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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

13.5K
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...
13.5K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.9K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

13.9K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
13.9K
The Two-State Receptor Model01:29

The Two-State Receptor Model

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The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...
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相关实验视频

Updated: Sep 18, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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通过数据扰动和增强建模来得分RNA-连接体相互作用.

Hongli Ma1,2,3,4, Letian Gao2,3, Yunfan Jin2,3

  • 1School of Mathematics, Harbin Institute of Technology, Harbin, China.

Nature computational science
|June 24, 2025
PubMed
概括

研究人员开发了RNAsmol,这是一个新的深度学习框架,用于预测RNA-小分子相互作用. 这种基于序列的方法通过准确识别结合模式而提高药物发现,而不需要RNA结构.

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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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科学领域:

  • 计算生物学 计算生物学
  • 药物发现 药物发现 药物发现
  • 生物信息学是一种生物信息学.

背景情况:

  • 开发针对RNA向药物的深度学习模型受到有限的相互作用数据和RNA结构的阻碍.
  • 准确预测RNA-小分子相互作用对于推进药物发现至关重要.

研究的目的:

  • 介绍RNAsmol,一个基于序列的深度学习框架,用于预测RNA-小分子相互作用.
  • 为了解决数据的局限性,并提高RNA药物相互作用预测的准确性.

主要方法:

  • 开发了RNAsmol,这是一个使用序列数据的深度学习框架.
  • 嵌入数据扰动与增强和基于图形的分子特征.
  • 利用基于注意力的功能融合模块进行增强的预测.

主要成果:

  • RNAsmol准确地预测了RNA-小分子结合相互作用.
  • 该模型在交叉验证,隐形和诱评估方面表现优于现有方法.
  • 案例研究提供了对约束性配置文件和模型预测的可解释的见解.

结论:

  • RNAsmol提供了一种可靠的,结构独立的方法来预测RNA-小分子相互作用.
  • 该框架可以适应各种药物设计场景,克服数据限制.
  • 这种方法通过准确和可解释的预测来推进RNA向药物发现.