Computational and theoretical studies of co-operative phenomena in biology at different scales
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Abstract
From the collective dynamics observed in starling murmuration to the intricate process of protein folding, co-operativity stands as a fundamental concept underlying biological phenomena across different scales. Consequently, the pursuit of a mechanistic comprehension of the complex co-operative phenomena in biology remains a central theme of biophysics. To this end, my research has been organized into two parts, which are dedicated to the understanding of co-operative phenomena in biology at two different scales respectively. The first part focuses on protein allostery (inter-residue co-operativity), a fundamental regulatory mechanism of protein function. Here, we first developed a machine learning model that helps us identify the defining molecular features of allosteric hotspots, where the results highlight the convergence and divergence of the allosteric mechanisms among homologous transcriptional factors. These findings further inspired us to build a statistical thermodynamic model, which quantitatively recapitulates in vivo experimental data and provides physical insights into the mutational effects on the allosteric response of two-domain systems. The second part focuses on studying biomolecular condensation (inter-molecular co-operativity), an emerging mechanism essential for the fast and reversible assembly of functional subcellular compartments characterized by diverse and distinct chemical features. Here, using molecular simulations and a mean-field theory, we demonstrate that biological membranes, by coupling its own phase separation to that of the surface biomolecules for co-operative domain growth, offers a robust mechanism for sensitive and selective condensate regulation. Our results add to the fundamental understanding of the mechanisms for spatiotemporal control of condensate assembly, and the revealed physical principle has broad implications beyond the specific biophysical problem.
Description
2024
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Attribution-NoDerivatives 4.0 International