Biology subjects

Dowling, Q.

Publications and source records attributed to Dowling, Q..

4 recordsLinked to original sources

bioRxiv2023

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

The design of novel protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. A new generation of deep learning methods promises 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.

biochemistry↗

bioRxiv2023

Hierarchical design of pseudosymmetric protein nanoparticles

Discrete protein assemblies ranging from hundreds of kilodaltons to hundreds of megadaltons in size are a ubiquitous feature of biological systems and perform highly specialized functions 1-3. Despite remarkable recent progress in accurately designing new self-assembling proteins, the size and complexity of these assemblies has been limited by a reliance on strict symmetry 4,5. Inspired by the pseudosymmetry observed in bacterial microcompartments and viral capsids, we developed a hierarchical computational method for designing large pseudosymmetric self-assembling protein nanomaterials. We computationally designed pseudosymmetric heterooligomeric components and used them to create discrete, cage-like protein assemblies with icosahedral symmetry containing 240, 540, and 960 subunits. At 49, 71, and 96 nm diameter, these nanoparticles are the largest bounded computationally designed protein assemblies generated to date. More broadly, by moving beyond strict symmetry, our work represents an important step towards the accurate design of arbitrary self-assembling nanoscale protein objects.

bioengineering↗

bioRxiv2022

Accurate Computational Design of 3D Protein Crystals

Protein crystallization plays a central role in structural biology1, with broad impact2 in pharmaceutical formulation3, drug delivery4, biosensing5, and biocatalysis6,7. Despite this importance, the process of protein crystallization remains poorly understood and highly empirical8-10, with largely unpredictable crystal contacts, lattice packing arrangements, and space group preferences, and the programming of protein crystallization through precisely engineered sidechain-sidechain interactions across multiple protein-protein interfaces is an outstanding challenge. Here we develop a general computational approach to designing three-dimensional (3D) protein crystals with pre-specified lattice architectures at atomic accuracy that hierarchically constrains the overall degree of freedoms (DOFs) of the system. We use the approach to design three pairs of oligomers that can be individually purified, and upon mixing, spontaneously self-assemble into large 3D crystals (>100 m). Small-angle X-ray scattering and X-ray crystallography show these crystals are nearly identical to the computational design models, with the design target F4132 and I432 space groups and closely corresponding overall architectures and protein-protein interfaces. The crystal unit cell dimensions can be systematically redesigned while retaining space group symmetry and overall architecture, and the crystals are both extremely porous and highly stable, enabling the robust scaffolding of inorganic nanoparticle arrays. Our approach thus enables the computational design of protein crystals with high accuracy, and since both structure and assembly are encoded in the primary sequence, provides a powerful new platform for biological material engineering.

synthetic biology↗

bioRxiv2022

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. Author SummaryProtein design methodologies are now able to generate, through a stepwise approach, a wide variety of self-assembling protein structures that begin to rival the structural complexity of naturally occurring protein nanomachines. Efficient methods for docking oligomeric protein building blocks in user-defined target symmetries are central to these techniques. We developed RPXDock as a fast and versatile method to systematically dock pre-existing proteins together into a multitude of asymmetrical and symmetrical architectures. RPXdock is also readily extendable to future applications through the addition of new symmetries, score functions, and filtering criteria.

bioengineering↗