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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Protein-protein Interfaces

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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...
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Physiological Pharmacokinetic Models: Assumption with Protein Binding

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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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....
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STAG-LLM: タンパク質言語モデルと計算生成3D構造によるTCR-pHLA結合予測

Jared K Slone1, Minying Zhang2, Peixin Jiang2

  • 1Computer Science, Rice University, Houston, 77005, TX, USA.

Computational and structural biotechnology journal
|January 16, 2026
PubMed
まとめ

T細胞受容体（TCR）とペプチド-HLA（pHLA）の結合予測は、免疫療法にとって極めて重要です。新しいマルチモーダルモデルであるSTAG-LLMは、3D構造と配列を使用して結合特異性予測を改善し、既存の方法を上回る性能を示します。

キーワード:
幾何学的深層学習免疫学タンパク質言語モデルプロテオミクス構造バイオインフォマティクスTCRHLA

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科学分野:

  • 免疫学
  • 計算生物学
  • 機械学習

背景:

  • T細胞受容体（TCR）とペプチド-HLA（pHLA）の結合は、獲得免疫にとって不可欠です。
  • 正確な結合特異性予測は、個別化免疫療法の設計に役立ちます。
  • 現在の方法では主にアミノ酸配列が使用されており、構造情報は無視されています。

研究 の 目的:

  • TCR-pHLA結合特異性予測のためのマルチモーダル機械学習（ML）モデルを開発すること。
  • 予測精度の向上のために、3D構造データと配列データを統合すること。
  • MLパイプラインでの計算生成3D構造の使用に関連する課題に対処すること。

主な方法:

  • タンパク質言語モデルと幾何学的深層学習を組み合わせたマルチモーダルMLモデル、STAG-LLMを開発しました。
  • アミノ酸配列とともに、計算生成された3Dタンパク質構造を利用しました。
  • 推論コスト、限られたトレーニングデータ、構造ノイズを管理するための戦略を組み込みました。

主要な成果:

  • STAG-LLMは、既存の方法と比較してTCR-pHLA結合特異性の予測において優れた性能を示しました。
  • モデルは、小さなトレーニングデータセットでも高い精度を達成しました。
  • invitroでのアラニンスキャン実験では、モデルの注意重みとの相関が示され、予測が検証されました。

結論:

  • STAG-LLMは、構造ベースのTCR-pHLA結合予測において significant な可能性を示しています。
  • このモデルは、モデル化された3D構造を使用した免疫学的およびプロテオミクス研究の進歩の基盤を提供します。
  • タンパク質構造および言語モデルの進歩に伴い、STAG-LLMの有用性は高まると予想されます。