Synthesizing Amino Acids Modified with Reactive Carbonyls in Silico to Assess Structural Effects Using Molecular Dynamics Simulations

Our work shows a route for the in silico creation of carbonylated amino acids with lipid peroxidation end products and how to incorporate them into a protein. This can help us understand how this post-translational modification can alter the structural function of carbonylated proteins. Programs based on artificial intelligence algorithms are the most powerful computational tools today for predicting the tertiary structure of protein.

However, they do not yet recognize carbonylated amino acids and, therefore, they cannot predict their effects on protein structure. To study the impact of post-translational modification on the structure and function of a protein, a wide range of the parameter methods are available that are combined with computational approaches to accelerate and deepen the desired finding. There are important challenges that must be faced in the study of post-translational modification of proteins, both in vivo, in vitro, and in silico.

At the in silico level, the improvement of force fields and data analysis programs is still required, along with the creation of new parameters. Due to the increase in carbonylated proteins in metabolic infections and aging-related diseases, among many others, our results can contribute to understanding the structural change that occur in this and how this can affect the function in silico strands. To begin using a computational chemistry software package, draw the amino acid molecules bound to the reactive aldehydes derived from lipid peroxidation.

Upon modification at the carboxyl group end of the amino acid, draw the shape of the methylamine group. At the amino end, draw an acetyl group to emulate the peptide bonds of the modified amino acid. Click on the Clean icon to clean the structure.

For structure optimization, click on Calculate, followed by Gaussian Calculation Setup. Then click on General and uncheck Write Connectivity. From the Job Type, select Optimization.

In Additional Keywords, enter the indicated text. To change the basis set, go to Method and select 631G. For optimization, click on Submit.

Then to optimize from a Gaussian terminal, enter the indicated command. Click on File, then Save. Enter the file name and save the file as com.

Once the optimization is complete, open the output file and check that there are no error messages at the end of the document. To begin, download the PDB file of the thioredoxin protein as the model protein system. Use an appropriate protein visualizer software to erase water molecules, dimers, and ligands from the protein structure.

Add the file with modified amino acid residues, from-lib. pdb, and overlay it on the amino acid residue to be modified. Ensure that the amino and carbonyl ends of from-lib.

pdb accurately matches the amino acid intended for modification. Delete the protein, leaving only the from-lib. pdb file in the three dimensional space occupied by the amino acid residue to be modified.

Remove hydrogen from the N-and C-terminal atoms. Save the from-lib. pdb as u00-moved.

pdb with the new coordinates. Using a text editor, open the cleaned protein PDB file and u00-moved. pdb file.

Copy the coordinates from u00-moved. pdb and paste them into the protein PDB file to adapt the bond between the modified residue and the protein system. Adjust the typology to be compatible with the protein PDB format, changing HEATATM to ATOM and updating the number 1 to the one corresponding to the residue to be modified.

Save the new file as complex.pdb. Structures from density functional theory at m062x/631g level compared with AMBER's molecular dynamic simulations correlated well with theoretical quantum mechanics showing minimal bond distance errors and angle parameter values. The RMSD values obtained for each of the modified amino acids were similar to native counterparts and maintained confirmational stability throughout the entire trajectory.