Design of Efficient Artificial Enzymes Using Crystallographically Enhanced Conformational Sampling.

The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing the catalytic efficiency of de novo enzymes by several orders of magnitude without relying on directed evolution and high-throughput screening. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (kcat/KM 125 M-1 s-1) and KE70 (kcat/KM 57 M-1 s-1), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM values improved by 100-250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models, with catalytic contacts present as designed and transition-state root-mean-square deviations of ≤0.65 Å. Our work shows how ensemble-based design can generate efficient artificial enzymes by exploiting the true conformational ensemble to design improved active sites.

Rational design of proteins that exchange on functional timescales.

Proteins are intrinsically dynamic molecules that can exchange between multiple conformational states, enabling them to carry out complex molecular processes with extreme precision and efficiency. Attempts to design novel proteins with tailored functions have mostly failed to yield efficiencies matching those found in nature because standard methods do not allow the design of exchange between necessary conformational states on a functionally relevant timescale. Here we developed a broadly applicable computational method to engineer protein dynamics that we term meta-multistate design. We used this methodology to design spontaneous exchange between two novel conformations introduced into the global fold of Streptococcal protein G domain ß1. The designed proteins, named DANCERs, for dynamic and native conformational exchangers, are stably folded and switch between predicted conformational states on the millisecond timescale. The successful introduction of defined dynamics on functional timescales opens the door to new applications requiring a protein to spontaneously access multiple conformational states.

Structural Determinants of the Stereoinverting Activity of Pseudomonas stutzeri d-Phenylglycine Aminotransferase.

Aromatic d-amino acids are key precursors for the production of many small molecule therapeutics. Therefore, the development of biocatalytic methods for their synthesis is of great interest. An enzyme that has great potential as a biocatalyst for the synthesis of d-amino acids is the stereoinverting d-phenylglycine aminotransferase (DPAT) from Pseudomonas stutzeri ST-201. This enzyme catalyzes a unique l to d transamination reaction that produces d-phenylglycine and α-ketoglutarate from benzoylformate and l-glutamate, via a mechanism that is poorly understood. Here, we present the crystal structure of DPAT, which shows that the enzyme folds into a two-domain structure representative of class III aminotransferases. Guided by the crystal structure, we performed saturation mutagenesis to probe the substrate binding pockets of the enzyme. These experiments helped us identify two arginine residues (R34 and R407), one in each binding pocket, that are essential to catalysis. Together with kinetic analyses using a library of amino acid substrates, our mutagenesis and structural studies allow us to propose a binding model that explains the dual l/d specificity of DPAT. Our kinetic analyses also demonstrate that DPAT can catalyze the transamination of ß- and γ-amino acids, reclassifying this enzyme as an ω-aminotransferase. Collectively, our studies highlight that the DPAT active site is amenable to protein engineering for expansion of its substrate scope, which offers the opportunity to generate new biocatalysts for the synthesis of a variety of valuable optically pure d-amino acids from inexpensive and abundant l-amino acids.

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.

Improving the accuracy of protein stability predictions with multistate design using a variety of backbone ensembles.

Multistate computational protein design (MSD) with backbone ensembles approximating conformational flexibility can predict higher quality sequences than single-state design with a single fixed backbone. However, it is currently unclear what characteristics of backbone ensembles are required for the accurate prediction of protein sequence stability. In this study, we aimed to improve the accuracy of protein stability predictions made with MSD by using a variety of backbone ensembles to recapitulate the experimentally measured stability of 85 Streptococcal protein G domain ß1 sequences. Ensembles tested here include an NMR ensemble as well as those generated by molecular dynamics (MD) simulations, by Backrub motions, and by PertMin, a new method that we developed involving the perturbation of atomic coordinates followed by energy minimization. MSD with the PertMin ensembles resulted in the most accurate predictions by providing the highest number of stable sequences in the top 25, and by correctly binning sequences as stable or unstable with the highest success rate (≈90%) and the lowest number of false positives. The performance of PertMin ensembles is due to the fact that their members closely resemble the input crystal structure and have low potential energy. Conversely, the NMR ensemble as well as those generated by MD simulations at 500 or 1000 K reduced prediction accuracy due to their low structural similarity to the crystal structure. The ensembles tested herein thus represent on- or off-target models of the native protein fold and could be used in future studies to design for desired properties other than stability.

Continuous colorimetric screening assay for detection of d-amino acid aminotransferase mutants displaying altered substrate specificity.

D-Amino acid aminotransferase (DAAT) catalyzes the synthesis of numerous d-amino acids, making it an attractive biocatalyst for the production of enantiopure d-amino acids. To bolster its biocatalytic applicability, improved variants displaying increased activity toward non-native substrates are desired. Here, we report the development of a high-throughput, colorimetric, continuous coupled enzyme assay for the screening of DAAT mutant libraries that is based on the use of d-amino acid oxidase (DAAO). In this assay, the d-amino acid product of DAAT is oxidized by DAAO with concomitant release of hydrogen peroxide, which is detected colorimetrically by the addition of horseradish peroxidase and o-dianisidine. Using this assay, we measured apparent KM and kcat values for DAAT and identified mutants displaying altered substrate specificity via the screening of cell lysates in 96-well plates. The DAAO coupled assay is sensitive in that it allowed the detection of a DAAT mutant displaying an approximately 2000-fold decrease in kcat/KM relative to wild type. In addition, the DAAO assay enabled the identification of two DAAT mutants (V33Y and V33G) that are more efficient than wild type at transaminating the non-native acceptor phenylpyruvate. We expect that this assay will be useful for the engineering of additional mutants displaying increased activity toward non-native substrates.

Bioanalysis for biocatalysis: multiplexed capillary electrophoresis-mass spectrometry assay for aminotransferase substrate discovery and specificity profiling.

In this work, we introduce an entirely automated enzyme assay based on capillary electrophoresis coupled to electrospray ionization mass spectrometry termed MINISEP-MS for multiple interfluent nanoinjections-incubation-separation-enzyme profiling using mass spectrometry. MINISEP-MS requires only nanoliters of reagent solutions and uses the separation capillary as a microreactor, allowing multiple substrates to be assayed simultaneously. The method can be used to rapidly profile the substrate specificity of any enzyme and to measure steady-state kinetics in an automated fashion. We used the MINISEP-MS assay to profile the substrate specificity of three aminotransferases (E. coli aspartate aminotransferase, E. coli branched-chain amino acid aminotransferase, and Bacillus sp. YM-1 D-amino acid aminotransferase) for 33 potential amino acid substrates and to measure steady-state kinetics. Using MINISEP-MS, we were able to recapitulate the known substrate specificities and to discover new amino acid substrates for these industrially relevant enzymes. Additionally, we were able to measure the apparent K(M) and k(cat) parameters for amino acid donor substrates of these aminotransferases. Because of its many advantages, the MINISEP-MS assay has the potential of becoming a useful tool for researchers aiming to identify or create novel enzymes for specific biocatalytic applications.

A high-throughput assay for screening L- or D-amino acid specific aminotransferase mutant libraries.

Aminotransferases are pyridoxal phosphate-dependent enzymes whose potential for the biocatalytic production of enantiopure amino acids is increasingly recognized. Because of this, there is a growing interest in engineering them to alter their substrate specificity and to increase their catalytic activity. Here, we report the development of a high-throughput assay for screening α-ketoglutarate-dependent aminotransferase mutant libraries. To achieve this, we exploited the L-glutamate dehydrogenase coupled assay that has previously been shown to allow for aminotransferase activity to be monitored in vitro. We adapted this assay to allow screening of mutant libraries of either L- or D-amino acid specific aminotransferases in a continuous fashion. This assay requiring clarified cell lysates is reproducible, rapid, and sensitive because it allowed for the identification of a catalytically active mutant of Bacillus sp. YM-1 D-amino acid aminotransferase displaying a decrease in k(cat)/K(M) of more than two orders of magnitude. In addition, this assay allowed us to discover a mutant of Escherichia coli branched-chain amino acid aminotransferase, F36W, which is approximately 60-fold more specific toward the natural substrate L-leucine than L-phenylalanine as compared with wild type. This result demonstrates the potential of our assay for the discovery of mutant aminotransferases displaying altered substrate specificity, an important goal of enzyme engineering.

Generation of longer emission wavelength red fluorescent proteins using computationally designed libraries.

The longer emission wavelengths of red fluorescent proteins (RFPs) make them attractive for whole-animal imaging because cells are more transparent to red light. Although several useful RFPs have been developed using directed evolution, the quest for further red-shifted and improved RFPs continues. Herein, we report a structure-based rational design approach to red-shift the fluorescence emission of RFPs. We applied a combined computational and experimental approach that uses computational protein design as an in silico prescreen to generate focused combinatorial libraries of mCherry mutants. The computational procedure helped us identify residues that could fulfill interactions hypothesized to cause red-shifts without destabilizing the protein fold. These interactions include stabilization of the excited state through H-bonding to the acylimine oxygen atom, destabilization of the ground state by hydrophobic packing around the charged phenolate, and stabilization of the excited state by a π-stacking interaction. Our methodology allowed us to identify three mCherry mutants (mRojoA, mRojoB, and mRouge) that display emission wavelengths > 630 nm, representing red-shifts of 20-26 nm. Moreover, our approach required the experimental screening of a total of ∼5,000 clones, a number several orders of magnitude smaller than those previously used to achieve comparable red-shifts. Additionally, crystal structures of mRojoA and mRouge allowed us to verify fulfillment of the interactions hypothesized to cause red-shifts, supporting their contribution to the observed red-shifts.

Design of efficient artificial enzymes using crystallographically-enhanced conformational sampling.

The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing catalytic efficiency of de novo enzymes to a level comparable to their natural counterparts without relying on directed evolution. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (kcat/KM 125 M-1 s-1) and KE70 (kcat/KM 57 M-1 s-1), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM values improved by 100-250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models. Our work shows how computational design can generate efficient artificial enzymes by exploiting the true conformational ensemble to more effectively stabilize the transition state.

Computational remodeling of an enzyme conformational landscape for altered substrate selectivity.

Structural plasticity of enzymes dictates their function. Yet, our ability to rationally remodel enzyme conformational landscapes to tailor catalytic properties remains limited. Here, we report a computational procedure for tuning conformational landscapes that is based on multistate design of hinge-mediated domain motions. Using this method, we redesign the conformational landscape of a natural aminotransferase to preferentially stabilize a less populated but reactive conformation and thereby increase catalytic efficiency with a non-native substrate, resulting in altered substrate selectivity. Steady-state kinetics of designed variants reveals activity increases with the non-native substrate of approximately 100-fold and selectivity switches of up to 1900-fold. Structural analyses by room-temperature X-ray crystallography and multitemperature nuclear magnetic resonance spectroscopy confirm that conformational equilibria favor the target conformation. Our computational approach opens the door to targeted alterations of conformational states and equilibria, which should facilitate the design of biocatalysts with customized activity and selectivity.

Generation of bright monomeric red fluorescent proteins via computational design of enhanced chromophore packing.

Red fluorescent proteins (RFPs) have found widespread application in chemical and biological research due to their longer emission wavelengths. Here, we use computational protein design to increase the quantum yield and thereby brightness of a dim monomeric RFP (mRojoA, quantum yield = 0.02) by optimizing chromophore packing with aliphatic residues, which we hypothesized would reduce torsional motions causing non-radiative decay. Experimental characterization of the top 10 designed sequences yielded mSandy1 (λ em = 609 nm, quantum yield = 0.26), a variant with equivalent brightness to mCherry, a widely used RFP. We next used directed evolution to further increase brightness, resulting in mSandy2 (λ em = 606 nm, quantum yield = 0.35), the brightest Discosoma sp. derived monomeric RFP with an emission maximum above 600 nm reported to date. Crystallographic analysis of mSandy2 showed that the chromophore p-hydroxybenzylidene moiety is sandwiched between the side chains of Leu63 and Ile197, a structural motif that has not previously been observed in RFPs, and confirms that aliphatic packing leads to chromophore rigidification. Our results demonstrate that computational protein design can be used to generate bright monomeric RFPs, which can serve as templates for the evolution of novel far-red fluorescent proteins.

Origin of acetylcholine antagonism in ELIC, a bacterial pentameric ligand-gated ion channel.

ELIC is a prokaryotic homopentameric ligand-gated ion channel that is homologous to vertebrate nicotinic acetylcholine receptors. Acetylcholine binds to ELIC but fails to activate it, despite bringing about conformational changes indicative of activation. Instead, acetylcholine competitively inhibits agonist-activated ELIC currents. What makes acetylcholine an agonist in an acetylcholine receptor context, and an antagonist in an ELIC context, is not known. Here we use available structures and statistical coupling analysis to identify residues in the ELIC agonist-binding site that contribute to agonism. Substitution of these ELIC residues for their acetylcholine receptor counterparts does not convert acetylcholine into an ELIC agonist, but in some cases reduces the sensitivity of ELIC to acetylcholine antagonism. Acetylcholine antagonism can be abolished by combining two substitutions that together appear to knock out acetylcholine binding. Thus, making the ELIC agonist-binding site more acetylcholine receptor-like, paradoxically reduces the apparent affinity for acetylcholine, demonstrating that residues important for agonist binding in one context can be deleterious in another. These findings reinforce the notion that although agonism originates from local interactions within the agonist-binding site, it is a global property with cryptic contributions from distant residues. Finally, our results highlight an underappreciated mechanism of antagonism, where agonists with appreciable affinity, but negligible efficacy, present as competitive antagonists.

Personalized oncology and BRAFK601N melanoma: model development, drug discovery, and clinical correlation.

PURPOSE: Mutations in BRAF are the most prominent activating mutations in melanoma and are increasingly recognized in other cancers. There is currently no accepted treatment regimen for patients with mutant BRAFK601N melanoma, and the study of melanoma driven by BRAF mutations at the 601 locus is lacking due to a paucity of cellular model systems. Therefore, we sought to better understand the treatment and clinical approach to patients with mutant BRAFK601N melanoma and subsequently develop a novel personalized oncology platform for rare or treatment-refractory cancers. METHODS: We developed and characterized the first patient-derived, naturally occurring BRAFK601N melanoma model, described herein as OHRI-MEL-13, and assessed efficacy using the Prestwick Chemical Library and select targeted therapeutics. RESULTS: OHRI-MEL-13 exhibits loss of heterozygosity of BRAF, closely mimics the original tumor's gene expression profile, is tumorigenic in immune-deficient murine models, and is available for public accession through American Type Culture Collection. We present in silico modeling data, which illustrates the therapeutic failure of BRAFV600E-targeted therapies in BRAFK601N mutants. Our platform elucidated a unique role for MEK inhibition with cobimetinib, which resulted in short-term clinical success by reducing the metastatic burden. CONCLUSION: Our model of BRAFK601N-activated melanoma was developed, thoroughly characterized, and made available for public accession. This model served to demonstrate the feasibility of a novel personalized oncology platform that could be optimized at an institutional level for rare variant or treatment-refractory cancers. We also demonstrate the clinical utility of monotherapy MEK inhibition in a case of BRAFK601N melanoma.

Genetically Encoded Fluorescent Biosensor for Rapid Detection of Protein Expression.

Fluorescent proteins are widely used as fusion tags to detect protein expression in vivo. To become fluorescent, these proteins must undergo chromophore maturation, a slow process with a half-time of 5 to >30 min that causes delays in real-time detection of protein expression. Here, we engineer a genetically encoded fluorescent biosensor to enable detection of protein expression within seconds in live bacteria. This sensor for transiently expressed proteins (STEP) is based on a fully matured but dim green fluorescent protein in which pre-existing fluorescence increases 11-fold in vivo following the specific and rapid binding of a protein tag (Kd 120 nM, kon 1.7 × 105 M-1 s-1). In live E. coli cells, our STEP biosensor enables detection of protein expression twice as fast as the use of standard fluorescent protein fusions. Our biosensor opens the door to the real-time study of short timescale processes in live cells with high spatiotemporal resolution.

Ensemble-based enzyme design can recapitulate the effects of laboratory directed evolution in silico.

The creation of artificial enzymes is a key objective of computational protein design. Although de novo enzymes have been successfully designed, these exhibit low catalytic efficiencies, requiring directed evolution to improve activity. Here, we use room-temperature X-ray crystallography to study changes in the conformational ensemble during evolution of the designed Kemp eliminase HG3 (kcat/KM 146 M-1s-1). We observe that catalytic residues are increasingly rigidified, the active site becomes better pre-organized, and its entrance is widened. Based on these observations, we engineer HG4, an efficient biocatalyst (kcat/KM 103,000 M-1s-1) containing key first and second-shell mutations found during evolution. HG4 structures reveal that its active site is pre-organized and rigidified for efficient catalysis. Our results show how directed evolution circumvents challenges inherent to enzyme design by shifting conformational ensembles to favor catalytically-productive sub-states, and suggest improvements to the design methodology that incorporate ensemble modeling of crystallographic data.

Photolabeling of tissue transglutaminase reveals the binding mode of potent cinnamoyl inhibitors.

We have recently developed a new class of cinnamoyl derivatives as potent tissue transglutaminase (TG2) inhibitors. Herein, we report the synthesis of a diazirine derivative of these inhibitors and its application to the photolabeling of its binding site on guinea pig liver transglutaminase. Two novel homology models were generated for this commonly studied TG2, which differ in the conformational state they represent. Tryptic digest and mass spectrometric analysis of the photolabeling experiment showed that only residue Cys230 was labeled, and our homology models were used to visualize these results. This visualization suggested that Cys230 is somewhat more solvent-exposed in the "closed" conformation of TG2, compared to the "open" conformation. Docking experiments suggested binding modes consistent with the labeling pattern that would block access to the tunnel leading to the active site, consistent with the observed mode of inhibition. However, while these modeling simulations favored the closed conformation as the target of our cinnamoyl inhibitors, native PAGE experiments indicated the open conformation of the enzyme in fact predominates in the presence of our photolabeling derivative. These results are important for understanding the binding modes of TG2 inhibitors in general and will be critical for the structure-based design of future inhibitors.

Origin of conformational dynamics in a globular protein.

Protein structures are dynamic, undergoing motions that can play a vital role in function. However, the link between primary sequence and conformational dynamics remains poorly understood. Here, we studied how conformational dynamics can arise in a globular protein by evaluating the impact of individual core-residue substitutions in DANCER-3, a streptococcal protein G domain ß1 variant that we previously designed to undergo a specific mode of conformational exchange that has never been observed in the wild-type protein. Using a combination of solution NMR experiments and molecular dynamics simulations, we demonstrate that only two mutations are necessary to create this conformational exchange, and that these mutations work synergistically, with one destabilizing the native structure and the other allowing two new conformational states to be accessed on the energy landscape. Overall, our results show how dynamics can appear in a stable globular fold, a critical step in the molecular evolution of dynamics-linked functions.