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相关概念视频

Protein Networks02:26

Protein Networks

3.9K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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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Cofactors and Coenzymes01:27

Cofactors and Coenzymes

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Enzymes require additional components for proper function. There are two such classes of molecules: cofactors and coenzymes. Cofactors are metallic ions and coenzymes are non-protein organic molecules. Both of these types of helper molecule can be tightly bound to the enzyme or bound only when the substrate binds.
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Synthetic Biology02:55

Synthetic Biology

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

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Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...
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A Protocol for Computer-Based Protein Structure and Function Prediction
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生物结构网 (BioStructNet):基于结构的网络,用于预测生物催化剂功能的转移学习.

Xiangwen Wang1,2, Jiahui Zhou1, Jane Mueller2

  • 1School of Chemistry and Chemical Engineering, Queen's University Belfast, BT9 5AG Belfast, Northern Ireland, U.K.

Journal of chemical theory and computation
|December 20, 2024
PubMed
概括
此摘要是机器生成的。

基于结构的深度学习网络BioStructNet增强了酶基质相互作用预测. 转移学习优化了小数据集的准确性,加速了生物催化剂的发现.

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科学领域:

  • 生物化学和分子生物学
  • 计算生物学 计算生物学
  • 化学工程是化学工程的重要组成部分.

背景情况:

  • 酶基质相互作用对于生物过程和工业应用至关重要.
  • 机器学习加速了生物催化剂研究,但由于对特定酶功能的数据有限,它面临着挑战.
  • 预测酶活性,转化效率和立体选择性对于发现新型生物催化剂至关重要.

研究的目的:

  • 开发BioStructNet,一个基于结构的深度学习网络,用于预测酶基质相互作用.
  • 整合蛋白质和配体结构数据,以提高预测准确度.
  • 通过转移学习解决生物催化剂研究中有限数据所带来的挑战.

主要方法:

  • 开发了BioStructNet,这是一个深度学习网络,集成蛋白质和配体结构信息.
  • 通过对大数据集的源模型进行训练和对特定数据集进行微调 (CalB) 来实现转移学习.
  • 使用注意热图和分子动力学模拟验证模型性能.

主要成果:

  • 与其他算法相比,BioStructNet显示了增强的预测准确性.
  • 转移学习显著优化了小的,功能特定的数据集的预测准确性.
  • 注意:来自BioStructNet的热图与分子动力学模拟对齐,验证了相互作用预测.

结论:

  • 使用结构数据,BioStructNet有效地捕捉了酶基质相互作用的复杂性.
  • 转移学习是一种可行的策略,可以在有限的数据中提高预测准确性.
  • 生物结构网可以加速用于工业应用的功能性酶的发现,特别是在小数据集的情况下.