Research

Computational chemistry has made significant progress in studying biomolecular properties over the last few decades. Molecular simulations provide a detailed examination of various macroscopic thermodynamic and structural properties. However, the accuracy of these calculated properties heavily depends on the quality of the empirical force field (MM) used to evaluate the underlying interactions. Empirical force fields, with their simplistic approach of assigning partial point charges to atoms, often fail to describe many nonclassical phenomena such as charge transfer, polarization or excited states. As a result, a quantum mechanical (QM) description becomes inevitable. Unfortunately, the computational effort required for QM calculations is immense, making continuous trajectories at the QM level unfeasible even for small systems. To address this challenge, hybrid QM/MM techniques have been developed. These methods bridge the gap between accuracy and time efficiency by enabling a precise QM-level description for a critical small part of a system, known as the inner region. More recently, machine-learned interatomic potentials (MLIPs) have demonstrated the ability to learn the potential energy surface at the underlying reference QM level, providing a significant speed-up in calculations. The BuRNN methodology seeks to further enhance traditional QM/MM techniques by transitioning to the MLIP/MM level, resulting in speed-ups by orders of magnitude, and by introducing an additional buffer region that is calculated at both levels of theory to reduce artifacts at the QM/MM interface. Since BuRNN has been shown to work for a hexaaquairon(III) complex in water, our first aim is to further develop and evaluate the BuRNN methodology in increasingly complex systems, such as protein-ligand systems and metalloorganic compounds. Our second aim involves implementing a robust workflow to generate MLIPs for complex systems by incorporating enhanced sampling techniques at the MLIP level. Finally, we aim to advance relative binding free-energy calculations by allowing perturbations at the MLIP level, thereby improving protein-ligand binding affinity predictions and enabling accurate calculations of free energies for coordinative binding in metalloorganic complexes.

This project is based on the integration of knowledge from biology, chemistry, and physics. It focuses on advancing understanding of how the physical properties of liquids influence the behavior of ligand molecules — small particles that form the basis of many medicines and can bind to proteins in the body. Special attention is given to how these molecules behave both in solutions and directly inside the "pockets" of proteins — small “niches” where important processes affecting cell function occur. The primary objective is to determine how external factors, such as the composition of the surrounding liquid or temperature (especially within the range close to human body temperature), affect the movement and interactions of these molecules with their liquid environment. Furthermore, the study seeks to clarify how these molecules interact with proteins and how these interactions relate to changes in the structure and behavior of solutions in such systems. Modern computational techniques — molecular modeling — are employed, enabling visualization of the movement and interaction of individual molecules, akin to a slow-motion movie. Additionally, physics methods traditionally used to study liquids and solutions are adapted and improved to better understand the complex biological systems involving liquids, ligands, and proteins. The project’s main innovation lies in revealing how molecules “dock” with proteins at the smallest scale — literally from the inside. Various computational models are compared to identify which best represent real processes. This is crucial because precise knowledge of the number of water molecules surrounding a drug molecule in a protein pocket significantly aids the interpretation of experimental data. Ultimately, the results contribute not only to a deeper understanding of fundamental molecular interactions but also to the development of new ideas for experimental research. Importantly, these insights support the creation of new medicines. Given the ongoing quest in modern medicine for effective and safe drugs, understanding molecular interactions at a fundamental level can substantially accelerate this process and reduce associated costs.

Based on the state-of-the-art of science, we assume that the nature of ligand binding to proteins depends significantly on the properties of the surrounding liquid medium, which is a full-fledged "player" in protein-ligand systems. Based on this assumption, we hypothesize that changes in the local structure of the liquid near the ligand (solute) in the liquid medium (solvent) depend on the properties of the solvent and lead to a change in the dynamic behavior of the components of the studied liquid-ligand systems. In the case of the solvent-ligand-protein system, the dissolution of the ligand in the protein pocket indirectly affects the properties of all components of the biofluid (water, saline, etc.) - ligand - protein system, accompanied by a reorganization of the local structure and dynamics of the liquid in the protein pocket. To test the above hypothesis, we will use molecular dynamics (MD).