Cov_DOX: A Method for Structure Prediction of Covalent Protein-Ligand Bindings

作者：Wei, Lin; Chen, Yaru; Liu, Jiaqi; Rao, Li^*; Ren, Yanliang; Xu, Xin^*; Wan, Jian^*

来源：Journal of Medicinal Chemistry, 2022, 65(7): 5528-5538.

DOI：10.1021/acs.jmedchem.1c02007

• EXTENDED ONIOM METHOD
• MOLECULAR DOCKING
• DRUG DISCOVERY
• ACCURATE
• OPTIMIZATION
• ENERGY
• THERMOCHEMISTRY
• VALIDATION
• XO

A handful of molecular docking tools have beenextended to enable a covalent docking. However, all of them facethe challenge brought by the covalent bond between proteins andligands. Many covalent drug design scenarios still heavily rely ondemanding crystallographic experiments for accurate bindingstructures. Aiming atfilling the gap between covalent dockingsand crystallographic experiments, we develop and validate a hybridmethod, dubbed as Cov_DOX, in this work. Cov_DOX achievesan overall success rate of 81% with RMSD < 2 A for the Top 1pose prediction in the validation against a test set including 405crystal structures for covalent protein-ligand complexes, coveringvarious types of the warhead chemistry and receptors. Suchaccuracy is not far from the much more demanding crystallo-graphic experiments, in sharp contrast to the performance of the covalent docking front runners (success rate: 40-60%).

• 单位
  复旦大学