序列库存的功能概况:基于蛋白质对的评估,用于in silico预测工具
R Prabakaran1,2, Yana Bromberg1,2
1Department of Biology, Emory University, Atlanta, GA 30322, United States.
Bioinformatics (Oxford, England)
|January 24, 2025
概括
目前的蛋白质功能预测工具很难识别与已知家族无关的蛋白质的分子功能. 这项研究揭示了大多数方法,包括深度学习,都局限于同源序列,突出了需要新的预测方法.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 在 silico 蛋白质功能注释对于理解生物过程和弥合基因组测序和功能知识之间的差距至关重要.
- 许多蛋白质功能预测方法的开发,特别是基于深度学习的方法,已经加速发展,但它们的真正预测能力仍然不确定.
- 一个关键的挑战是评估这些工具是否可以识别与已知的蛋白质家族不同类或进化远离的蛋白质的分子功能.
研究的目的:
- 评估现有的蛋白功能注释方法的功能和局限性.
- 研究这些方法在预测缺乏同源引用的蛋白质分子功能的潜力.
- 开发一种新的方法来评估函数预测准确度,超出传统的基于词汇的方法.
主要方法:
- 功能预测评估转化为评估蛋白质对具有可能共享但未注释的功能的功能相似性.
- 专注于功能上相似但与已知的注释蛋白质序列不同的蛋白质对.
- 开发了一种评估不同本体学注释方法的方法,超越了词汇限制.
主要成果:
- 大多数现有的蛋白质功能注释方法都局限于预测同源序列的功能.
- 这些方法无法准确预测缺乏具有已知功能的近亲进化亲属的蛋白质的分子功能.
- 即使是先进的深度学习方法在从缺乏同质性的蛋白质序列中捕获功能信号方面也存在局限性.
结论:
- 目前的in silico蛋白质功能预测工具对于没有已知的同类蛋白质的蛋白质来说基本上是不够的.
- 有必要开发下一代预测方法,能够识别新型或与之远距离相关的蛋白质的功能.
- 这项工作为未来的研究提供了基础,旨在推进蛋白质功能发现,并扩大我们对蛋白质组的理解.
相关概念视频
02:26Protein Networks
3.7K
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,...
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,...
3.7K
03:21RNA-seq
9.4K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.4K
02:24Ribosome Profiling
3.2K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.2K