Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein–ligand binding affinities

Ikki Yasuda, Katsuhiro Endo, Eiji Yamamoto, Yoshinori Hirano, Kenji Yasuoka

研究成果: Article査読

抄録

Prediction of protein–ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). The energy gap implicitly includes the effects of changes in protein dynamics induced by ligand binding. However, the relationship between protein dynamics and binding affinity remains unclear. Here, we propose a method that represents ligand-binding-induced protein behavioral change with a simple feature that can be used to predict protein–ligand affinity. From unbiased molecular simulation data, an unsupervised deep learning method measures the differences in protein dynamics at a ligand-binding site depending on the bound ligands. A dimension reduction method extracts a dynamic feature that strongly correlates to the binding affinities. Moreover, the residues that play important roles in protein–ligand interactions are specified based on their contribution to the differences. These results indicate the potential for binding dynamics-based drug discovery.

本文言語English
論文番号481
ジャーナルCommunications biology
5
1
DOI
出版ステータスPublished - 2022 12月

ASJC Scopus subject areas

  • 医学(その他)
  • 生化学、遺伝学、分子生物学(全般)
  • 農業および生物科学(全般)

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