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相关概念视频

Ribosome Profiling02:24

Ribosome Profiling

3.6K
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...
3.6K
Protein Networks02:26

Protein Networks

4.0K
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,...
4.0K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

11.0K
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...
11.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.7K
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...
11.7K
Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

5.5K
Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
5.5K
Protein Families02:47

Protein Families

15.5K
Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
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相关实验视频

Updated: Jul 26, 2025

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

143

使用深度变换器蛋白语言模型识别蛋白质工程的有希望的序列.

Trevor S Frisby1, Christopher James Langmead1

  • 1Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

Proteins
|June 20, 2023
PubMed
概括

这项研究引入了一种新的承诺分数,来自深度变压器蛋白质语言模型,以有效地识别工程方面的有希望的蛋白质序列. 该得分有助于发现新型纳米体,并通过预测结合相互作用来优化现有蛋白质.

科学领域:

  • 计算生物学 计算生物学
  • 蛋白质工程是指蛋白质工程.
  • 人工智能在生物化学中的应用

背景情况:

  • 发现具有所需性质的新型蛋白序列是具有挑战性的,因为序列空间广.
  • 对于纳米体发现和蛋白质优化等应用来说,识别有前途的序列往往是昂贵和耗时的.

研究的目的:

  • 开发和验证基于深度学习的方法,以有效地识别高潜力的蛋白质序列.
  • 引入"承诺分数"以指导蛋白质工程在序列发现和优化方面的努力.

主要方法:

  • 利用深度变压蛋白语言模型及其自我注意力图来计算承诺分数.
  • 将Promise Score应用于纳米体 (Nb) 发现和蛋白质优化工作流程.
  • 分析了自我注意力图,以确定参与分子间相互作用的关键蛋白质区域.
  • 探索了用于预测性质建模的蛋白质语言模型的微调策略.

主要成果:

  • 答应分数有效地从纳米体谱中选择有希望的序.
  • 承诺得分指导特定地点的突变发生实验,识别了高比例的改善的蛋白质序列.
  • 自我注意地图提供了对驱动特定相互作用的蛋白质区域的洞察.
  • 证明了微调蛋白质语言模型对目标蛋白质工程任务的有用性.
关键词:
关注注意力注意力注意力注意力精细调整 精细调整蛋白质设计 蛋白质设计蛋白质工程工程 蛋白质工程蛋白质语言模型转移学习转移学习变压器变压器变压器变压器

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A Protocol for Computer-Based Protein Structure and Function Prediction
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An Integrated Approach for Microprotein Identification and Sequence Analysis
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相关实验视频

Last Updated: Jul 26, 2025

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

143
A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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An Integrated Approach for Microprotein Identification and Sequence Analysis
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An Integrated Approach for Microprotein Identification and Sequence Analysis

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结论:

  • 承诺分数提供了一个计算高效的方法来加速蛋白质工程.
  • 深度学习模型,特别是基于变压器的语言模型,是预测和设计功能蛋白质的强大工具.
  • 通过自我注意地图来了解蛋白质相互作用,可以增强设计过程.