Computational Redesign Research Articles

Computational Redesign of an ω-Transaminase from Pseudomonas jessenii for Asymmetric Synthesis of Enantiopure Bulky Amines.

ω-Transaminases (ω-TA) are attractive biocatalysts for the production of chiral amines from prochiral ketones via asymmetric synthesis. However, the substrate scope of ω-TAs is usually limited due to steric hindrance at the active site pockets. We explored a protein engineering strategy using computational design to expand the substrate scope of an (S)-selective ω-TA from Pseudomonas jessenii (PjTA-R6) toward the production of bulky amines. PjTA-R6 is attractive for use in applied biocatalysis due to its thermostability, tolerance to organic solvents, and acceptance of high concentrations of isopropylamine as amino donor. PjTA-R6 showed no detectable activity for the synthesis of six bicyclic or bulky amines targeted in this study. Six small libraries composed of 7–18 variants each were separately designed via computational methods and tested in the laboratory for ketone to amine conversion. In each library, the vast majority of the variants displayed the desired activity, and of the 40 different designs, 38 produced the target amine in good yield with >99% enantiomeric excess. This shows that the substrate scope and enantioselectivity of PjTA mutants could be predicted in silico with high accuracy. The single mutant W58G showed the best performance in the synthesis of five structurally similar bulky amines containing the indan and tetralin moieties. The best variant for the other bulky amine, 1-phenylbutylamine, was the triple mutant W58M + F86L + R417L, indicating that Trp58 is a key residue in the large binding pocket for PjTA-R6 redesign. Crystal structures of the two best variants confirmed the computationally predicted structures. The results show that computational design can be an efficient approach to rapidly expand the substrate scope of ω-TAs to produce enantiopure bulky amines.

Development of a versatile and efficient C–N lyase platform for asymmetric hydroamination via computational enzyme redesign

Although C–N bonds are ubiquitous in natural products, pharmaceuticals and agrochemicals, biocatalysts forging these bonds with high atom-efficiency and enantioselectivity have been limited to a few select enzymes. In particular, ammonia lyases have emerged as powerful catalysts to access C–N bond formation via hydroamination. However, the use of ammonia lyases is rather restricted due to their narrow synthetic scope. Herein, we report the computational redesign of aspartase, a highly specific ammonia lyase, to yield C–N lyases with cross-compatibility of non-native nucleophiles and electrophiles. A wide range of non-canonical amino acids (ncAAs) are afforded with excellent conversion (up to 99%), regioselectivity >99% and enantioselectivity >99%. The process is scalable under industrially relevant protocols (exemplified in kilogram-scale synthesis) and can be facilely integrated in cascade reactions (demonstrated in the synthesis of β-lactams with N-1 and C-4 substitutions). This versatile and efficient C–N lyase platform supports the preparation of ncAAs and their derivatives, and will present opportunities in synthetic biology. Ammonia lyases are powerful catalysts to access C–N bond formation via hydroamination, but show a narrow synthetic scope. Now, by computational redesign of an aspartase, a C–N lyase is developed that shows cross-compatibility of non-native nucleophiles and electrophiles expanding the synthetic scope.

Computational Redesign of a PETase for Plastic Biodegradation under Ambient Condition by the GRAPE Strategy

Nature has provided a fantastic array of enzymes that are responsible for essential biochemical functions but not usually suitable for technological applications. Not content with the natural reper...

Reengineering biocatalysts: Computational redesign of chondroitinase ABC improves efficacy and stability.

Maintaining biocatalyst stability and activity is a critical challenge. Chondroitinase ABC (ChABC) has shown promise in central nervous system (CNS) regeneration, yet its therapeutic utility is severely limited by instability. We computationally reengineered ChABC by introducing 37, 55, and 92 amino acid changes using consensus design and forcefield-based optimization. All mutants were more stable than wild-type ChABC with increased aggregation temperatures between 4° and 8°C. Only ChABC with 37 mutations (ChABC-37) was more active and had a 6.5 times greater half-life than wild-type ChABC, increasing to 106 hours (4.4 days) from only 16.8 hours. ChABC-37, expressed as a fusion protein with Src homology 3 (ChABC-37-SH3), was active for 7 days when released from a hydrogel modified with SH3-binding peptides. This study demonstrates the broad opportunity to improve biocatalysts through computational engineering and sets the stage for future testing of this substantially improved protein in the treatment of debilitating CNS injuries.

A physics-based energy function allows the computational redesign of a PDZ domain

Computational protein design (CPD) can address the inverse folding problem, exploring a large space of sequences and selecting ones predicted to fold. CPD was used previously to redesign several proteins, employing a knowledge-based energy function for both the folded and unfolded states. We show that a PDZ domain can be entirely redesigned using a “physics-based” energy for the folded state and a knowledge-based energy for the unfolded state. Thousands of sequences were generated by Monte Carlo simulation. Three were chosen for experimental testing, based on their low energies and several empirical criteria. All three could be overexpressed and had native-like circular dichroism spectra and 1D-NMR spectra typical of folded structures. Two had upshifted thermal denaturation curves when a peptide ligand was present, indicating binding and suggesting folding to a correct, PDZ structure. Evidently, the physical principles that govern folded proteins, with a dash of empirical post-filtering, can allow successful whole-protein redesign.

Computational and structure-guided design of phosphoinositide substrate specificity into the tyrosine specific LMW-PTP enzyme

We have used a combination of computational and structure-based redesign of the low molecular weight protein tyrosine phosphatase, LMW-PTP, to create new activity towards phosphoinositide substrates for which the wild-type enzyme had little or no activity. The redesigned enzymes retain catalytic activity despite residue alterations in the active site, and kinetic experiments confirmed specificity for up to four phosphoinositide substrates. Changes in the shape and overall volume of the active site where critical to facilitate access of the new substrates for catalysis. The kinetics data suggest that both the position and the combination of amino acid mutations are important for specificity towards the phosphoinositide substrates. The introduction of basic residues proved essential to establish new interactions with the multiple phosphate groups in the inositol head, thus promoting catalytically productive complexes. The crystallographic structures of the top-ranking designs confirmed the computational predictions and showed that residue substitutions do not alter the overall folding of the phosphatase or the conformation of the active site P-loop. The engineered LMW-PTP mutants with new activities can be useful reagents in investigating cell signalling pathways and offer the potential for therapeutic applications.

Modeled structure-based computational redesign of a glycosyltransferase for the synthesis of rebaudioside D from rebaudioside A

Engineering of enzymes without crystal structures and homologous structures is a great challenge for structure-based computational enzyme design. In this study, a computational strategy that combines protein structure prediction, sequence optimization, and molecular dynamics simulation was developed to improve the catalytic efficiency of the UDP-dependent glycosyltransferase EUGT11 for the synthesis of rebaudioside D from rebaudioside A. The modeled structure of EUGT11 was constructed using a threading-based structure prediction method and the H27–D128 catalytic dyad for glycosylation was identified by bioinformatics analysis. Variant sequences of EUGT11 were created using the computational enzyme design method, and a single variant M2(F379A) was confirmed experimentally to have a catalytic efficiency 2.18-fold higher than that of the wild-type enzyme. The molecular dynamics simulation results revealed that the F379A mutation improved the binding of the sugar acceptor, which may explain the increased catalytic activity of the M2 variant. Although the catalytic efficiency of the M2 variant needs to be further improved for practical use, the developed computational strategy can be applied to improve the properties of enzymes when their structures are unknown.

Semi-explicit Solvation Improves Ligand Binding Site Design in an Allosteric Protein

A protein's function is closely derived from its structure; the ability to understand and precisely design structures can thus help create proteins with specific functions. An often-overlooked aspect of protein structural design is the placement of individual water molecules, which are normally approximated as bulk solvent. Previous work has shown that specifying key water molecules improves structural predictions. In particular, designing binding sites in proteins could benefit from modeling waters because the binding affinity and resulting response of proteins to their ligands are influenced by water-mediated hydrogen bonds. Here, we applied solvation to the design of the allosteric transcriptional repressor LacI, with the design goal of inverting the inducibility of both an inducing and anti-inducing ligand. We used a statistical semi-explicit solvation model to solvate LacI; this elucidated specific hydrogen bonds we used to inform our computational redesign of the LacI binding site. We experimentally screened the top six designs to measure their response to various ligands and observed a variety of altered responses compared to wild type; a future challenge is to pinpoint the interactions responsible for the observed allosteric effects. Altogether, our results indicate that considering semi-explicit waters yields greater insight into ligand-protein interactions and can improve current protein design protocols.

Creation of (R)-Amine Transaminase Activity within an α-Amino Acid Transaminase Scaffold.

The enzymatic transamination of ketones into (R)-amines represents an important route for accessing a range of pharmaceuticals or building blocks. Although many publications have dealt with enzyme discovery, protein engineering, and the application of (R)-selective amine transaminases [(R)-ATA] in biocatalysis, little is known about the actual in vivo role and how these enzymes have evolved from the ubiquitous α-amino acid transaminases (α-AATs). Here, we show the successful introduction of an (R)-transaminase activity in an α-amino acid aminotransferase with one to six amino acid substitutions in the enzyme's active site. Bioinformatic analysis combined with computational redesign of the d-amino acid aminotransferase (DATA) led to the identification of a sextuple variant having a specific activity of 326 milliunits mg-1 in the conversion of (R)-phenylethylamine and pyruvate to acetophenone and d-alanine. This value is similar to those of natural (R)-ATAs, which typically are in the range of 250 milliunits mg-1. These results demonstrate that (R)-ATAs can evolve from α-AAT as shown here for the DATA scaffold.

Computational redesign of the Escherichia coli ribose-binding protein ligand binding pocket for 1,3-cyclohexanediol and cyclohexanol

Bacterial periplasmic-binding proteins have been acclaimed as general biosensing platform, but their range of natural ligands is too limited for optimal development of chemical compound detection. Computational redesign of the ligand-binding pocket of periplasmic-binding proteins may yield variants with new properties, but, despite earlier claims, genuine changes of specificity to non-natural ligands have so far not been achieved. In order to better understand the reasons of such limited success, we revisited here the Escherichia coli RbsB ribose-binding protein, aiming to achieve perceptible transition from ribose to structurally related chemical ligands 1,3-cyclohexanediol and cyclohexanol. Combinations of mutations were computationally predicted for nine residues in the RbsB binding pocket, then synthesized and tested in an E. coli reporter chassis. Two million variants were screened in a microcolony-in-bead fluorescence-assisted sorting procedure, which yielded six mutants no longer responsive to ribose but with 1.2–1.5 times induction in presence of 1 mM 1,3-cyclohexanediol, one of which responded to cyclohexanol as well. Isothermal microcalorimetry confirmed 1,3-cyclohexanediol binding, although only two mutant proteins were sufficiently stable upon purification. Circular dichroism spectroscopy indicated discernable structural differences between these two mutant proteins and wild-type RbsB. This and further quantification of periplasmic-space abundance suggested most mutants to be prone to misfolding and/or with defects in translocation compared to wild-type. Our results thus affirm that computational design and library screening can yield RbsB mutants with recognition of non-natural but structurally similar ligands. The inherent arisal of protein instability or misfolding concomitant with designed altered ligand-binding pockets should be overcome by new experimental strategies or by improved future protein design algorithms.

Computational Redesign of PD-1 Interface for PD-L1 Ligand Selectivity

Chronic or persistent stimulation of the programmed cell death-1 (PD-1) pathway prevents Tcells from mounting anti-tumor and anti-viral immune responses. Blockade of this inhibitory checkpoint pathway has shown therapeutic importance by rescuing Tcells from their exhausted state. Cognate ligands of the PD-1 receptor include the tissue-specific PD-L1 and PD-L2 proteins. Engineering a human PD-1 interface specific for PD-L1 or PD-L2 can provide a specific reagent and therapeutic advantage for tissue-specific disruption of the PD-1 pathway. We utilized ProtLID, a computational framework, which constitutes a residue-based pharmacophore approach, to custom-design a human PD-1 interface specific to human PD-L1 without any significant affinity to PD-L2. In subsequent cell assay experiments, half of all single-point mutant designs proved to introduce a statistically significant selectivity, with nine of these maintaining a close to wild-type affinity to PD-L1. This proof-of-concept study suggests a general approach to re-engineer protein interfaces for specificity.

A Self-Assembling Two-Dimensional Protein Array is a Versatile Platform for the Assembly of Multicomponent Nanostructures.

Rationally designed two-dimensional (2D) arrays that support the assembly of nanoscale components are of interest for catalysis, sensing, and biomedical applications. The computational redesign of a protein called TTM that undergoes calcium-induced self-assembly into nanostructured lattices capable of growing to dozens of micrometers are previously reported. The work demonstrates here that the N- and C-termini of the constituent monomers are solvent-accessible and that they can be modified with a hexahistidine extension, a gold-binding peptide, or a biotinylation tag to decorate nickel-nitriloacetic acid beads with self-assembled protein islands, conjugate gold nanoparticles to planar arrays, or control the immobilization density of avidin molecules onto 2D lattices through co-polymerization of biotinylated and wild type TTM monomers. These results showcase the potential of TTM as a versatile 2D scaffold for the fabrication of hierarchical structures comprising a broad range of nanoscale elements.

Engineered Biosensors from Dimeric Ligand-Binding Domains.

Biosensors are important components of many synthetic biology and metabolic engineering applications. Here, we report a second generation of Saccharomyces cerevisiae digoxigenin and progesterone biosensors based on destabilized dimeric ligand-binding domains that undergo ligand-induced stabilization. The biosensors, comprising one ligand-binding domain monomer fused to a DNA-binding domain and another fused to a transcriptional activation domain, activate reporter gene expression in response to steroid binding and receptor dimerization. The introduction of a destabilizing mutation to the dimer interface increased biosensor dynamic range by an order of magnitude. Computational redesign of the dimer interface and functional selections were used to create heterodimeric pairs with further improved dynamic range. A heterodimeric biosensor built from the digoxigenin and progesterone ligand-binding domains functioned as a synthetic "AND"-gate, with 20-fold stronger response to the two ligands in combination than to either one alone. We also identified mutations that increase the sensitivity or selectivity of the biosensors to chemically similar ligands. These dimerizing biosensors provide additional flexibility for the construction of logic gates and other applications.

DisruPPI: structure-based computational redesign algorithm for protein binding disruption.

MotivationDisruption of protein–protein interactions can mitigate antibody recognition of therapeutic proteins, yield monomeric forms of oligomeric proteins, and elucidate signaling mechanisms, among other applications. While designing affinity-enhancing mutations remains generally quite challenging, both statistically and physically based computational methods can precisely identify affinity-reducing mutations. In order to leverage this ability to design variants of a target protein with disrupted interactions, we developed the DisruPPI protein design method (DISRUpting Protein–Protein Interactions) to optimize combinations of mutations simultaneously for both disruption and stability, so that incorporated disruptive mutations do not inadvertently affect the target protein adversely.ResultsTwo existing methods for predicting mutational effects on binding, FoldX and INT5, were demonstrated to be quite precise in selecting disruptive mutations from the SKEMPI and AB-Bind databases of experimentally determined changes in binding free energy. DisruPPI was implemented to use an INT5-based disruption score integrated with an AMBER-based stability assessment and was applied to disrupt protein interactions in a set of different targets representing diverse applications. In retrospective evaluation with three different case studies, comparison of DisruPPI-designed variants to published experimental data showed that DisruPPI was able to identify more diverse interaction-disrupting and stability-preserving variants more efficiently and effectively than previous approaches. In prospective application to an interaction between enhanced green fluorescent protein (EGFP) and a nanobody, DisruPPI was used to design five EGFP variants, all of which were shown to have significantly reduced nanobody binding while maintaining function and thermostability. This demonstrates that DisruPPI may be readily utilized for effective removal of known epitopes of therapeutically relevant proteins.Availability and implementationDisruPPI is implemented in the EpiSweep package, freely available under an academic use license.Supplementary information Supplementary data are available at Bioinformatics online.

Computational redesign of enzymes for regio- and enantioselective hydroamination.

Introduction of innovative biocatalytic processes offers great promise for applications in green chemistry. However, owing to limited catalytic performance, the enzymes harvested from nature's biodiversity often need to be improved for their desired functions by time-consuming iterative rounds of laboratory evolution. Here we describe the use of structure-based computational enzyme design to convert Bacillus sp. YM55-1 aspartase, an enzyme with a very narrow substrate scope, to a set of complementary hydroamination biocatalysts. The redesigned enzymes catalyze asymmetric addition of ammonia to substituted acrylates, affording enantiopure aliphatic, polar and aromatic β-amino acids that are valuable building blocks for the synthesis of pharmaceuticals and bioactive compounds. Without a requirement for further optimization by laboratory evolution, the redesigned enzymes exhibit substrate tolerance up to a concentration of 300 g/L, conversion up to 99%, β-regioselectivity >99% and product enantiomeric excess >99%. The results highlight the use of computational design to rapidly adapt an enzyme to industrially viable reactions.

Computational Re-design of Synthetic Genetic Oscillators for Independent Amplitude and Frequency Modulation.

To perform well in biotechnology applications, synthetic genetic oscillators must be engineered to allow independent modulation of amplitude and period. This need is currently unmet. Here, we demonstrate computationally how two classic genetic oscillators, the dual-feedback oscillator and the repressilator, can be re-designed to provide independent control of amplitude and period and improve tunability-that is, a broad dynamic range of periods and amplitudes accessible through the input "dials." Our approach decouples frequency and amplitude modulation by incorporating an orthogonal "sink module" where the key molecular species are channeled for enzymatic degradation. This sink module maintains fast oscillation cycles while alleviating the translational coupling between the oscillator's transcription factors and output. We characterize the behavior of our re-designed oscillators over a broad range of physiologically reasonable parameters, explain why this facilitates broader function and control, and provide general design principles for building synthetic genetic oscillators that are more precisely controllable.

Optimization of Cholinesterase-Based Catalytic Bioscavengers Against Organophosphorus Agents.

Organophosphorus agents (OPs) are irreversible inhibitors of acetylcholinesterase (AChE). OP poisoning causes major cholinergic syndrome. Current medical counter-measures mitigate the acute effects but have limited action against OP-induced brain damage. Bioscavengers are appealing alternative therapeutic approach because they neutralize OPs in bloodstream before they reach physiological targets. First generation bioscavengers are stoichiometric bioscavengers. However, stoichiometric neutralization requires administration of huge doses of enzyme. Second generation bioscavengers are catalytic bioscavengers capable of detoxifying OPs with a turnover. High bimolecular rate constants (kcat/Km > 106 M−1min−1) are required, so that low enzyme doses can be administered. Cholinesterases (ChE) are attractive candidates because OPs are hemi-substrates. Moderate OP hydrolase (OPase) activity has been observed for certain natural ChEs and for G117H-based human BChE mutants made by site-directed mutagenesis. However, before mutated ChEs can become operational catalytic bioscavengers their dephosphylation rate constant must be increased by several orders of magnitude. New strategies for converting ChEs into fast OPase are based either on combinational approaches or on computer redesign of enzyme. The keystone for rational conversion of ChEs into OPases is to understand the reaction mechanisms with OPs. In the present work we propose that efficient OP hydrolysis can be achieved by re-designing the configuration of enzyme active center residues and by creating specific routes for attack of water molecules and proton transfer. Four directions for nucleophilic attack of water on phosphorus atom were defined. Changes must lead to a novel enzyme, wherein OP hydrolysis wins over competing aging reactions. Kinetic, crystallographic, and computational data have been accumulated that describe mechanisms of reactions involving ChEs. From these studies, it appears that introducing new groups that create a stable H-bonded network susceptible to activate and orient water molecule, stabilize transition states (TS), and intermediates may determine whether dephosphylation is favored over aging. Mutations on key residues (L286, F329, F398) were considered. QM/MM calculations suggest that mutation L286H combined to other mutations favors water attack from apical position. However, the aging reaction is competing. Axial direction of water attack is not favorable to aging. QM/MM calculation shows that F329H+F398H-based multiple mutants display favorable energy barrier for fast reactivation without aging.

Computational redesign of human respiratory syncytial virus epitope as therapeutic peptide vaccines against pediatric pneumonia.

Human respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections in infants and young children. Here, the RSV fusion (F) glycoprotein epitope FFL was redesigned based on its complex crystal structure with motavizumab, an mAb drug in development for the prevention of RSV infections, aiming to obtain therapeutic peptide vaccines with high affinity to induce RSV-specific neutralizing antibodies. Computational modeling and analysis found that only a small region covering the helix-turn-helix (HTH) motif of FFL can directly interact with motavizumab and confer stability and specificity to the complex system, while the rest of the epitope primarily serves as a structural scaffold that stabilizes the HTH conformation of motavizumab-binding site. Molecular dynamics simulations revealed a large flexibility and intrinsic disorder for the isolated linear HTH peptide, which would incur a considerable entropy penalty upon binding to motavizumab. In this respect, the FFL epitope was redesigned by truncation, mutation, and cyclization to derive a number of small cyclic peptide immunogens. We also employed in vitro fluorescence-based assays to demonstrate that the linear epitope peptide has no observable affinity to motavizumab, whereas redesigned versions of the peptide can bind with a moderate or high potency. Graphical abstract Computationally modeled complex structure of RSV F glycoprotein with motavizumab and zoom up of the complex binding site.

Computational redesign of penicillin acylase for cephradine synthesis with high kinetic selectivity

A penicillin acylase variant was designed to catalyze the enzymatic synthesis of β-lactam antibiotic cephradine under industrial conditions.

Computational Redesign of Acyl-ACP Thioesterase with Improved Selectivity toward Medium-Chain-Length Fatty Acids.

Enzyme and metabolic engineering offer the potential to develop biocatalysts for converting natural resources into a wide range of chemicals. To broaden the scope of potential products beyond natural metabolites, methods of engineering enzymes to accept alternative substrates and/or perform novel chemistries must be developed. DNA synthesis can create large libraries of enzyme-coding sequences, but most biochemistries lack a simple assay to screen for promising enzyme variants. Our solution to this challenge is structure-guided mutagenesis in which optimization algorithms select the best sequences from libraries based on specified criteria (i.e. binding selectivity). Here, we demonstrate this approach by identifying medium-chain (C6-C12) acyl-ACP thioesterases through structure-guided mutagenesis. Medium-chain fatty acids, products of thioesterase-catalyzed hydrolysis, are limited in natural abundance compared to long-chain fatty acids; the limited supply leads to high costs of C6-C10 oleochemicals such as fatty alcohols, amines, and esters. Here, we applied computational tools to tune substrate binding to the highly-active 'TesA thioesterase in Escherichia coli. We used the IPRO algorithm to design thioesterase variants with enhanced C12- or C8-specificity while maintaining high activity. After four rounds of structure-guided mutagenesis, we identified three thioesterases with enhanced production of dodecanoic acid (C12) and twenty-seven thioesterases with enhanced production of octanoic acid (C8). The top variants reached up to 49% C12 and 50% C8 while exceeding native levels of total free fatty acids. A comparably sized library created by random mutagenesis failed to identify promising mutants. The chain length-preference of 'TesA and the best mutant were confirmed in vitro using acyl-CoA substrates. Molecular dynamics simulations, confirmed by resolved crystal structures, of 'TesA variants suggest that hydrophobic forces govern 'TesA substrate specificity. We expect that the design rules we uncovered and the thioesterase variants identified will be useful to metabolic engineering projects aimed at sustainable production of medium-chain oleochemicals.