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相关概念视频

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.8K
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
18.8K
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 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
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
Protein Organization01:24

Protein Organization

6.4K
Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
6.4K
Improving Translational Accuracy02:07

Improving Translational Accuracy

10.2K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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相关实验视频

Updated: Jun 27, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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评估用于注释蛋白质的大型语言模型.

Rosario Vitale1, Leandro A Bugnon1, Emilio Luis Fenoy1

  • 1Research Institute for Signals, Systems and Computational Intelligence sinc(i) (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina.

Briefings in bioinformatics
|May 6, 2024
PubMed
概括

本研究介绍了一种使用蛋白质大语言模型 (LLM) 进行Pfam域注释的新型转移学习协议. 这种方法显著提高了蛋白质家族分类的准确性,与现有方法相比,预测错误减少了60%.

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:

  • UniProtKB含有超过25100万种蛋白质,但只有0.25%的蛋白质与Pfam家族域进行注释.
  • 当前的Pfam注释方法虽然有效,但难以跟上蛋白质发现的速度.
  • 对于Pfam注释的深度学习模型需要大量的训练数据,这给代表性不足的蛋白质家族带来了挑战.

研究的目的:

  • 开发和评估一种新的转移学习协议,以增强蛋白质域注释.
  • 利用蛋白质大语言模型 (LLM) 和它们的序列嵌入来改进Pfam分类.
  • 为了解决在注释人口稀少的蛋白质家族中的数据稀缺问题.

主要方法:

  • 使用蛋白质大语言模型 (LLM),在大型未注释数据集上进行自我监督训练,以生成序列嵌入.
  • 在小型注释数据集上应用监督学习,使用这些嵌入式用于专门的蛋白质域预测任务.
  • 在拟议协议中评估了多个最先进的蛋白质LLM和机器学习架构.

主要成果:

  • 新的协议取得的结果明显优于最先进的蛋白质家族分类方法.
  • 与标准蛋白质注释技术相比,预测错误减少了60%.
关键词:
大型语言模型.蛋白质注释 蛋白质注释蛋白质家族是一种蛋白质家族.转移学习转移学习

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  • 成功展示了LLM嵌入用于蛋白质注释的实际应用.

    • 结论:

    • 使用蛋白质LLM转移学习为蛋白质域注释提供了一种强大而高效的方法.
    • 拟议的方法大大提高了Pfam分类的准确性和效率,特别是对于具有挑战性的家庭.
    • 随时可用的管道和代码有助于采用这种先进的注释策略.