Roadmap-Based Methods for Studying Protein Folding Kinetics

Protein motions play an essential role in many biochemical processes. Protein folding kinetics has helped define the properties of protein motion. For example, folding rates describe the speed at which a protein folds while population kinetics give insight into the equilibrium folding process. In this paper, we present two new techniques to study kinetics-based functions for proteins such as folding rates and population kinetics. Previously, folding rates and population kinetics were calculated by methods such as the Master Equation and Monte Carlo simulation. However, these methods are computationally expensive and often impractical for full protein structures. In our previous work, we presented an approximate model, or map, of the protein folding landscape. While these maps enabled us to study some important properties of the folding process, such as secondary structure formation order, we had not been able to use them to extract kinetic measures such as folding rates or population kinetics. In this work, we show that these approximate maps in fact provide an appropriate framework for studying kinetic properties as well. In particular, we propose two new analysis techniques: Map-based Monte Carlo simulation and Map-based Master Equation solution. An important benefit of this approach is that it enables us to study the kinetics of much larger proteins that can be handled by traditional Master Equation methods or Monte Carlo simulation. We validate our map-based kinetics techniques by comparing folding rates to known experimental results. We also look in depth at the population kinetics of a variety of proteins. ∗Research supported in part by NSF Grants ACI-9872126, EIA-9975018, EIA-0103742, EIA-9805823, ACR-0081510, ACR-0113971, CCR-0113974, EIA-9810937, EIA-0079874 and by the DOE. Tapia supported in part by a Molecular Biophysics NIH Training Grant T32GM065088 and a Department of Education GAANN Fellowship. Thomas supported in part by an NSF Graduate Research Fellowship, a P.E.O. Scholarship, and a Department of Education GAANN Fellowship. “Roadmap-Based Methods...”, Tapia et al. TR06-011, Parasol Lab, Texas A&M, October 2006 1