Structural Modelling

To evaluate the structural fitting of miRNAs into TLR-9 We started by constructing structural models for both miRNAs, we have used SimRNA [5] Tool for simulating tertiary structure of miRNA mir-134 nad mir-370 as SimRNA generates a circular starting conformation with the 5’ and 3’ ends close to each other as a starting structure for simulation. By building the internal list of restraints on dots-and brackets format, the default parameters for simulation energy and simulations steps have been set to 500 steps and 1% of the lowest energy frames taken to clustering, while the TLR-9 structure was cleaned out of PDB structures of code 5Y3K,5Y3J to build a model of human TLR-9 through Swiss-model.

Figure 1: Simulated Model of miR-134 through SimRNA visualized using Pymol
Figure 2: Simulated Model of miR-370 through SimRNA visualized using Pymol

Nucleic acid Modelling


For verification of our proposal for colibactin interference in carcinogenic strains of e.coli we tried to investigate the binding of dCas9 into the target DNA of colibactin genes [clbN Escherichia coli colibactin non-ribosomal peptide synthetase ClbN, Escherichia coli precolibactin peptidase ClbP and Colibactin hybrid non-ribosomal peptide synthetase/type I polyketide synthase ClbB] along with its designed guide RNA using benchling to interfere with the transcription of the whole gene cluster of colibactin. To build a final model of complementary guide RNA binded to target DNA of colibactin -studied through EBI samples of UCI121 strain- , we used 3D-DART along with Rosetta FARFAR package to generate a 3D structure of the target DNA representing the cleavage site and PAM of cas9 as well as PAM flexibility.





Docking protocol


We have followed HADDOCK docking protocol, consisting of randomized orientations and rigid body energy minimization, calculating 1,000 complex structures, with HADDOCK scoring performed according to the weighted sum (HADDOCK score) of different energy terms which includes van der Waals energy, electrostatic energy, distance restraints energy, direct RDC restraint energy, intervector projection angle restraints energy, diffusion anisotropy energy, dihedral angle restraints energy, symmetry restraints energy, binding energy, desolvation energy and buried surface area. This lowest energy structure displayed no AIR restraint violations within 0.3 A˚ threshold and was accepted as the final docked structure for the complex.




Results analysis

The graphs below represents water-refined models generated by HADDOCK where clusters are calculated based on the interface-ligand RMSDs calculated by HADDOCK, with the interface defined automatically based on all observed contacts. The various structural analysis (FCC, i-RMSD and l-RMSD) are made with respect to the best HADDOCK model (the one with the lowest HADDOCK score).


Table-2 Docking scores of both complexes using HADDOCK





dCas9/grna 1

Dcas9 grna2

Dcas9 grna3


-126.5+/- 6.8

-142.4 +/- 5.6

-119.9+/- 19.2

-87.7+/- 16.1

-83.8+/- 23.6







Total Interaction energy(Kcal mol-1)

0.5 +/- 0.3

1.0 +/- 0.1

3.8 +/- 0.1

1.3 +/- 0.1

13.1 +/- 0.1

Van der Waals energy(Kcal mol-1)

-57.3 +/- 5.6

-74.6 +/- 5.5

-85.2 +/- 2.3

-84.8+/- 6.7

-60.3 +/- 3.7

Electrostatic Energy(Kcal mol-1)

-498.0 +/- 13.2

-419.6 +/- 3.1




Desolvation energy(Kcal mol-1)

22.2 +/- 4.4

13.9 +/- 7.6

61.2 +/- 18.7

62.7+/- 17.9

39.4+/- 21.2

Restraints violation energy(Kcal mol-1)

82.1 +/- 15.55

22.1 +/- 19.67

476.7+/- 24.14

618.6+/- 116.90

566.8+/- 147.70

Buried Surface area

1734.1 +/- 39.0

2116.0 +/- 87.2

2390.1 +/- 163.2

2276.8 +/- 223.2

1791.7 +/- 106.9








Generated Poses of Docking

Figure 4: Figure shows the best docking pose of miR-134 into TLR-9 (left) and its HADDOCK clusters RMSD graph (right)




Figure 5: Figure shows the best docking pose of miR-370 into TLR-9 (left) and its HADDOCK clusters RMSD graph (right)




Insights from structural docking of TLR-9 and miRNAs

Residues of large contact with both miRNAs constituted a large contact area to bind nucleic acids of both miRNAs at CpG rich motifs including residues; 248Val, 290Val, 312Val, 314Asp, 337Arg, 338Lys, 340Asn, 364Val, 365Ala, 367Lys, 392Met, and 397Arg.

Figure 6: Figure shows the best docking pose of dCas9 into target e.coli DNA of colibactin B (left) and its HADDOCK clusters RMSD graph (right)




Figure 8: Figure shows the best docking pose of dCas9 into target e.coli DNA of colibactin N (left) and its HADDOCK clusters RMSD graph (right)




Cellular Transfection Modelling

The aim of this model is to provide us with mathematical representation of the proposed kinetics of our proposed lentiviral delivery system, deSolve R package was used for solving ordinary differential equations while FME package was used for fitting parameters based on our proposed model of interactions and dynamics between molecular species in the modeling environment represented in the following table and diagram.


Table shows parameters and constants of our lentiviral transfection model





Uninfected CRC cell

Cells uL-1


Lentiviral Infected CRC cell

Cells uL-1


Mir Transfected CRC cells

Cells uL-1


Apoptotic CRC cells to mIRNA transfection

Cells uL-1


Lentiviral vector

Focus Forming unit (FFU) uL-1


miRNA specie

Ng mL-1


Intrinsic division rate of CRC cells

Day -1


Rate of conversion of CRC cells to lentiviral transfected cells

Ng Day -1


Rate of reversion from lentiviral transfected cells to mIRNA-expressing cell

Day -1


Rate of CRC cells RNA induced-apoptosis

Day -1


Rate of lentiviral infection

ul Day-1 FFU-1


Lentivirus secretion from infected CRC cells

FFU cells Day-1


Lentivirus decay rate



Saturated rate of miRNA expression

Ng mL-1


mIRNA specie decay rate


System of ordinary differential equations

This Circuit diagram represents our Model of lentiviral transfection




System of ordinary differential equations

This plot shows the kinetics of our model reaching equilibrium after 2.5 time units on 0.01 framing rate




Model Conclusion:

The time to equilibrium is highly dependent on the rate of transformation and not on the initial value of untransfected cells in the experiment. The value of miRNA expressing cells to miRNA reaches a simultaneous level of equilibrium with equilibration of vector production out of transfected cell. On successful trials of transfection the rate of apoptotic induction is dependent on the success of expressing the apoptotic mIRNA as well as its regulatory effect.


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