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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

13.3K
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...
13.3K
Ligand Binding Sites02:40

Ligand Binding Sites

13.2K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
13.2K
Conserved Binding Sites01:49

Conserved Binding Sites

4.4K
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...
4.4K
Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K
G Protein-coupled Receptors01:15

G Protein-coupled Receptors

13.5K
G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
GPCRs are also called heptahelical, 7TM, or serpentine receptors, and consist of seven (H1-H7) transmembrane alpha-helices that span the bilayer to form a cylindrical core. The transmembrane helices are connected by three extracellular loops and three...
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Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

55.1K
Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
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相关实验视频

Updated: Sep 13, 2025

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

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深度学习单独从受体结构的分子相互作用动机.

Seeun Kim1, Simaek Oh1, Hyeonuk Woo1

  • 1Department of Chemistry, Seoul National University, Seoul, 08826, Republic of Korea.

Journal of cheminformatics
|July 31, 2025
PubMed
概括

深度学习网络MotifGen可以直接从结构中预测蛋白质结合因子的图案. 这种新的方法有助于发现挑战性目标的新结合剂,超越传统方法.

关键词:
有约束力的图案.的设计 的设计蛋白质结构分析分析蛋白质结构分析基于结构的药物设计.

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Peptide-based Identification of Functional Motifs and their Binding Partners
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Peptide-based Identification of Functional Motifs and their Binding Partners

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Peptide-based Identification of Functional Motifs and their Binding Partners
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科学领域:

  • 计算生物学是一种计算生物学.
  • 结构生物信息学 结构生物信息学
  • 机器学习在药物发现中的作用

背景情况:

  • 蛋白质相互作用依赖于表面结合基因.
  • 传统的粘合剂设计仅限于已知的图案,限制了新奇性.
  • 开发新的方法来识别潜在的结合地点至关重要.

研究的目的:

  • 介绍MotifGen,一个深度学习网络,用于从受体结构中预测潜在的绑定因子.
  • 为了生成功能组和化学相互作用的人类可解读的图案配置文件.
  • 为了使少数拍摄的粘合剂设计应用程序,并增强新型粘合剂的发现.

主要方法:

  • 开发了MotifGen,这是一个深度学习网络.
  • 创建了14个功能组和6个化学相互作用类别的图案配置文件.
  • 应用MotifGen对结体设计和小分子结合部位预测.

主要成果:

  • MotifGen可以直接从受体结构中预测潜在的结合剂图案.
  • 生成的图案配置文件是人类可解读的,并作为预先训练的嵌入.
  • 在结体设计和小分子结合部位预测方面证明有效性,优于或补充现有方法.

结论:

  • MotifGen提供了一种新的,以动机为中心的方法来发现粘合剂.
  • 这种方法扩展了设计策略,以挑战受体目标.
  • 这种方法有助于识别新型结合剂,而不依赖于先前存在的模式知识.