Devarajan Thirumalai of the University of Maryland is supported by an award from the Theoretical and Computational Chemistry division for studies that discover new principles governing the folding of larger proteins under conditions that more closely mimic the cellular environment. These studies span a wide range of scales, from the single molecule to chaperone-assisted rescue of substrate proteins. Particular problems include 1) understanding single molecule spectroscopy; 2) examining folding and kinetics of peptides under confinement, 3) modeling the effects of crowding on protein folding and 4) simulating chaperonin-assisted folding. In order to solve these problems Thirumalai uses a variety of theoretical and computational methods. They include ideas from statistical mechanics, principles of polymer and colloid science, and novel simulation methods.
This research is addressing one of the central challenges of modern biology: to understand the physics and dynamics of large molecules such as proteins in a living cell. Thirumalai's work is expected to provide a conceptual framework for understanding how proteins fold. The outcome of the researches will set the stage for providing a quantitative and integrated picture of folding and dynamics with potential applications to a number of other topics including protein-protein and protein-RNA association.
This project is supported by the Theoretical and Computational Chemistry Program in a co-funding arrangement with the Molecular Biophysics Program and with the Physics of Living Systems Program.
The over arching goal of the work conducted during the funding period was to provide hoe proteins, which are the wok horses performing myriads of functions in cells, adopt three-dimensional structure. It is known that if they do not adopt the shapes needed for function they could aggregate, leading to a number of diseases. We made major advances using theoretical and computational methods to describe their folding and function. The highlights of our accomplishments are: 1) Protein Folding: For the first time, we were able to create models and use computer simulations to precisely predict how a completely unfolded protein reaches the folded functionally competent state. Our predictions for large proteins such as Green Fluorescent Protein (GFP) quantitatively explained all the known experiments. By understanding this process precisely one can design variants of GFP, which will become increasingly important because these proteins are used as markers to image in detail how cells function. 2) Describing cellular protein folding: A major gap has existed between folding in the laboratory and how those lessons can be used understand the corresponding processes in cells. It turns out that cells are very crowded containing other proteins, RNA, lipids, and ribosomes. How to describe folding in such a crowded environment has remained a major challenge. We created novel theories to understand how crowding affects folding process. This is a complicated problem and we were able to dissect it into bite size pieces by solving subsets of issues quantitatively. As a result, we have provided major guidance for understanding how proteins even in a cellular environment can facilitate folding. Experimentalists have used these theories in analyzing their data. 3) Chaperones to the rescue: Folding is such a key cellular activity that in cases where this does not occur spontaneously there are helper proteins that assist in folding. The bacterial chaperone, GroEL, has been the most well studied system. It is not possible to understand the mechanism by which GroEL helps proteins fold. We realized that GroEL is a machine that consumes energy (generated by hydrolysis) and gives those proteins, which tend to misfold another chance to fold. In the funding period, we translated this picture into precise model capable of rationalizing all known experiments. This is the first and only theory that can account for how this complex machine works in solving a fundamental cellular problem, which is to help proteins reach their functional state.
