 |
Center for Nonlinear Studies |
Anurag Sethi Macromolecules can be visualized as information processing machines that have evolved to perform certain tasks in specific contexts. Information is typically transmitted within the context of a single macromolecule or it is communicated through a cascade of proteins to ensure a particular outcome within the cell. I will discuss two methods that I have developed recently to theoretically model disordered regions in signaling proteins: 1) Signaling pathways employ a number of proteins to transfer information over long distances and timescales. Signaling proteins often utilize multivalent interactions to increase the specificity and affinity for their binding partners. I employed a combination of evolutionary analysis, polymer models, molecular dynamics simulations, and thermodynamic modeling to quantify the stoichiometry of multivalent complexes formed under physiological conditions. 2) One of the proteins in a multivalent signaling complex typically contains regions that lack any stable structure. The heterogeneity in conformations displayed by large disordered regions makes it difficult to characterize these proteins at a high resolution both experimentally and computationally. I developed a network analysis method combined with molecular dynamics simulations to split large disordered regions into small minimally interacting peptides that can be investigated individually using experiments and simulations at a high resolution. These methods will aid in gaining an understanding of how biomolecules are able to function efficiently and can be applied in biomolecular and drug design studies.
Host: Kipton Barros, T-4 and CNLS