  Recent advancements in protein docking site prediction have highlighted the
limitations of traditional rigid docking algorithms, like PIPER, which often
neglect critical stochastic elements such as solvent-induced fluctuations.
These oversights can lead to inaccuracies in identifying viable docking sites
due to the complexity of high-dimensional, stochastic energy manifolds with low
regularity. To address this issue, our research introduces a novel model where
the molecular shapes of ligands and receptors are represented using
multi-variate Karhunen-Lo `eve (KL) expansions. This method effectively
captures the stochastic nature of energy manifolds, allowing for a more
accurate representation of molecular interactions.Developed as a plugin for
PIPER, our scientific computing software enhances the platform, delivering
robust uncertainty measures for the energy manifolds of ranked binding sites.
Our results demonstrate that top-ranked binding sites, characterized by lower
uncertainty in the stochastic energy manifold, align closely with actual
docking sites. Conversely, sites with higher uncertainty correlate with less
optimal docking positions. This distinction not only validates our approach but
also sets a new standard in protein docking predictions, offering substantial
implications for future molecular interaction research and drug development.
