DOI: 10.5281/zenodo.15787986
title: Therapeutic Potential of the Laminin-1-Derived Peptide C16 in Interactions with αvβ3 and α5β1 Integrins: In Silico Analysis and Implications for Angiogenesis, Cancer, and Tissue Regeneration

description: Molecular DynamicsMolecular Docking there has also been performed molecular dynamics simulation studies by GROMACS 2023 to study the relationship between structure and function and properties by the analysis of molecular conformation sampling (cluster analysis, dominant conformation identification), interaction analysis (hydrogen bonding network, Contact-Map, Binding free energy calculations (MM-PBSA), backbone fluctuation analysis (RMSD, RMSF), Conformational transition analysis (simple normal mode analysis, dominant conformation identification), and physicochemical property analysis (energy, volume, pressure, temperature, density monitoring) (Luzik et al., 2019; Zhang et al., 2003).

For simulation purposes, a simulation system was set up for the protein-ligand complex with solvent using GPU-enabled GROMACS 2024.2 package (Abraham et al., 2024). The αvβ3 and α5β1 in water simulation were performed with GROMACS standard protocols also, αvβ3_S1@C16, αvβ3_S2@C16, α5β1@C16 complex, and αvβ3, αvβ3 proteins MD simulation was carried out in triplicate. All necessary topology files were generated using CHARMM-GUI (Lee et al., 2016; Park et al., 2023). CHARMM-GUI was used to build the simulation system (Allouche, 2012). And provided scripts compatible with GROMACS  (Lee et al., 2016), provideding TIP3P water model to solvate the system.

The dimensions of the box were defined by ensuring at least a 10 Å distance between the protein and the box edges to avoid boundary effects. Proper neutralization of the system was achieved by adding counterions (Na+ and Cl−) based on the protein’s charge automatically calculated by CHARMM-GUI. CHARMM36 force field was chosen, which provides parameters for proteins and peptides. Before the production MD simulation, energy minimization was performed to remove any unfavorable contacts or steric clashes in the system. CHARMM-GUI The system was equilibrated in two stages. First, the system was equilibrated under an NVT ensemble (constant number of particles, volume, and temperature) with restraints applied to the heavy atoms of the protein to allow the water and ions to relax around the solute. This phase runs for 100 ps. Second, the restraints were removed, and the system was equilibrated under an NPT ensemble (constant number of particles, pressure, and temperature) for an additional 1 ns to stabilize the density of the system (Galmozzi et al., 2014; K. W. Wang et al., 2022).

The minimization, equilibration, and production steps were performed using the GROMACS 2024.2 The steepest-descent energy minimization was used, and the maximum force was set to 100 kJ/(mol∙nm) on any atom. The solvated system was equilibrated with two steps. First, the system was equilibrated for 1 ns under a constant volume ensemble (NVT) without restraints applied. Second, the system was equilibrated for another 1 ns under a constant pressure ensemble (NPT) without any restraint. Production simulation was conducted for 100 ns under the NPT ensemble. All bonds containing hydrogen atoms were constrained using the default LINCS constraint algorithm. The coupling algorithm of Nose-Hoover was used to maintain temperature (310 K) and Parrinello-Rahman algorithm to maintain temperature pressure (1 atm, 101 325 Pa) with a constant of 1.0 ps. The electrostatic interactions were treated with the particle mesh-Ewald (PME) method. The integration time step was set to 2 fs and periodic boundary conditions were applied in all directions (Bansal et al., 2021; H. Yu et al., 2015). 

2.4.1. MMPBSA analyses GROMACS modules gmx rms for root mean square deviation (RMSD), gmx rmsf for root mean square fluctuation (RMSF), gmx hbond for numbers of hydrogen-bond (Hbond), gmx gyrate for the radius of gyration (Rg), gmx sasa for solvent accessible surface area (SASA) were used to analyze each complex system. The xmgrace module was employed to generate plots and graphs to represent binding energies, interaction frequencies, and structural changes. The Molecular Mechanics/Poisson-Boltzmann Surface Area (MM-PBSA) binding free energies like van der Waals and electrostatic interactions, potential energy, polar, and non-polar solvation energies were calculated by gmx_MMPBSA a tool based on AMBER's MMPBSA.py aiming to perform end-state free energy calculations with GROMACS files (Valdés-Tresanco et al., 2021). It has employed the visualization tools VMD and PyMOL to examine the trajectory and interaction details.

References:

Allouche, A. (2012). Software News and Updates Gabedit — A Graphical User Interface for Computational Chemistry Softwares. Journal of Computational Chemistry, 32, 174–182. https://doi.org/10.1002/jcc.

Galmozzi, A., Dominguez, E., Cravatt, B. F., & Saez, E. (2014). CHARMM-GUI Ligand Binder for Absolute Binding Free Energy Calculations and Its Application. Methods Enzymol., 538(1), 151–169.

Bansal, R., Mohagaonkar, S., Sen, A., Khanam, U., & Rathi, B. (2021). In-silico study of peptide-protein interaction of antimicrobial peptides potentially targeting SARS and SARS-CoV-2 nucleocapsid protein. In Silico Pharmacology, 9(1), 1–14. https://doi.org/10.1007/s40203-021-00103-z.

Park, S. J., Kern, N., Brown, T., Lee, J., & Im, W. (2023). CHARMM-GUI PDB Manipulator: Various PDB Structural Modifications for Biomolecular Modeling and Simulation. Journal of Molecular Biology, 435(14), 167995. https://doi.org/10.1016/j.jmb.2023.167995.

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Luzik, D. A., Rogacheva, O. N., Izmailov, S. A., Indeykina, M. I., Kononikhin, A. S., & Skrynnikov, N. R. (2019). Molecular Dynamics model of peptide-protein conjugation: case study of covalent complex between Sos1 peptide and N-terminal SH3 domain from Grb2. Scientific Reports, 9(1), 1–18. https://doi.org/10.1038/s41598-019-56078-7.

Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A., & Moreno, E. (2021). gmx\_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. Journal of Chemical Theory and Computation, 17(10), 6281–6291. https://doi.org/10.1021/acs.jctc.1c00645.

Wang, K. W., Lee, J., Zhang, H., Suh, D., & Im, W. (2022). CHARMM-GUI Implicit Solvent Modeler for Various Generalized Born Models in Different Simulation Programs. Journal of Physical Chemistry B, 126(38), 7354–7364. https://doi.org/10.1021/acs.jpcb.2c05294. 

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