Existing, more expensive, antibiotic detection systems for fresh and wastewater have been designed to very precisely determine the concentration of a specific molecule. For example, complex and expensive electrochemical and optical biosensors were built to determine the presence of Penicillin G or Ampicillin in nanogram range. (Zeng et al. 2022)
In their natural state, our two-component systems cannot distinguish between Penicillin G, Ampicillin, or other types of β-lactam antibiotics found in wastewater probes. They can only determine the presence or absence of any β-lactam antibiotic. To tackle this, we wanted to establish a toolbox of receptors, each with a high specificity for a specific β-lactam antibiotic. Our goal is to create two variants of the BlaR1 receptor, each variant exhibiting increased specificity for a particular β-lactam antibiotic (as shown in Fig. 1).
Figure 1: Illustration showing the naive BlaR1 receptor (yellow receptor) that binds to all types of β-lactam antibiotics. BlaR1 will be mutated and optimized for high binding affinity to either Penicillin G (red receptor) or Ampicillin (blue receptor).
Ultimately, we decided to use the two different β-lactam antibiotics: Penicillin G and Ampicillin. These are often used to cure diseases connected to bacterial infections (Yip et al. 2022, Peechakara et al. 2022). Both antibiotics are highly concentrated in wastewater, especially in developing countries as shown by a case study (Samandari et al. 2022), potentially connected to higher consumption rates. The intriguing aspect of these antibiotics lies in their remarkable similarity, which presents a significant challenge in the process of receptor design. To enhance binding affinity, it is essential to establish novel interactions between the protein and the antibiotic. However, when dealing with antibiotics that bear a high degree of similarity, enhancing binding affinity can only be attributed to the minimal chemical distinctions that exist among these closely related antibiotics.
Fig. 2 shows the structural formula of the two selected β-lactam antibiotics. Ampicillin contains an amino group which is absent in Penicillin G. The difference is rather marginal but detectable. Mutants could favor ampicillin by forming hydrogen bonds to the amino group located next to the aromatic ring.
Figure 2: Structural formula of Penicillin G (A) and Ampicillin (B) showing small structural distances.
To design mutants effectively, it is crucial to have a comprehensive understanding of how antibiotics interact with the receptor, ideally with a high degree of clarity. To that end, we used current literature and investigated crystal structures that already include a bound Penicillin G in the sensor domain (PDB: 1xa7). Fig 3. shows the crystal structure of BlaR1's sensor domain including the covalent bound Penicillin G (Wilke et al., 2004). By interacting with BlaR1, the highly-reactive β-lactam ring breaks, and binds covalently to the serine located in the binding pocket (Wilke et al 2004).
As indicated by Fig. 3B, the Penicillin G bound to the BlaR1 is fixed in the binding pocket by 7 hydrogen bonds, the carboxylate group is connected by an ionic bond, and the benzyl ring is held in place by multiple hydrophobic interactions. Overall, the antibiotic positioned well in the binding pocket with many present polar interactions.
Figure 3: Interactions between Penicillin G and BlaR1 as observed in the crystal structure (PDB: 1xa7). A: Formation of a covalent bond with the serine residue in an open conformation. B: Polar and non-polar interactions occurring between Penicillin G and the receptor estimated by PLIP (Adamse et al., 2021) Interactions: ion bond (yellow dashed line), hydrogen bond (blue solid lines), hydrophobic interactions (grey dashed lines)
The newest research by Alexander et al. (Alexander et al., 2023) revealed the entire structure of BlaR1, also covering the intercellular part. We used one monomer of this structure (PDB: 8exq - chain B) to determine the interaction between the receptor and the considered antibiotics, and prepared mutants with higher specificity.
Besides Penicillin G, no other β-lactam antibiotic was considered in a crystal structure experiment using BlaR1, as outlined before. Therefore, we first investigated the binding behavior of all selected antibiotics using covalent docking using the MOE software (MOE 2022).
Figure 4: PLIP results of the naive BlaR1 receptor docked covalent to Penicillin G (A) and Ampicillin (B) indicating similar polar and non-polar interactions. Interactions: ion bond (yellow dashed line), hydrogen bond (blue solid lines), hydrophobic interactions (grey dashed lines)
Fig. 4 illustrates the covalent docking of Penicillin G and ampicillin. Comparing the covalent dockings from the structure revealed by Alexander et al. to the crystal structure that includes the Penicillin G less hydrogen bond can be observed. Additionally, the experiment indicates that Penicillin G and Ampicillin exhibit similar interactions. As a result, there appears to be no residue engaging with the amino group adjacent to the benzyl ring in Ampicillin. This intriguing finding paves the way for the potential development of a mutant strain that could specifically enhance the affinity for Ampicillin due to its unique amino group.
BlaR1 exhibited no discernible preference in its binding affinity towards specific ß-lactam antibiotics. Consequently, we can proceed with designing mutants. To our advantage, the development of sophisticated algorithms offers an efficient solution to automate this protein design process, sparing us from a manual design approach. We considered the Rosetta interface design protocol (Moretti et al., 2016) to design our protein interface interacting with the antibiotics. In this process, certain amino acids in the binding pocket, which potentially get in contact with the antibiotics, are mutated. Those amino acids are automatically detected by Rosetta itself.
Our first approach was to discover potential mutants by a non-covalent approach. Hereby, we placed the antibiotics in their activated state (closed β-lactam ring) in the binding pocket, created a rotamer library, and used the Rosetta interface design protocol to establish 1000 mutated BlaR1 receptors. Afterward, the top 3 mutants based on Rosetta's total score were considered and evaluated using MOE and PLIP (Adamse et al., 2021). Thereby, the interaction between the antibiotics and the receptor was assessed after docking them covalently again in the optimized receptors.
The approach is driven by the intricate process of the antibiotic's interaction with the protein. Specifically, the antibiotic must initially reach the serine residue while still in its activated state, before subsequently undergoing a reaction and forming a covalent connection with it.
In the case of Penicillin G, no receptor could be established that showed a significant increase in the binding affinity towards Penicillin G. However in the case of Ampicillin, a mutant was found that indicated a higher binding affinity compared to the state where the receptor binds covalent to Penicillin G indicated by more hydrogen bonds and hydrophobic interactions. In contrast to the naive receptor, the mutant receptor exhibited a noticeable reduction in critical interactions, notably the high-energy ionic bond that typically binds to the carboxylate group and specific hydrogen bonds. However, it did manage to establish a distinctive T-shaped π-stacking interaction between the benzyl ring and a phenylalanine residue.
Figure 5: Most optimized BlaR1 mutant for ampicillin after docking covalent: Quantification of polar and non-polar interactions between the mutated receptor and the targeted antibiotics (A). Visualization of interactions as 3D structures between the mutated receptor and Ampicillin (B) or Penicillin G (C). Results were discovered using PLIP. Interactions: π-stacking (green dashed line), hydrogen bond (blue solid lines), hydrophobic interactions (grey dashed lines).
Fine-tuning non-covalent bonds did indeed lead to a notable differentiation between the two antibiotics, particularly with Ampicillin. However, achieving a more pronounced distinction between them will require more substantial alterations in protein-antibiotic interactions.
Since our initial approach did not uncover tremendous differences in the interactions between the BlaR1 and the considered antibiotics, we explored an alternative method. In the previous non-covalent approach, we optimized the protein's interface considering potential conformations of the antibiotics. This approach could lead to changes in amino acids that do not interact with the antibiotic in its inactive state (open β-lactam ring). However, the protocol proposed by Rosetta was not suitable for dealing with covalent binding. Therefore, we pursued optimization of the protein interface using the inactive state of the antibiotics without actually forming a covalent bond with the protein. The rest of the pipeline remained. We call this the pseudo-covalent approach.
The pseudo-covalent approach, in a similar vein, did not produce a structure with markedly increased estimated binding affinities for Penicillin G. Surprisingly, the optimization consistently leaned towards Ampicillin, regardless of the intended antibiotic in the interface design. However, upon closer inspection of the top three results for Penicillin G, we did discover mutants that exhibited notably exceptional estimated binding affinities for Ampicillin.
Figure 6: Most optimized BlaR1 mutant optimized for Penicillin G show much higher estimated binding affinity towards Ampicillin in the pseudo-covalent approach: Quantification of polar and non-polar interactions between the mutated receptor and the targeted antibiotics (A). Visualization of interactions as 3D structures between the mutated receptor and Ampicillin (B) or Penicillin G (C). Results were discovered using PLIP. Interactions: ion bond (yellow dashed line), hydrogen bond (blue solid lines), hydrophobic interactions (grey dashed lines).
Fig. 6 shows the discovered mutant of BlaR1 which indicates many more hydrogen bonds for Ampicillin, resulting in a higher estimated binding affinity. This finding implies that utilizing similar antibiotics in the optimization process can help achieve results that are more optimal for the desired antibiotic.
Creating receptors with enhanced binding affinities for specific antibiotics is an essential requirement for our whole-cell biosensor to remain competitive in the field of antibiotic detection systems. In total, we successfully developed two mutants of BlaR1 that exhibited significantly increased estimated binding affinities for Ampicillin. In doing so, we leveraged Rosetta's interface design protocol, introducing a novel approach to identify receptor mutants tailored for specific ligands.