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相关概念视频

Protein Networks02:26

Protein Networks

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,...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein Families02:47

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key locations, protein...
Proteomics01:33

Proteomics

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 proteomics...

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相关实验视频

Updated: Jun 16, 2026

Protein Target Prediction and Validation of Small Molecule Compound
10:21

Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

在AI和大数据时代预测蛋白质功能

Riccardo Percudani1, Carlo De Rito1

  • 1Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy.

Biochemistry
|May 17, 2025
PubMed
概括

深度学习和人工智能正在彻底改变蛋白质功能预测. 这些先进的方法丰富了蛋白质数据库的3D结构,提高了我们对分子生命的理解.

科学领域:

  • 蛋白质组学和生物信息学
  • 计算生物学 计算生物学
  • 分子生物学分子生物学

背景情况:

  • 从基因组序列来确定蛋白质功能的传统方法在很大程度上已经过时了.
  • 大数据分析的近期进展刺激了功能基因组学的进步.
  • 人工智能 (AI) 和深度学习 (DL) 的整合正在改变生物研究.

研究的目的:

  • 突出AI和DL对预测蛋白质功能的影响.
  • 强调以结构和功能信息丰富蛋白质数据库.
  • 讨论这些方法在促进分子层面对生命的理解方面的潜力.

主要方法:

  • 利用深度学习算法来分析基因组和蛋白质组数据.
  • 采用基于AI的方法来预测蛋白质结构和功能.
  • 整合大规模的生物数据集来训练预测模型.

主要成果:

  • 人工智能和DL方法正在增强蛋白质数据库,提供关键的三维结构信息.
  • 这些先进的技术提供了准确预测生化特性和生物分子相互作用的潜力.
  • 预计功能注释蛋白质的显著增加.

结论:

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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

  • 人工智能和深度学习代表了理解蛋白质功能的范式转变.
  • 增强的功能洞察力将加深我们对分子层面的生物过程的理解.
  • 这一进展有望加速分子生物学和相关领域的发现.