Difference between revisions of "Part:BBa K4176004"

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optimized sacB

This is a sequence-optimized sacB with lower toxicity

Sequence and Features

Abstract

Expression of sacB converts sucrose to levan, which accumulates in the periplasm and is toxic to E. coli. We used sacB gene as a counter-selection marker in our strainer system. However, the CFU/ug using the wild-type sacB gene (BBa_K322921) is low even using 0.01% sucrose in the media. Structural insights into the wild-type SacB reveals that S164 is important to ensure the stabilization of D86 that is the nucleophilic agent. We speculate that the S164T mutation could decrease the catalytic efficiency. Thus, we model the new hydrogen bond formation from S164T and the position of the D86 carboxyl group by molecular dynamics, and test our conjecture in our wet experiments.

Introduction

The strainer system utilizes the double stranded DNA breaks (DSBs) as a signal to start the transcription of gRNA targeting on the plasmid harboring sacB gene. Expression of sacB converts sucrose to levan, which accumulates in the periplasm and is toxic to E. coli. When the sacB plasmid is cured by CRISPR/Cas system, the successful recombined strain can survival in the media with sucrose. The strain without DSBs, still retains the plasmid harboring sacB gene, cannot survival in the media with the sucrose. In our wet-lab experiments, we found that the toxicity of sacB in our strainer system is too high. When we use original CRISPR/Cas method and the strainer method for gene editing. Although we can increase the editing efficiency by our strain compared to original CRISPR/Cas method, the CFU/ug using the strainer is much lower than that of original CRISPR/Cas method even use 0.01% sucrose. To this end, we sought to use dry-lab experiment to design a sacB mutant with lower toxicity for E. coli, and this sacB mutant can increase the CFU/μg of the strainer method with high editing efficiency.

Structural insights into the wild-type SacB reveals that S164 forms a hydrogen bond with the nucleophilic agent D86 and the 4-OH of the fructose group, and S164 is important to ensure the stabilization of D86 (Fig. 1). We speculate that the S164T mutation with an additional -methyl would change the orientation of the-OH and would effectively form new hydrogen bonds. Thus, the conformation of the D86 carboxyl group is restricted by hydrogen bonding, results in the reduced hydrolysis rate and cell toxicity. We model the new hydrogen bond formation and the position of the D86 carboxyl group by molecular dynamics, and test our conjecture in our wet experiments.

Fig. 1 The first layer means that the amino acid shown in the figure is the closest layer to the substrate (sucrose), and the distance between all amino acids and the substrate is less than 3.5 Å. W85, D86, W163, R246, D247, E342 are completely conservative in GH68 family.

Result and Discussion

Molecular dynamics (MD) simulations

MD simulations were performed using Amber software and the ff99SB force field.[1] The selected docking complexes of SacB-sucrose were solvated in the OPC water model. A simulated truncated octahedral box was built for calculating protein–ligand interactions. The box size was set to avoid interactions through periodic boundaries. Nonbonded interactions were truncated at a cutoff distance of 11 Å. The system was initially equilibrated using the steepest descent method for 5000 steps twice while restraining the atoms of protein and ligand with 10 kcal/mol and 0 kcal/mol, respectively. Then, the system was gradually heated to 300K within 20 ps while maintaining the 20 kcal/mol constraint on protein–ligand. Next, a 1 ns isothermal–isobaric (NPT) ensemble and 1 ns canonical ensemble (NVT) run were performed, both with 5 kcal/mol restraint. Finally, a 20 ns MD run was adopted for equilibration and sampling. All MD simulations were performed with 2 fs time steps with the temperature maintained via a Berendsen thermostat. Protein–sucrose complexes were equilibrated by detecting the root-mean-square deviation (RMSD) of compounds and protein backbone, and reasonable and equilibrated conformations of the ligand were extracted from the MD simulations (Fig. 2). The RMSD values of SacB backbone fluctuated around 1.2 Å indicated the conformations of sucrose were stable.

Fig. 2 RMSD of SacB Wt (A) (PDB ID: 1OYG) and variant S164T (B) using sucrose as ligand. In the mutants, the fluctuating value of RMSD is large, indicating the low catalytic efficiency of the mutant.

Fig. 3 Binding free energy decomposition calculated by MM/GBSA, including van der Waals energy (A) electrostatic energy (B) non-polar solvation energy (C) polar solvation energy (D) The complexes of SacB and variant S164T with sucrose are indicated in blueness and orange, respectively.

As a whole, we used the MMGBSA (Molecular Mechanics / Poisson Boltzmann (Generalized Born) Surface Area) approach (Fig. 3). The overall binding free energy of sucrose molecules is: -18.84 ± 4.10 kcal / mol. In the mutation group, this value is: -21.27 ± 3.77 kCal / mol, which doesn't change much compared to the wild-type SacB (Based on our previous experimental data, the increased binding energy per 4.5 kCal / mol corresponds to a 2 to 3-fold increase in the inhibitor inhibition capacity, which not directly correspond to the catalytic capacity). S164 plays an important role for the stabilization of sucrose molecule in the pocket, with total free energy calculated as -1.61 kcal / mol. The value of the locus in S164T mutation is only -0.41 kcal / mol. Thus, the S164T is the key mutation, which directly leads to the shift of the sucrose molecules in the catalytic pocket (Fig. 4), enhanced interaction with amino acid D53 and with E214, and diminished interaction with amino acid GLU307. This mutation breaks the delicate balance of the ternary catalytic amino acid with the ligand. Therefore, it is speculated to reduce the cytotoxicity.

Fig. 4 Comparation between variant S164T and SacB Wt. MD simulations of variant S164T (A) and SacB Wt (B) (PDB ID: 1OYG) using sucrose as ligand. The parameters of hydrogen bonds, variant S164T (C) and SacB Wt (D). Conformational change of the D86 orientation results in the partially broken hydrogen bond formed by nucleophilic agent D86 and 4-OH of fructose group , which reduced the efficiency of sucrose hydrolysis.

Improve the CFU/μg of "strainer" Method with SacB Mutant

The results showed that the editing efficiency of SacB_S164T mutant (in EC85) is 25% higher than the control without “strainer” system. The CFU/μg of SacB_S164T mutant (in EC85) increased 3-fold compared to the original “strainer” system while still keep high editing efficiency (Fig. 5A).

We also used the same condition to test SacB_S164T mutant in EC88. The results showed that the editing efficiency using “strainer” system with SacB_S164T mutant is 4-fold higher than the control, although the CFU/μg of SacB_S164T mutant (in EC88) is still 71% lower than the control without “strainer” system (Fig. 5B). We also used the same condition to test SacB_S164T mutant in EC88. However, the results showed that the toxicity of the modified SacB protein was low, and the screening effect served by the lower concentration of sucrose chosen at this point was no longer obvious, so we further optimized the sucrose concentration and found that testing the mutants at 0.1% concentration conditions, the CFU/μg using the "strainer" system with the SacB_S164T mutant was 2-fold higher than the control, although the SacB_ S164T mutant (in EC88) still had a lower CFU/μg than the control without the "strainer" system.

These results showed that our strainer system works as a good purification system to remove the unedited cells, and SacB_S164T mutant is less toxicity than the original SacB.

Fig. 5. Improve the CFU/μg of “strainer” method with SacB mutant. (A) The CFU/μg and editing efficiency of the control group, SacB and SacB_S164T under 0.01% sucrose condition in EC85. (B) The CFU/μg and editing efficiency of the control group, SacB and SacB_S164T under 0.1% sucrose and 0.2% sucrose condition in EC88.

References

[1] Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins. 2006, 65, 712–725.

Contribution: iGEM24_DUT-China

We developed sacB_S164A and sacB_E262Q mutants based on sacB_S164T. Through wet experiments, the above two mutants, together with sacB_WT and sacB_S164T proposed by DUT_China's team in 2022, constitute sacB toolkits with different fatality rates.

Abstract

The sacB gene encodes a fructosyltransferase that catalyzes the hydrolysis of sucrose to produce levans, which, when accumulated in Gram-negative bacteria, can lead to their death[1]. To develop a more versatile screening toolkit of sacB with different lethality capabilities, we predicted single and combined mutation sites affecting sacB toxicity through molecular docking simulations and Funclib. Integrating predictions from different technologies, we proposed sacB_S164A and sacB_E262Q mutants with varying toxicity levels and validated their toxicity through wet lab experiments. These two mutants, along with sacB_S164T and sacB_WT proposed by the DUT_China team in 2022, enriched our sacB toolkit covering different lethality rates.

3.1 Molecular Docking Predicts Different Toxicity of sacB Mutation Sites

When bacteria grow in sucrose-containing media, sacB enzyme converts sucrose to levans. Levans is a large molecular polysaccharide accumulated in the outer membrane of bacterial cells. Its accumulation in Gram-negative bacteria can lead to impaired cell membrane function, thereby exerting toxic effects. To identify critical sites affecting enzyme binding with sucrose, we performed molecular docking of fructosyltransferase with sucrose using AutoDock Vina[2][3], with sucrose molecule information from the PubChem database[4] and protein structure data from the PDB database, running the docking program in a Cygwin64 environment. By optimizing the size and position of the docking box, we improved the accuracy and precision of docking simulations. We selected 15 docking results with high affinity and similar to real crystal structures for analysis, counted the number of polar interactions and hydrogen bonds formed between sacB residues and the ligand, and ranked them(Figure 6). According to the results, we identified 13 critical sites for substrate binding: 164, 342, 86, 85, 163, 246, 360, 412, 433, 411, 343, 247, 262. Based on literature and crystal structures, these sites are presumed to significantly impact the catalytic activity of fructosyltransferase. We selected 10 sites that intersect with literature for sacB mutation: 86, 164, 246, 247, 262, 340, 342, 343, 360, 411. Some sites such as 86, 164, 247, 340, 342, have mutation data that can serve as evidence for our experimental results.[4]

Fig. 1 T The Number of Polar Interactions and Hydrogen

3.2 Using Funclib to Predict Combined Mutations of sacB

Based on the analysis, we used FunLib[5]—an automated multi-point mutation design method based on phylogenetic analysis and Rosetta design calculations—to perform mutation space searches on these 10 high-impact sites and predicted the stability and affinity of the enzyme for fructose receptors after multiple mutations. Considering the stability and affinity post-mutation, we scored and ranked the results using the following formulas to negate the effects of sign and absolute magnitude. We quantified post-mutation stability, binding ability with small molecules, and overall capacity, with the calculation formulas as follows:

- Formula for Stability Calculation

- Formula for Binding Ability Calculation

- Overall Formula

Through this method, we selected mutations that surpassed the wild-type in stability, small molecule binding ability, and overall capacity. Then we quantified the proportion of different mutations at each unit point superior to the wild-type, verifying mutation results described in the literature, such as 86: S>N>T>D, 247: D>N, etc. We also tested new sites 262 and 164, conducting experimental validations for E262Q and S164A mutations.

Fig.2 Overall Scoring Chart. The x-axis represents the mutation sites, while the y-axis displays the count of specific mutations at each site in mutation combinations that outperform the wild type. The frequency of each mutation is measured across the spectrum of tested combinations, providing a quantitative assessment of mutational impacts relative to the baseline wild type performance.

Fig.3 Stability Scoring Chart. The horizontal axis displays the mutation sites, while the vertical axis indicates the count of specific mutations at each site across mutation combinations that demonstrate greater stability than the wild type. This chart quantitatively assesses the stabilizing effects of mutations, highlighting those that enhance protein stability beyond the baseline wild type characteristics.

Fig.4 Small Molecule Binding Ability Scoring Chart. The horizontal axis displays the mutation sites, while the vertical axis indicates the count of specific mutations at each site across mutation combinations that demonstrate greater small molecule binding ability than the wild type. This chart quantitatively assesses the binding capacity enhancements of mutations, highlighting those that improve protein interaction with small molecules beyond the baseline wild type characteristics.

3.3 Toxicity Test of sacB Mutants

We set sucrose concentration gradients of 0.01%, 0.1% and 1% to detect the fatality rate of sacB mutants. At a sucrose concentration of 0.1%, the mortality rates of sacB_S164A and sacB_E262Q were 19.33% and 41.35%, respectively. According to the results, we found that the fatality rate of sacB increased significantly with the increase of sucrose concentration. Through the combination of sacB mutants and sucrose concentrations, we obtained sacB screening kits covering different fatality rates.

Fig.5 The fatality rate of ''sacB'' mutants at sucrose concentration of 0.1%

Fig.6 The fatality rate of ''sacB'' mutants at sucrose concentration of 1%, 0.1% and 0.01%

Reference

[1] Huang, H., Huang, G., Tan, Z. et al. Engineered Cas12a-Plus nuclease enables gene editing with enhanced activity and specificity. BMC Biol 20, 91 (2022). https://doi.org/10.1186/s12915-022-01296-1
[2] Eberhardt, J., Santos-Martins, D., Tillack, A.F., Forli, S. (2021). AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling.
[3] Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461.
[4] Meng G, Fütterer K. Structural framework of fructosyl transfer in Bacillus subtilis levansucrase. Nat Struct Biol. 2003 Nov;10(11):935-41. doi: 10.1038/nsb974. Epub 2003 Sep 28. PMID: 14517548.
[5] Olga Khersonsky, Rosalie Lipsh, Ziv Avizemer, Yacov Ashani, Moshe Goldsmith, Haim Leader, Orly Dym, Shelly Rogotner, Devin L. Trudeau, Jaime Prilusky, Pep Amengual-Rigo, Victor Guallar, Dan S. Tawfik, Sarel J. Fleishman, Automated Design of Efficient and Functionally Diverse Enzyme Repertoires, Molecular Cell, Volume 72, Issue 1, 2018, Pages 178-186.e5, ISSN 1097-2765, https://doi.org/10.1016/j.molcel.2018.08.033.