Theoretical Characterization of Kinetically Stable Proteins
Co-Investigator: Angel Garcia
Professor of Physics & Senior Constellation Chaired Professor in Biocomputation and Bioinformatics, Rensselaer Polytechnic Institute
In this module, we propose to study the Transition State Ensembles (TSE) of kinetically trapped proteins. We will determine the TSE by using multiple scale models ranging from atomic models with explicit solvent treatment, to ca and all atom minimalist models. Once we identify the TSE, we will examine interactions that stabilize the folded state ensemble, and destabilize the TSE. Features that are likely to be important are electrostatic interactions, electrostatic complementarity, hydrophobic core formation, water penetration, and dynamics. The complexity of the models used will be tailored to the protein size and complexity of the system. One simple approach to understand kinetically trapped proteins is to use a two state model for the folding/unfolding transition, and defining the folding, unfolding, and transition state ensembles (TSE). In instances (which are more likely to be the case for larger multi domain proteins that form multimers) where the folding kinetic is not two states, we can still identify the rate limiting step for unfolding, and call it the TSE. Within this simplified model, slow unfolding kinetics is due to a large energy difference between the folded and TSE states. Approaches that identify features associated with protein over stabilization by electrostatics (cite Sanchez Ruiz), hydrophobic, or protein dynamics, are based on the structure of the folded state. In the case of kinetically trapped states, we must consider the TSE properties. The TSE, being a high energy state, occurs rarely and cannot be easily characterized by equilibrium methods. However, phi value analysis and high T MD simulations, and coarse grained models of the folding/unfolding kinetics are able to define many features of the TSE. In many instances, the folding kinetics is strongly determined by the protein topology. In those instances, coarse grained models, such as Go models (C alpha and all atom models) and knowledge-based models can accurately define the effect of mutation on the folding/unfolding kinetics. Also, atomic, explicit solvent simulations have successfully been used to describe the phi values, TSE, and folding/unfolding kinetics of proteins and peptides.