GPDOCK: highly accurate docking strategy for metalloproteins based on geometric probability

Enhancing HDAC Inhibitor Screening: Addressing Zinc Parameterization and Ligand Protonation in Docking Studies

Precise binding free-energy predictions for ligands targeting metalloproteins, especially zinc-containing histone deacetylase (HDAC) enzymes, require specialized computational approaches due to the unique interactions at metal-binding sites. This study evaluates a docking algorithm optimized for zinc coordination to determine whether it could accurately differentiate between protonated and deprotonated states of hydroxamic acid ligands, a key functional group in HDAC inhibitors (HDACi). By systematically analyzing both protonation states, we sought to identify which state produces docking poses and binding energy estimates most closely aligned with experimental values. The docking algorithm was applied across HDAC 2, 4, and 8, comparing protonated and deprotonated ligand correlations to experimental data. The results demonstrate that the deprotonated state consistently yielded stronger correlations with experimental data, with R2 values for deprotonated ligands outperforming protonated counterparts in all HDAC targets (average R2 = 0.80 compared to the protonated form where R2 = 0.67). These findings emphasize the significance of proper ligand protonation in molecular docking studies of zinc-binding enzymes, particularly HDACs, and suggest that deprotonation enhances predictive accuracy. The study's methodology provides a robust foundation for improved virtual screening protocols to evaluate large ligand libraries efficiently. This approach supports the streamlined discovery of high-affinity, zinc-binding HDACi, advancing therapeutic exploration of metalloprotein targets. A comprehensive, step-by-step tutorial is provided to facilitate a thorough understanding of the methodology and enable reproducibility of the results.

Unlocking Precision Docking for Metalloproteins

Metalloproteins play a fundamental role in molecular biology, contributing to various biological processes. However, the discovery of high-affinity ligands targeting metalloproteins has been delayed due, in part, to a lack of suitable tools and data. Molecular docking, a widely used technique for virtual screening of small-molecule ligand interactions with proteins, often faces challenges when applied to metalloproteins due to the particular nature of the ligand metal bond. To address these limitations associated with docking metalloproteins, we introduce a knowledge-driven docking approach known as "metalloprotein bias docking" (MBD), which extends the AutoDock Bias technique. We assembled a comprehensive data set of metalloprotein-ligand complexes from 15 different metalloprotein families, encompassing Ca, Co, Fe, Mg, Mn, and Zn metal ions. Subsequently, we conducted a performance analysis of our MBD method and compared it to the conventional docking (CD) program AutoDock4, applied to various metalloprotein targets within our data set. Our results demonstrate that MBD outperforms CD, significantly enhancing accuracy, selectivity, and precision in ligand pose prediction. Additionally, we observed a positive correlation between our predicted ligand free energies and the corresponding experimental values. These findings underscore the potential of MBD as a valuable tool for the effective exploration of metalloprotein-ligand interactions.