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

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.

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.

Computational design of SARS-CoV-2 spike glycoproteins to increase immunogenicity by T cell epitope engineering

The development of effective and safe vaccines is the ultimate way to efficiently stop the ongoing COVID-19 pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Built on the fact that SARS-CoV-2 utilizes the association of its Spike (S) protein with the human angiotensin-converting enzyme 2 (ACE2) receptor to invade host cells, we computationally redesigned the S protein sequence to improve its immunogenicity and antigenicity. Toward this purpose, we extended an evolutionary protein design algorithm, EvoDesign, to create thousands of stable S protein variants that perturb the core protein sequence but keep the surface conformation and B cell epitopes. The T cell epitope content and similarity scores of the perturbed sequences were calculated and evaluated. Out of 22,914 designs with favorable stability energy, 301 candidates contained at least two pre-existing immunity-related epitopes and had promising immunogenic potential. The benchmark tests showed that, although the epitope restraints were not included in the scoring function of EvoDesign, the top S protein design successfully recovered 31 out of the 32 major histocompatibility complex (MHC)-II T cell promiscuous epitopes in the native S protein, where two epitopes were present in all seven human coronaviruses. Moreover, the newly designed S protein introduced nine new MHC-II T cell promiscuous epitopes that do not exist in the wildtype SARS-CoV-2. These results demonstrated a new and effective avenue to enhance a target protein's immunogenicity using rational protein design, which could be applied for new vaccine design against COVID-19 and other pathogens.

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.

Rational Design of SARS-CoV-2 Spike Glycoproteins To Increase Immunogenicity By T Cell Epitope Engineering

The current COVID-19 pandemic caused by SARS-CoV-2 has resulted in millions of confirmed cases and thousands of deaths globally. Extensive efforts and progress have been made to develop effective and safe vaccines against COVID-19. A primary target of these vaccines is the SARS-CoV-2 spike (S) protein, and many studies utilized structural vaccinology techniques to either stabilize the protein or fix the receptor-binding domain at certain states. In this study, we extended an evolutionary protein design algorithm, EvoDesign, to create thousands of stable S protein variants without perturbing the surface conformation and B cell epitopes of the S protein. We then evaluated the mutated S protein candidates based on predicted MHC-II T cell promiscuous epitopes as well as the epitopes' similarity to human peptides. The presented strategy aims to improve the S protein's immunogenicity and antigenicity by inducing stronger CD4 T cell response while maintaining the protein's native structure and function. The top EvoDesign S protein candidate (Design-10705) recovered 31 out of 32 MHC-II T cell promiscuous epitopes in the native S protein, in which two epitopes were present in all seven human coronaviruses. This newly designed S protein also introduced nine new MHC-II T cell promiscuous epitopes and showed high structural similarity to its native conformation. The proposed structural vaccinology method provides an avenue to rationally design the antigen's structure with increased immunogenicity, which could be applied to the rational design of new COVID-19 vaccine candidates.

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.

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.

Recent advances in automated protein design and its future challenges

INTRODUCTION

Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.

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 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.