Methods and Applications of Computational Design in Multiple States

Thesis (Ph.D.)--University of Washington, 2023The balance between flexibility and stability is a key property of proteins, contributing to the vast area of structures and functions observed in nature. Flexibility arises from the complex energy landscape that determines the available conformational states of a protein. Over the past two decades, our ability to design proteins has advanced significantly, however, our understanding of and ability to design multistate proteins remains an area of significant challenge. The multistate design challenge hinders our ability to engineer functional proteins like enzymes, and limits the size and complexity of designed protein materials. The focus of my doctoral research has been developing multistate design approaches and applying those methods to practical design problems; a peudosymmetric trimer used as a building block for assembling large, closed, cage-like nanostructures; and re-engineering a natural enzyme to remove allosteric dependence on double-stranded DNA for activity. In both cases I started by applying simple computational methods to identify mutations that are likely to shift the equilibrium from an undesired state to a desired state. I then incorporated bioinformatics data to improve the design pipeline. This approach was applied to convert a naturally occurring, symmetric, homotrimeric protein, 1WA3, into a pseudosymmetric heterotrimer. I used the resulting pseudosymmetric trimer as a building block for designing cage-like protein assemblies. Because of the choice of trimer as starting material I was able to efficiently build cage-like structures containing 240, 540, or 960 protein chains, significantly larger than any previous computationally designed, bounded, protein nanostructure. I also applied this multistate design approach to a naturally occurring enzyme, cyclic GMP-AMP synthase (cGAS). Under normal circumstances cGAS adopts an enzymatically active conformation only when bound to double-stranded DNA (dsDNA). By applying multistate design I was able to engineer constitutively active variants of cGAS, which adopt the active conformation independent of dsDNA. We then showed the utility of CA-cGAS in an in vivo model. The methods developed here are generally applicable to multistate design problems in naturally occurring building blocks and also highlight the practical utility of the proteins engineered using these approaches

Fast and versatile sequence-independent protein docking for nanomaterials design using RPXDock.

Computationally designed multi-subunit assemblies have shown considerable promise for a variety of applications, including a new generation of potent vaccines. One of the major routes to such materials is rigid body sequence-independent docking of cyclic oligomers into architectures with point group or lattice symmetries. Current methods for docking and designing such assemblies are tailored to specific classes of symmetry and are difficult to modify for novel applications. Here we describe RPXDock, a fast, flexible, and modular software package for sequence-independent rigid-body protein docking across a wide range of symmetric architectures that is easily customizable for further development. RPXDock uses an efficient hierarchical search and a residue-pair transform (RPX) scoring method to rapidly search through multidimensional docking space. We describe the structure of the software, provide practical guidelines for its use, and describe the available functionalities including a variety of score functions and filtering tools that can be used to guide and refine docking results towards desired configurations

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology