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Updated: Jul 1, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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PoseBusters:基于人工智能的对接方法无法生成物理有效的姿势或将其概括为新的序列.

Martin Buttenschoen1, Garrett M Morris1, Charlotte M Deane1

  • 1Department of Statistics 24-29 St Giles' Oxford OX1 3LB UK deane@stats.ox.ac.uk.

Chemical science
|March 1, 2024
PubMed
概括

深度学习蛋白质-连接体对接方法通常会产生物理上不合理的结构,需要超出根-平均-平方偏差 (RMSD) 的评估. "PoseBusters"评估了固态和能量标准,揭示了经典方法仍然优于深度学习方法.

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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

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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