思 酵素工程

    背景情况:

    • 酶功能注释对于医学和工业生物学至关重要,它依赖于酶委员会 (EC) 的数字.
    • 目前的酶功能预测工具缺乏实际应用所需的精度.
    • 对酶功能的准确预测对于推进各种科学和工业领域至关重要.

    研究的目的:

    • 开发一种新的深度学习方法,以提高酶功能预测的精度.
    • 为了应对酶功能数据集中的类失衡的挑战.
    • 为了提高多功能酶的预测准确度.

    主要方法:

    • 利用ESM-2蛋白语言模型进行先进的蛋白序列预处理.
    • 开发了一个并行卷积对比学习 (PCCL) 框架,集成卷积神经网络 (CNN) 和对比学习.
    • 采用深度学习架构与三个并行CNN进行全面的特征提取.

    主要成果:

    • 通过PCCL方法,比先进的酶功能预测技术表现得更好.
    • 该模型在两个独立的测试集上显示了增强的预测准确性.
    • 在较小的试验组中观察到AUC显著改善2.57%,突出显示PCCL的有效性.

    结论:

    • 拟议的PCCL方法在酶功能预测准确度方面取得了重大进展.
    • PCCL有效地处理了阶级不平衡问题,导致了更强大的预测.
    • 这种方法有望在医疗和工业生物技术中得到更广泛的应用.