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相关概念视频

Induced-fit Model01:13

Induced-fit Model

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Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
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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.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes...
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深度学习引导蛋白酶基质的设计.

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概括

人工智能管道CleaveNet有效地设计蛋白酶基质. 该工具加速了蛋白酶活性用于诊断和治疗的研究和应用.

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科学领域:

  • 生物化学 生化学
  • 计算生物学 计算生物学
  • 酶学 是一种酶学.

背景情况:

  • 蛋白酶是参与各种生物过程和疾病的关键酶.
  • 识别蛋白质酶基质对于理解蛋白质酶功能和开发诊断/治疗方法至关重要.
  • 目前的基板设计受到巨大的序列空间和缺乏高吞吐量工具的限制.

研究的目的:

  • 开发一个人工智能管道,CleaveNet,用于高效和可调节的蛋白质酶基质设计.
  • 为了提高蛋白酶基质识别的规模和准确性.
  • 为了实现具有特定裂痕配置的基板的有针对性的设计.

主要方法:

  • 开发了CleaveNet,这是一个端到端的人工智能管道,用于蛋白酶基质设计.
  • 应用CleaveNet到基底生成的矩阵金属蛋白酶 (MMP).
  • 包含一个调节标签,用于控制生成具有所需裂解配置的基板.
  • 通过大规模的体外查验证了CleaveNet产生的基质.

主要成果:

  • CleaveNet成功地产生了具有可取生物物理性质的基质.
  • 确定了针对蛋白酶的已知和新的裂解动机.
  • 证明了高度选择性的基板的成功设计,以MMP13为例.
  • 实验验证证了CleaveNet产生的基质的有效性.

结论:

  • CleaveNet显著提高了蛋白酶基质设计的效率,规模和可调性.
  • 人工智能管道可以捕捉复杂的裂变图案,并使目标基板生成成为可能.
  • CleaveNet显示了加速基于蛋白酶的诊断和治疗研究和开发的前景.
  • 这种方法为跨越不同类型的酶的in silico设计工具铺平了道路.