Computational protein design enables a novel one-carbon assimilation pathway.

We describe a computationally designed enzyme, formolase (FLS), which catalyzes the carboligation of three one-carbon formaldehyde molecules into one three-carbon dihydroxyacetone molecule. The existence of FLS enables the design of a new carbon fixation pathway, the formolase pathway, consisting of a small number of thermodynamically favorable chemical transformations that convert formate into a three-carbon sugar in central metabolism. The formolase pathway is predicted to use carbon more efficiently and with less backward flux than any naturally occurring one-carbon assimilation pathway. When supplemented with enzymes carrying out the other steps in the pathway, FLS converts formate into dihydroxyacetone phosphate and other central metabolites in vitro. These results demonstrate how modern protein engineering and design tools can facilitate the construction of a completely new biosynthetic pathway.

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.

Kemp elimination catalysts by computational enzyme design.

The design of new enzymes for reactions not catalysed by naturally occurring biocatalysts is a challenge for protein engineering and is a critical test of our understanding of enzyme catalysis. Here we describe the computational design of eight enzymes that use two different catalytic motifs to catalyse the Kemp elimination-a model reaction for proton transfer from carbon-with measured rate enhancements of up to 10(5) and multiple turnovers. Mutational analysis confirms that catalysis depends on the computationally designed active sites, and a high-resolution crystal structure suggests that the designs have close to atomic accuracy. Application of in vitro evolution to enhance the computational designs produced a >200-fold increase in k(cat)/K(m) (k(cat)/K(m) of 2,600 M(-1)s(-1) and k(cat)/k(uncat) of >10(6)). These results demonstrate the power of combining computational protein design with directed evolution for creating new enzymes, and we anticipate the creation of a wide range of useful new catalysts in the future.

Alteration of enzyme specificity by computational loop remodeling and design.

Altering the specificity of an enzyme requires precise positioning of side-chain functional groups that interact with the modified groups of the new substrate. This requires not only sequence changes that introduce the new functional groups but also sequence changes that remodel the structure of the protein backbone so that the functional groups are properly positioned. We describe a computational design method for introducing specific enzyme-substrate interactions by directed remodeling of loops near the active site. Benchmark tests on 8 native protein-ligand complexes show that the method can recover native loop lengths and, often, native loop conformations. We then use the method to redesign a critical loop in human guanine deaminase such that a key side-chain interaction is made with the substrate ammelide. The redesigned enzyme is 100-fold more active on ammelide and 2.5e4-fold less active on guanine than wild-type enzyme: The net change in specificity is 2.5e6-fold. The structure of the designed protein was confirmed by X-ray crystallographic analysis: The remodeled loop adopts a conformation that is within 1-A Calpha RMSD of the computational model.

Leukocyte telomere length predicts cancer risk in Barrett's esophagus.

PURPOSE: Leukocyte telomere length has gained attention as a marker of oxidative damage and age-related diseases, including cancer. We hypothesize that leukocyte telomere length might be able to predict future risk of cancer and examined this in a cohort of patients with Barrett's esophagus, who are at increased risk of esophageal adenocarcinoma and thus were enrolled in a long-term cancer surveillance program. PATIENTS AND METHODS: In this prospective study, telomere length was measured by quantitative PCR in baseline blood samples in a cohort of 300 patients with Barrett's esophagus followed for a mean of 5.8 years. Leukocyte telomere length hazard ratios (HR) for risk of esophageal adenocarcinoma were calculated using multivariate Cox models. RESULTS: Shorter telomeres were associated with increased esophageal adenocarcinoma risk (age-adjusted HR between top and bottom quartiles of telomere length, 3.45; 95% confidence interval, 1.35-8.78; P = 0.009). This association was still significant when individually or simultaneously adjusted for age, gender, nonsteroidal anti-inflammatory drug (NSAID) use, cigarette smoking, and waist-to-hip ratio (HR, 4.18; 95% confidence interval, 1.60-10.94; P = 0.004). The relationship between telomere length and cancer risk was particularly strong among NSAID nonusers, ever smokers, and patients with low waist-to-hip ratio. CONCLUSION: Leukocyte telomere length predicts risk of esophageal adenocarcinoma in patients with Barrett's esophagus independently of smoking, obesity, and NSAID use. These results show the ability of leukocyte telomere length to predict the risk of future cancer and suggest that it might also have predictive value in other cancers arising in a setting of chronic inflammation.

Computational design of an enzyme catalyst for a stereoselective bimolecular Diels-Alder reaction.

The Diels-Alder reaction is a cornerstone in organic synthesis, forming two carbon-carbon bonds and up to four new stereogenic centers in one step. No naturally occurring enzymes have been shown to catalyze bimolecular Diels-Alder reactions. We describe the de novo computational design and experimental characterization of enzymes catalyzing a bimolecular Diels-Alder reaction with high stereoselectivity and substrate specificity. X-ray crystallography confirms that the structure matches the design for the most active of the enzymes, and binding site substitutions reprogram the substrate specificity. Designed stereoselective catalysts for carbon-carbon bond-forming reactions should be broadly useful in synthetic chemistry.

De novo computational design of retro-aldol enzymes.

The creation of enzymes capable of catalyzing any desired chemical reaction is a grand challenge for computational protein design. Using new algorithms that rely on hashing techniques to construct active sites for multistep reactions, we designed retro-aldolases that use four different catalytic motifs to catalyze the breaking of a carbon-carbon bond in a nonnatural substrate. Of the 72 designs that were experimentally characterized, 32, spanning a range of protein folds, had detectable retro-aldolase activity. Designs that used an explicit water molecule to mediate proton shuffling were significantly more successful, with rate accelerations of up to four orders of magnitude and multiple turnovers, than those involving charged side-chain networks. The atomic accuracy of the design process was confirmed by the x-ray crystal structure of active designs embedded in two protein scaffolds, both of which were nearly superimposable on the design model.