Optimization of predictive energy landscapes for membrane and globular protein structure prediction
Kim, Bobby Lee
Wolynes, Peter G.
Doctor of Philosophy
This thesis discusses recent results using the Associative-memory, Water-mediated, Structure and Energy Model (AWSEM), an optimized, coarse-grained molecular dynamics model. AWSEM and its membrane protein extension, AWSEM-membrane, are capable of de novo protein structure prediction and through the use of statistical estimators, allow construction of free energy landscapes which can provide insight about the dynamics of protein systems. We review the origins of energy landscape theory and how one can learn energy functions using the results of spin glass-inspired statistical mechanics models. We explore the similarities and differences between the energy landscapes of proteins that have been selected by nature and those of some proteins designed by humans. We also study how robust the folding of these designs would be to the simplification of the sequences using fewer amino acid types. Using an optimized extension of AWSEM, AWSEM-membrane, we explore the hypothesis that the folding landscapes of membrane proteins are funneled once the proteins’ topology within the membrane is established. We also show that the AWSEM-membrane force field is able to sample near native binding interfaces of several oligomeric systems.
protein folding; protein structure prediction; energy landscape theory; molecular dynamics