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相关概念视频

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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Protein Networks02:26

Protein Networks

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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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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
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Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Updated: Jun 5, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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可解释和可解释的预测机器学习模型用于数据驱动的蛋白质工程.

David Medina-Ortiz1, Ashkan Khalifeh2, Hoda Anvari-Kazemabad3

  • 1Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany; Departamento de Ingeniería En Computación, Universidad de Magallanes, Avenida Bulnes, 01855, Punta Arenas, Chile.; Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Beauchef 851, Santiago, Chile.

Biotechnology advances
|December 7, 2024
PubMed
概括
此摘要是机器生成的。

可解释的人工智能 (XAI) 通过使人工智能模型可解释来增强蛋白质工程. 这种方法提高了信任,并指导机器学习辅助的定向进化,以获得更好的蛋白质设计.

关键词:
可解释的人工智能 (XAI)可解释的机器学习 (XML)可以解释的机器学习 (IML)蛋白质的设计 蛋白质的设计蛋白质工程是一种蛋白质工程.

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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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相关实验视频

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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科学领域:

  • 生物技术是生物技术.
  • 计算生物学 计算生物学
  • 蛋白质工程是指蛋白质的工程.

背景情况:

  • 蛋白质工程利用定向进化和理性设计来优化蛋白质的特性.
  • 人工智能 (AI) 通过数据驱动的预测模型加速蛋白质工程.
  • 当前的人工智能模型缺乏可解释性,限制了它们的现实应用和可靠性.

研究的目的:

  • 探索可解释的人工智能 (XAI) 的原则和方法.
  • 突出XAI在生物技术和蛋白质设计中的相关性和潜力.
  • 提出理论管道,将XAI与蛋白质工程的预测模型集成在一起.

主要方法:

  • 审查XAI的原则和方法.
  • 对XAI在生物技术中的应用进行分析.
  • 在蛋白质工程中整合XAI的理论管道的开发.

主要成果:

  • XAI提供了对人工智能决策的见解,提高了模型可靠性.
  • 蛋白质工程中的XAI应用尚未得到充分探索,但具有显著的潜力.
  • 为蛋白质设计提出了三个XAI集成的理论管道.

结论:

  • 通过提高模型的可解释性和可信度,XAI可以显著提高蛋白质工程.
  • 整合XAI可以引导机器学习辅助的定向进化和蛋白质设计.
  • 需要进一步的研究来应对挑战,并开发XAI作为传统蛋白质工程的支持工具.