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相关概念视频

Proteomics01:33

Proteomics

7.2K
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...
7.2K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.8K
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...
10.8K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.4K
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...
12.4K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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...
4.2K
Protein Families02:47

Protein Families

15.2K
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...
15.2K
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,...
3.9K

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相关实验视频

Updated: May 30, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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现代机器学习方法用于蛋白质性质预测.

Arjun Dosajh1, Prakul Agrawal1, Prathit Chatterjee1

  • 1Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, Telangana, India.

Current opinion in structural biology
|January 30, 2025
PubMed
概括
此摘要是机器生成的。

人工智能和机器学习 (AI/ML) 通过预测功能性质并从已知的特征实现蛋白质序列和结构的反向工程来推进蛋白质科学.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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相关实验视频

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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: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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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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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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科学领域:

  • 生物分子科学是生物分子科学.
  • 计算生物学是一种计算生物学.
  • 人工智能的人工智能是人工智能.

背景情况:

  • 人工智能和机器学习 (AI/ML) 是解决复杂生物分子问题的越来越强大的工具.
  • 人工智能/ML模型擅长识别数据中的模式,以对新输入进行预测.
  • 生成性AI (GenAI) 提供了基于所需属性的蛋白质结构或序列生成的能力.

研究的目的:

  • 审查AI/ML在预测关键蛋白质功能性质方面的应用.
  • 从属性数据中探索逆向工程蛋白质序列和结构的潜力.

主要方法:

  • 审查当前用于蛋白质性质预测的AI/ML技术.
  • 对蛋白序列和结构生成的生成性AI能力的分析.
  • 检查使用蛋白质属性关系的反向工程方法.

主要成果:

  • 人工智能/ML模型在预测各种蛋白质功能性质方面取得了显著的成功.
  • 基因人工智能在设计具有目标特征的新型蛋白质序列和结构方面表现有前途.
  • 反向工程方法正在出现,用于从功能数据中推断蛋白质细节.

结论:

  • 人工智能/ML正在彻底改变蛋白质科学,使精确的属性预测和新设计成为可能.
  • 整合AI / ML有助于更深入地理解和操纵蛋白质序列-结构-功能关系.
  • 未来的前景包括通过先进的AI/ML应用程序增强蛋白质工程和发现.