Hydrogen bonds (HB)s are the most abundant motifs in biological systems. They play a key role in determining protein-ligand binding affinity and selectivity. We designed two pharmaceutically beneficial HB databases, database A including ca. 12,000 protein-ligand complexes with ca. 22,000 HBs and their geometries, and database B including ca. 400 protein-ligand complexes with ca. 2200 HBs, their geometries, and bond strengths determined via our local vibrational mode analysis. We identified seven major HB patterns, which can be utilized as a de novo QSAR model to predict the binding affinity for a specific protein-ligand complex. Glycine was reported as the most abundant amino acid residue in both donor and acceptor profiles, and N-H⋯O was the most frequent HB type found in database A. HBs were preferred to be in the linear range, and linear HBs were identified as the strongest. HBs with HB angles in the range of 100-110°, typically forming intramolecular five-membered ring structures, showed good hydrophobic properties and membrane permeability. Utilizing database B, we found a generalized Badger's relationship for more than 2200 protein-ligand HBs. In addition, the strength and occurrence maps between each amino acid residue and ligand functional groups open an attractive possibility for a novel drug-design approach and for determining drug selectivity and affinity, and they can also serve as an important tool for the hit-to-lead process.
Keywords: XTB; hydrogen bond patterns; hydrogen bonds; local mode analysis; protein–ligand interaction; semiempirical quantum chemistry.