Research

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).

In recent years, molecular informatics has transformed from a niche discipline into a driving force of the research and development of functional small molecules such as drugs and agrochemicals. Advanced algorithms as well as powerful computer hardware are now opening unprecedented opportunities for the targeted design of safe and efficacious small molecules. However, the full potential of computational methods in the biosciences is by far not exploited yet. One of the main reasons for this situation is the fact that the most powerful technologies in molecular informatics, machine learning and simulations in particular, depend on the availability of substantial amounts of high-quality data for development and validation. Despite recently launched initiatives to boost collaborative research and learning, the vast majority of high-quality chemical, biological and structural data remain behind corporate firewalls, inaccessible for research by experts in academia. This initiative for the Christian Doppler Laboratory for Molecular Informatics in the Biosciences seeks to push the frontiers of machine learning and molecular dynamics simulations technologies for the prediction of small-molecule bioactivity by supporting three expert academic research groups of the University of Vienna and the University of Natural Resources and Life Sciences (BOKU) with big data on the chemical and biological properties of small molecules, and with significant capacities for experimental testing and method validation. The unique synergy that will be generated by this consortium stems from two important factors: First, the two industry partners of this consortium have strong interest in cheminformatics but their business areas are non-competing. Second, and from a scientific point highly important, these industry partners focus on distinct chemical spaces, opening a unique opportunity for academics to boost the capacity and applicability of in silico methods with uniquely diverse, high-quality data.