Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.9K
VSEPR Theory for Determination of Electron Pair Geometries
45.9K
Prediction Intervals01:03

Prediction Intervals

3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.4K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.2K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.2K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.3K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.3K
Protein Complex Assembly02:41

Protein Complex Assembly

16.8K
Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types.  Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
Many viruses self-assemble into a fully functional unit using the infected host cell to...
16.8K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Structure-Directed Optimization of Ebselen Derivatives as Potent NDM-1 Inhibitors Reverses Meropenem Resistance.

Journal of medicinal chemistry·2025
Same author

Expanding Balloons: Robust Computational Method for Determining Supramolecular Cage Cavity Morphology Based on the "Inflating Balloon" Metaphor.

Journal of chemical information and modeling·2025
Same author

PocketSCP: A Method for Spatiotemporal Topological Visualization and Analysis of Protein Pocket Dynamics.

Journal of chemical information and modeling·2025
Same author

Antibacterial and cytotoxic angucyclines discovered by heterologous expression of a type II polyketide gene cluster.

Organic & biomolecular chemistry·2025
Same author

Increase Docking Score Screening Power by Simple Fusion With CNNscore.

Journal of computational chemistry·2025
Same author

A potent and selective anti-glutathione peroxidase 4 nanobody as a ferroptosis inducer.

Chemical science·2024
Same journal

Engineered HSP90-MP65 Bivalent Fusion Antigen: A Novel Vaccine Candidate Against Invasive Candidiasis.

Proteins·2026
Same journal

Physics-Based Energy Functions for Computational Protein Design.

Proteins·2026
Same journal

Impact of Stabilizing Osmolytes on the Conformational Dynamics of Human and Rat Islet Amyloid Polypeptides.

Proteins·2026
Same journal

Stabilization of Bone Morphogenetic Protein-2 at Physiological pH: Contrasting Roles of CHAPS and Arginine in Aggregation Inhibition.

Proteins·2026
Same journal

Structural Insights Into the Function of Leishmania major Adenylosuccinate Lyase.

Proteins·2026
Same journal

Generalizing the Gaussian Network Model: Spanning-Tree Thermodynamics Shows Entropy-Driven KRAS Activation.

Proteins·2026
関連記事をすべて見る

関連する実験動画

Updated: Feb 5, 2026

Author Spotlight: Evaluating Biophysical Assays for Characterizing PROTACS Ternary Complexes
07:22

Author Spotlight: Evaluating Biophysical Assays for Characterizing PROTACS Ternary Complexes

Published on: January 12, 2024

4.5K

PROTAC三者複合体予測のための深層学習のベンチマーキング

Haoyu Chen1, Fengjiao Wei1, Jiajie Li1

  • 1Key Laboratory of Marine Drugs, Chinese Ministry of Education, School of Medicine and Pharmacy, Ocean University of China, Qingdao, Shandong, China.

Proteins
|February 4, 2026
PubMed
まとめ
この要約は機械生成です。

プロテオリシス標的キメラ（PROTAC）は、標的タンパク質を分解することで新しい薬剤開発戦略を提供します。この研究では、PROTAC三者複合体を予測するためのAIツールのベンチマーキングを行い、Chai-1、AlphaFold3、Protenixが最も優れた性能を発揮することを発見しました。

キーワード:
AlphaFold3PROTAC深層学習構造予測ベンチマーキング三者複合体予測

さらに関連する動画

Thermochemical Studies of NiII and ZnII Ternary Complexes Using Ion Mobility-Mass Spectrometry
16:11

Thermochemical Studies of NiII and ZnII Ternary Complexes Using Ion Mobility-Mass Spectrometry

Published on: June 8, 2022

2.8K
Preparation of Binary and Ternary Deep Eutectic Systems
06:15

Preparation of Binary and Ternary Deep Eutectic Systems

Published on: October 31, 2019

12.8K

関連する実験動画

Last Updated: Feb 5, 2026

Author Spotlight: Evaluating Biophysical Assays for Characterizing PROTACS Ternary Complexes
07:22

Author Spotlight: Evaluating Biophysical Assays for Characterizing PROTACS Ternary Complexes

Published on: January 12, 2024

4.5K
Thermochemical Studies of NiII and ZnII Ternary Complexes Using Ion Mobility-Mass Spectrometry
16:11

Thermochemical Studies of NiII and ZnII Ternary Complexes Using Ion Mobility-Mass Spectrometry

Published on: June 8, 2022

2.8K
Preparation of Binary and Ternary Deep Eutectic Systems
06:15

Preparation of Binary and Ternary Deep Eutectic Systems

Published on: October 31, 2019

12.8K

科学分野:

  • 生化学および構造生物学
  • 創薬および開発
  • 計算生物学

背景:

  • プロテオリシス標的キメラ（PROTAC）は、標的タンパク質分解のためにユビキチン-プロテアソームシステムを利用します。
  • PROTACは、E3リガーゼバインダー、標的タンパク質バインダー、およびリンカーから構成され、三者複合体を形成します。

研究 の 目的:

  • 4つの計算ツール（Chai-1、AlphaFold2、AlphaFold3、Protenix）のPROTAC誘発三者複合体構造予測における精度をベンチマークすること。
  • 標的タンパク質、E3リガーゼ、PROTAC分子の相対的な向きと位置の予測におけるこれらのツールの性能を評価すること。

主な方法:

  • Chai-1、AlphaFold2、AlphaFold3、およびProtenixによって予測された三者複合体構造の比較分析。
  • 標的タンパク質（POI）、E3リガーゼ、およびPROTACの位置に関する全体的な複合体、POI、E3リガーゼのCα-RMSDメトリクスを使用した予測精度の評価。

主要な成果:

  • すべてのツールは、三者複合体予測（Cα-RMSD < 10 Å）において満足のいく全体的な精度を達成しました。
  • Chai-1、AlphaFold3、ProtenixはAlphaFold2を上回り、50%以上のテストで優れた性能を示しました。
  • POIとE3リガーゼの向き、およびPROTAC分子の正確な位置決めにおいて、依然として重大な課題が残っています。

結論:

  • タンパク質構造予測ツールの最近の進歩は、PROTAC三者複合体のモデリングに有望です。
  • PROTAC三者複合体の正確な構造予測は、特に特定の構成要素の向きと位置に関して、依然として困難です。
  • このベンチマーキングは、現在の予測ツールの機能に関する洞察を提供し、PROTACベースの創薬のための将来の開発を導きます。