Systematic optimization model and algorithm for binding sequence selection in computational enzyme design

Computational design of highly efficient thermostable MHET hydrolases and dual enzyme system for PET recycling

Recently developed enzymes for the depolymerization of polyethylene terephthalate (PET) such as FAST-PETase and LCC-ICCG are inhibited by the intermediate PET product mono(2-hydroxyethyl) terephthalate (MHET). Consequently, the conversion of PET enzymatically into its constituent monomers terephthalic acid (TPA) and ethylene glycol (EG) is inefficient. In this study, a protein scaffold (1TQH) corresponding to a thermophilic carboxylesterase (Est30) was selected from the structural database and redesigned in silico. Among designs, a double variant KL-MHETase (I171K/G130L) with a similar protein melting temperature (67.58 °C) to that of the PET hydrolase FAST-PETase (67.80 °C) exhibited a 67-fold higher activity for MHET hydrolysis than FAST-PETase. A fused dual enzyme system comprising KL-MHETase and FAST-PETase exhibited a 2.6-fold faster PET depolymerization rate than FAST-PETase alone. Synergy increased the yield of TPA by 1.64 fold, and its purity in the released aromatic products reached 99.5%. In large reaction systems with 100 g/L substrate concentrations, the dual enzyme system KL36F achieved over 90% PET depolymerization into monomers, demonstrating its potential applicability in the industrial recycling of PET plastics. Therefore, a dual enzyme system can greatly reduce the reaction and separation cost for sustainable enzymatic PET recycling.

Computational redesign of cytochrome P450 CYP102A1 for highly stereoselective omeprazole hydroxylation by UniDesign

Cytochrome P450 CYP102A1 is a prototypic biocatalyst that has great potential in chemical synthesis, drug discovery, and biotechnology. CYP102A1 variants engineered by directed evolution and/or rational design are capable of catalyzing the oxidation of a wide range of organic compounds. However, it is difficult to foresee the outcome of engineering CYP102A1 for a compound of interest. Here, we introduce UniDesign as a computational framework for enzyme design and engineering. We tested UniDesign by redesigning CYP102A1 for stereoselective metabolism of omeprazole (OMP), a proton pump inhibitor, starting from an active but nonstereoselective triple mutant (TM: A82F/F87V/L188Q). To shift stereoselectivity toward (R)-OMP, we computationally scanned three active site positions (75, 264, and 328) for mutations that would stabilize the binding of the transition state of (R)-OMP while destabilizing that of (S)-OMP and picked three variants, namely UD1 (TM/L75I), UD2 (TM/A264G), and UD3 (TM/A328V), for experimentation, based on computed energy scores and models. UD1, UD2, and UD3 exhibit high turnover rates of 55 ± 4.7, 84 ± 4.8, and 79 ± 5.7 min-1, respectively, for (R)-OMP hydroxylation, whereas the corresponding rates for (S)-OMP are only 2.2 ± 0.19, 6.0 ± 0.68, and 14 ± 2.8 min-1, yielding an enantiomeric excess value of 92, 87, and 70%, respectively. These results suggest the critical roles of L75I, A264G, and A328V in steering OMP in the optimal orientation for stereoselective oxidation and demonstrate the utility of UniDesign for engineering CYP102A1 to produce drug metabolites of interest. The results are discussed in the context of protein structures.

Rational Design of Daunorubicin C-14 Hydroxylase Based on the Understanding of Its Substrate-Binding Mechanism

Doxorubicin is one of the most widely used antitumor drugs and is currently produced via the chemical conversion method, which suffers from high production costs, complex product separation processes, and serious environmental pollution. Biocatalysis is considered a more efficient and environment-friendly method for drug production. The cytochrome daunorubicin C-14 hydroxylase (DoxA) is the essential enzyme catalyzing the conversion of daunorubicin to doxorubicin. Herein, the DoxA from Streptomyces peucetius subsp. caesius ATCC 27952 was expressed in Escherichia coli, and the rational design strategy was further applied to improve the enzyme activity. Eight amino acid residues were identified as the key sites via molecular docking. Using a constructed screening library, we obtained the mutant DoxA(P88Y) with a more rational protein conformation, and a 56% increase in bioconversion efficiency was achieved by the mutant compared to the wild-type DoxA. Molecular dynamics simulation was applied to understand the relationship between the enzyme's structural property and its substrate-binding efficiency. It was demonstrated that the mutant DoxA(P88Y) formed a new hydrophobic interaction with the substrate daunorubicin, which might have enhanced the binding stability and thus improved the catalytic activity. Our work lays a foundation for further exploration of DoxA and facilitates the industrial process of bio-production of doxorubicin.

Using High-Throughput Molecular Dynamics Simulation to Enhance the Computational Design of Kemp Elimination Enzymes

Computational enzyme design has been successfully applied to identify new alternatives to natural enzymes for the biosynthesis of important compounds. However, the moderate catalytic activities of de novo designed enzymes indicate that the modeling accuracy of current computational enzyme design methods should be improved. Here, high-throughput molecular dynamics simulations were used to enhance computational enzyme design, thus allowing the identification of variants with higher activities in silico. Different time schemes of high-throughput molecular dynamics simulations were tested to identify the catalytic features of evolved Kemp eliminases. The 20 × 1 ns molecular dynamics simulation scheme was sufficiently accurate and computationally viable to screen the computationally designed massive variants of Kemp elimination enzymes. The developed hybrid computational strategy was used to redesign the most active Kemp eliminase, HG3.17, and five variants were generated and experimentally confirmed to afford higher catalytic efficiencies than that of HG3.17, with one double variant (D52Q/A53S) exhibiting a 55% increase. The hybrid computational enzyme design strategy is general and computationally economical, with which we anticipate the efficient creation of practical enzymes for industrial biocatalysis.

Tuning the Catalytic Activity of Synthetic Enzyme KE15 with DNA

Efficiency improvement of synthetic enzymes through scaffold modifications suffers from limitations in terms of effectiveness, cost, and potential devastating consequences for protein structural stability. Here, we propose an alternative to scaffold modification, within electrostatic preorganization theory, where the enzyme's greater environment is designed to support the evolution of the reaction in the active site. We demonstrate the feasibility of such an approach by placing a (polar) DNA fragment in the surroundings of the Kemp eliminase enzyme KE15 (structure from Houk's group) and computing the resulting change in catalytic activity. We find that the introduction of a DNA fragment magnifies the contribution of protein residues to the stabilization of the transition state, estimated from electric field calculations with polarizable molecular dynamics. Our randomly generated test systems reveal a 2.0 kcal/mol reduction in activation energy, suggesting that even more significant catalytic improvements could be made by optimizing DNA size, sequence, and orientation with respect to the enzyme, validating our approach.

FASPR: an open-source tool for fast and accurate protein side-chain packing

MOTIVATION

Protein structure and function are essentially determined by how the side-chain atoms interact with each other. Thus, accurate protein side-chain packing (PSCP) is a critical step toward protein structure prediction and protein design. Despite the importance of the problem, however, the accuracy and speed of current PSCP programs are still not satisfactory.

RESULTS

We present FASPR for fast and accurate PSCP by using an optimized scoring function in combination with a deterministic searching algorithm. The performance of FASPR was compared with four state-of-the-art PSCP methods (CISRR, RASP, SCATD and SCWRL4) on both native and non-native protein backbones. For the assessment on native backbones, FASPR achieved a good performance by correctly predicting 69.1% of all the side-chain dihedral angles using a stringent tolerance criterion of 20°, compared favorably with SCWRL4, CISRR, RASP and SCATD which successfully predicted 68.8%, 68.6%, 67.8% and 61.7%, respectively. Additionally, FASPR achieved the highest speed for packing the 379 test protein structures in only 34.3 s, which was significantly faster than the control methods. For the assessment on non-native backbones, FASPR showed an equivalent or better performance on I-TASSER predicted backbones and the backbones perturbed from experimental structures. Detailed analyses showed that the major advantage of FASPR lies in the optimal combination of the dead-end elimination and tree decomposition with a well optimized scoring function, which makes FASPR of practical use for both protein structure modeling and protein design studies.

AVAILABILITY AND IMPLEMENTATION

The web server, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/FASPR and https://github.com/tommyhuangthu/FASPR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

EvoEF2: accurate and fast energy function for computational protein design

MOTIVATION

The accuracy and success rate of de novo protein design remain limited, mainly due to the parameter over-fitting of current energy functions and their inability to discriminate incorrect designs from correct designs.

RESULTS

We developed an extended energy function, EvoEF2, for efficient de novo protein sequence design, based on a previously proposed physical energy function, EvoEF. Remarkably, EvoEF2 recovered 32.5%, 47.9% and 22.3% of all, core and surface residues for 148 test monomers, and was generally applicable to protein-protein interaction design, as it recapitulated 30.9%, 42.4%, 31.3% and 21.4% of all, core, interface and surface residues for 88 test dimers, significantly outperforming EvoEF on the native sequence recapitulation. We further used I-TASSER to evaluate the foldability of the 148 designed monomer sequences, where all of them were predicted to fold into structures with high fold- and atomic-level similarity to their corresponding native structures, as demonstrated by the fact that 87.8% of the predicted structures shared a root-mean-square-deviation less than 2 Å to their native counterparts. The study also demonstrated that the usefulness of physical energy functions is highly correlated with the parameter optimization processes, and EvoEF2, with parameters optimized using sequence recapitulation, is more suitable for computational protein sequence design than EvoEF, which was optimized on thermodynamic mutation data.

AVAILABILITY AND IMPLEMENTATION

The source code of EvoEF2 and the benchmark datasets are freely available at https://zhanglab.ccmb.med.umich.edu/EvoEF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Enhancing cellulosic ethanol production through coevolution of multiple enzymatic characteristics of β-glucosidase from Penicillium oxalicum 16

In previous studies, we isolated a novel β-glucosidase from Penicillium oxalicum 16 (16BGL), which is useful for producing cellulosic ethanol. However, 16BGL has a relatively low enzyme activity and product tolerance; besides, a huge gap exists between the optimum temperature of 16BGL (70 °C) and the fermentation temperature for producing cellulosic ethanol (40 °C). Here, we present a directed evolution-based study, which combines one-round error-prone PCR with three rounds of high-throughput screening, for coevolving multiple enzymatic characteristics of 16BGL. We identified an improved variant Y-1-B1 with a triple mutant (G414S/D421V/T441S). Y-1-B1 had an optimum temperature of 50 °C, much closer to the fermentation temperature. The catalytic efficiency of Y-1-B1 for hydrolyzing pNPG was 1355 mM^-1 s^-1 at 50 °C and pH 5, significantly higher than that of 16BGL (807 mM^-1 s^-1). Y-1-B1 also achieved a slightly reduced strength of product inhibition of 1.1 at a glucose concentration of 20 mM, compared with the ratio of 1.3 for 16BGL. A maximum titer of 6.9 g/L for ethanol production was achieved in the reaction with Y-1-B1, which was 22% higher than that achieved with 16BGL. Structure modeling revealed that the mutations are distant from the active-site pocket. Therefore, we performed molecular dynamics (MD) simulations to understand why these mutations can improve catalytic efficiency. MD simulation revealed that the nucleophilic residue Asp261 had a much closer contact with the glucosidic center of pNPG in the simulation with Y-1-B1 than that with 16BGL, suggesting that the mutant is more favorable for catalysis. KEY POINTS: • Multiple enzymatic properties of Penicillium oxalicum 16 BGL were coevolved. • A catalytically efficient triple mutant G414S/D421V/T441S was reported. • Molecular dynamics simulation supported the enhanced catalytic activity.

Computational design of new enzymes for hydrolysis and synthesis of third-generation cephalosporin antibiotics

Engineering active sites in inert scaffolds to catalyze chemical transformations with unnatural substrates is still a great challenge for enzyme catalysis. In this research, a p-nitrobenzyl esterase from Bacillus subtilis was identified from the structural database, and a double mutant E115A/E188A was designed to afford catalytic activities toward the hydrolysis of ceftizoxime. A quadruple mutant E115A/E188A/L362S/I270A with enhanced catalytic efficiency was created to catalyze the condensation reaction of ethyl-2-methoxy-amino-2-(2-aminothiazole-4-yl) acetate with 7-amino-3-nor-cephalosporanic acid to produce ceftizoxime in a fully aqueous medium. The catalytic efficiencies of the computationally designed mutants E115A/E188A/L362S/I270A and E115A/Y118 K/E188 V/I270A/L362S can be taken as starting points to further improve their properties towards the practical application in designing more ecology-friendly production of third-generation cephalosporins.

Computer-aided engineering of adipyl-CoA synthetase for enhancing adipic acid synthesis

OBJECTIVE

To enhance adipic acid production, a computer-aided approach was employed to engineer the adipyl-CoA synthetase from Thermobifida fusca by combining sequence analysis, protein structure modeling, in silico site-directed mutagenesis, and molecular dynamics simulation.

RESULTS

Two single mutants of T. fusca adipyl-CoA synthetase, E210βN and E210βQ, achieved a specific enzyme activity of 1.95 and 1.84 U/mg, respectively, which compared favorably with the 1.48 U/mg for the wild-type. The laboratory-level fermentation experiments showed that E210βN and E210βQ achieved a maximum adipic acid titer of 0.32 and 0.3 g/L. In contrast, the wild-type enzyme yielded a titer of 0.15 g/L under the same conditions. Molecular dynamics (MD) simulations revealed that the mutants (E210βN and E210βQ) could accelerate the dephosphorylation process in catalysis and enhance enzyme activity.

CONCLUSIONS

The combined computational-experimental approach provides an effective strategy for enhancing enzymatic characteristics, and the mutants may find a useful application for producing adipic acid.

Co-evolution of β-glucosidase activity and product tolerance for increasing cellulosic ethanol yield

β-Glucosidase (BGL) plays a key role in cellulose hydrolysis. However, it is still a great challenge to enhance product tolerance and enzyme activity of BGL simultaneously. Here, we utilized one round error-prone PCR to engineer the Penicillium oxalicum 16 BGL (16BGL) for improving the cellulosic ethanol yield. We identified a new variant (L-6C), a triple mutant (M280T/V484L/D589E), with enhanced catalytic efficiency ([Formula: see text]) for hydrolyzing pNPG and reduced strength of inhibition ([Formula: see text]) by glucose. To be specific, L-6C achieved a [Formula: see text] of 0.35 at a glucose concentration of 20 mM, which was 3.63 times lower than that attained by 16BGL. The catalytic efficiency for L-6C to hydrolyze pNPG was determined to be 983.68 mM^-1 s^-1, which was 22% higher than that for 16BGL. However, experiments showed that L-6C had reduced binding affinity (2.88 mM) to pNGP compared with 16BGL (1.69 mM). L-6C produced 6.15 g/L ethanol whose yield increased by about 10% than 16BGL. We performed molecular docking and molecular dynamics (MD) simulation, and binding free energy calculation using the Molecular Mechanics/Poisson Boltzmann surface area (MM/PBSA) method. MD simulation together with the MM/PBSA calculation suggested that L-6C had reduced binding free energy to pNPG, which was consistent with the experimental data.

Toward the Accuracy and Speed of Protein Side-Chain Packing: A Systematic Study on Rotamer Libraries

Protein rotamers refer to the conformational isomers taken by the side-chains of amino acids to accommodate specific structural folding environments. Since accurate modeling of atomic interactions is difficult, rotamer information collected from experimentally solved protein structures is often used to guide side-chain packing in protein folding and sequence design studies. Many rotamer libraries have been built in the literature but there is little quantitative guidance on which libraries should be chosen for different structural modeling studies. Here, we performed a comparative study of six widely used rotamer libraries and systematically examined their suitability for protein folding and sequence design in four aspects: (1) side-chain match accuracy, (2) side-chain conformation prediction, (3) de novo protein sequence design, and (4) computational time cost. We demonstrated that, compared to the backbone-dependent rotamer libraries (BBDRLs), the backbone-independent rotamer libraries (BBIRLs) generated conformations that more closely matched the native conformations due to the larger number of rotamers in the local rotamer search spaces. However, more practically, using an optimized physical energy function incorporated into a simulated annealing Monte Carlo searching scheme, we showed that utilization of the BBDRLs could result in higher accuracies in side-chain prediction and higher sequence recapitulation rates in protein design experiments. Detailed data analyses showed that the major advantage of BBDRLs lies in the energy term derived from the rotamer probabilities that are associated with the individual backbone torsion angle subspaces. This term is important for distinguishing between amino acid identities as well as the rotamer conformations of an amino acid. Meanwhile, the backbone torsion angle subspace-specific rotamer search drastically speeds up the searching time, despite the significantly larger number of total rotamers in the BBDRLs. These results should provide important guidance for the development and selection of rotamer libraries for practical protein design and structure prediction studies.

Computational design of enhanced detoxification activity of a zearalenone lactonase from Clonostachys rosea in acidic medium

Computational design of pH-activity profiles for enzymes is of great importance in industrial applications. In this research, a computational strategy was developed to engineer the pH-activity profile of a zearalenone lactonase (ZHD101) from Clonostachys rosea to promote its activity in acidic medium. The active site pK _a values of ZHD101 were computationally designed by introducing positively charged lysine mutations on the enzyme surface, and the experimental results showed that two variants, M2(D157K) and M9(E171K), increased the catalytic efficiencies of ZHD101 modestly under acidic conditions. Moreover, two variants, M8(D133K) and M9(E171K), were shown to increase the turnover numbers by 2.73 and 2.06-fold with respect to wild type, respectively, though their apparent Michaelis constants were concomitantly increased. These results imply that the active site pK _a value change might affect the pH-activity profile of the enzyme. Our computational strategy for pH-activity profile engineering considers protein stability; therefore, limited experimental validation is needed to discover beneficial mutations under shifted pH conditions.

Using molecular dynamics simulations to evaluate active designs of cephradine hydrolase by molecular mechanics/Poisson–Boltzmann surface area and molecular mechanics/generalized Born surface area methods

The poor predictive accuracy of current computational enzyme design methods has led to low success rates of producing highly active variants that target non-natural substrates. In this report, a quantitative assessment approach based on molecular dynamics (MD) simulations was developed to eliminate false-positive enzyme designs at the computational stage. Taking cephradine hydrolase as an example, the apparent Michaelis binding constant (K_m) and catalytic efficiency (k_cat/K_m) of designed variants were correlated with binding free energies and activation energy barriers, respectively, as calculated by molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) methods with explicit water considered based on general MD simulation protocols. The correlation results showed that both the MM/GBSA and MM/PBSA methods with a protein dielectric constant (ε_p = 4) could rank the variants well based on the predicted binding free energies between enzyme and the substrate. Furthermore, the activation energy barriers calculated by the MM/PBSA method with an ε_p = 24 correlated well with k_cat/K_m. Thus, false-positive variants obtained by the enzyme design program PRODA were eliminated prior to experimentation. Therefore, MD simulation-based quantitative assessment of designed variants greatly enhanced the predictive accuracy of computational enzyme design tools and should facilitate the construction of artificial enzymes with high catalytic activities toward non-natural substrates.

A quantitative assessment method for computational enzyme design was developed to rank the active designs of cephradine hydrolase based on molecular dynamics simulation.

Computational design of cephradine synthase in a new scaffold identified from structural databases

Computational enzyme design exhibits excellent performance for identifying potential scaffolds from structural databases and creating new enzymatic catalysts from naught. Using the active site-matching algorithm ProdaMatch, we identified a new scaffold cocaine esterase from Rhodococcus sp. that showed modest activity (k_cat/K_m = 0.018 M^-1 s^-1) towards the hydrolysis of β-lactam antibiotic cephradine. The identified cocaine esterase scaffold afforded low sequence identity (<30%) with the known β-lactam synthases, such as penicillin G acylase or α-amino acid ester hydrolase, and was able to catalyze the condensation reaction between d-dihydrophenylglycine methyl ester and 7-aminodesacetoxycephalosporanic acid to produce cephradine via a kinetically controlled synthesis. By virtue of the computational enzyme design protocol, hundreds of sequences were predicted in the cocaine esterase scaffold to promote the catalytic activity towards the hydrolytic reaction of cephradine. Moreover, a single mutant (F261T) was experimentally confirmed to have improved the catalytic efficiency by ten times (k_cat/K_m = 0.193 M^-1 s^-1), indicating that the novel scaffold cocaine esterase may be potentially redesigned to become an industrially useful cephradine synthase.

Computational design to improve catalytic activity of cephalosporin C acylase from Pseudomonas strain N176

Engineering enzymes with high catalytic activities using enzyme designin silicoand a limited number of experimental evaluations is the new trend for the discovery of highly efficient biocatalysts.

Computational tools for the evaluation of laboratory-engineered biocatalysts

Biocatalysis is based on the application of natural catalysts for new purposes, for which enzymes were not designed. Although the first examples of biocatalysis were reported more than a century ago, biocatalysis was revolutionized after the discovery of an in vitro version of Darwinian evolution called Directed Evolution (DE). Despite the recent advances in the field, major challenges remain to be addressed. Currently, the best experimental approach consists of creating multiple mutations simultaneously while limiting the choices using statistical methods. Still, tens of thousands of variants need to be tested experimentally, and little information is available on how these mutations lead to enhanced enzyme proficiency. This review aims to provide a brief description of the available computational techniques to unveil the molecular basis of improved catalysis achieved by DE. An overview of the strengths and weaknesses of current computational strategies is explored with some recent representative examples. The understanding of how this powerful technique is able to obtain highly active variants is important for the future development of more robust computational methods to predict amino-acid changes needed for activity.

Computational design of variants for cephalosporin C acylase from Pseudomonas strain N176 with improved stability and activity

In this report, redesigning cephalosporin C acylase from the Pseudomonas strain N176 revealed that the loss of stability owing to the introduced mutations at the active site can be recovered by repacking the nearby hydrophobic core regions. Starting from a quadruple mutant M31βF/H57βS/V68βA/H70βS, whose decrease in stability is largely owing to the mutation V68βA at the active site, we employed a computational enzyme design strategy that integrated design both at hydrophobic core regions for stability enhancement and at the active site for activity improvement. Single-point mutations L154βF, Y167βF, L180βF and their combinations L154βF/L180βF and L154βF/Y167βF/L180βF were found to display improved stability and activity. The two-point mutant L154βF/L180βF increased the protein melting temperature (T _m) by 11.7 °C and the catalytic efficiency V _max/K _m by 57 % compared with the values of the starting quadruple mutant. The catalytic efficiency of the resulting sixfold mutant M31βF/H57βS/V68βA/H70βS/L154βF/L180βF is recovered to become comparable to that of the triple mutant M31βF/H57βS/H70βS, but with a higher T _m. Further experiments showed that single-point mutations L154βF, L180βF, and their combination contribute no stability enhancement to the triple mutant M31βF/H57βS/H70βS. These results verify that the lost stability because of mutation V68βA at the active site was recovered by introducing mutations L154βF and L180βF at hydrophobic core regions. Importantly, mutation V68βA in the six-residue mutant provides more space to accommodate the bulky side chain of cephalosporin C, which could help in designing cephalosporin C acylase mutants with higher activities and the practical one-step enzymatic route to prepare 7-aminocephalosporanic acid at industrial-scale levels.

Use of an Improved Matching Algorithm to Select Scaffolds for Enzyme Design Based on a Complex Active Site Model

Active site preorganization helps native enzymes electrostatically stabilize the transition state better than the ground state for their primary substrates and achieve significant rate enhancement. In this report, we hypothesize that a complex active site model for active site preorganization modeling should help to create preorganized active site design and afford higher starting activities towards target reactions. Our matching algorithm ProdaMatch was improved by invoking effective pruning strategies and the native active sites for ten scaffolds in a benchmark test set were reproduced. The root-mean squared deviations between the matched transition states and those in the crystal structures were < 1.0 Å for the ten scaffolds, and the repacking calculation results showed that 91% of the hydrogen bonds within the active sites are recovered, indicating that the active sites can be preorganized based on the predicted positions of transition states. The application of the complex active site model for de novo enzyme design was evaluated by scaffold selection using a classic catalytic triad motif for the hydrolysis of p-nitrophenyl acetate. Eighty scaffolds were identified from a scaffold library with 1,491 proteins and four scaffolds were native esterase. Furthermore, enzyme design for complicated substrates was investigated for the hydrolysis of cephalexin using scaffold selection based on two different catalytic motifs. Only three scaffolds were identified from the scaffold library by virtue of the classic catalytic triad-based motif. In contrast, 40 scaffolds were identified using a more flexible, but still preorganized catalytic motif, where one scaffold corresponded to the α-amino acid ester hydrolase that catalyzes the hydrolysis and synthesis of cephalexin. Thus, the complex active site modeling approach for de novo enzyme design with the aid of the improved ProdaMatch program is a promising approach for the creation of active sites with high catalytic efficiencies towards target reactions.

Computational strategies for the design of new enzymatic functions

In this contribution, recent developments in the design of biocatalysts are reviewed with particular emphasis in the de novo strategy. Studies based on three different reactions, Kemp elimination, Diels-Alder and Retro-Aldolase, are used to illustrate different success achieved during the last years. Finally, a section is devoted to the particular case of designed metalloenzymes. As a general conclusion, the interplay between new and more sophisticated engineering protocols and computational methods, based on molecular dynamics simulations with Quantum Mechanics/Molecular Mechanics potentials and fully flexible models, seems to constitute the bed rock for present and future successful design strategies.

Computational design of enzyme-ligand binding using a combined energy function and deterministic sequence optimization algorithm

Enzyme amino-acid sequences at ligand-binding interfaces are evolutionarily optimized for reactions, and the natural conformation of an enzyme-ligand complex must have a low free energy relative to alternative conformations in native-like or non-native sequences. Based on this assumption, a combined energy function was developed for enzyme design and then evaluated by recapitulating native enzyme sequences at ligand-binding interfaces for 10 enzyme-ligand complexes. In this energy function, the electrostatic interaction between polar or charged atoms at buried interfaces is described by an explicitly orientation-dependent hydrogen-bonding potential and a pairwise-decomposable generalized Born model based on the general side chain in the protein design framework. The energy function is augmented with a pairwise surface-area based hydrophobic contribution for nonpolar atom burial. Using this function, on average, 78% of the amino acids at ligand-binding sites were predicted correctly in the minimum-energy sequences, whereas 84% were predicted correctly in the most-similar sequences, which were selected from the top 20 sequences for each enzyme-ligand complex. Hydrogen bonds at the enzyme-ligand binding interfaces in the 10 complexes were usually recovered with the correct geometries. The binding energies calculated using the combined energy function helped to discriminate the active sequences from a pool of alternative sequences that were generated by repeatedly solving a series of mixed-integer linear programming problems for sequence selection with increasing integer cuts.

Evaluation of active designs of cephalosporin C acylase by molecular dynamics simulation and molecular docking

Optimization to identify the global minimum energy conformation sequence in in silico enzyme design is computationally non-deterministic polynomial-time (NP)-hard, with the search time growing exponentially as the number of design sites increases. This drawback forces the modeling of protein-ligand systems to adopt discrete amino acid rotamers and ligand conformers, as well as continuum solvent treatment of the environment; however, such compromises produce large numbers of false positives in sequence selection. In this report, cephalosporin acylase, which catalyzes the hydrolytic reaction of cephalosporin C to 7-aminocephalosporanic acid, was used to investigate the dynamic features of active-site-transition-state complex structures using molecular dynamics (MD) simulations to potentially eliminate false positives. The molecular docking between cephalosporin C and wild type acylase N176 and its eight mutants showed that the rate-limiting step in the hydrolytic reaction of cephalosporin C is the acylation process. MD simulations of the active-site-transition-state complex structures of the acylation processes for N176 and its eight mutants showed that the geometrical constraints between catalytic residues and small molecule transition states are always well maintained during the 20 ns simulation for mutants with higher activities, and more hydrogen bonds between binding residues and functional groups of the ligand side chain in the active pocket are formed for mutants with higher activities. The conformations of the ligand transition states were changed greatly after the simulation. This indicates that the hydrogen bond network between the ligand and protein could be improved to enhance the activity of cephalosporin C acylase in subsequent design.