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関連する概念動画

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Multi-Step Reactions02:31

Multi-Step Reactions

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Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...
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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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Insertion of Multi-pass Transmembrane Proteins in the RER

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The rough ER membrane synthesizes, assembles, and embeds transmembrane proteins in diverse topologies. These proteins function as transporters or channels and can remain in the ER membrane or are sent to the Golgi complex, lysosome, and cell membrane.
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Multi-pass Transmembrane Proteins and β-barrels01:09

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In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as...
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Peptide Bonds02:43

Peptide Bonds

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A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...
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関連する実験動画

Updated: Feb 4, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Multi-AOP：抗酸化ペプチド発見のための軽量マルチビュー深層学習フレームワーク

Jianxiu Cai1,2, Xinpo Lou1,3, Chak Fong Chong1,2

  • 1Faculty of Applied Sciences, Macao Polytechnic University, Rua de Luís Gonzaga Gomes, Macau SAR, China.

Bioresources and bioprocessing
|February 2, 2026
PubMed
まとめ

抗酸化ペプチド（AOP）の発見は、健康と食品保存にとって重要です。新しい深層学習フレームワークであるMulti-AOPは、シーケンスとグラフデータを使用してAOPを効率的に特定し、既存の方法を上回るパフォーマンスを発揮します。

キーワード:
抗酸化ペプチド深層学習フレームワークペプチド発見シーケンス学習グラフ学習

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関連する実験動画

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10:25

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科学分野:

  • 生化学とバイオインフォマティクス
  • 計算化学
  • 創薬における人工知能

背景:

  • 抗酸化ペプチド（AOP）は、フリーラジカル捕捉特性により、疾患予防と食品保存の可能性を示しています。
  • AOPを発見するための従来の実験的方法は、非効率的でリソースを大量に消費します。

研究 の 目的:

  • 強化された抗酸化ペプチド発見のための効率的な計算フレームワークを開発すること。
  • AOP予測精度の向上に、シーケンスと構造情報の統合。

主な方法:

  • Extended Long Short-Term Memory（xLSTM）を使用してシーケンス埋め込みを利用する軽量マルチビュー深層学習フレームワークであるMulti-AOPを開発しました。
  • 分子グラフの特徴を抽出するためにSMILES表現でMessage Passing Neural Network（MPNN）を採用し、物理化学的特性を捉えました。
  • 包括的なペプチド分析のために、シーケンスとグラフの特徴の階層的融合を実装しました。

主要な成果:

  • Multi-AOPは、0.8043（AnOxPePred）、0.9684（AnOxPP）、および0.9043（AOPP）の高い予測精度を達成しました。
  • このフレームワークは、従来の機械学習および最先端の深層学習アプローチを常に上回りました。
  • 一般化可能なAOP予測モデルの開発を促進するために、統一されたAOPデータセットが作成されました。

結論:

  • Multi-AOPフレームワークは、効率的で正確な抗酸化ペプチド発見において大きな進歩を提供します。
  • 統合されたシーケンスとグラフ学習は、ペプチド機能の予測に強力なアプローチを提供します。
  • 公開されているデータセットとモデルは、AOP開発における将来の研究を加速します。