Abstract

For the past few decades, Cardiovascular diseases (CVD) have been recorded as the number one cause of death globally. Imbalanced diet, unhealthy lifestyle, and genetic heritages endangered everyone to the risk of CVD[1]. Subtilisin is an example of a potent fibrinolytic agent. As primary fibrinolysis is typically found in fermented foods, such as natto, which are also rich in bacterial-flora growth that is beneficial for our body. This ASA research Project with the UPH university focuses on discovering the fibrinolysis activity and its mechanism of action from Subtilisin, the enzyme that gives natto its unique characteristic in degrading blood clot lysis. This innovative research discovered the fibrinolytic characteristic, the mechanism of Subtilisin that was extracted from Bacillus Subtilis G8, and it has also displayed a strong interaction between Subtilisin and Fibrin.

Blood Coagulation (CVD)

Thrombus formation in the vascular system is the major cause of various lethal vascular obstructive diseases, including venous thrombosis, obstructive coronary artery disease, and ischemic stroke. In healthy homeostasis, blood clotting events are tracked by fibrinolytic activity, preventing obstructive blood flow. In the search for safe and cost-effective fibrinolytic agents, functional foods made of microbes with fibrinolytic activity are of interest. Among them, the fermented food, Natto is a Japanese cheese-like traditional food made of soybeans fermented with Bacillus subtilis (natto).

Subtilisin and Its Previous Studies

Subtilisin, a serine proteinase from Bacillus subtilis (natto), is an important enzyme for the taste and flavor of natto [10]. It is one of the important enzymes for natto’s blood clot lysis activity. Moreover, previous studies by Pinotoan et al (2021) and Dikson et al (2022) also show that one of the enzymes from Bacillus subtilis G8 is responsible for its fibrinolytic activity is Subtilisin (Figure 1). Though information about subtilisin hydrolyzing raw materials has been reported, the mechanism of how it degrades fibrin is still limited. To identify the interaction of enzyme and its substrate by using in vitro method, excessive time, energy, and money, will be required. Computational approaches (In silico) alleviate the excessive necessity of resources. Simulating and predicting the interaction of enzyme and its substrate with Molecular Docking protocol prevents waste of resources.

Nattokinase very well-studied protein and an enzyme, especially for the fibrinolytic activity. Subtilisin, an enzyme, is also a protease similar to Nattokinase.

Nattokinase and Subtilisin have similar features, including the action mechanism and the catalytic triad: they are identical to each other, except for their structure.

Nattokinase is used for reference, but this enzyme is not used to research the fibrinolytic activity in this research. This research only uses the nattokinase for the catalytic triad and the action mechanism. The main model of subtilisin in this study was generated from the subtilisin amino acid sequence of Bacillus subtilis G8 which was reported as a Natto starter culture in the previous study by Dikson et al (2022).

Sequence Alignment

The sequence of subtilisin was aligned to the known subtilisin sequence from reference (PDB id: 1S01) in order to find the information about; sequence similarity and important amino acids that have the catalysis role using CLUSTAL-Omega web server.

Subtilisin has 86.18% similarities with the reference subtilisin. The reference’s important amino acids that have the role of catalysis were aligned with Subtilisin at the same pattern and therefore concluded as conserved. Therefore, Subtilisin’s important amino acids based on the reference are Serine (Ser) 327, Histidine (His) 170, Aspartic Acid (Asp) 138, and Asparagine (Asn) 261, whereas the reference important amino acids are: Ser 328, His 171, Asp 139, and Asn 262 .

Structural Modeling an Preparation

2.1. STRUCTURAL MODELING

The 3D structure of Subtilisin was constructed through the SWISS-MODEL workspace [4], using the Nattokinase structure as the template. This structure - from SWISS-MODEL (RRID:SCR_018123) - was then validated by examining Phi/Psi Ramachandran plot, while active residues were predicted through the cPORT web server [5]. The RCSB database [6] in crystallographic form was used to identify the residues for the 3D Structure of the Fibrin protein. For each chain, the active residues were predicted using cPORT.

RESULTS:

Subtilisin’s model predicted by SWISS-MODEL showed the QMEAN-Z Score value of 0.29 and Ramachandran favored value of 96.7% (Figure 2.1). In this context, the QMEAN-Z Score value determines the quality of the model, by indicating whether the predicted model is as close as the native structure (from SWISS-Model Database) or not. The closer the QMEAN-Z Score value to 0, the native-like and accurate the structure model is. Meanwhile, the Ramachandran Favored score determines the resolution of the model. Predicted models that have the Ramachandran Favored score greater than 90% are then considered a great model, and therefore can be considered/selected. The predicted SWISS-MODEL of Subtilisin satisfied these given standards and was selected for the next steps.

2.2. STRUCTURAL PREPARATION

In order to perform the molecular docking, each active residue’s structures are necessary. Active residues are defined as the amino acids that will interact with each other and these were predicted through the CPORT web server.

RESULTS:

CPORT prediction result for Subtilisin shows that the enzyme has 42 active residues, in which the important amino acids of the enzyme; Ser327, His170, Asp138, and Asn261, are all included. On the other hand, CPORT prediction for each domain of the fibrin protein shows that the A, B, C, D, E, and F domain has a total of 50, 55, 52, 36, 29, and 43 active residues.

Protein-Protein Docking and Visualization

Subtilisin and Fibrin protein docking were processed to examine the interaction between these two proteins. Protein-protein docking was simulated using the HADDOCK web server (“HADDOCK Web Server”) with the parameters for each fibrin chain defaulted. These results - from HADDOCK - were then analyzed through the PRODIGY web server [8], to determine the energy of binding affinity. Visualization, followed by the preceding step, was conducted by using LigPlot+ analysis [9].

RESULTS:

Delta G and Kd values determine interactions’ strengths. Delta G values were categorized into weak, moderate, and strong, whereas Kd values were categorized as: low, moderate, and high (range visually presented in Figure 3.1). RMSD value - in this case, i-RMSD - is a value that as it approaches 0, implies a higher resolution of the complexes. i-RMSD results are categorized into: acceptable, medium, and high quality with a range of 2.0 < x ≤ 4.0, 1.0 < x ≤ 2.0, and ≤ 1.0 respectively. Among these complexes, all 6 domains of fibrin protein resulted in acceptable RMSD values and high Kd Values. The visualization performed using Ligplot+ informs that Subtilisin’s significant amino acids interact with all 6 domains of fibrin, which indicates that the enzymatic activity would occur at all domains. However, most of the complexes’ interactions are hydrophobic bonds instead of hydrogen bonds. Syahbanu et al (2019) theorized that the phenomenon might occur because the molecular docking was using two large proteins.

Protein-Ligand Docking and Visualization

Fibrin protein - the substrate - was then fragmented through Rapid Peptide Generator [7] and modeled into ligand through PHENIX eLBOW. This step is necessary to enter the Protein-Ligand docking, which enhances the docking results and ultimately decreases the research scope. Subtilisin and the predicted fragments were docked, analyzed, and visualized by repeating the methods presented in the Protein-Protein docking. The predicted fragments were then identified through the NCBI Database with Protein Blast for supplementations (BLAST).

RESULTS:

RPG simulation result shows that the subtilisin will digest fibrin chain into 7 fragments in total (2 from Alpha chain, 2 from Betta chain, and 3 from Gamma chain), which the Protein-ligand docking result shows the subtilisin + Fragments from Betta chain complex (SubE-B12) is the best among the rest. Complex SubE-B12 visualization using Ligplot+ confirmed the interaction between Ser327 from Subtilisin (which serves as the amino acids that will cut the substrate) with corresponding amino acids from the fibrin fragments.

Conclusion

The interaction between Subtilisin and Fibrin has been shown to have fibrinolytic activity. Protein-Protein Docking results suggest that Subtilisin will bind and digest the fibrin protein at all domains, with overall affinity considered a strong interaction. The results from Protein-Ligand Docking also support the conclusion, followed up with the better interaction between subtilisin’s Ser327 position in the fibrinolytic activity. However, the computational prediction results still contain potential errors and mispredictions.

Han Ah Song

PUBLICATION AND PRESENTATION

EVENTS ATTENDED