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関連する概念動画

Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
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...
11.8K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

116
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Synthetic Biology02:55

Synthetic Biology

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
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Effects of feedback01:24

Effects of feedback

698
Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
698
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

19.3K
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.
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Updated: Sep 9, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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実験的なフィードバックを統合することで,生物学的配列の生成モデルが改善される.

Francesco Calvanese1,2, Giovanni Peinetti1,3, Polina Pavlinova2

  • 1Sorbonne Université, CNRS, Department of Computational, Quantitative and Synthetic Biology-CQSB, 75005 Paris, France.

Nucleic acids research
|September 3, 2025
PubMed
まとめ
この要約は機械生成です。

生物分子設計の生成モデルは 偽陽性と闘っています 実験的なフィードバックを統合することで,機能的なRNAとタンパク質の配列の生成が大幅に改善され,アクティブデザインは6.7%から63.7%に増加しました.

さらに関連する動画

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

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関連する実験動画

Last Updated: Sep 9, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

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科学分野:

  • 計算生物学
  • 分子生物学
  • バイオ分子工学

背景:

  • 人工RNAとタンパク質の配列を設計する可能性を示しています.
  • 主な制限は,予測された機能的なシーケンスが実験的検証に失敗する高率の偽陽性です.

研究 の 目的:

  • ゲネラティブ・バイオモレキュア・デザインの 偽陽性問題に対処するためです
  • 実験的なフィードバックをモデル設計に再統合する影響を調査する.
  • 機能的な生物分子配列の生成を改善する.

主な方法:

  • 可能性に基づく再統合計画を提案した.
  • RNAとタンパク質のデータセットに関する広範な計算実験を行った.
  • グループIのイントロンRNAのセルフスプライシングリボ酵素のウェットラボ実験を行った.

主要な成果:

  • フィードバックベースのアプローチは,機能的なシーケンスを生成するモデルの能力を大幅に高めました.
  • アクティブデザインは実験データを統合した後に6.7%から63.7%に増加した (45の変異).
  • この方法は,自己結合リボ酵素の設計において特に有効であることが示された.

結論:

  • 最近の実験データを統合することで 生物分子の設計における 偽陽性の課題に 直接取り組むことができます
  • このフィードバックベースのアプローチは,機能的なRNAとタンパク質の配列を設計する上で大きな改善をもたらします.
  • 提案されたスキームは,生成的生物分子設計の信頼性と成功率を高めます.