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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.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
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The mammalian target of rapamycin  (mTOR) is a serine/threonine kinase that regulates growth, proliferation, and cell survival in response to hormones, growth factors, or nutrient availability. This kinase exists in two structurally and functionally distinct forms: mTOR complex 1  (mTORC1) and mTOR complex 2  (mTORC2). The first form (mTORC1) is composed of a rapamycin-sensitive Raptor and proline-rich Akt substrate, PRAS40. In contrast,  mTORC2 consists of a...
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Protein Kinases and Phosphatases02:54

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Proteins undergo chemical modifications that trigger changes in the charge, structure, and conformation of the proteins. Phosphorylation, acetylation, glycosylation, nitrosylation, ubiquitination, lipidation, methylation, and proteolysis are various protein modifications that regulate protein activity. Such modifications are usually enzyme-driven.
Protein kinases
Many proteins in the cell are regulated by phosphorylation, the addition of a phosphate group. A family of enzymes called kinases...
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Enzymes02:34

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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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Introduction to Enzyme Kinetics01:19

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Enzyme kinetics studies the rates of biochemical reactions. Scientists monitor the reaction rates for a particular enzymatic reaction at various substrate concentrations. Additional trials with inhibitors or other molecules that affect the reaction rate may also be performed.
The experimenter can then plot the initial reaction rate or velocity (Vo) of a given trial against the substrate concentration ([S]) to obtain a graph of the reaction properties. For many enzymatic reactions involving a...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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相关实验视频

Updated: May 15, 2025

Identification of Kinase-substrate Pairs Using High Throughput Screening
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使用自回归模型进行激酶基质预测.

Farzaneh Esmaili1, Yongfang Qin1, Duolin Wang1

  • 1Data Science and Informatics Institute, Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.

Computational and structural biotechnology journal
|April 7, 2025
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种新型的自身回归模型,以预测蛋白质水平上的酶-基质关系. 这种方法通过识别酶点来推进细胞信号研究,即使对于没有已知的基质的酶.

关键词:
自动回归的语言模型基因酶激酶的使用方法激酶基质基质是激酶基质.酸化是指酸化的方法.这是一次零射击.

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相关实验视频

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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
  • 基因组学就是基因组学.

背景情况:

  • 酶特异性酸化对于细胞信号传递和疾病发病过程至关重要.
  • 鉴定酶基质仍然是一个重大挑战,阻碍了对细胞过程的全面理解.
  • 现有的方法缺乏可靠的方法来预测酶-基质关系.

研究的目的:

  • 引入一种创新的自回归模型,用于在蛋白质水平上预测酶-基质对.
  • 通过专注于酶-蛋白质相互作用来解决特定地点酸化预测的局限性.
  • 为识别酶标提供可靠的计算工具.

主要方法:

  • 使用了一种自回归模型,将ESM-2蛋白大型语言模型集成为编码器.
  • 将问题重新定义为蛋白质-蛋白质相互作用预测任务,使用二进制分类方法.
  • 实施了基于激酶嵌入距离的硬负策略,用于强大的模型训练.
  • 进行了top-k分析,以评估潜在的激酶候选者的优先级.

主要成果:

  • 该模型成功地预测了蛋白质水平上的酶-基质对.
  • 证明了零射击预测的能力,识别了没有先前已知的相互作用的激酶的基质.
  • 展示了对新型激酶和代表性不足的蛋白质群体的强有力的概括.
  • 通过硬负策略,有效地区分了积极的激酶基质相互作用与负的相互作用.

结论:

  • 开发的自回归模型为预测酶-基质关系提供了一种新且有效的解决方案.
  • 这种方法通过使蛋白质水平的基质识别和零射击预测能够显著推进该领域.
  • 该模型的多功能性和概括能力凸显了其在各种生物研究和疾病研究中的潜在实用性.