Interaction of nano silica and membrane

[rezafahri11]

Zhang et al. 2021 (https://doi.org/10.1038/s41427-021-00320-0): This paper has reviewed several simulations to study the interaction of nanoparticles (NPs) and membrane. This paper said there are several parameters of NPs that can affect the interaction. The parameters are size, elasticity, shape, charge, hydrophobicity, and surface modification. 1) For the size of NPs: we can conclude that the cellular uptake and internalization behavior of NPs can vary based on their size, and the optimal size is dependent on other properties of NPs (such as surface modification, aggregation degree, and dimension). 2) For the shape of NPs :

• the penetrating capability of an NP across a lipid bilayer is determined based on the contact area between the particle and lipid bilayer and the local curvature of the particle at the contact point.
• The membrane wrapping efficiency of NPs during endocytosis was a result of the competition between the receptor diffusion kinetics and thermodynamic driving force. The receptor diffusion kinetics are referred to as the kinetics of receptor recruitment and are affected by the contact edge length between the NP and membrane. The thermodynamic driving force represents the amount of free energy required to drive NPs into a cell.
• ligand-modified NPs have advantages and disadvantages in endocytosis, which need to be balanced when applied practically.
• the efficiency of passive endocytosis was higher for spherocylindrical NPs than for spheres and that endocytosis was suppressed for NPs with sharp edges
• NPs with various shapes displayed different favorable orientations for the two stages, and one or more orientation rearrangements might be required during the endocytosis process 3) For the elasticity of NPs : compared to soft NPs, rigid NPs tend to be transported across the membrane, which can help in designing NPs with high efficiencies for potential applications. 4) For the charge of NPs :
• NPs with negatively charged surface functionalization were electrostatically repelled from the cell membrane, while neutral and positive NPs could interact with the cell membrane.
• Furthermore, the surface charge density of neutral and positive NPs played a key role in their translocation processes: asymmetrically charged NPs undergo an electrostatics-driven reorientation in the vicinity of the bilayer, thereby enhancing the NP–membrane contact area and disrupting the self-assembly of the membrane lipids.
• negatively charged NPs tended to adsorb onto the lipid bilayer more easily 5) For the hydrophobicity of NPs :
• the hydrophobic effect of an NP is a result of the interplay of all component beads: for hydrophilic beads, the cell membrane acts as a potential barrier, making beads tend to concentrate in the hydrophilic head regions of the bilayer membrane; for hydrophobic beads, the cell membrane acts as a potential trap, confining beads in the hydrophobic interior region of the bilayer membrane
• NPs with moderate hydrophobicities exhibited the best cellular uptake properties. 6) Surface Modification :

Other References (I have not read yet):

https://doi.org/10.1371/journal.pone.0107696 http://dx.doi.org/10.2147/IJN.S49770 https://doi.org/10.3390/ani9121041 https://doi.org/10.3762%2Fbjnano.11.25

[rrkurnova]

• Try to read deeper on AAMD, CGMD, and DPD simulations.
• Find open source software for them.
• Find the hardware specification for the simulation.

[rezafahri11]

Open Source software For AAMD : DivCon by QuantumBio, Gromos, Molpro, Paramfit by Ambertools For CGMD : Cafemol, Gromacs For DPD : LAMMPS

Reference :

1. Divcon : https://www.quantumbioinc.com/solutions/structural-biology/divcon-plugin/ (I think we should contact the company to ask about the pc requirements)
2. Gromos :
   • Official website : https://www.gromos.net/
   • Guide : https://ethz.ch/content/dam/ethz/special-interest/chab/physical-chemistry/csms-dam/doc/CSCBP-res/CSBMS_gro_man_HS19.pdf
3. Molpro
   • Official website : https://www.molpro.net/
   • Guide : https://www.molpro.net/manual/doku.php?id=installation_guide
4. Paramfit by Ambertools : http://ambermd.org/doc12/Amber23.pdf (Tutorial for Ambertools)
5. Cafemol
   • Official website : https://www.cafemol.org/
   • Guide : http://www.csrp.riken.jp/2014/CafeMol_practice_201405.pdf
6. Gromacs
   • Official website : https://www.gromacs.org
   • Guide : https://manual.gromacs.org/documentation/current/install-guide/index.html
7. LAMMPS
   • Official website : https://www.lammps.org
   • Guide : https://docs.lammps.org/Manual.html

I have not found yet the hardware specification for each software. But I try to ask chatgpt, and I think the answer can be considered.

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From chatgpt :

When it comes to molecular modeling, the requirements can vary depending on the complexity of the simulations and the software being used. However, here's a general recommendation for a PC specification that should provide good performance for molecular modeling tasks:

Processor (CPU): Look for a high-performance multicore processor. Intel Core i7 or i9, or AMD Ryzen 7 or 9 processors are suitable options. More cores and higher clock speeds will generally result in better performance.

Random Access Memory (RAM): Molecular modeling software can be memory-intensive, so having a sufficient amount of RAM is crucial. Aim for a minimum of 16 GB, but consider getting 32 GB or even 64 GB if your simulations involve larger systems or extensive calculations.

Graphics Processing Unit (GPU): While many molecular modeling applications primarily rely on CPU performance, some software, such as GPU-accelerated molecular dynamics simulations, can benefit from a powerful GPU. NVIDIA GPUs, especially those from the GeForce RTX or Quadro series, tend to offer good performance for scientific applications.

Storage: Opt for a solid-state drive (SSD) as the primary storage device. SSDs are faster than traditional hard disk drives (HDDs) and can significantly improve loading times for large molecular structures and datasets. Consider getting an SSD with a capacity of 500 GB or more to accommodate your software and data.

Operating System: Ensure that your PC is running a 64-bit operating system (e.g., Windows 10, macOS, or Linux) to take full advantage of the available hardware resources.

Additional Considerations:

Check the software requirements of the specific molecular modeling software you intend to use. Some applications may have additional recommendations or hardware dependencies. Consider a high-resolution display to comfortably view complex molecular structures and visualize simulation results. If you plan to run large-scale simulations or parallel computing tasks, you may want to explore options for a workstation-grade system or cluster computing. Remember to check the system requirements of the specific software you plan to use for molecular modeling, as they may have specific recommendations or optimizations for hardware configurations.

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[rezafahri11]

What is MD?

Molecular Dynamics : One method for doing simulation of atom or molecules physical movement. More physically, we can see MD as a method that compute the equilibrium and transport properties of classical many-body system. How to do the MD :

1. We prepare the sample
2. Choose a model that consisting N particles
3. Solve the Newton's equation for that model until reached the equilibrium state
4. Doing the measurement

What's the different about CGMD and AAMD? The one significant different about them is about the levels of detail. AAMD is used usually for the system that we want to look more details, and this approach will represent the systems more detail / more accurate. Meanwhile CGMD used usually for the system that have a large systems.

Doing real experiment and MD simulation have some similarities. For example, when you can't define the system correctly, the outcome also will be incorrect.

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What is DPD?

Dissipative particle dynamics (DPD) is used for detailed molecular simulation of chemical reactions, mass transfer, and phase separation near the interface (liquid-liquid)

References : http://course.sdu.edu.cn/Download2/20211011140445728.pdf https://www.eng.uc.edu/~beaucag/Classes/AdvancedMaterialsThermodynamics/Books/%5BComputational%20science%20(San%20Diego,%20Calif.)%5D%20Daan%20Frenkel_%20Berend%20Smit%20-%20Understanding%20molecular%20simulation%20_%20from%20algorithms%20to%20applications%20(2002,%20Academic%20Press%20)%20-%20libgen.lc.pdf https://doi.org/10.2147/AABC.S70333 https://doi.org/10.1142/S1758825109000381