Exploring the landscape of protein-ligand interaction energy using probabilistic approach

PIDiff: Physics informed diffusion model for protein pocket-specific 3D molecular generation

Designing drugs capable of binding to the structure of target proteins for treating diseases is essential in drug development. Recent remarkable advancements in geometric deep learning have led to unprecedented progress in three-dimensional (3D) generation of ligands that can bind to the protein pocket. However, most existing methods primarily focus on modeling the geometric information of ligands in 3D space. Consequently, these methods fail to consider that the binding of proteins and ligands is a phenomenon driven by intrinsic physicochemical principles. Motivated by this understanding, we propose PIDiff, a model for generating molecules by accounting in the physicochemical principles of protein-ligand binding. Our model learns not only the structural information of proteins and ligands but also to minimize the binding free energy between them. To evaluate the proposed model, we introduce an experimental framework that surpasses traditional assessment methods by encompassing various essential aspects for the practical application of generative models to actual drug development. The results confirm that our model outperforms baseline models on the CrossDocked2020 benchmark dataset, demonstrating its superiority. Through diverse experiments, we have illustrated the promising potential of the proposed model in practical drug development.

Effect of molecular properties of the protein-ligand complex on the prediction accuracy of AutoDock

Molecular docking approach has been extensively used to predict the ligand's binding conformation in the binding pocket of protein. However, its prediction accuracy is still limited and highly dependent on target protein-ligand complexes. In this study, we investigated the effects of ligand torsion number, ligand hydrophobicity, and binding-site hydrophobicity on the prediction accuracy of Autodock, a popular molecular docking tool, combinatorially as well as respectively. A clear understanding of how these properties affect the prediction accuracy was observed when these properties were studied combinatorially rather than individually. The combination of low ligand torsion number-hydrophilic ligand-hydrophobic binding site provided the best prediction accuracy while the high ligand torsion number-hydrophilic ligand-hydrophobic binding pocket combination showed the least prediction accuracy. This study allowed us to determine the molecular properties of complex, showing relatively higher or low prediction accuracy and can be employed as a reference in the molecular docking studies using Autodock.

Estimation of the protein-ligand interaction energy for model building and validation

Macromolecular X-ray crystallography is one of the main experimental techniques to visualize protein-ligand interactions. The high complexity of the ligand universe, however, has delayed the development of efficient methods for the automated identification, fitting and validation of ligands in their electron-density clusters. The identification and fitting are primarily based on the density itself and do not take into account the protein environment, which is a step that is only taken during the validation of the proposed binding mode. Here, a new approach, based on the estimation of the major energetic terms of protein-ligand interaction, is introduced for the automated identification of crystallographic ligands in the indicated binding site with ARP/wARP. The applicability of the method to the validation of protein-ligand models from the Protein Data Bank is demonstrated by the detection of models that are `questionable' and the pinpointing of unfavourable interatomic contacts.