According to the results of this assay, Sortase A7M is definitely Ca²⁺-independent, since it shows linking activity without calcium in the vicinity. The enzyme mutant also works in presence of Ca²⁺, but these FRET experiments made us suspect that Ca²⁺ may even inhibit Sortase A7M, since it shows less activity with calcium around than without calcium.

To better address this question, an ELISA was performed. Therefore, a piece of paper functionalized with GGGβA was connected to a protein domain, which binds antibodies to the LPTEG-tag. The results are shown in Fig. 18.

As shown in Fig. 18, the highest absorption was measured in well 2. Thus, Sortase A7M works more efficiently when no Ca²⁺ is around. The absorption is also relatively high for the negative control, which can be explained by poor washing before the substrate for Horeseradish peroxidase (HPR) was added. This assay shows the functionality of Sortase A7M even in context of surfaces since we confirmed that Sortase A7M is able to connect tags attached to paper. This shows that the surface structure is not a relevant factor for the enzyme.

When we compare the reaction speed of Sortase A5M and Sortase A7M, Sortase A5M is the clear winner (see Fig. : 19). However, this means of course that the reverse reaction is also faster in the case of Sortase A5M. Consequently, Sortase A7M is the best variant for in vivo modification of our VLPs as it is Ca²⁺-independent. On the other hand, Sortase A5M is a suitable enzyme variant for in vitro modification due to its high efficiency.

What about other substrates?

Primary Amines

The literature describes Sortase A7M as somewhat „promiscuous“ towards other substrates than GGGG(polyG) as long as the substrate possesses a primary amine. The Sortase A7M used for this assay was stored in the fridge at 4 °C for two weeks. The substrates were TAMRA with a KLPETG bound to TAMRA via the lysine side chain and 3-azidopropanamine as the example for a primary amine. The reaction was performed for two hours at 37 °C.
It was then analyzed by electron spray ionization mass spectrometry (ESI-MS) (Fig. 20).

Fig. 20 shows the educt-peak in the mass spectrum. TAMRA with the LPETG-tag weighs 1054 g/mol. Shown above in green is the single charged molecule at 1054.27 g/mol and the double charged molecule at 528.75 g/mol.

Fig. 21 shows the product-peak in the mass spectrum. The primary amine that was taken as an example has a molecular weight of 100 g/mol. After the reaction the glycine of the LPETG-tag has been removed and therefore the product only consists of TAMRA-KLPET-3-azidopropanamine. When adding the two molecular weights and subtracting the weight of the glycine it adds up to a total weight of 1078 g/mol which can be seen in the single loaded 1079.37 g/mol peak (Fig. 21), since the ESI-MS we used has a small error margin. The peak in black again is the double loaded peak at 541.55 g/mol. This clearly shows that the sortase reaction took place.
Furthermore, we can conclude that the Sortase A7M accepts any primary amine as a substrate. However, mass spectrum does not show the ratio of educt and product, which is why we cannot estimate whether the turnover is as high as when using a polyG-tag as substrate. Additionally this assay confirms our suspicion that the Sortase A7M is stable at 4 °C and still functional if stored at said temperature for at least two weeks.

Yield

For the characterization of Sortase A7M an assay was designed to show the coupling efficiency between the TAMRA-LEPTG and the tetrapeptide GGG-Beta-Alanin (GGGβA) catalyzed by the Sortase. The Sortase reaction was performed for 1h at 30˚C and was stopped by enzyme separation through centrifugal filtration. For analysis mass spectrometry (ESI-MS) was used. The mass spectrometry enables differentiation between products and educts. It allowed us to make an estimate of the product yield. The calculated theoretical molecular masses are 1054 g/mol for TAMRA and 1240 g/mol for TAMRA-LPETGGGβA. Therefore, peaks are expected at mass/n, with n ∈ N. By comparison of the number of corresponding peaks, estimation of the product yield is possible as both molecules possess the same amount of ionizable groups and thus the difference in the ionizability of both molecules is negligible.

In Fig. 22 the 621.56 peak can be assigned to the TAMRA-LEPTGGGβA and the 528.85 to the TAMRA-LPETG. The count ratios of the two molecules mentioned show an excess of the product.

Is Sortase A7M able to attach cargo to P22 coat protein?

We performed the linking reaction with CP-LPETGG and GGGG-mCherry as substrates and applied them to an SDS-PAGE. We saw products at the expected size (28 kDa + 49 kDa = 77 kDa) thus the requirement is fulfilled. However, a lot of additional bands appeared that we did not expect. These bands also appeared when only Sortase A7M and CP were mixed.

Figure 23:

a) Sortase A7M band is at expected height (17.85 kDa). The two negative controls containing only GGGG-mCherry (28 kDa) and CP-LPETGG (49 kDa) at the expected respective heights. b) Shown are sfGFP-SP and CP-LPETGG each incubated with both Sortase A7M and Sortase A5M. Both gels display multimers when coat and a sortase variant are in a sample together.

To investigate this issue, we had a look at the literature and found a matching description in the publication of Patterson et al.. They performed a similar experiment with P22 capsid proteins and observed the same multimers in their SDS-PAGEs . Comparing both SDS-PAGEs, we came to the following assumption:

Because of the promiscuity of Sortase A7M to accept primary amines as substrates, as we discussed previously, the formation of CP multimers occurs, unspecifically catalyzed by Sortase A7M.

Parallel to these experiments, we successfully modified the exterior of pre-assembled VLPs in vitro (VLP assembly). These modified VLPs were homogenous and overall correctly assembled. Therefore, we conclude that the described multimer problem only occurs when Sortase A7M encounters free CP.

Does methionine affect Sortase linking?

Sortase A7M preferably attaches N-terminal poly-G to C-terminal LPETGG. However, the first amino acid of a protein is methionine (to be specific, formylmethionine in bacteria). For our constructs that possess N-terminal polyG-tags, we have to ask ourselves the question: If the initial methionines are not cleaved off after the proteins have been produced, will this interfere with the Sortase reaction?

To investigate this, we cloned and purified two other proteins: TVMVsite-GGGG-mCherry and TEVsite-GGGG-sfGFP. Then we treated these proteins with the respective proteases, resulting in *GGGG-mCherry and *GGGG-sfGFP. Following this *GGGG-mCherry was then compared to (M)GGGG-mCherry which we used in all previous assays. Assays were also conducted on Fig. 24 the processed *GGGG-sfGFP substrate. Fig. 24 confirmed our assumptions that the unprocessed substrate cannot be linked to the sorting motif via Sortase A7M. Subsequently, *GGGG-sfGFP (after protease digest) demonstrate successful linkage via sortase-mediated ligation.

Due to these findings we modified our VLPs with *GGGG-sfGFP.

We performed FRET-assays with TAMRA-LPETG and either of the following reaction partners:

• (M)GGGG-mCherry, a protein sample that might still carry an N-terminal methionine
• *GGGG-mCherry that does not carry any additional N-terminal residue

Before the FRET-assay was started, we adjusted the mCherry-concentrations of both fluorescent protein solutions to the same level. To do so, we diluted them until both showed the same fluorescence at 610 nm.

Figure 25: FRET of the sortase reaction connecting TAMRA-LPETG and GGGG-sfGFP mediated by Sortase A7M. The concentration of the Sortase A7M and TAMRA-LPETG was kept at the same level why the concentration of sfGFP was either 10,7 µM or 1,4 µM. The graphs show that the reverse reaction happens earlier if the GGGG-substrate concentration is lower. The ΔRFU refers to the respective negative control without each sortase at 514 nm. The mean ΔRFU value of the duplicates was normalized to zero for better visualization.

Strikingly, only the (M)GGGG-mCherry construct showed a clear decrease in delta RFU after the maximum delta RFU was reached (at about 160 min).

We assume the following: Although we adjusted the overall mCherry concentration by fluorescence, we cannot determine the absolute amount of MGGGG-mCherry in the (M)GGGG-mCherry sample. However, if this amount was relatively high, the effective substrate concentration that could enter the sortase reaction would be low. That is because MGGGG is a worse sortase substrate than GGGG – if any at all. If we furthermore consider that a low substrate concentration correlates with a faster reverse reaction, we can explain the observed decrease in delta RFU for the (M)GGGG-mCherry sample that contrasts the delta RFU trend of the *GGGG-mCherry sample.

On this basis we can assume that a certain, yet unknown portion of the (M)GGGG-mCherry sample still carries an N-terminal methionine.

These FRET-assays let us assume that methionine disturbs or at least interferes with the sortase reaction mechanism. Indeed, our modeling suggests that methionine affects the interaction of polyG and the flexible loop near the active site of Sortase A7M. Click here if you want to know more about our modeling results!

This strengthens our hypothesis: If there is any amino acid in front of the poly-glycine sequence, substrate binding to Sortase A7M is negatively influenced.

Modeling

Introduction

In synthetic biology, theoretical models are often used to gain insights, predict and improve experiments. In our project we are modifying Virus-like particles (VLPs) by attaching proteins to the surface of the P22 capsid through a linker. The linking is catalyzed using the enzyme Sortase A7M, which is a calcium-independent mutant of the wild type Sortase A from Staphylococcus aureus. We performed modeling to predict the unknown structure of the Sortase A7M, to improve the linker between proteins and therefore optimizing the modification efficiency.
Here Rosetta Comparative Modeling was performed to predict the structure of Sortase A7M. The generated structure was characterized in terms of the molecular mechanisms and binding affinities of ligand peptides using molecular dynamics and docking simulations.

Structure determination

In silico modeling and simulation of proteins requires a 3D structure, which can be obtained from the RCSB Protein Data Bank. However, if no 3D structures are annotated, as it is the case with sortase A7M, the structure has to be determined by other means. The structure prediction of sortase A7M was done using two different approaches.

RosettaCM

Results

The simulation of our Sortase A7M yielded 15,000 structures which have been compared using the Rosetta scoring functions (talaris2013). From the 15,000 structures generated, we inspected the ten best scoring structures.

As can be seen in Fig. 27, the most prominent differences can be found in the regions close to the N- and C-terminus. As fluctuations in those regions are not untypical, we decided to use the best scoring structure, candidate S_14771 (Fig. 28), as the input for the simulations to follow.

In order to evaluate the secondary structure of the Sortase A7M candidate S_14771 a Ramachandran plot has been created and compared to the five sortases used as input for the comparitive modeling.

Figure 29: The Ramachandran plot of randomly sampled proteins and the input structures of the comparative modeling show similar secondary structures. Secondary structure analysis of both sortase candidates reveals absence of secondary structures for the ML candidate. This is not the case with candidate S_14771 as the Ramachandran plot shows all relevant structures.

The Ramachandran plot (Fig. 29) showing α-helices and β-sheets is a strong indicator of a successful structure determination, as those secondary structures are crucial for the functionality of sortases.

Conclusion

We used monte-carlo simulations to determine the structure of the mutated transpeptidase Sortase A7M. As a first investigation of the secondary structure a Ramachandran plot of candidate S_14771 was made. The plot showing secondary structures was a first indicator of successful structure prediction. The approach, using Rosetta Comparative Modeling, yielded 15,000 structures scored with the talaris2013 scoring function. The ten best structures were aligned and exhibited almost identical secondary structures (Fig. 27). The greatest structural differences are present in the N- and C-terminal regions. Since terminal regions tend to fluctuate more strongly than non-terminal segments of the protein, we deemed those fluctuations non-relevant for the proteins functionality.
Being the best scoring candidate, structure S_14771 was analyzed structurally using a Ramachandran plot (Fig. 29). The plot shows all the relevant and typical structures sortases exhibit and serves as an indicator for a successful structure prediction.
In the steps to follow, a molecular dynamics (MD) simulation was performed on both structures.

Molecular dynamics

Results

The first possible indicators of a stable protein structure are converging root-mean-square deviation (RMSD), small root-mean-square fluctuation (RMSF) values as well as converging radii of gyration. Using the Python software package and the module Biotite we calculated these quantities and plotted the result for candidate S_14771.

Typical RMSDs and radii of gyration converge towards a value dependent on the size of the protein. Convergence of those quantities can be interpreted as a stable state of the protein structure. As it can be seen in Fig. 30 and Fig. 31 both the RMSD and the radius of gyration stabilize at the same time as the simulation reaches 110,000 ps (110 ns), suggesting a now stabilized structure of candidate S_14771 solvated in water. Another indicator of a functional protein is the RMSF. Instead of being averaged over all atoms, the RMSF is averaged over time with respect to each amino acid. It provides insights in both protein stability and functionality. Fig Fig. 32 reveals the RMSF of residues 105 to 115 to be significantly higher than that of other residues. This hints at the presence of a functional unit along these residues. As commented on in the section describing our structure prediction approaches, the N- and C-terminal regions tend to fluctuate more strongly as a result of the absence of stabilizing structures.

To further analyse the potential binding site, we performed a Principle Component Analysis.

Principle component analysis

To analyze our system further Principle Component Analysis (PCA) was performed using GROMACS. By applying PCA to a protein it is possible to gain insights into the relevant vibrational motions and thereby the physical mechanism of the protein.

Animation 33: A Principle Component Analysis of a fast (blue) and a slow (red) mode showing the most prominent movements of the Cα-chain of candidate S_14771. Both modes show movement of the β6/β7 loop consisting of residues 105 to 115 towards the active site . Thus we can assume that the closing β6/β7 loop is involved in the reaction mechanism.

The results from the Principle Component Analysis of candidate S_14771 (Amination 33) show a movement of the residues 105 to 115 towards the active site, supporting our theory that residues 105 to 115 are important for the reaction mechanism. Since the most relevant eigenvector (red), which shows the most relevant movement of the sortase, moves further towards the active site, it is possible that the β6/β7 loop either closes the binding site of the ligand peptides or even transports one peptide towards the other.

Conclusion

Candidate S_14771 that was generated using RosettaCM appears to be a fitting candidate not only due to successful analyses, but also due to the residues of the active site being in close proximity. Not the RMSF but also Principle Component Analysis indicate that residues 105 to 115 forming the β6/β7 loop are crucial for the molecular mechanism of our model.

Docking

Now that the binding site of the Sortase had been found, the peptide ligand needed to be inserted into the binding site to create a peptide-protein complex. The procedure of choice for the introduction of a ligand into the binding site of a protein is called docking. In the following sections, we will present the protocol and methods we used as well as the results they yielded.

Results

For sequences MGGGGPPPPPP(M-polyG), GGGGPPPPPP(polyG) and PPPPPPLPETGG(LPETGG) 50,000 structures have been created and clustered. After the clustering the sample consisted of 100 structures of docked complexes.

Analysis of the scores has shown a similar score for all the three dockings. The best scoring results of the LPETGG docking show a tendency of the glycines to face the active site while also being in close proximity to the active site.

Fig. 34 shows the docking result of the LPETGG peptide to the sortase. The results shown are the best scoring structures of the clustering with respect to the total score, interface score and reweighted score. As the best scoring structure is the same for the total score and the reweighted score only two peptides are shown. This also applies to Fig. 35 and Fig. 36. For both results the reacting glycin residues (yellow) are facing the active site. Additionally, the same residues are in close proximity to the active site.

The figures Fig. 35 and Fig. 36 show the docking of the both polyG and M-polyG. While polyG results align well and seem to be interacting with the β6/β7 loop rather than with the active site, this does not seem to be the case for M-polyG. Instead of both structures interacting with the β6/β7 loop or active site one (best interaction score; dark blue) interacts with the β6/β7 loop and the other (best reweighted/total score; light blue-gray) appears to interact with the active site.

As can be seen in Fig. 37 visualizing the result of the the docking simulation (total/reweighted score) suggests an interaction of methionine and two of the active sites namely arginine₁₃₉ and cysteine₁₂₆. Fig. 38 shows the interaction of M-polyG with the β6/β7 loop. The glycines still interact with the β6/β7 loop. Instead of binding above the β6/β7 loop, which is the case for polyG as illustrated in Fig. 36, the interaction seems to be influenced by methionine. By interacting with the residues in the β-helix methionine could potentially hinder binding of glycine to the β6/β7 loop by partial immobilization of the peptide. Overall peptide binding and orientation is less uniform compared polyG without the leading methionine, which could be an indicator of lesser binding affinity of M-PolyG towards the β6/β7 loop.

Conclusion

To computationally investigate binding affinities of the polyG and M-polyG as well as the LPETGG tags we performed docking simulations using the Rosetta FlexPepDock application. We used a modified version of the recommended protocol as the modified version was easier to automate and served our purpose better than the standard protocol. From the calculated scores only, we could not see a difference in binding affinities. Thus, we inspected the best scoring structures regarding the total score, the interface score and the reweighted score using PyMOL. Since the best structures with respect to total score and reweighted score were the same for all simulations, only two structures have been inspected per run. A polyproline tag was appended to all the peptides to simulate the modification of the VLPs with a small peptide.

As expected, the results showed that for LPETGG, the glycines of both peptides oriented towards the active site. This is unsurprising as peptides with the sequence LPXTGG are known to be substrate of the Sortase. It was more surprising to see the polyG tag oriented away from the active site since polyG also is a known substrate of the sortase. Both polyG peptides were facing the β6/β7 loop (residues 105 to 115) uniformly and appeared to be interacting with it. The M-polyG peptides did not show a uniform orientation or interaction scheme. On one hand the visualization of the best result concerning the total and reweighted score has shown interaction of methionine with the cysteine₁₂₆ and arginine₁₃₉, two residues of the active site. On the other hand, the visualization of the best result with respect to the interface score shows the M-polyG facing the mobile β6/β7 loop. In contrast to the polyG peptide the lacking the methionine, the M-polyG peptide is pulled down below the β6/β7 loop by the methionine interacting with one of the β-sheets leading to the active site. This is not the case with the polgG results, which lie aligned in one plane with the β6/β7 loop.

Modeling Conclusion

For our project it was key to understand and characterize Sortase A7M. As there is no annotated 3D structure for this specific Sortase, an in silico structure determination was performed. Comparative modeling with Rosetta produced valid structures. We used the best structure, candidate S_14771, for extensive characterization. We evaluated the model with regard to its secondary structure using Ramachandran plots. The Ramachandran plot suggested plausible secondary structures.

Molecular Dynamics simulations were used to investigate stability and dynamic properties of the candidate. The RMSD and radius of gyration stabilized over the course of the simulation, a first indicator of an equilibrated structure. Interestingly, RMSF analysis showed strond fluctuations of residues 105 to 115. We further investigated this by performing Principle Component Analysis. Doing so, we extracted the principle movements of the model. We could observe movement of the β6/β7 loop towards the active site, suggesing the presence of a binding site. Consequently, we performed docking simulations.

FlexPepDock was used to conduct the docking simulations with target peptides. Each run yielded 50,000 structures. In multiple steps we reduced the amount of complexes to 100 clusters with respect to total, reweighted and interface score. We extracted the best scoring complexes and investigated interactions.

For LPETGG we observed a uniform binding to the active site, fullfilling our expectation. Strikingly, polyG appeared to bind to the β6/β7 loop in a uniform manner. As it is know from literature polyG is a functioning ligand of sortase. Supported by literature and our data, we postulate the following mechanism: the β6/β7 loop transports bound polyG towards the active site of Sortase A7M, thereby lowering the activation energy of the linking reaction.

As the theory is neither backed up by nor contradicts experimental data, further research is required.

References

For our references, please have a look at our wiki.

Sequence and Features