Modeling of macromolecular assemblies in the cellular environment: linking theory and simulations with experiment – CellModeling

Recent developments in the field of protein structure prediction, notably those based on AI, showed that protein models can routinely reach unprecedented levels of near-experimental accuracy. In this context, modeling protein interactions in the living cell is becoming more central than ever before. Classical techniques for modeling protein interactions include molecular docking and biomolecular simulations. While the latter can give access to the dynamics and kinetics of the interactions, they are either relatively slow if carried out at the all-atom representation or largely coarse-grained, with one particle representing a protein. Consequently, there are only a few examples of simulations at the scale of the entire cell. Molecular docking methods are more efficient, especially those relying on systematic Fast Fourier Transform (FFT) sampling algorithms. However, they lack a reliable account of the kinetics of the association, and modeling the competition between several molecules is difficult in this framework. Due to these current limits in temporal and spatial resolutions, there has been a distinct lack of investigation on how the crowded environment of the cell impacts the physiological function of protein interactions in vivo.

Grounded on our preliminary results published in PNAS last year, this proposal aims to address this gap through the application of a novel framework for modeling the dynamics of protein interactions in crowded environments combined with detailed experimental tests. We aim to bridge the two simulation approaches and reach unprecedented simulation timescales of milliseconds to seconds at all-atom resolution while correctly accounting for intermolecular interactions and protein flexibility. We will model bacterial proteasome assembly kinetics in a crowded environment as a test system. We will study the formation of the proteasome in vivo with mass spectrometry (MS) and cryo-EM techniques and compare it with the simulation results.

The two PIs, Grudinin and Deeds, are recognized leaders in their respective fields with complementary expertise in the study of protein interactions in the cell. They have been actively collaborating for about three years on the development of novel approaches to simulating the dynamics of protein interactions in crowded environments. This proposal builds off of this already strong collaboration. The proposed project involves the complementary expertise of the PIs: Grudinin in the development of rigorous and efficient computational methods and Deeds in the application of biophysical modeling, computational tools, and wet-lab experiments to answer critical biological questions.

The long-term goals of this project are to gain insights into fundamental principles of molecular processes in living systems, including dynamics and kinetics of macromolecular interactions, leading to the structure-based description of the cell. We will publicly release the developed tools and communicate our research results in popular science formats. The younger members of the consortium will be strongly encouraged to regularly present their work at international events and visit partners' teams.