Dissecting the Protein Dynamics Coupled Ligand Binding with Kinetic Models and Single-Molecule FRET

Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning

Rational structure-based drug design relies on accurate predictions of protein-ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting binding affinity have shown promise in computational drug design, certain approaches have faced criticism for their potential to inadequately capture the fundamental physical interactions between ligands and their macromolecular targets or for being susceptible to dataset biases. Herein, we propose to include bond-critical points based on the electron density of a protein-ligand complex as a fundamental physical representation of protein-ligand interactions. Employing a geometric deep learning model, we explore the usefulness of these bond-critical points to predict absolute binding affinities of protein-ligand complexes, benchmark model performance against existing methods, and provide a critical analysis of this new approach. The models achieved root-mean-squared errors of 1.4-1.8 log units on the PDBbind dataset, and 1.0-1.7 log units on the PDE10A dataset, not indicating significant advantages over benchmark methods, and thus rendering the utility of electron density for deep learning models context-dependent. The relationship between intermolecular electron density and corresponding binding affinity was analyzed, and Pearson correlation coefficients r > 0.7 were obtained for several macromolecular targets.

Single-Molecule Kinetic Analysis of Oxygenation of Co(II) Porphyrin at the Solution/Solid Interface

Kinetic analysis of surface reactions at the single molecule level is important for understanding the influence of the substrate and solvent on reaction dynamics and mechanisms, but it is difficult with current methods. Here we present a stochastic kinetic analysis of the oxygenation of cobalt octaethylporphyrin (CoOEP) at the solution/solid interface by monitoring fluctuations from equilibrium using scanning tunneling microscopy (STM) imaging. Image movies were used to monitor the oxygenated and deoxygenated state dwell times. The rate constants for CoOEP oxygenation are k_a = 4.9 × 10^-6 s^-1·Torr^-1 and k_d = 0.018 s^-1. This is the first use of stochastic dwell time analysis with STM to study a chemical reaction, and the results suggest that it has great potential for application to a wide range of surface reactions. Expanding these stochastic studies to further systems is key to unlocking kinetic information for surface-confined reactions at the molecular level, especially at the solution/solid interface.