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Conserved Binding Sites

Conserved Binding Sites

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...

Conserved Binding Sites

Conserved Binding Sites

Ligand Binding Sites

Ligand Binding Sites

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...

Ligand Binding Sites

Ligand Binding Sites

Nucleic Acid Structure

Nucleic Acid Structure

The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA...

Nonsense-mediated mRNA Decay

Nonsense-mediated mRNA Decay

The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...

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相关实验视频

Updated: Jan 13, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

CoBRA:使用RNA语言模型预测化合物结合部位.

Wonkyeong Jang¹, Woong-Hee Shin^1,2

¹Department of Biomedical Informatics, Korea University College of Medicine, 161 Jeongneung-ro, Seongbuk-gu, Seoul 02708, Republic of Korea.

Briefings in bioinformatics

|January 11, 2026

概括

此摘要是机器生成的。

一个新的深度学习工具,RNA的复合结合位预测 (CoBRA),仅使用序列数据就能准确预测RNA与药物相互作用. 这种方法优于基于结构的方法,提供了一种灵活的方式来发现新的RNA向治疗方法.

关键词:

RNA语言模型RNA语言模型 RNA小分子结合部位的预测卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.预先训练有素的嵌入方式

更多相关视频

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

Published on: July 2, 2010

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

相关实验视频

Last Updated: Jan 13, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

Published on: July 2, 2010

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

科学领域:

生物化学生物化学
计算生物学计算生物学
药物发现药物发现药物发现

背景情况:

核糖核酸 (RNA) 在细胞功能中起着至关重要的作用,并与许多人类疾病有关.
用小分子向RNA是一个有前途的治疗策略,因为蛋白质的药用性有限.

研究的目的:

开发一种用于准确预测RNA分子上小分子结合点的计算工具.
评估深度学习方法的有效性,利用RNA语言模型进行结合部位预测.

主要方法:

介绍RNA (CoBRA) 的化合物结合位预测,这是一个轻量级的深度学习程序.
从预先训练的RNA语言模型中利用了残留级嵌入,绕过了对结构信息的需求.
采用多层感知子分类器用于核酸结合部位的二进制分类.

主要成果:

与现有方法相比,CoBRA在马修相关系数中取得了22.1%的相对改善,敏感度增加了45.6%.
基于序列的语言模型嵌入显示了与基于结构的预测方法相比或超过的性能.
该模型在TR60和HARIBOSS数据集上进行了训练,并在四个独立的基准集上进行了验证.

结论:

在不需要结构数据的情况下,CoBRA提供了一种灵活有效的工具来预测RNA与药物的结合点.
这种基于序列的方法促进了RNA向治疗的发展.
这些发现突出了语言模型在理解RNA - 连接体相互作用方面的潜力.