A High-Quality Data Set of Protein-Ligand Binding Interactions Via Comparative Complex Structure Modeling.
J Chem Inf Model
; 64(7): 2454-2466, 2024 04 08.
Article
em En
| MEDLINE
| ID: mdl-38181418
ABSTRACT
High-quality protein-ligand complex structures provide the basis for understanding the nature of noncovalent binding interactions at the atomic level and enable structure-based drug design. However, experimentally determined complex structures are scarce compared with the vast chemical space. In this study, we addressed this issue by constructing the BindingNet data set via comparative complex structure modeling, which contains 69,816 modeled high-quality protein-ligand complex structures with experimental binding affinity data. BindingNet provides valuable insights into investigating protein-ligand interactions, allowing visual inspection and interpretation of structural analogues' structure-activity relationships. It can also be used for evaluating machine-learning-based scoring functions. Our results indicate that machine learning models trained on BindingNet could reduce the bias caused by buried solvent-accessible surface area, as we previously found for models trained on the PDBbind data set. We also discussed strategies to improve BindingNet and its potential utilization for benchmarking the molecular docking methods and ligand binding free energy calculation approaches. The BindingNet complements PDBbind in constructing a sufficient and unbiased protein-ligand binding data set and is freely available at http//bindingnet.huanglab.org.cn.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Desenho de Fármacos
/
Proteínas
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article