Gert Kiss

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.

Iterative approach to computational enzyme design.

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.

Evaluation and ranking of enzyme designs

In 2008, a successful computational design procedure was reported that yielded active enzyme catalysts for the Kemp elimination. Here, we studied these proteins together with a set of previously unpublished inactive designs to determine the sources of activity or lack thereof, and to predict which of the designed structures are most likely to be catalytic. Methods that range from quantum mechanics (QM) on truncated model systems to the treatment of the full protein with ONIOM QM/MM and AMBER molecular dynamics (MD) were explored. The most effective procedure involved molecular dynamics, and a general MD protocol was established. Substantial deviations from the ideal catalytic geometries were observed for a number of designs. Penetration of water into the catalytic site and insufficient residue‐packing around the active site are the main factors that can cause enzyme designs to be inactive. Where in the past, computational evaluations of designed enzymes were too time‐extensive for practical considerations, it has now become feasible to rank and refine candidates computationally prior to and in conjunction with experimentation, thus markedly increasing the efficiency of the enzyme design process.

Computational Design of Catalytic Dyads and Oxyanion Holes for Ester Hydrolysis

Nucleophilic catalysis is a general strategy for accelerating ester and amide hydrolysis. In natural active sites, nucleophilic elements such as catalytic dyads and triads are usually paired with oxyanion holes for substrate activation, but it is difficult to parse out the independent contributions of these elements or to understand how they emerged in the course of evolution. Here we explore the minimal requirements for esterase activity by computationally designing artificial catalysts using catalytic dyads and oxyanion holes. We found much higher success rates using designed oxyanion holes formed by backbone NH groups rather than by side chains or bridging water molecules and obtained four active designs in different scaffolds by combining this motif with a Cys-His dyad. Following active site optimization, the most active of the variants exhibited a catalytic efficiency (k(cat)/K(M)) of 400 M(-1) s(-1) for the cleavage of a p-nitrophenyl ester. Kinetic experiments indicate that the active site cysteines are rapidly acylated as programmed by design, but the subsequent slow hydrolysis of the acyl-enzyme intermediate limits overall catalytic efficiency. Moreover, the Cys-His dyads are not properly formed in crystal structures of the designed enzymes. These results highlight the challenges that computational design must overcome to achieve high levels of activity.

Computational design of enone-binding proteins with catalytic activity for the Morita-Baylis-Hillman reaction.

The Morita-Baylis-Hillman reaction forms a carbon-carbon bond between the α-carbon of a conjugated carbonyl compound and a carbon electrophile. The reaction mechanism involves Michael addition of a nucleophile catalyst at the carbonyl β-carbon, followed by bond formation with the electrophile and catalyst disassociation to release the product. We used Rosetta to design 48 proteins containing active sites predicted to carry out this mechanism, of which two show catalytic activity by mass spectrometry (MS). Substrate labeling measured by MS and site-directed mutagenesis experiments show that the designed active-site residues are responsible for activity, although rate acceleration over background is modest. To characterize the designed proteins, we developed a fluorescence-based screen for intermediate formation in cell lysates, carried out microsecond molecular dynamics simulations, and solved X-ray crystal structures. These data indicate a partially formed active site and suggest several clear avenues for designing more active catalysts.

Identification of significantly mutated regions across cancer types highlights a rich landscape of functional molecular alterations

Cancer sequencing studies have primarily identified cancer driver genes by the accumulation of protein-altering mutations. An improved method would be annotation independent, sensitive to unknown distributions of functions within proteins and inclusive of noncoding drivers. We employed density-based clustering methods in 21 tumor types to detect variably sized significantly mutated regions (SMRs). SMRs reveal recurrent alterations across a spectrum of coding and noncoding elements, including transcription factor binding sites and untranslated regions mutated in up to ∼15% of specific tumor types. SMRs demonstrate spatial clustering of alterations in molecular domains and at interfaces, often with associated changes in signaling. Mutation frequencies in SMRs demonstrate that distinct protein regions are differentially mutated across tumor types, as exemplified by a linker region of PIK3CA in which biophysical simulations suggest that mutations affect regulatory interactions. The functional diversity of SMRs underscores both the varied mechanisms of oncogenic misregulation and the advantage of functionally agnostic driver identification.

Discovery of a regioselectivity switch in nitrating P450s guided by molecular dynamics simulations and Markov models

The dynamic motions of protein structural elements, particularly flexible loops, are intimately linked with diverse aspects of enzyme catalysis. Engineering of these loop regions can alter protein stability, substrate binding, and even dramatically impact enzyme function. When these flexible regions are structurally unresolvable, computational reconstruction in combination with large-scale molecular dynamics simulations can be used to guide the engineering strategy. Here, we present a collaborative approach consisting of both experiment and computation that led to the discovery of a single mutation in the F/G loop of the nitrating cytochrome P450 TxtE that simultaneously controls loop dynamics and completely shifts the enzymes regioselectivity from the C4 to the C5 position of L-tryptophan. Furthermore, we find that this loop mutation is naturally present in a subset of homologous nitrating P450s and confirm that these uncharacterized enzymes exclusively produce 5-nitro-L-tryptophan, a previously unknown biosynthetic intermediate.

Automatic Selection of Order Parameters in the Analysis of Large Scale Molecular Dynamics Simulations

Given the large number of crystal structures and NMR ensembles that have been solved to date, classical molecular dynamics (MD) simulations have become powerful tools in the atomistic study of the kinetics and thermodynamics of biomolecular systems on ever increasing time scales. By virtue of the high-dimensional conformational state space that is explored, the interpretation of large-scale simulations faces difficulties not unlike those in the big data community. We address this challenge by introducing a method called clustering based feature selection (CB-FS) that employs a posterior analysis approach. It combines supervised machine learning (SML) and feature selection with Markov state models to automatically identify the relevant degrees of freedom that separate conformational states. We highlight the utility of the method in the evaluation of large-scale simulations and show that it can be used for the rapid and automated identification of relevant order parameters involved in the functional transitions of two exemplary cell-signaling proteins central to human disease states.

Molecular Dynamics Simulations for the Ranking, Evaluation, and Refinement of Computationally Designed Proteins

Computational methods have been developed to redesign proteins so that they can perform novel functions such as the catalysis of nonnatural reactions. Active sites are constructed from the inside out by stochastically exploring mutations that favor the binding of transition states, small molecule binders, and protein surfaces-depending on the task at hand. The approach allows the use of many proteins for engineering scaffolds upon which to erect the necessary functionality. Beyond being of practical value for producing proteins with new applications, the approach tests our understanding of protein chemistry. The current success rate, however, is rather modest, and so the designers have become good only at making catalysts with low catalytic efficiencies. Directed evolution can be used to enhance function and stability, while more advanced computational techniques and physics-based simulations are useful at elucidating structural flaws and at guiding the design process. Here, we summarize work that focuses on the dynamic properties of computationally designed enzymes and their directed evolution variants. We utilized in silico methods to address three questions: (1) What are the shortcomings of these designs? (2) Can they be improved? (3) Can we screen out designs that are likely to be inactive?

Efficacy of SHP2 phosphatase inhibition in cancers with nucleotide-cycling oncogenic RAS, RAS-GTP dependent oncogenic BRAF and NF1 loss

Oncogenic alterations in the RAS-RAF-MEK-ERK pathway, including mutant forms of KRAS, BRAF, and loss of the tumor suppressor and RAS GTPase-activating protein (GAP) NF1, drive the growth of a wide spectrum of human cancers. While BRAF and MEK inhibitors are effective in many patients with oncogenic BRAF V600E, there are no effective targeted therapies for individuals with cancers driven by other pathway alterations, including oncogenic KRAS, non-V600E BRAF, and NF1 loss. Here, we show that targeting the PTPN11/SHP2 phosphatase with a novel small molecule allosteric inhibitor is effective against cancers bearing nucleotide-cycling oncogenic RAS (e.g. KRAS G12C), RAS-GTP dependent oncogenic BRAF (e.g. class 3 BRAF mutants), or NF1 loss in multiple preclinical models in vitro and in vivo. SHP2 inhibition suppressed the levels of RAS-GTP and phosphorylated ERK in these models and induced growth inhibition. Expression of a constitutively active mutant of the RAS guanine nucleotide exchange factor (GEF) SOS1 rescued cells from the effects of SHP2 inhibition, suggesting that SHP2 blockade decreases oncogenic RAS-RAF-MEK-ERK signaling by disrupting SOS1-mediated RAS-GTP loading. Our findings illuminate a critical function for SHP2 in promoting oncogenic RAS activation and downstream signaling in cancers with nucleotide-cycling oncogenic RAS, RAS-GTP dependent oncogenic BRAF, and NF1 loss. SHP2 inhibition thus represents a rational, biomarker-driven therapeutic strategy to be tested in patients with cancers of diverse origins bearing these oncogenic drivers and for which current treatments are largely ineffective.