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.

Recent developments in computational chemistry and biology have come together in the "inside-out" approach to enzyme engineering. Proteins have been designed to catalyze reactions not previously accelerated in nature. Some of these proteins fold and act as catalysts, but the success rate is still low. The achievements and limitations of the current technology are highlighted and contrasted to other protein engineering techniques. On its own, computational "inside-out" design can lead to the production of catalytically active and selective proteins, but their kinetic performances fall short of natural enzymes. When combined with directed evolution, molecular dynamics simulations, and crowd-sourced structure-prediction approaches, however, computational designs can be significantly improved in terms of binding, turnover, and thermal stability.

A general approach for the computational design of enzymes to catalyze arbitrary reactions is a goal at the forefront of the field of protein design. Recently, computationally designed enzymes have been produced for three chemical reactions through the synthesis and screening of a large number of variants. Here, we present an iterative approach that has led to the development of the most catalytically efficient computationally designed enzyme for the Kemp elimination to date. Previously established computational techniques were used to generate an initial design, HG-1, which was catalytically inactive. Analysis of HG-1 with molecular dynamics simulations (MD) and X-ray crystallography indicated that the inactivity might be due to bound waters and high flexibility of residues within the active site. This analysis guided changes to our design procedure, moved the design deeper into the interior of the protein, and resulted in an active Kemp eliminase, HG-2. The cocrystal structure of this enzyme with a transition state analog (TSA) revealed that the TSA was bound in the active site, interacted with the intended catalytic base in a catalytically relevant manner, but was flipped relative to the design model. MD analysis of HG-2 led to an additional point mutation, HG-3, that produced a further threefold improvement in activity. This iterative approach to computational enzyme design, including detailed MD and structural analysis of both active and inactive designs, promises a more complete understanding of the underlying principles of enzymatic catalysis and furthers progress toward reliably producing active enzymes.computational protein design | de novo enzyme design | proton transfer T he high efficiency, chemoselectivity, regio-and stereospecificity, and biodegradability of enzymes make them extremely attractive catalysts. However, the finite repertoire of naturally occurring enzymes limits their applicability to broad problems in biotechnology. A general method for the computational design of enzymes that can efficiently catalyze arbitrary chemical reactions would allow the benefits of enzymatic catalysis to be applied to chemical transformations of interest that are currently inaccessible via natural enzymes. Bolon and Mayo provided important early evidence that such an approach is feasible (1), which motivated significant progress toward this goal in recent years. Using quantum mechanics-based active site design and the Rosetta software suite, Baker, Houk, and coworkers designed enzymes for three chemically unrelated nonnatural reactions in a variety of catalytically inert scaffolds (2-4).In early incarnations of computational protein design, a strategy for methods development was put forth in terms of the so-called "protein design cycle" in which experimental evaluation of an initial design is used to inform adjustments to the design process for subsequent rounds of design (5, 6). Ideally, these steps would be continued iteratively until the protein sequences predicted by the algorithm exhibit the desired char...

Oncogenic alterations in the RAS/RAF/MEK/ERK pathway drive the growth of a wide spectrum of cancers. While BRAF and MEK inhibitors are efficacious against BRAF-driven cancers, effective targeted therapies are lacking for most cancers driven by other pathway alterations, including non-V600E oncogenic BRAF, RAS GTPase-activating protein (GAP) NF1 (neurofibromin 1) loss and oncogenic KRAS. Here, we show that targeting the SHP2 phosphatase (encoded by PTPN11) with RMC-4550, a small-molecule allosteric inhibitor, is effective in human cancer models bearing RAS-GTP-dependent oncogenic BRAF (for example, class 3 BRAF mutants), NF1 loss or nucleotide-cycling oncogenic RAS (for example, KRAS). SHP2 inhibitor treatment decreases oncogenic RAS/RAF/MEK/ERK signalling and cancer growth by disrupting SOS1-mediated RAS-GTP loading. Our findings illuminate a critical function for SHP2 in promoting oncogenic RAS/MAPK pathway activation in cancers with RAS-GTP-dependent oncogenic BRAF, NF1 loss and nucleotide-cycling oncogenic KRAS. SHP2 inhibition is a promising molecular therapeutic strategy for patients with cancers bearing these oncogenic drivers.

Computational design is a test of our understanding of enzyme catalysis and a means of engineering novel, tailor-made enzymes. While the de novo computational design of catalytically efficient enzymes remains a challenge, designed enzymes may comprise unique starting points for further optimization by directed evolution. Directed evolution of two computationally designed Kemp eliminases, KE07 and KE70, led to low to moderately efficient enzymes (k cat ∕K m values of ≤5 × 10 4 M −1 s −1 ). Here we describe the optimization of a third design, KE59. Although KE59 was the most catalytically efficient Kemp eliminase from this design series (by k cat ∕K m , and by catalyzing the elimination of nonactivated benzisoxazoles), its impaired stability prevented its evolutionary optimization. To boost KE59's evolvability, stabilizing consensus mutations were included in the libraries throughout the directed evolution process. The libraries were also screened with less activated substrates. Sixteen rounds of mutation and selection led to >2,000-fold increase in catalytic efficiency, mainly via higher k cat values. The best KE59 variants exhibited k cat ∕K m values up to 0.6 × 10 6 M −1 s −1 , and k cat ∕k uncat values of ≤10 7 almost regardless of substrate reactivity. Biochemical, structural, and molecular dynamics (MD) simulation studies provided insights regarding the optimization of KE59. Overall, the directed evolution of three different designed Kemp eliminases, KE07, KE70, and KE59, demonstrates that computational designs are highly evolvable and can be optimized to high catalytic efficiencies.computational protein design | enzyme mimic T he endeavor of making enzyme-like catalysts spans several decades (1) with the Kemp elimination being a thoroughly explored model (2-7). In this activated model system, basecatalyzed proton elimination from carbon is concerted with the cleavage of nitrogen-oxygen bond, thus leading to the cyanophenol product (Fig. 1A). Activation of a carboxylate base catalyst by desolvation is efficiently mimicked by aprotic dipolar solvents such as acetonitrile and by various enzyme mimics. Effective alignment of the substrate and charge-dispersing interactions that stabilize the negatively charged transition state (TS) have also been achieved by various enzyme mimics that catalyze the Kemp elimination (8, 9). However, generation of a (wo)manmade active site that exhibits all these features and performs as well as natural enzymes, remains a challenge.Computational methods for predicting structure from sequence at atomic accuracy provide a new approach to enzyme engineering (10-12). The design involves two steps: (i) designing an active-site configuration that may confer efficient catalysis, (ii) computing a sequence that confers the desired configuration. Both steps are currently far from optimal, as the catalytic efficiency of designed enzymes falls far behind that of natural enzymes. Further, as with other enzyme mimics, computational designs tackle activated model systems and fail to catalyze cha...