Cheminformatic analysis of protein surfaces provides binding site insights and informs identification strategies

Understanding protein-ligand binding site behavior is central to structure-based drug design. We analyzed amino acid composition and interactions in protein-ligand small-molecule binding sites and developed a novel method for binding site prediction.

We analyzed the PDBBind+ database, which contains the largest protein-ligand binding site dataset known to us, using existing cheminformatics packages and in-house code. We used the resulting data to train a binding site prediction model.

Within solvent-accessible binding regions, tryptophan, phenylalanine, tyrosine, methionine, and glycine, were enriched. Interaction analysis revealed hydrophobic contacts as the most frequent, followed by hydrogen bonds, water-bridged hydrogen bonds, salt bridges, π-π, π-cation, and occasional halogen interactions. We introduced the amino acid binding site enrichment index (ABSE), to support small-molecule binding site detection, and developed a model that discriminates binding site sequences from protein surface patches with 0.91 accuracy.

This work offers interpretable composition-interaction relationships and practical tool for binding site characterization. To facilitate application, we provide a free, open-source, fast, bindingsite identification tool (AABS), available at https://gitlab.com/Jukic/aabs. We anticipate that these findings and tool will advance binding site prediction and accelerate computationally intensive drug discovery within medicinal chemistry.