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Multi-pass Transmembrane Proteins and β-barrels01:09

Multi-pass Transmembrane Proteins and β-barrels

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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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Insertion of Multi-pass Transmembrane Proteins in the RER01:29

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.
The multipass transmembrane proteins are the type IV integral membrane proteins with multiple topogenic sequences determining their spatial arrangement in the ER membrane. Nearly all multipass proteins lack a cleavable signal sequence and use...
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The Significance of Membrane Transport01:44

The Significance of Membrane Transport

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The transport of solutes across the cell membrane is essential for metabolic processes, like maintaining cell size and volume, generating the action potential, exchanging nutrients and gases, etc. Membrane transport can be either passive or active. It can be simple diffusion, facilitated, or mediated transport aided by transport proteins such as transporters and channels.
Transporters facilitate either an active or passive movement of solutes. They can allow a single-molecule transport down its...
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Single-pass Transmembrane Proteins01:25

Single-pass Transmembrane Proteins

5.3K
Integral membrane proteins are tightly associated with the cell membrane and play a crucial role in cell communication, signaling, adhesion, and transport of the molecules. Some integral membrane proteins are present only in the membrane monolayer. For example, the enzyme fatty acid amide hydrolase is present in the cytoplasmic side of the membrane monolayer. In contrast, another type of integral membrane protein, also known as a transmembrane protein, spans across the membrane. Transmembrane...
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Mitochondrial Protein Sorting01:39

Mitochondrial Protein Sorting

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Mitochondria are double-membrane organelles of the eukaryotes involved in cellular metabolism, signaling, ATP synthesis, and programmed cell death.  Each of these processes requires specific proteins and enzymes that must be correctly sorted to the right mitochondrial subcompartment for the proper functioning of the organelle.
Most of these mitochondrial proteins are encoded by the nucleus and imported to the mitochondria as unfolded or loosely folded precursors. Mitochondrial precursors...
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Cotranslational Protein Translocation01:20

Cotranslational Protein Translocation

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Translocation of proteins across membranes is an ancient process that occurs even in bacteria and archaebacteria. In fact, the components of the translocation machinery are still conserved between prokaryotes and eukaryotes.
Sec61 channel partners for cotranslational translocation
During cotranslational translocation, the Sec61 channel partners with the signal recognition particle (SRP), the signal recognition particle receptor (SR), and the ribosomes to transport the nascent polypeptide chain...
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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TCS-TP:マルチスケール特征抽出に基づくトランスポータータンパク質予測

Fan Yu1, Qianying Zheng1, Qingwei Fu1

  • 1College of Physics and Information Engineering, Fuzhou University, Fuzhou, China.

Biotechnology and applied biochemistry
|August 29, 2025
PubMed
まとめ

トランスフォーマーとコンボリューションニューラルネットワークを使って トランスポータータンパク質 (TP) を正確に識別する 新規の予測装置である TCS-TP を開発しました このツールは機能的ゲノミクスと新しいTPの発見に役立ちます.

キーワード:
コンヴォルションニューラルネットワーク (CNN)タンパク質の予測配列情報サポートベクトルマシン (SVM)トランスフォーマートランスポータータンパク質

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科学分野:

  • 生物化学
  • ゲノミクス
  • バイオ情報学

背景:

  • トランスポータータンパク質 (TP) は,ホメオスタシスやコミュニケーションなどの細胞機能に不可欠です.
  • 新しいTPを特定することは 機能的ゲノミクスと薬の開発に不可欠です
  • 現在の方法では TPを正確に予測することが困難です.

研究 の 目的:

  • トランスポータータンパク質 (TP) を予測するための正確な計算モデルを開発する.
  • タンパク質の配列から複数の特性を抽出するための ディープラーニングを活用する.
  • 大規模なゲノムデータにおける新しいTPの発見を強化する.

主な方法:

  • トランスフォーマーとコンボリューションニューラルネットワーク (CNN) を組み合わせたハイブリッドのディープラーニングアーキテクチャ,TCS-TPを使用しました.
  • GLUアクティベーションのトランスフォーマーと,特徴抽出のための並列サブネットワークのCNNを使用しています.
  • 最終的なTP分類のための適用されたサポートベクトルマシン.

主要な成果:

  • TCS-TPは,AUROC 0.89,AUPRC 0.81,精度 91.66%で高い性能を達成しました.
  • このモデルは,既存のTP予測方法と比較して優れたパフォーマンスを示した.
  • タンパク質の配列から トランスポータータンパク質を 発見しました

結論:

  • TCS-TPは,トランスポータータンパク質を予測するための強力で正確なツールです.
  • このモデルは,大規模なゲノム研究プロジェクトや,新しいTPの発見を容易にする.
  • このアプローチは機能的ゲノミクスと 潜在的治療標的の特定を 促進します