Accurate design of co-assembling multi-component protein nanomaterials

King, N., Bale, J.B., Sheffler, W., et al. Nature (2014)

The self-assembly of proteins into highly ordered nanoscale architectures is a hallmark of biological systems. The sophisticated functions of these molecular machines have inspired the development of methods to engineer self-assembling protein nanostructures; however, the design of multi-component protein nanomaterials with high accuracy remains an outstanding challenge. Here we report a computational method for designing protein nanomaterials in which multiple copies of two distinct subunits co-assemble into a specific architecture. We use the method to design five 24-subunit cage-like protein nanomaterials in two distinct symmetric architectures and experimentally demonstrate that their structures are in close agreement with the computational design models. The accuracy of the method and the number and variety of two-component materials that it makes accessible suggest a route to the construction of functional protein nanomaterials tailored to specific applications.

Removing T-cell epitopes with computational protein design

King, C. et al. PNAS 111, 8577-82 (2014)

Immune reposes can make protein therapeutics ineffective or even dangerous. Here we describe a general computational protein design method for reducing immunogenicity by eliminating known and predicted T-cell epitopes and maximizing the content of human peptide sequences without disrupting protein structure and function. We show that the method recapitulates previous experimental results on immunogenicity reduction, and we use it to disrupt T-cell epitopes in GFP and Pseudomonas exotoxin A without disrupting function.

Design of activated serine-containing catalytic triads with atomic-level accuracy

Rajagopalan, S., Wang, C., et al. Nat Chem Bio. 10(5), 386-391 (2014)

A challenge in the computational design of enzymes is that multiple properties, including substrate binding, transition state stabilization and product release, must be simultaneously optimized, and this has limited the absolute activity of successful designs. Here, we focus on a single critical property of many enzymes: the nucleophilicity of an active site residue that initiates catalysis. We design proteins with idealized serine-containing catalytic triads and assess their nucleophilicity directly in native biological systems using activity-based organophosphate probes. Crystal structures of the most successful designs show unprecedented agreement with computational models, including extensive hydrogen bonding networks between the catalytic triad (or quartet) residues, and mutagenesis experiments demonstrate that these networks are critical for serine activation and organophosphate reactivity. Following optimization by yeast display, the designs react with organophosphate probes at rates comparable to natural serine hydrolases. Co-crystal structures with diisopropyl fluorophosphate bound to the serine nucleophile suggest that the designs could provide the basis for a new class of organophosphate capture agents.

Proof of principle for epitope-focused vaccine design

Correia, B.E. et al. Nature 507, 201-6 (2014)

Vaccines prevent infectious disease largely by inducing protective neutralizing antibodies against vulnerable epitopes. Several major pathogens have resisted traditional vaccine development, although vulnerable epitopes targeted by neutralizing antibodies have been identified for several such cases. Hence, new vaccine design methods to induce epitope-specific neutralizing antibodies are needed. Here we show, with a neutralization epitope from respiratory syncytial virus, that computational protein design can generate small, thermally and conformationally stable protein scaffolds that accurately mimic the viral epitope structure and induce potent neutralizing antibodies. These scaffolds represent promising leads for the research and development of a human respiratory syncytial virus vaccine needed to protect infants, young children and the elderly. More generally, the results provide proof of principle for epitope-focused and scaffold-based vaccine design, and encourage the evaluation and further development of these strategies for a variety of other vaccine targets, including antigenically highly variable pathogens such as human immunodeficiency virus and influenza.

Computational design of ligand-binding proteins with high affinity and selectivity

Tinberg, C.E., Khare, S.D., et al. Nature. 501(7466), 212-216. (2013) 



We describe a computational approach for designing proteins that bind small molecules and use it to create binders for digoxin, a steroid toxin deriving from the foxglove plant. The method relies on the explicit design of highly energetically favorable interactions with the ligand. Directed laboratory evolution guided by deep mutational sequencing can be used to tailor the binding affinity of the designed protein towards the application of interest. The computational method presented here should enable the development of a new generation of biosensors and diagnostics for the detection of small molecule compounds as well as therapeutic proteins for the treatment of small molecule toxicity.

Computational design of an α-gliadin peptidase

Gordon, S.R., Stanley, E.J., et al. J Am Chem Soc. 134(50), 20513-20520. (2012)

The ability to rationally modify enzymes to perform novel chemical transformations is essential for the rapid production of next-generation protein therapeutics. Here we describe the use of chemical principles to identify a naturally occurring acid-active peptidase, and the subsequent use of computational protein design tools to reengineer its specificity toward immunogenic elements found in gluten that are the proposed cause of celiac disease. The engineered enzyme exhibits a k(cat)/K(M) of 568 M(-1) s(-1), representing a 116-fold greater proteolytic activity for a model gluten tetrapeptide than the native template enzyme, as well as an over 800-fold switch in substrate specificity toward immunogenic portions of gluten peptides. The computationally engineered enzyme is resistant to proteolysis by digestive proteases and degrades over 95% of an immunogenic peptide implicated in celiac disease in under an hour. Thus, through identification of a natural enzyme with the pre-existing qualities relevant to an ultimate goal and redefinition of its substrate specificity using computational modeling, we were able to generate an enzyme with potential as a therapeutic for celiac disease.


Koga, N., Tasumi-Koga R., et al., Nature. 491(7423), 222-227. (2012)

We describe an approach to designing ideal protein structures stabilized by completely consistent local and non-local interactions. The approach is based on a set of rules relating secondary structure patterns to protein tertiary motifs, which make possible the design of strongly funneled protein folding energy landscapes.  Guided by these rules, we designed sequences predicted to fold into ideal protein structures consisting of alpha helices, beta strands, and minimal loops. Designs for five different topologies were found to be monomeric, very stable, and adopt structures in solution nearly identical to the computational models. These results illuminate how the folding funnels of natural proteins arise and provide the foundation for engineering a new world of functional proteins free from natural. 

Computational Design of Self-Assembling Protein Nanomaterials with Atomic Level Accuracy

King, N.P., Sheffler, W., et al. Science. 336(6085), 1171-1174. (2012)

We describe a general computational method for designing proteins that self-assemble to a desired symmetric architecture.  Protein building blocks are docked together symmetrically to identify complementary packing arrangements, and low-energy protein-protein interfaces are then designed between the building blocks in order to drive self-assembly.  Here we use trimeric protein building blocks to design a 24-subunit, 13 nm diameter complex with octahedral symmetry and two related variants of a 12-subunit, 11 nm diameter complex with tetrahedral symmetry.  The designed proteins assembled to the desired oligomeric states in solution, and crystal structures of the complexes revealed that the resulting materials closely match the design models. The method can be used to design a wide variety of self-assembling protein nanomaterials.

Atomic model of the type III secretion system needle

The ability of Gram-negative bacteria, such as the agents of plague, dysentery and typhoid fever to infect host cells is dependent on a syringe-like molecular machine known as the Type-III secretion system (T3SS). The core of T3SS consists of a hollow filament, the needle; composed of identical, symmetric repeats of an 80-residue protein, the needle forms a conduit for unfolded effector proteins to be delivered to the cytoplasm of the host cell at the early stages of infection. Determination of the three-dimensional structure of the needle by X-ray crystallography or solution NMR has been challenging thus far due to the inherent non-crystallinity and insolubility of the complex. Modeling based on docking of the known monomeric structure into EM reconstructions of isolated needle particles has been limited by the inability of such approaches to capture conformational change as a result of tertiary interactions. We have developed an alternative, hybrid approach through a combination of solid-state NMR data collected in the group of Prof. Adam Lange at the Max Planck Institute, previously published EM data and Rosetta modeling to determine a high-resolution model of in vitro reconstructed needle filaments. We show that the 80-residue subunits form a right-handed helical assembly with roughly 11 subunits per two turns of a 24A-pitch helix. While the more conserved C-terminus is forming key stabilizing towards the inside of the 25A needle pore, the more sequence variant N-terminus is positioned on the surface of the structure. The approach developed here presents a powerful way towards structure determination of large protein assemblies.

Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing

Whitehead, T. A., Chevalier A. et al. Nature biotechnology (2012)

We show that comprehensive sequence-function maps obtained by deep sequencing can be used to reprogram interaction specificity and to leapfrog over bottlenecks in affinity maturation by combining many individually small contributions not detectable in conventional approaches. We use this approach to optimize two computationally designed inhibitors against H1N1 influenza hemagglutinin and, in both cases, obtain variants with subnanomolar binding affinity. The most potent of these, a 51-residue protein, is broadly cross-reactive against all influenza group 1 hemagglutinins, including human H2, and neutralizes H1N1 viruses with a potency that rivals that of several human monoclonal antibodies, demonstrating that computational design followed by comprehensive energy landscape mapping can generate proteins with potential therapeutic utility.

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