Dry Lab

MD simulation of micelle formation

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We have simulated the micelle formation by nonionic monorhamnolipid alpha-rhamnopyranosyl-β-hydroxydecanoyl-β-hydroxydecanoate (Rha-C10-C10) with Cu2+ ions and Decane hydrocarbon. The simulations were performed on GROMACS 2019.6 and using CHARMM36 Force Field Package. Visualisation was done on Visual Molecular Dynamics(VMD), PyMOL and Avogadro.

Protocol

Stage	Metal ion (Cu+2)	Oil (decane)
1	The structure of nonionic monorhamnolipid α-rhamnopyranosyl-β-hydroxydecanoyl-β-hydroxydecanoate (Rha-C10-C10) was taken from the supplementary information section of the paper 1	The structure of decane was generated using Avogadro 2
2	The PDB files were converted into .mol2 files using Avogadro	The PDB files were converted into .mol2 files using Avogadro
3	Force field files were generated using the CHARMM Generate Force Field (CGenFF) server. It used charmm36-jul2022.ff for generating the modified force fields3	Force field files were generated using the CHARMM Generate Force Field (CGenFF) server. It used charmm36-jul2022.ff for generating the modified force fields3
4	The stream (.str) files generated by CGenFF server were converted into parameter (.prm), topology(.top) and included topology(.itp) using the cgenff_charmm2gmx_py3_nx2.py from charmm4	The stream (.str) files generated by CGenFF server were converted into parameter (.prm), topology(.top) and included topology(.itp) using the cgenff_charmm2gmx_py3_nx2.py from charmm4
5	Using the command gmx insert-molecules, 50 molecules were inserted into a 70x70x70 Angstrom box. The system was solvated using SPC/E Water Model then ionised with 50 CU2P ions.	For the Decane system, 210 molecules of Rhamnolipid and 90 molecules of Decane were inserted into a 90x90x90 Angstrom box. This was then solvated with the SPC/E Water Model.
6	System was minimised to the point where maximum force applied on any atom was 100 kJ/mol/nm.	System was minimised to the point where maximum force applied on any atom was 100 kJ/mol/nm.
7	Restraint Files for the Rhamnolipid were generated and used for equilibration. For equilibration three groups were created (exhaustive), namely, RLIP, Water and CU2P	Restraint Files for the Rhamnolipid were generated and used for equilibration. For equilibration three groups were created (exhaustive), namely, RLIP, Water and DEC
8	System was equilibrated by NVT ensemble for 2 ns after minimising using the V-rescale thermostat with a tau_t of 0.1 and a ref_t of 300K	System was equilibrated by NVT ensemble for 2 ns after minimising using the V-rescale thermostat with a tau_t of 0.1 and a ref_t of 300K
9	System was equilibrated by NPT ensemble for 100 ns after NVT equilibration using Nose-Hoover Thermostat(tau_t = 0.5 and ref_t = 300) and Parrinello-Rahman Barostat (tau_p = 5 and ref_p = 1.0).	System was equilibrated by NPT ensemble for 100 ns after NVT equilibration using Nose-Hoover Thermostat(tau_t = 0.5 and ref_t = 300) and Parrinello-Rahman Barostat (tau_p = 5 and ref_p = 1.0).
10	Constraints were removed and after a 5ns production run, the micelle formation was observed and data was collected.	Constraints were removed and after a 5ns production run, the micelle formation was observed and data was collected.

Results

Binding to Cu ions: As seen in the video and a stable RMSD value, we observe that the micelles are formed around the Cu2+ ions hence confirming the hypothesis that the biosurfactant chelates the ions. These ions will then be extracted by reverse micelle extraction process. P.S. :- The system is contracting and expanding because of insufficient equilibration. But it shows promising results for micelle formation around the ions.

Binding to decane molecule: RMSD and the box video look promising which proves that in-silico test for chelation of oil molecules is successful.

Mathematical modelling of promoter dynamics

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Modelling

Promoter dynamics

Function: The promoter is a region of DNA that initiates the transcription of a particular gene. It serves as a binding site for RNA polymerase, the enzyme responsible for copying the DNA sequence into RNA.
States: Promoters can exist in different states, typically an active state (on) and an inactive state (off).
Activation and Inactivation: P_on represents the concentration of active promoters, where RNA polymerase can bind and initiate transcription. P_off represents the concentration of inactive promoters. Activation occurs when P_off transitions to P_on, often in response to external signals or stimuli. Inactivation occurs when P_on transitions back to P_off
Mathematical representation
C : Represents the concentration or activity of the external stimulus.
k_on : Rate constant for promoter activation in the presence of the external stimulus.
k_off : Rate constant for promoter inactivation.

Transcription

Function: Transcription is the process by which RNA polymerase synthesizes a complementary RNA strand from the DNA template. It occurs in the nucleus in eukaryotes and in the cytoplasm in prokaryotes.
Transcription Rate: The rate of transcription (k_transcription) depends on the concentration of active promoters (P_on).
Transcription Inhibition: Transcription is inhibited when promoters are in the inactive state (P_off).
Mathematical representation
k_transcription : Transcription rate constant
k_degrade : Rate constant for mRNA degradation

Translation

Function: Translation is the process where ribosomes synthesize a polypeptide chain from the mRNA template.
Translation Rate: The rate of translation (k_translation) depends on the concentration of mRNA.
Translation Inhibition: Translation is inhibited when there is no mRNA available.
Biosurf Degradation: The concentration of the biosurf (biosurf) is governed by the rate of translation minus the rate of biosurf degradation.
Mathematical representation
k_translation : Translation rate constant
k_degrade` : Rate constant for biosurf degradation

Differential equations

Simulation run

Intial Parameters

p_off = [1] # promoters in "off" state
p_on = [0] # promoters in "on" state
c_inducer = [1] # init conc of inducer
mRNA = [0] # assuming no mRNA initially
protein = [0] # assuming no protein initially

# Typical rate constants [SI units]
k_on = 0.01
k_off = 0.01
k_transcription = 0.01
k_degradation_mRNA = 0.01
k_translation = 0.01
k_degradation_protein = 0.01

Results

Inducer, P_on and P_off concentration saturates to 0.63 au in around 20000 au time duration itself. Further, a delayed saturation time is observed for the production of mRNA (which is triggered by P_on) at a time of 50000 au. The biosurfactant produced is triggered by the production of mRNA and reaches its saturation value in around 100000 au of time. The model that we propose, qualitatively matches the real world observations hence the circuit/model is consistent. [Note : saturation refers to around 90% of the conc at t = infinite]

The plots of P_on, P_off, C, mRNA, biosurfactant vs time were obtained by numerically solving the differential equations