Community computing allows everyone to get involved from home

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Foldit is a computer game which enables you to contribute to cutting edge scientific research. Join this free online game and help us to design new proteins to cure diseases, create new materials, and develop new ways of capturing and storing energy.

 

rosetta @ home

Rosetta@home needs your help to determine the 3-dimensional shapes of proteins in research that may ultimately lead to finding cures for some major human diseases. By running the Rosetta program on your computer while you don't need it you will help us speed up and extend our research in ways we couldn't possibly attempt without your help. You will also be helping our efforts at designing new proteins to fight diseases such as HIV, Malaria, Cancer, and Alzheimer's (See our Disease Related Research for more information). Please join us in our efforts! Rosetta@home is not for profit.

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Robetta: Full-chain Protein Structure Prediction

High thermodynamic stability of parametrically designed helical bundles

Huang, P. et. al. Science 346, 481-85. (2014) 

We describe a procedure for designing proteins with backbones produced by varying the parameters in the Crick coiled coil–generating equations. Combinatorial design calculations identify low-energy sequences for alternative helix supercoil arrangements, and the helices in the lowest-energy arrangements are connected by loop building. We design an antiparallel monomeric untwisted three-helix bundle with 80-residue helices, an antiparallel monomeric right-handed four-helix bundle, and a pentameric parallel left-handed five-helixbundle. The designed proteins are extremely stable, and their crystal structures are close to those of the design models with nearly identical core packing between the helices. The approach enables the custom design of hyperstable proteins with fine-tuned geometries for a wide range of applications.

Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information

Ovchinnikov, S. et al. eLife 3:e02030. (2014)

Do the amino acid sequence identities of residues that make contact across protein interfaces covary during evolution? If so, such covariance could be used to predict contacts across interfaces and assemble models of biological complexes. We find that residue pairs identified using a pseudo-likelihood-based method to covary across protein–protein interfaces in the 50S ribosomal unit and 28 additional bacterial protein complexes with known structure are almost always in contact in the complex, provided that the number of aligned sequences is greater than the average length of the two proteins. We use this method to make subunit contact predictions for an additional 36 protein complexes with unknown structures, and present models based on these predictions for the tripartite ATP-independent periplasmic (TRAP) transporter, the tripartite efflux system, the pyruvate formate lyase-activating enzyme complex, and the methionine ABC transporter.

A computationally designed inhibitor of an Epstein-Barr viral Bcl-2 protein induces apoptosis in infected cells

Procko, E. et al. Cell 157, 1644-1656. (2014)

Since apopotosis of infected cells can limit virus production and spread, some viruses have co-opted prosurvival genes from the host. This includes the Epstein-Barr virus gene BHRF1, a homologue of human Bcl-2 proteins that block apoptosis and are associated with cancer. Here we describe an approach where computational design and experimental optimization were used to generate a novel protein called BINDI that binds BHRF1 with picomolar affinity. BINDI recognizes the hydrophobic cleft of BHRF1 in a similar manner to other Bcl-2 protein interactions, but makes many additional contacts to acheive exceptional affinity and specificity. BINDI induces apoptosis in EBV-infected cancer lines, and when delivered with an antibody-targeted intracellular delivery carrier, BINDI suppressed tumor growth and extended survival in a xenograft disease model of EBV-positive human lymphoma. High specificity designed proteins that selectively kill target cells may provide an advantage over the toxic compounds used in current generation antibody-drug conjugates.

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 Early Edition (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.

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.

PRINCIPLES FOR DESIGNING IDEAL PROTEIN STRUCTURES

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. 


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