We report for the first time the
use of experimental electron density
(ED) in the Protein Data Bank for modeling of noncovalent interactions
(NCIs) for protein–ligand complexes. Our methodology is based
on reduced electron density gradient (RDG) theory describing intermolecular
NCIs by ED and its first derivative. We established a database named
Experimental NCI Database (ExptNCI; ) containing ED saddle points, indicating ∼200,000 NCIs from
over 12,000 protein–ligand complexes. We also demonstrated
the usage of the database in the case of depicting amide−π
interactions in protein–ligand binding systems. In summary,
the database provides details on experimentally observed NCIs for
protein–ligand complexes and can support future studies including
studies on rarely documented NCIs and the development of artificial
intelligence models for protein–ligand binding prediction.
The Protein Data Bank (PDB) contains a massive amount of experimental electron density (ED) data. Such data are traditionally used to determine atomic coordinates. We report for the first time the use of experimental ED in the PDB for modeling of noncovalent interactions (NCIs) for protein-ligand complexes. Our methodology is based on the reduced electron density gradient (RDG) theory describing intermolecular NCI by ED and its first derivative. We established a database named Experimental NCI Database (ExptNCI; http://ncidatabase.stonewise.cn/#/nci) containing ED saddle points, indicating ~200,000 NCIs from over 12,000 protein-ligand complexes. The value of such data is demonstrated in a usage case of understanding amide-pi; interaction geometry in the protein-ligand binding system by using the database to facilitate quantum mechanics-based potential energy landscape scan. In summary, the database provides details on experimentally observed NCIs for protein-ligand complexes and can support future studies on rarely documented NCIs. The potential of fueling artificial intelligence algorithm development by using the database is also discussed.
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