parameterization of modified amino acid residues into the thioredoxin protein structure

In Silico Structural Analysis of Protein Carbonylation by Lipid Derivatives

In the constant pursuit of understanding the molecular behavior of proteins with oxidative modifications, computational chemistry has become a fundamental pillar in the broad field of scientific research. This relies on the use of theoretical models capable of interpreting physical phenomena in electronic systems, using mathematical equations to describe the atomic behavior of molecules. Within this landscape, computational simulations of proteins stand out as crucial tools to analyze the atomic behavior of molecular systems. Based on the evaluation of structural behavior, energetic calculations, and conformational states1, these methods become strategic allies to predict the behavior of biomolecular systems.
These simulations specialize in studying structural changes and assessing the loss or gain of biological functions in protein systems. However, computational approaches have shown significant limitations when applied to protein systems containing modified residues formed by covalent post-translational modifications in the sequence. This is because many available methods lack resources with parameters adaptable to force fields that are compatible with the most common packages of programs for molecular dynamics simulations of proteins2,3,4,5,6. Therefore, the standardization of computational software-compatible force-field adaptive parameters is essential to facilitate the precise coupling of topologies and atomic coordinates with the equation governing the potential energy of the system7.
In response to these challenges, a protocol adaptable to new modified amino acid residues with aldehydes derived from lipid peroxidation has been developed using ab initio methods. In that sense, the optimization of the structural geometry of the new residues allows the assignment of adaptive charges to new bond, angle, and dihedral parameters that can be run in general force fields such as AMBER. Subsequent validation of these parameters allows the determination of the consistency and robustness of the method applicable to molecular dynamics simulations.
One of the notable strengths of this method lies in its ability to adapt to diverse post-translational modifications, from carbonylation to phosphorylation, acetylation, and methylation, among others. This versatility is not only limited to protein systems but extends to macromolecular structures, allowing coupling with atomic topologies and coordinates. In contrast, previous studies reveal that the standard parameterization of post-translational modifications is only suited to a specific type of modification and can only be obtained from published repositories, lacking the ability to create new structures8.
Currently, challenges in protein structure prediction and design are becoming more evident when modeling structures with post-translational modifications. The scarcity of parameters describing alterations at specific amino acid sites underlines the urgent need to develop and apply computational methods that can be adjusted to standard parameterizations. The aim of this protocol is to provide a route for the in silico construction of new parameters for amino acids covalently modified with reactive carbonyl species derived from fatty acid oxidation. These modified amino acids are recognized by the general amber force field (GAFF) and can, therefore, be used to evaluate in silico the structural and functional effects that this kind of carbonylation has on their target proteins.

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

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 on 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

Parameterization of Modified Amino Acid Residues into the Thioredoxin Protein Structure

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 one 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 dynamics 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.

parameterization-modified-amino-acid-residues-into-thioredoxin

Research

JoVE Journal

Biochemistry

41 Views. 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 fro...

Video: Parameterization of Modified Amino Acid Residues into the Thioredoxin Protein Structure

Protein carbonylation by reactive aldehydes derived from lipid peroxidation leads to cross-linking, oligomerization, and aggregation of proteins, causing intracellular damage, impaired cell functions, and, ultimately, cell death. It has been described in aging and several age-related chronic conditions. However, the basis of structural changes related to the loss of function in protein targets is still not well understood. Hence, a route to the in silico construction of new parameters for amino acids carbonylated with reactive carbonyl species derived from fatty acid oxidation is described. The Michael adducts for Cys, His, and Lys with 4-hydroxy-2-nonenal (HNE), 4-hydroxy-2-hexenal (HHE), and a furan ring form for 4-Oxo-2-nonenal (ONE), were built, while malondialdehyde (MDA) was directly attached to each residue. The protocol describes details for the construction, geometry optimization, assignment of charges, missing bonds, angles, dihedral angles parameters, and its validation for each modified residue structure. As a result, structural effects induced by the carbonylation with these lipid derivatives have been measured by molecular dynamics simulations on different protein systems such as the thioredoxin enzyme, bovine serum albumin and the membrane Zu-5-ankyrin domain employing root-mean-square deviation (RMSD), root mean square fluctuation (RMSF), structural secondary prediction (DSSP) and the solvent-accessible surface area analysis (SASA), among others.

Here, we describe a protocol for the optimization and parameterization of amino acid residues modified with reactive carbonyl species, adaptable to protein systems. The protocol steps include structure design and optimization, charge assignments, parameter construction, and preparation of protein systems.

Protein carbonylation by reactive aldehydes derived from lipid peroxidation leads to cross-linking, oligomerization, and aggregation of proteins, causing ...

Computational Design and Energy Optimization of Modified Amino Acids Reacting with Lipid Peroxidation Aldehydes

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 bend 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 631 G.For optimization, click on submit.Then to optimize from 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.