Ligand B-Factor Index: A Metric for Prioritizing Protein-Ligand Complexes in Docking.

Abstract

Docking is a structure-based cheminformatics tool broadly employed in early drug discovery. Based on the tridimensional structure of the protein target, docking is used to predict the binding interactions between the protein and a ligand, estimate the corresponding binding affinity, or perform virtual screenings (VSs) to identify new active compounds. This study introduces the ligand B-factor index (LBI), a novel computational metric for prioritizing protein-ligand complexes for docking. Unlike other metrics, LBI directly compares atomic displacements in the ligand and binding site. LBI is defined as the ratio of the median atomic B-factor of the binding site to that of the bound ligand. Using the comparative assessment of scoring functions (CASF-2016) dataset, we evaluated the effectiveness of LBI in guiding the selection of protein-ligand complexes to enhance docking performance. Our results show a moderate correlation (Spearman ρ ~ 0.48) between LBI and the experimental binding affinities, outperforming several docking scoring functions. Additionally, LBI correlates with improved redocking success (root mean square deviation < 2 Å), underlying the significance of a ligand-focused metric. While LBI outperforms other metrics such as the protein B-factor index and resolution, its utility in VS docking remains to be further investigated. LBI is easy to compute, interpretable, applicable in structure-based cheminformatics, and freely available for calculation at https://chembioinf.ro/tool-bi-computing.html.