LeScore: a scoring function incorporating hydrogen bonding penalty for protein–ligand docking

Abstract

Context

Molecular docking is vital for structure-based virtual screening and heavily depends on accurate and robust scoring functions. Scoring functions often inadequately account for the breakage of solvent hydrogen bonds, hindering the accuracy of predicting binding energy. Here, we introduce LeScore, a novel scoring function that specifically incorporates the hydrogen bonding penalty (HBP) in an aqueous environment, aiming to penalize unfavorable polar interactions when hydrogen bonds with water are broken but the energy loss is not fully compensated by newly formed protein–ligand interactions. LeScore was optimized for descriptor combinations and subsequently validated using a testing data set, achieving a Pearson correlation coefficient (rp) of 0.53 in the training set and 0.52 in the testing set. To evaluate its screening capability, a subset of the Directory of Useful Decoys: Enhanced (DUD-E) was used. And LeScore achieved an AUC of 0.71 for specific targets, outperforming models without HBP and enhancing the ranking and classification of active compounds. Overall, LeScore provides a robust tool for improving virtual screening, especially in cases where hydrogen bonding is crucial for ligand binding.

Method

LeScore is formulated as a linear combination of descriptors, including van der Waals interactions, hydrogen bond energy, ligand strain energy, and newly integrated HBP. The function was optimized using multiple linear regression (MLR) on the PDBbind 2019 dataset. Evaluation metrics, such as Pearson and Spearman correlation coefficients were utilized to assess the performance of 12 descriptor combinations. Additionally, the study employed datasets from the DUD-E to evaluate LeScore’s ability to distinguish active ligands from decoys across multiple target proteins.