Multistate Computational Protein Design: Theories, Methods, and Applications

Abstract

Traditional computational protein design (CPD) calculations model sequence perturbations and evaluate their stabilities using a single fixed protein backbone template in an approach referred to as single‐state design (SSD). However, certain design objectives require the explicit consideration of multiple conformational states. Cases where a multistate framework may be advantageous over the single‐state approach include the computer aided discovery of new enzyme substrates, the prediction of protein stabilities, and the design of protein dynamics. These design objectives can be tackled using multistate design (MSD). However, it is often the case that a design objective requires the consideration of a protein state having no available structure information. For such circumstances the multistate framework cannot be applied. In this thesis I present the development of two template and ensemble preparation methodologies and their application to three projects. The purpose of which is to demonstrate the necessary ensemble modeling strategies to overcome limitations in available structure information. Particular emphasis is placed on the ability to recapitulate experimental data to guide modelling of the design space. Specifically, the use of MSD allowed for the accurate prediction of a methyltransferase recognition motif and new substrates, the prediction of mutant sequence stabilities with quantitative accuracy, and the design of dynamics into the rigid Gβ1 scaffold producing a set of dynamic variants whose tryptophan residue exchanges between two conformations on the millisecond timescale. Implementation of both the ensemble, coordinate perturbation followed by energy minimization (PertMin), and template, rotamer optimization followed by energy minimization (ROM), generation protocols developed here allow for exploration and manipulation of the structure space enabling the success of these applications.