From Biomolecular Interactions to Protein Networks: A Multiscale Modeling Study

Biomolecular cooperation is essential for life, facilitating the complex and highly regulated processes necessary for cellular function, organismal development, and maintenance. Scientists can manipulate biomolecular systems by studying these interactions for various medical, therapeutic, and biotechnological purposes. This cooperation refers to the collaborative behavior between different biomolecules that work together to successfully execute and maintain intricate biological functions, like signaling, metabolism, and genetic regulation. Molecules’ cooperation often depends on specific binding events, conformational changes, and coordinated actions. The physiological function of all living organisms depends on complex, multiscale phenomena. At the molecular level, interactions between biomolecules and their environment, such as water and membranes, or small ligands, govern specific pathways, such as trafficking and regulation. At a larger scale, the crowded cellular environment influences subcellular compartments and whole-cell behavior. Among biological macromolecules, proteins are essential for cellular development and function. Their coordination with other molecules regulates most of their activities. Complexes are well-organized assemblies whose parts interact to perform specific functions, often essential for cellular processes. These complexes can involve biological macromolecules of the same or different types. Protein and protein-nucleic acid complexes perform several activities, such as signal transduction, metabolism, gene expression, and structural integrity. They often carry out tasks that a single protein cannot autonomously accomplish. Protein and complex interactions and aggregation are crucial in healthy and pathological conditions. Some molecules naturally aggregate under physiological conditions. Additionally, complexes can undergo phase separation to form liquid-like systems that may condense into aggregates, as seen in membraneless organelles. Aggregation is also associated with many disorders, where misfolded proteins promote aggregation, disrupting cellular functions and causing toxicity. Understanding the molecular mechanisms of proteins’ and complexes’ behavior is key for comprehending physiological and pathological situations. By combining i) advanced theoretical and computational techniques and ii) sophisticated and reliable models, we can gain insights into molecular processes at different time- and space scales. By investigating physio-chemical and topological features of mechanisms relying on molecular coordination with the correct instruments, we can decipher and drive experiments and provide a magnifying glass at a subcellular scale to deeply understand phenomena. We use computer simulations, statistical mechanics, and fractal theory to give a powerful framework for studying biomolecular interactions, with insights into molecular dynamic behavior under varying conditions. In particular, we obtain a deeper knowledge of the mechanism underlying the liquid-phase transition of nucleosomes and demonstrate that is possible to predict the organization of the large-scale condensate by investigating the lower-scale topology. In addition, though currently underutilized, statistical mechanics applied to protein-protein interactions is highly effective in identifying key proteins, uncovering pathways, and distinguishing between healthy and diseased states. By analyzing various protein-protein interaction models related to the physiopathology of the Unfolded Protein Response mechanism, we observe similarities among phylogenetically related organisms and identify conserved pathways and key proteins. Furthermore, we emphasize commonalities across different pathological conditions and the specific impact of diseases on the physiological mechanism. This understanding may facilitate the identification of shared molecular pathways among diseases, potentially aiding in drug development and the broader application of therapeutic strategies.