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

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)

The ability to design proteins with high affinity and selectivity for any given small molecule is a rigorous test of our understanding of the physiochemical principles that govern molecular recognition. Attempts to rationally design ligand-binding proteins have met with little success, however, and the computational design of protein-small-molecule interfaces remains an unsolved problem. Current approaches for designing ligand-binding proteins for medical and biotechnological uses rely on raising antibodies against a target antigen in immunized animals and/or performing laboratory-directed evolution of proteins with an existing low affinity for the desired ligand, neither of which allows complete control over the interactions involved in binding. Here we describe a general computational method for designing pre-organized and shape complementary small-molecule-binding sites, and use it to generate protein binders to the steroid digoxigenin (DIG).Of seventeen experimentally characterized designs, two bind DIG; the model of the higher affinity binder has the most energetically favourable and pre-organized interface in the design set. A comprehensive binding-fitness landscape of this design, generated by library selections and deep sequencing, was used to optimize its binding affinity to a picomolar level, and X-ray co-crystal structures of two variants show atomic-level agreement with the corresponding computational models. The optimized binder is selective for DIG over the related steroids digitoxigenin, progesterone and β-oestradiol, and this steroid binding preference can be reprogrammed by manipulation of explicitly designed hydrogen-bonding interactions. The computational design method presented here should enable the development of a new generation of biosensors, therapeutics and diagnostics.

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. 


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.

Computational redesign of a mononuclear zinc metalloenzyme for organophosphate hydrolysis

The ability to redesign enzymes to catalyze noncognate chemical transformations would have wide-ranging applications. We developed a computational method for repurposing the reactivity of metalloenzyme active site functional groups to catalyze new reactions. Using this method, we engineered a zinc-containing mouse adenosine deaminase to catalyze the hydrolysis of a model organophosphate with a catalytic efficiency (k(cat)/K(m)) of ∼10(4) M(-1) s(-1) after directed evolution. In the high-resolution crystal structure of the enzyme, all but one of the designed residues adopt the designed conformation. The designed enzyme efficiently catalyzes the hydrolysis of the R(P) isomer of a coumarinyl analog of the nerve agent cyclosarin, and it shows marked substrate selectivity for coumarinyl leaving groups. Computational redesign of native enzyme active sites complements directed evolution methods and offers a general approach for exploring their untapped catalytic potential for new reactivities.

 

Increased Diels-Alderase activity through backbone remodeling guided by Foldit players

Eiben, C. B., Siegel J. B., Bale J. B.  et alNature biotechnology. 30(2), 190-2. (2012)


Computational enzyme design holds promise for the production of renewable fuels, drugs and chemicals. De novo enzyme design has generated catalysts for several reactions, but with lower catalytic efficiencies than naturally occurring enzymes. Here we report the use of game-driven crowdsourcing to enhance the activity of a computationally designed enzyme through the functional remodeling of its structure. Players of the online game Foldit were challenged to remodel the backbone of a computationally designed bimolecular Diels-Alderase to enable additional interactions with substrates. Several iterations of design and characterization generated a 24-residue helix-turn-helix motif, including a 13-residue insertion, that increased enzyme activity >18-fold. X-ray crystallography showed that the large insertion adopts a helix-turn-helix structure positioned as in the Foldit model. These results demonstrate that human creativity can extend beyond the macroscopic challenges encountered in everyday life to molecular-scale design problems.

Substrate in cyan. DA2010 crystal structure in yellow. Player designed loop in purple.

Solution structure of a minor and transiently formed state of a T4 lysozyme mutant

Proteins are inherently plastic molecules, whose function often critically depends on excursions between different molecular conformations (conformers). However, a rigorous understanding of the relation between a protein’s structure, dynamics and function remains elusive. This is because many of the conformers on its energy landscape are only transiently formed and marginally populated (less than a few per cent of the total number of molecules), so that they cannot be individually characterized by most biophysical tools. Here we study a lysozyme mutant from phage T4 that binds hydrophobic molecules and populates an excited state transiently (about 1 ms) to about 3% at 25 6C. We show that such binding occurs only via the ground state, and present the atomic-level model of the ‘invisible’, excited state obtained using a combined strategy of relaxation-dispersion NMR and CSRosetta model building that rationalizes this observation. The model was tested using structure-based design calculations identifying point mutants predicted to stabilize the excited state relative to the ground state. In this way a pair of mutations were introduced, inverting the relative populations of the ground and excited states and altering function. Our results suggest a mechanism for the evolution of a protein’s function by changing the delicate balance between the states on its energy landscape. More generally, they show that our approach can generate and validate models of excited protein states.

a-c, Selected regions from 1H-13C HSQC spectra (recorded at 1 degree C) of (a) L99A, G113A T4L (b) and L99A, G113A, R119P T4L (c), with the peaks from the ground and excited states colored in blue and red, respectively. d-f, Corresponding energy landscapes, showing the structures of the ground and excited states and their fractional populations.
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