Justin B. Siegel

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

Biocatalytic Boost Enzymes tend to direct reactions toward specific products much more selectively than synthetic catalysts. Unfortunately, this selectivity has evolved for cellular purposes and may not promote the sorts of reactions chemists are seeking to enhance (see the Perspective by Lutz). Siegel et al. (p. 309) now describe the design of enzymes that catalyze the bimolecular Diels-Alder reaction, a carbon-carbon bond formation reaction that is central to organic synthesis but unknown in natural metabolism. The enzymes display high stereoselectivity and substrate specificity, and an x-ray structure of the most active enzyme confirms that the structure matches the design. Savile et al. (p. 305, published online 17 June) applied a directed evolution approach to modify an existing transaminase enzyme so that it recognized a complex ketone in place of its smaller native substrate, and could tolerate the high temperature and organic cosolvent necessary to dissolve this ketone. This biocatalytic reaction improved the production efficiency of a drug that treats diabetes. Synthetic enzymes catalyze a carbon-carbon bond-forming reaction with high stereoselectivity and substrate specificity. 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.

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

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.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.

Computational protein design enables a novel one-carbon assimilation pathway

Significance This paper describes the development of a computationally designed enzyme that is the cornerstone of a novel metabolic pathway. This enzyme, formolase, performs a carboligation reaction, directly fixing one-carbon units into three-carbon units that feed into central metabolism. By combining formolase with several naturally occurring enzymes, we created a new carbon fixation pathway, the formolase pathway, which assimilates one-carbon units via formate. Unlike native carbon fixation pathways, this pathway is linear, not oxygen sensitive, and consists of a small number of thermodynamically favorable steps. We demonstrate in vitro pathway function as a proof of principle of how protein design in a pathway context can lead to new efficient metabolic pathways. 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.

Impact of scaffold rigidity on the design and evolution of an artificial Diels-Alderase

Significance Creating artificial enzymes that catalyze arbitrary chemical reactions is challenging. Although computational approaches to this problem hold great promise, starting designs typically exhibit low efficiency and require extensive optimization through directed evolution. In this study, we chronicle the evolution of a modestly active, computationally designed Diels-Alderase into a proficient biocatalyst for an abiological [4+2] cycloaddition reaction. Biochemical and structural characterization of the evolved enzyme reveals the molecular origins of its enhanced efficiency. The close match between the experimental structure, which changed only subtly over the course of evolution, and the original design model is particularly notable. In addition to enhancing our understanding of the principles of enzymatic catalysis, these findings should aid future efforts to produce active enzymes more reliably. By combining targeted mutagenesis, computational refinement, and directed evolution, a modestly active, computationally designed Diels-Alderase was converted into the most proficient biocatalyst for [4+2] cycloadditions known. The high stereoselectivity and minimal product inhibition of the evolved enzyme enabled preparative scale synthesis of a single product diastereomer. X-ray crystallography of the enzyme–product complex shows that the molecular changes introduced over the course of optimization, including addition of a lid structure, gradually reshaped the pocket for more effective substrate preorganization and transition state stabilization. The good overall agreement between the experimental structure and the original design model with respect to the orientations of both the bound product and the catalytic side chains contrasts with other computationally designed enzymes. Because design accuracy appears to correlate with scaffold rigidity, improved control over backbone conformation will likely be the key to future efforts to design more efficient enzymes for diverse chemical reactions.

Expanding the Product Profile of a Microbial Alkane Biosynthetic Pathway

Microbially produced alkanes are a new class of biofuels that closely match the chemical composition of petroleum-based fuels. Alkanes can be generated from the fatty acid biosynthetic pathway by the reduction of acyl-ACPs followed by decarbonylation of the resulting aldehydes. A current limitation of this pathway is the restricted product profile, which consists of n-alkanes of 13, 15, and 17 carbons in length. To expand the product profile, we incorporated a new part, FabH2 from Bacillus subtilis , an enzyme known to have a broader specificity profile for fatty acid initiation than the native FabH of Escherichia coli . When provided with the appropriate substrate, the addition of FabH2 resulted in an altered alkane product profile in which significant levels of n-alkanes of 14 and 16 carbons in length are produced. The production of even chain length alkanes represents initial steps toward the expansion of this recently discovered microbial alkane production pathway to synthesize complex fuels. This work was conceived and performed as part of the 2011 University of Washington international Genetically Engineered Machines (iGEM) project.

Computational design of an α-gliadin peptidase

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 kcat/KM 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.

Integrative genomic mining for enzyme function to enable engineering of a non-natural biosynthetic pathway

The ability to biosynthetically produce chemicals beyond what is commonly found in Nature requires the discovery of novel enzyme function. Here we utilize two approaches to discover enzymes that enable specific production of longer-chain (C5–C8) alcohols from sugar. The first approach combines bioinformatics and molecular modelling to mine sequence databases, resulting in a diverse panel of enzymes capable of catalysing the targeted reaction. The median catalytic efficiency of the computationally selected enzymes is 75-fold greater than a panel of naively selected homologues. This integrative genomic mining approach establishes a unique avenue for enzyme function discovery in the rapidly expanding sequence databases. The second approach uses computational enzyme design to reprogramme specificity. Both approaches result in enzymes with >100-fold increase in specificity for the targeted reaction. When enzymes from either approach are integrated in vivo, longer-chain alcohol production increases over 10-fold and represents >95% of the total alcohol products.

Computational enzyme design: transitioning from catalytic proteins to enzymes.

The widespread interest in enzymes stem from their ability to catalyze chemical reactions under mild and ecologically friendly conditions with unparalleled catalytic proficiencies. While thousands of naturally occurring enzymes have been identified and characterized, there are still numerous important applications for which there are no biological catalysts capable of performing the desired chemical transformation. In order to engineer enzymes for which there is no natural starting point, efforts using a combination of quantum chemistry and force-field based protein molecular modeling have led to the design of novel proteins capable of catalyzing chemical reactions not catalyzed by naturally occurring enzymes. Here we discuss the current status and potential avenues to pursue as the field of computational enzyme design moves forward.

Engineering of Kuma030: A Gliadin Peptidase That Rapidly Degrades Immunogenic Gliadin Peptides in Gastric Conditions

Celiac disease is characterized by intestinal inflammation triggered by gliadin, a component of dietary gluten. Oral administration of proteases that can rapidly degrade gliadin in the gastric compartment has been proposed as a treatment for celiac disease; however, no protease has been shown to specifically reduce the immunogenic gliadin content, in gastric conditions, to below the threshold shown to be toxic for celiac patients. Here, we used the Rosetta Molecular Modeling Suite to redesign the active site of the acid-active gliadin endopeptidase KumaMax. The resulting protease, Kuma030, specifically recognizes tripeptide sequences that are found throughout the immunogenic regions of gliadin, as well as in homologous proteins in barley and rye. Indeed, treatment of gliadin with Kuma030 eliminates the ability of gliadin to stimulate a T cell response. Kuma030 is capable of degrading >99% of the immunogenic gliadin fraction in laboratory-simulated gastric digestions within physiologically relevant time frames, to a level below the toxic threshold for celiac patients, suggesting great potential for this enzyme as an oral therapeutic for celiac disease.

Janus: Prediction and Ranking of Mutations Required for Functional Interconversion of Enzymes

Identification of residues responsible for functional specificity in enzymes is a challenging and important problem in protein chemistry. Active-site residues are generally easy to identify, but residues outside the active site are also important to catalysis and their identities and roles are more difficult to determine. We report a method based on analysis of multiple sequence alignments, embodied in our program Janus, for predicting mutations required to interconvert structurally related but functionally distinct enzymes. Conversion of aspartate aminotransferase into tyrosine aminotransferase is demonstrated and compared to previous efforts. Incorporation of 35 predicted mutations resulted in an enzyme with the desired substrate specificity but low catalytic activity. A single round of DNA back-shuffling with wild-type aspartate aminotransferase on this variant generated mutants with tyrosine aminotransferase activities better than those previously realized from rational design or directed evolution. Methods such as this, coupled with computational modeling, may prove invaluable in furthering our understanding of enzyme catalysis and engineering.