This work was supported by research grant code 1107-844-67943 from Ministerio de Ciencia, Tecnolog&#237;a e Innovaci&#243;n (Minciencias) and the University of Cartagena (Colombia) for grant to support the research groups 2021 and Acta 017-2022.

1. Design and optimization of the new modified amino acid
NOTE: This stage involves drawing the structures of modified residues and optimizing their energy.
<ol>
	<li>Designing the modified structures and optimizing their structure.
	<ol>
		<li>Use a computational chemistry software package to draw the amino acid molecules bound to the reactive aldehydes derived from lipid peroxidation, i.e. with HNE, HHE, MDA and ONE. Once modified, 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, as shown in Figure 1.</li>
		<li>Click on the Clean Icon for structure cleanup. For structure optimization, click on Calculate &#62; Gaussian calculation setup&#8230; or Ctrl+G, then click on General and uncheck Write connectivity. Click on Job Type &#62; Optimization, as shown in Figure 2. In additional keywords type the following line: 
		SCF=tight test Pop=MK iop(6/33=2) iop(6/42=6) opt 
		NOTE: Here, GaussView automatically sets Hartree Fock (HF) as the functional and the basis set to 3-21. HF is commonly used as a functional in various applications, although other functionals, such as M062X, have also been used, depending on the specific system and the researcher&#39;s objectives. Remember that since it is a neutral charge molecule, the charge and multiplicity must be 0 and 1, respectively.</li>
		<li>To change the basis set, click on Method &#62; 6-31G for the Basis set.</li>
		<li>To execute the optimization on the same computer, click on Submit. To optimize from a Gaussian terminal, write the following command: 
		&#8203;g16 name_of_the_file.com &#38;</li>
		<li>Click on File &#62; Save. Save the file as .com for Linux or. gjf for Windows. Once the optimization is complete, open the output file (.out in Windows and .log in Linux) and verify that everything went well. There should be no error messages at the end of the document. 
		NOTE: If there are no error messages at the end of the output file, it means that the optimization was performed correctly.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66605/66605fig01.jpg" /> 
Figure 1: Cysteine modified with reactive carbonyls. Representation of the chemical structure of cysteine (black line) modified with HNE, HHE, MDA and ONE (green line), and linked with acetylamide (blue line) and methylamide (red line) substituent groups. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66605/66605fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66605/66605fig02.jpg" /> 
Figure 2: Menu to optimize modified residues synthetized. Reference image illustrating step 1.1 of the protocol, which shows the optimization step of the modified structure in Gaussian program. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66605/66605fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Parameterization of the modified amino acid residues
<ol>
	<li>Create the prepin file using the antechamber program from the AmberTools 16 package or the available version. Refer to Figure 3 for an image of how the prepin file should look like. 
	antechamber -i init-gau.log -fi gout -o u00.prepin -fo prepi -c resp -s 2 -rn U00 -at gaff2 -nc 0 
	NOTE: Hereafter, the italicized text corresponds to the file name and varies according to the researcher&#39;s criteria. In this case, init-gau.log corresponds to the file obtained after optimization.</li>
	<li>For building the parameter file, type the following command: 
	parmchk -i u00.prepin -f prepi -o u00.frcmod 
	&#8203;At this point verify that the. frcmod file has been created. Refer to Figure 3&#160;for an example of what the .frcmod file might look like.</li>
	<li>Building the library file
	<ol>
		<li>Open XLEaP, universe editor with the xleap command. A window resembling the one shown in Figure 4&#160;will open. Then, follow the steps below to generate the library file that contains relevant data. Type the following commands: 
		source leaprc.gaff2 
		loadamberparams u00.frcmod 
		loadamberprep u00.prepin 
		list 
		&#8203;NOTE: Verify that the U00 file has been created by using the list command.</li>
		<li>Edit the ends of modified structures and adjust the resulting charges by typing the following commands: 
		edit U00 
		&#8203;A graphical interface will be displayed (see Figure 4).</li>
		<li>Select Erase option. Click on the Atoms of the Acetyl and Methylamine Ends that were added in step 1.1 to delete them (see Figure 4 for a reference of what the carboxyl and amino ends of the modified residue should resemble).</li>
		<li>Charge neutralization
		<ol>
			<li>At this point, the molecule&#39;s charge is no longer neutral due to the elimination of atoms in step 2.3.3. The charge comes from both the carboxyl end and the amino end. To neutralize both the charge of the amino end and the carboxyl end, follow the steps below.</li>
			<li>To obtain the total charge value (see Figure 5), type: 
			charge U00 
			Divide by two the charge obtained. Use the absolute value for the total charge value.</li>
			<li>In the graphical interface select the Whole Molecule. Click on Display &#62; Names. Click on Edit &#62; Edit selected atoms. At this point a window with a table should appear.</li>
			<li>Verify the name of the N and C terminal atoms. In the table, add the value obtained for the division of the total charge (absolute value; see Figure 5). Then save and quit by clicking on Table &#62; Save and quit.</li>
			<li>Ensure the charge is zero (see Figure 5): 
			&#8203;charge U00</li>
			<li>To exit the program and save the library file type: 
			desc U00 
			saveoff U00 u00.lib 
			quit</li>
			<li>Verify that the library file (.lib) has been created correctly (see Figure 6 for reference).</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Build the pdb file of the modified residue with the new parameters as described below. 
	tleap 
	source leaprc.gaff2 
	loadamberparams u00.frcmod 
	loadoff u00.lib 
	x = U00 
	savepdb U00 from-lib.pdb 
	quit</li>
	<li>Preparation of the protein
	<ol>
		<li>Download the PDB file of the protein to be modified. Thioredoxin was selected as the model protein system (PDB ID: 2IFQ). Use an appropriate protein visualizer to erase water molecules, dimers (if necessary), ligands, etc. 
		NOTE: This step can be performed in viewers such as UCSF Chimera or Discovery</li>
		<li>Add the file from-lib.pdb (file obtained in step 2.4) and overlay it on the amino acid residue to be modified (as shown in Figure 7). Ensure the amino and carbonyl terminal ends of the from-lib.pdb match the amino acid to be modified.</li>
		<li>Delete the protein, only the from-lib.pdb file should remain in the three-dimensional space occupied by the residue to be modified. Remove H from the N- and C-terminal atoms.</li>
		<li>Save the from-lib.pdb as u00-moved.pdb with the new coordinates.</li>
		<li>Once the coordinates of the modified residue have been saved, with a text editor open the u00-moved.pdb and the protein PDB file that was cleaned previously. Here, we use Notepad++ v8.4.8 text editor.</li>
		<li>Copy the coordinates from u00-moved.pdb as shown in Figure 8 and paste them into the protein pdb file, replacing the residue to be modified. This is intended to adapt the bond between the modified residue and the protein system.</li>
		<li>Adjust the typology to be compatible with the protein PDB format, changing HEATATM to ATOM and change the numbering 1 to the one corresponding to the residue to be modified. Save the new file as complex.pdb.</li>
	</ol>
	</li>
	<li>Generation of modified protein-residue binding connections
	<ol>
		<li>In the protein visualizer program open the file from-lib.pdb. Select the entire structure. Click on Structure &#62; Labels &#62; Add... &#62; OK.</li>
		<li>Verify the nomenclature assigned to the N- and C-terminal atoms. In another window, open the u00.lib file in the text editor.</li>
		<li>In the list that appears, verify the position of N- and C-terminal, taking into account the nomenclature assigned.</li>
		<li>In the u00.lib file, locate the line: !entry.U00.unit.connect array int. Below that line, two numbers will appear. Change the first number to the position of the N-terminal and change the second number to the position of the C-terminal and save.</li>
	</ol>
	</li>
	<li>Create the parameter list by typing the following lines: 
	tleap 
	source leaprc.gaff2 
	source leaprc.protein.ff14SB 
	loadoff u00.lib 
	loadamberparams u00.frcmod 
	x = loadpdb complex.pdb 
	check x 
	&#8203;NOTE: At this point, tleap will provide a list of bonds, angles, and dihedral angles to parameterize.</li>
	<li>Typology identification
	<ol>
		<li>Open the complex.pdb file in the protein visualizer. Select the modified residue and the adjacent residues on either side. 
		NOTE: In the tertiary structure of the protein, it is common for a gap to occur at the site of the modified residue.</li>
		<li>Display ball and stick structure for the selected residues. Display nomenclature only for the modified residue as shown in step 2.6.1 Open library file (.lib) in the chosen text editor.</li>
		<li>Based on the nomenclature observed, identify in the library file (.lib) the assigned topology (found in quotation marks next to the nomenclature) that corresponds to the one used in the list of bonds, angles, and dihedral angles to be parameterized, created in step 2.7. 
		NOTE: In the list of bonds, angles, and dihedral angles provided by tleap, capital letters represent the atoms of the amino acids adjacent to the modified residue.</li>
	</ol>
	</li>
	<li>Parameterization of bonds, angles, and dihedral angles with parmcal (a program in Amber) 
	&#8203;NOTE: For this step, it will be necessary to use the parmcal program of the Amber package. It will also be indispensable to have the frcmod file (u00.frcmod) and the library file open in the text editor. The protein visualizer should be used to visualize the angles and bond distances. In the protein visualizer, the amino acid residues that are bound to the modified one will be selected in order to generate the bond distances, angles, and dihedrals (see list step 2.7). These data will be implemented to calculate the constants in parmcal and add them in the frcmod file for the parameter&#39;s creation.
	<ol>
		<li>Generation of bond distances and angles in the Visualizer
		<ol>
			<li>In the Visualizer, select the atoms involved in the bond or angle. Click on Structure &#62; Monitor &#62; Distance or Angle.</li>
			<li>Perform the following procedure for each new parameter to be added. The data to be entered in parmcal is indicated in bold. Below is an example of how to create the binding parameter between the N-terminal of the modified amino acid and the adjacent C of the other amino acid. 
			Parmcal 
			Please select: 
			0. set parameter se (gaff) 
			1. calculate the bond length parameter: a-b 
			2. calculate the bond angle parameter: a-b-c 
			3. exit 
			0 
			Please select which parameter set to use: 1-gaff (the default) or 2-gaff2 
			2 
			Force field parameters set has been set to gaff2 
			Please select: 
			0. set parameter se (gaff) 
			1. calculate the bond length parameter: a-b 
			2. calculate the bond angle parameter: a-b-c 
			3. exit 
			1 
			Please input the element name of atom A in A-B 
			C 
			Please input the element name of atom B in A-B 
			ns 
			Please input the bond length in non-positive number 
			means to calculate it according to empirical rules 
			1.455 
			BOND C-ns 270.256 1.455 
			NOTE: The double underline is copied and added in the frcmod file. For this example, it is added below the last line of the BOND section. The dihedral angles are added according to the values reported by Alviz-Amador et al.9.</li>
			<li>After creating all the bond, angle, and dihedral parameters and adding them to the frcmod file, save the frcmod file, ensuring that the new parameters are included.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>For topology and coordinate file generation, type the following commands: 
	tleap 
	source leaprc.gaff2 
	source leaprc.protein.ff14SB 
	loadoff u00.lib 
	loadamberparams u00.frcmod 
	x = loadpdb complejo.pdb 
	source leaprc.water.tip3p 
	&#8203;charge x
	<ol>
		<li>Add the number of Na or Cl ions necessary to neutralize the charge by typing: 
		addions x Na+ 5 
		solvateoct x TIP3PBOX 10.0 
		saveamberparm x prot.topo prot.coords 
		NOTE: If it is desired to add Cl ions instead of Na, replace Na+ by Cl-. The 5 corresponds to the number of ions to be added and is adjusted in order to neutralize the charge.</li>
	</ol>
	</li>
	<li>For molarity calculation type: 
	&#8203;tail -f prot.coords
	<ol>
		<li>Copy the final line produced, substituting it with the content in bold within the subsequent instruction. 0.15 corresponds to the target molarity. 
		usr/bin/perl molarity.perl 0.15 101.3356150 101.3356150 101.3356150 109.4712190 109.4712190 109.4712190 Kconts 
		This will generate the amount of Cl- and Na+ ions to be added, as described in step 2.10. At this point the generation of the topology and coordinate files of the modified amino acid residue with the new parameters takes place.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66605/66605fig03.jpg" /> 
Figure 3: Parameter file preparation. (A) Reference image illustrating the expected appearance of the prepin file generated in step 2.1. The visualization of the file was conducted using the GNU nano text editor v2.3.1.&#160;(B)&#160;Reference image illustrating the expected appearance of the frcmod file generated in step 2.1. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66605/66605fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/66605/66605fig04.jpg" /> 
Figure 4: Reference image of XLEaP window. (A) Shows the expected response when typing the mentioned commands.(B) Shows the atoms that need to be removed (yellow) and the option that needs to be selected in order to do so (red). (C) Shows a reference image of what the amino and carbonyl terminal ends of the modified residue should look like after the acetyl and methylamine groups are deleted. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66605/66605fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/66605/66605fig05.jpg" /> 
Figure 5: Charge neutralization procedure. (A) Calculation of the total charge after the removal of acetyl and methylamine groups. (B) Determination of the assigned nomenclature for the atoms of the residue. Pay attention to the assigned nomenclature for the N of the amino terminal and C of the carboxyl terminal. (C) Identification of the assigned charges for these two atoms (N1 and C3) in the table. Take the charge value of the atoms (divided by 2) and add the absolute value of the obtained charge. (D) Substitution of the charge values of N1 and C3 with the obtained values. (E) Verification that the resulting charge is now zero. (all data provided is for reference only and may vary depending on the modified residue). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66605/66605fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/66605/66605fig06.jpg" /> 
Figure 6: Reference image of the desired structure of the library file (.lib). It is important to note that the image provided only displays a condensed representation of the complete file. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66605/66605fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/66605/66605fig07.jpg" /> 
Figure 7: Reference image illustrating the correct positioning of the from-lib.pdb file. It is important to note that the displayed image includes the hydrogens on the N and C termini, which should be excluded before saving the file. The image was taken in the Visualizer software. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66605/66605fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/66605/66605fig08.jpg" /> 
Figure 8: PDB file update. Reference image of the procedure for replacing the residue coordinates (in this case Cys32) with the modified residue. The modified residue PDB file refers to the u00-moved.pdb file. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66605/66605fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

In Silico Structural Analysis of Protein Carbonylation by Lipid Derivatives

<ol>
	<li>Cornell, W. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+second+generation+force+field+for+the+simulation+of+proteins,+nucleic+acids,+and+organic+molecules.">A second generation force field for the simulation of proteins, nucleic acids, and organic molecules.</a> J Am Chem Soc. 117 (19), 5179-5197 (1995).</li><li>Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A., Case, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+testing+of+a+general+amber+force+field.">Development and testing of a general amber force field.</a> J Comput Chem. 25 (9), 1157-1174 (2004).</li><li>Brooks, B. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CHARMM:+A+program+for+macromolecular+energy,+minimization,+and+dynamics+calculations.">CHARMM: A program for macromolecular energy, minimization, and dynamics calculations.</a> J Comput Chem. 4 (2), 187-217 (1983).</li><li>Mayo, S. L., Olafson, B. D., Goddard, W. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DREIDING:+a+generic+force+field+for+molecular+simulations.">DREIDING: a generic force field for molecular simulations.</a> J Phys Chem. 94 (26), 8897-8909 (1990).</li><li>Daura, X., Mark, A. E., van Gunsteren, W. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Parametrization+of+aliphatic+CHn+united+atoms+of+GROMOS96+force+field.">Parametrization of aliphatic CHn united atoms of GROMOS96 force field.</a> J Comput Chem. 19 (5), 535-547 (1998).</li><li>Robertson, M. J., Tirado-Rives, J., Jorgensen, W. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+peptide+and+protein+torsional+energetics+with+the+OPLS-AA+force+field.">Improved peptide and protein torsional energetics with the OPLS-AA force field.</a> J Chem Theory Comput. 11 (7), 3499-3509 (2015).</li><li>Guvench, O., MacKerell, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+protein+force+fields+for+molecular+dynamics+simulations.">Comparison of protein force fields for molecular dynamics simulations.</a> Methods Mol Biol. 443, 63-88 (2008).</li><li>Petrov, D., Margreitter, C., Grandits, M., Oostenbrink, C., Zagrovic, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+systematic+framework+for+molecular+dynamics+simulations+of+protein+post-translational+modifications.">A systematic framework for molecular dynamics simulations of protein post-translational modifications.</a> PLoS Comput Biol. 9 (7), e1003154 (2013).</li><li>Alviz-Amador, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+benchmark+to+obtain+AMBER+parameters+dataset+for+non-standard+amino+acids+modified+with+4-hydroxy-2-nonenal.">Development and benchmark to obtain AMBER parameters dataset for non-standard amino acids modified with 4-hydroxy-2-nonenal.</a> Data Brief. 21, 2581-2589 (2018).</li><li>Pineda-Alem&#225;n, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cysteine+carbonylation+with+reactive+carbonyl+species+from+lipid+peroxidation+induce+local+structural+changes+on+thioredoxin+active+site.">Cysteine carbonylation with reactive carbonyl species from lipid peroxidation induce local structural changes on thioredoxin active site.</a> J Mol Graph Model. 124, 108533 (2023).</li><li>Alviz-Amador, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effect+of+4-HNE+modification+on+ZU5-ANK+domain+and+the+formation+of+their+complex+with+&#946;-Spectrin:+A+molecular+dynamics+simulation+study.">Effect of 4-HNE modification on ZU5-ANK domain and the formation of their complex with &#946;-Spectrin: A molecular dynamics simulation study.</a> J Chem Info Model. 60 (2), 805-820 (2020).</li><li>Zhou, A., Schauperl, M., Nerenberg, P. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Benchmarking+electronic+structure+methods+for+accurate+fixed-charge+electrostatic+models.">Benchmarking electronic structure methods for accurate fixed-charge electrostatic models.</a> J Chem Info Model. 60 (1), 249-258 (2020).</li><li>G&#281;gotek, A., Skrzydlewska, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biological+effect+of+protein+modifications+by+lipid+peroxidation+products.">Biological effect of protein modifications by lipid peroxidation products.</a> Che Phys Lipids. 221, 46-52 (2019).</li><li>Moldogazieva, N. T., Zavadskiy, S. P., Astakhov, D. V., Terentiev, A. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lipid+peroxidation:+Reactive+carbonyl+species,+protein/DNA+adducts,+and+signaling+switches+in+oxidative+stress+and+cancer.">Lipid peroxidation: Reactive carbonyl species, protein/DNA adducts, and signaling switches in oxidative stress and cancer.</a> Biochem Biophys Res Comm. 687, 149167 (2023).</li></ol>

The authors have nothing to disclose.

One of the critical steps in developing the AMBER parameterization protocol was the quantum optimization of the new amino acid residues modified with the lipid peroxidation derivatives, due to the energetic variability related to the minimization and the way of assigning RESP charges in the AMBER antechamber. For this, ab initio optimization methods with Hartree-Fock (HF/6-31G) and semiempirical density functional theory (DFT; B3LYP/6-31G and M062X/6-31G) were established to evaluate the response to the load assignment. As a result, the HF functional presented better performance/computational cost ratio, taking this into account as a previous step to the protocol. This was also demonstrated in the study by Zhou et al.12.
During the application of the protocol there can be several sources of errors. The possible steric hindrances generated by the additional structures of the modification often lead to errors that are often solved through the minimization steps of the molecular system. On the other hand, the parameters of the dihedral angles are usually adjusted at the end of the parameterization process and therefore, sometimes they tend to show as a possible error, in this particular case it is suggested to adjust the parameters by homology, as reported by Alviz-Amador9 and add it in the new format to eliminate the error.
One of the limitations of the method is the effort required for the step-by-step development of the parameterizations. The generation of new parameters from the new electronic structures and then adapting these parameters to protein systems requires a lot of dedication for its good execution. Therefore, a good strategy when implementing our protocol is to follow the step-by-step instructions and read the guide carefully.
In the landscape of molecular dynamics simulations, the significance of the AMBER protocol becomes evident. Its adaptive nature and versatility make it a valuable tool for researchers exploring diverse research areas. Beyond its application in protein systems, its extension to macromolecular structures opens doors to new possibilities. This adaptability not only addresses the existing gaps in standard parameterization methods but also offers a pathway for the creation of novel structures, thus expanding the horizons of molecular dynamics research. On the contrary, other investigations demonstrate that the conventional parameterization of post-translational modifications is limited to a particular modification type and is exclusively derived from publicly available repositories8, lacking the capability to generate novel structures.
Modifications resulting from the presence of reactive carbonyl species are frequently associated with a range of pathologies, including cancer, metabolic disorders, and degenerative diseases following different mechanisms13,14 . The support provided by this protocol is useful to assess various crucial properties, such as conformational stability, atomic flexibility, loss of secondary structures, solvent accessibility, and protein-protein interaction energy, among others. Consequently, the measurement of these properties could prove beneficial in situations where carbonylated proteins can induce irreversible alterations in biological systems, leading to conformational instability, increased or decreased atomic flexibility, and loss of secondary structure10,11.
In conclusion, the AMBER parameterization protocol, with its critical steps, adaptability, and versatility, stands as a pioneering method in the realm of molecular dynamics simulations. While acknowledging its limitations, its significance is underscored by its ability to address the shortcomings of existing methods, providing researchers with a powerful tool to explore the intricacies of molecular structures and behaviors across a spectrum of biological and chemical systems.

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.

<table><tbody><tr><td>AmberTools16 or Upper</td><td>The Amber Project</td><td></td><td>Amber is a suite of biomolecular simulation programs</td></tr><tr><td>Gaussian 09 or Upper</td><td>Gaussian Inc</td><td></td><td>Draw and optimize structures</td></tr><tr><td>Linux Ubuntu</td><td>GNU/Linux</td><td></td><td>Platform for AmberTools</td></tr><tr><td>NVIDIA GPUs GTX 1080 or Upper</td><td>Nvidia</td><td></td><td>Compatible with PMEMD</td></tr></tbody></table>

synthesizing amino acids modified with reactive carbonyls in silico to assess structural effects using molecular dynamics simulations

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.

To illustrate the implementation of the protocol and evaluate the results, the following analyses will be considered. The data set generated by assigning new parameters to modified amino acid residues was constructed based on optimization of the electronic structures, which were supported for partial RESP loadings. Figure 9 shows the structural conformation of one of the amino acid residues optimized with the parameter assignment.
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/66605/66605fig09.jpg" /> 
Figure 9: Cys-HHE residue synthesized in silico. Representation of HHE-modified cysteine amino acid with assigned topology and coordinate parameters. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66605/66605fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The structures obtained from theoretical DFT levels with M062X/6-31G were compared with the classical mechanics structures through molecular dynamics simulations in AMBER. Each of the parameters obtained from the simulations showed a good correlation with the theoretical data from quantum mechanics. The average bond distance errors showed values of approximately 0.001 - 0.002 &#197;, while the angles were ~ 8.2&#176;. The typology, distances and constants of bonds and angles are listed in Table 1. These data were similar to those reported in the data article by Alviz-Amador et al.9. Parameter files are available at&#160;http://research.bmh.manchester.ac.uk/bryce/amber/.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="7">Cys-HHE</td>
		</tr>
		<tr>
			<td rowspan="2">Methods</td>
			<td colspan="3">Bond&#160;</td>
			<td colspan="3">Angle</td>
		</tr>
		<tr>
			<td colspan="3">(&#197;, &#177; Stdev)</td>
			<td colspan="3">&#160;(&#176;, &#177; Stdev)</td>
		</tr>
		<tr>
			<td>QM</td>
			<td>S1 &#8211;C4</td>
			<td>C6-C8</td>
			<td>C8-C9</td>
			<td>S1-C4-C5</td>
			<td>O2-C6-C8</td>
			<td>C6-C8-C9</td>
		</tr>
		<tr>
			<td>&#160;(m062x/631g(d)</td>
			<td>1.82</td>
			<td>1.52</td>
			<td>1.53</td>
			<td>115.9</td>
			<td>109.25</td>
			<td>112.21</td>
		</tr>
		<tr>
			<td rowspan="2">MM (AMBER) aa alone</td>
			<td>1.85&#177;</td>
			<td>1.55&#177;</td>
			<td>1.54&#177;</td>
			<td>111.66&#177;</td>
			<td>109.77&#177;</td>
			<td>113.16&#177;</td>
		</tr>
		<tr>
			<td>0.002</td>
			<td>0.002</td>
			<td>0.002</td>
			<td>0.152</td>
			<td>0.14</td>
			<td>0.148</td>
		</tr>
	</tbody>
</table>
Table 1: Bond distance and angle parameters comparison. The values of bond distances and angle obtained by quantum (QM) and classical methods showed no significant differences.
Once each of the parameters for the modified amino acid residues was generated and validated, the dynamic behaviors were examined through molecular dynamics simulations with trajectories of 1 &#181;s in order to evaluate the effect on the stability of each residue compared to its native counterpart (Figure 10). The RMSD values obtained for each of the modified amino acids did not show significant differences from their native counterpart, and they maintained their conformational stability throughout the entire trajectory.
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/66605/66605fig10.jpg" /> 
Figure 10:&#160;RMSD graph of synthesized residues in silico. Representative RMSD of unmodified and modified cysteine residue with HHE, HNE, MDA and ONE. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66605/66605fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The files resulting from the parameterization of modified amino acid residues have been utilized to substitute natural structural amino acids in proteins that have experimental evidence of carbonylation. This substitution was undertaken to evaluate the structural and functional impacts that may occur in the protein as a result of these modifications. It had been reported by in silico studies of carbonylation by reactive carbonyl species on protein systems like Ankyrin and Thioredoxin10,11.

Read this Scientific Article Protocol about Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function at JoVE.com

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

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.

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

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

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Nanyang Technological University

Research

JoVE Journal

Biochemistry

313 Views.  University of Cartagena. Ons werk toont een route voor de in silico creatie van gecarbonyleerde aminozuren met eindproducten van lipideperoxidatie en hoe deze in een eiwit kunnen worden opgenomen. Dit kan ons helpen begrijpen hoe deze post-translationele modificatie de structurele functie van gecarbonyleerde eiwitten kan veranderen. Programma's op basis van kunstmatige intelligentie-algoritmen zijn tegenwoordig de krachtigste rekeninstrumenten voor het voorspellen van de tertiaire structuur van eiwitten.Ze her...

Video: Auteur in de schijnwerpers: in silico creatie en impact van gecarbonyleerde aminozuren op de eiwitstructuur en -functie

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Protein alignments are commonly used to evaluate the similarity of protein residues, and the derived consensus sequence used for identifying functional units (e.g., domains). Traditional consensus-building models fail to account for interpositional dependencies &#8211; functionally required covariation of residues that tend to appear simultaneously throughout evolution and across the phylogentic tree. These relationships can reveal important clues about the processes of protein folding, thermostability, and the formation of functional sites, which in turn can be used to inform the engineering of synthetic proteins. Unfortunately, these relationships essentially form sub-motifs which cannot be predicted by simple &#8220;majority rule&#8221; or even HMM-based consensus models, and the result can be a biologically invalid &#8220;consensus&#8221; which is not only never seen in nature but is less viable than any extant protein. We have developed a visual analytics tool, StickWRLD, which creates an interactive 3D representation of a protein alignment and clearly displays covarying residues. The user has the ability to pan and zoom, as well as dynamically change the statistical threshold underlying the identification of covariants. StickWRLD has previously been successfully used to identify functionally-required covarying residues in proteins such as Adenylate Kinase and in DNA sequences such as endonuclease target sites.

Synthetic protein sequences based on consensus motifs typically ignore co-evolving residues, that imply interpositional dependencies (IPDs). IPDs can be essential to activity, and designs that disregard them may result in suboptimal results. This protocol uses StickWRLD to identify IPDs and help inform rational protein design, resulting in more efficient results.

Protein alignments are commonly used to evaluate the similarity of protein residues, and the derived consensus sequence used for identifying functional ...

Chemistry

Synthesis and Characterization of 1,2-Dithiolane Modified Self-Assembling Peptides

This report focuses on the synthesis of an N-terminus 1,2-dithiolane modified self-assembling peptide and the characterization of the resulting self-assembled supramolecular structures. The synthetic route takes advantage of solid-phase peptide synthesis with the on-resin coupling of the dithiolane precursor molecule, 3-(acetylthio)-2-(acetylthiomethyl)propanoic acid, and the microwave-assisted thioacetate deprotection of the peptide N-terminus before final cleavage from the resin to yield the 1,2-dithiolane modified peptide. After the high-performance liquid chromatography (HPLC) purification of the 1,2-dithiolane peptide, derived from the nucleating core of the A&#946; peptide associated with Alzheimer&#39;s disease, the peptide is shown to self-assemble into cross-&#946; amyloid fibers. Protocols to characterize the amyloid fibers by Fourier-transform infrared spectroscopy (FT-IR), circular dichroism spectroscopy (CD) and transmission electron microscopy (TEM) are presented. The methods of N-terminal modification with a 1,2-dithiolane moiety to well-characterized self-assembling peptides can now be explored as model systems to develop post-assembly modification strategies and explore dynamic covalent chemistry on supramolecular peptide nanofiber surfaces.

A protocol for the synthesis of a 1,2-dithiolane modified peptide and the characterization of the supramolecular structures resulting from the peptide self-assembly.

This report focuses on the synthesis of an N-terminus 1,2-dithiolane modified self-assembling peptide and the characterization of the resulting ...

Synthesis of Information-bearing Peptoids and their Sequence-directed Dynamic Covalent Self-assembly

This protocol presents the use of Lewis acidic multi-role reagents to circumvent kinetic trapping observed during the self-assembly of information-encoded oligomeric strands mediated by paired dynamic covalent interactions in a manner mimicking the thermal cycling commonly employed for the self-assembly of complementary nucleic acid sequences. Primary amine monomers bearing aldehyde and amine pendant moieties are functionalized with orthogonal protecting groups for use as dynamic covalent reactant pairs. Using a modified automated peptide synthesizer, the primary amine monomers are encoded into oligo(peptoid) strands through solid-phase submonomer synthesis. Upon purification by high-performance liquid chromatography (HPLC) and characterization by electrospray ionization mass spectrometry (ESI-MS), sequence-specific oligomers are subjected to high-loading of a Lewis acidic rare-earth metal triflate which both deprotects the aldehyde moieties and affects the reactant pair equilibrium such that strands completely dissociate. Subsequently, a fraction of the Lewis acid is extracted, enabling annealing of complementary sequence-specific strands to form information-encoded molecular ladders characterized by matrix assisted laser desorption/ionization mass spectrometry (MALDI-MS). The simple procedure outlined in this report circumvents kinetic traps commonly experienced in the field of dynamic covalent assembly and serves as a platform for the future design of robust, complex architectures.

A protocol is presented for the synthesis of information-encoded peptoid oligomers and for the sequence-directed self-assembly of these peptoids into molecular ladders using amines and aldehydes as dynamic covalent reactant pairs and Lewis acidic rare-earth metal triflates as multi-role reagents.

This protocol presents the use of Lewis acidic multi-role reagents to circumvent kinetic trapping observed during the self-assembly of information-encoded ...

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Exhaustive generation of molecular structures has numerous chemical and biochemical applications such as drug design, molecular database construction, exploration of alternative biochemistries, and many more. Mathematically speaking, these are graph generators with chemical constraints. In the field, the most efficient generator currently (MOLGEN) is a commercial product, limiting its use. Alternative to that, another molecular structure generator, MAYGEN, is a recent open-source tool with efficiency comparable to MOLGEN and the capacity for users to increase its performance by adding new features. One of the research fields that can benefit from this development is astrobiology; structure generators allow researchers to supplement experimental data with computational possibilities for alternative biochemistry. This protocol details one use case for structure generation in astrobiology, namely the generation and curation of alpha-amino acid libraries. Using open-source structure generators and cheminformatics tools, the practices described here can be implemented beyond astrobiology for the low-cost creation and curation of chemical structure libraries for any research question.

The purpose of this protocol is to efficiently generate and curate small-molecule structure libraries using open-source software.

Exhaustive generation of molecular structures has numerous chemical and biochemical applications such as drug design, molecular database construction, ...

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

The role of RNA structure in virtually any biological process has become increasingly evident, especially in the past decade. However, classical approaches to solving RNA structure, such as RNA crystallography or cryo-EM, have failed to keep up with the rapidly evolving field and the need for high-throughput solutions. Mutational profiling with sequencing using dimethyl sulfate (DMS) MaPseq is a sequencing-based approach to infer the RNA structure from a base&#39;s reactivity with DMS. DMS methylates the N1 nitrogen in adenosines and the N3 in cytosines at their Watson-Crick face when the base is unpaired. Reverse-transcribing the modified RNA with the thermostable group II intron reverse transcriptase (TGIRT-III) leads to the methylated bases being incorporated as mutations into the cDNA. When sequencing the resulting cDNA and mapping it back to a reference transcript, the relative mutation rates for each base are indicative of the base&#39;s &#34;status&#34; as paired or unpaired. Even though DMS reactivities have a high signal-to-noise ratio both in vitro and in cells, this method is sensitive to bias in the handling procedures. To reduce this bias, this paper provides a protocol for RNA treatment with DMS in cells and with in vitro transcribed RNA.

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

The protocol provides instruction for modifying RNA with dimethyl sulfate for mutational profiling experiments. It includes in vitro and in vivo probing with two alternative library preparation methods.

The role of RNA structure in virtually any biological process has become increasingly evident, especially in the past decade. However, classical ...

Predicting Amino Acid Preferences of Protein-Protein Binding Interfaces

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Many protein-protein interactions involve the binding of short protein segments to peptide-binding domains. Usually, such interactions require the recognition of linear motifs with variable conservation. The combination of highly conserved and more variable regions in the same ligands often contributes to the multispecificity of binding, a common property of enzymes and cell signaling proteins. Characterization of amino acid preferences of peptide-binding domains is important for the design of mediators of protein-protein interactions (PPIs). Computational methods are an efficient alternative to the often costly and cumbersome experimental techniques, enabling the design of potential mediators that can be later validated in downstream experiments. Here, we described a methodology using the Pepspec application of the Rosetta molecular modeling package to predict the amino acid preferences of peptide-binding domains. This methodology is useful when the structure of the receptor protein and the nature of the peptide ligand are both known or can be inferred. The methodology starts with a well-characterized anchor from the ligand, which is extended by randomly adding amino acid residues. The binding affinity of peptides generated this way is then evaluated by flexible-backbone peptide docking in order to select the peptides with the best predicted binding scores. These peptides are then used to calculate amino acid preferences and to optionally compute a position-weight matrix (PWM) that can be used in further studies. To illustrate the application of this methodology, we used the interaction between subunits of human interferon regulatory factor 5 (IRF5), previously known to be multispecific but globally guided by a short conserved motif called pLxIS. The estimated amino acid preferences were consistent with previous knowledge about the IRF5 binding surface. Positions occupied by phosphorylatable serine residues exhibited a high frequency of aspartate and glutamate, likely because their negatively charged side chains are similar to phosphoserine.

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

We describe a methodology based on sequence diversification to estimate the amino acid preferences of multispecific binding sites in protein-protein interactions (PPIs). In this strategy, thousands of potential peptide ligands are generated and screened in silico, thus overcoming some limitations of available experimental methods.

Many protein-protein interactions involve the binding of short protein segments to peptide-binding domains. Usually, such interactions require the ...

Targeting Cysteine Thiols for in Vitro Site-specific Glycosylation of Recombinant Proteins

Stromal interaction molecule-1 (STIM1) is a type-I transmembrane protein located on the endoplasmic reticulum (ER) and plasma membranes (PM). ER-resident STIM1 regulates the activity of PM Orai1 channels in a process known as store operated calcium (Ca2+) entry which is the principal Ca2+ signaling process that drives the immune response. STIM1 undergoes post-translational N-glycosylation at two luminal Asn sites within the Ca2+ sensing domain of the molecule. However, the biochemical, biophysical, and structure biological effects of N-glycosylated STIM1 were poorly understood until recently due to an inability to readily obtain high levels of homogeneous N-glycosylated protein. Here, we describe the implementation of an in vitro chemical approach which attaches glucose moieties to specific protein sites applicable to understanding the underlying effects of N-glycosylation on protein structure and mechanism. Using solution nuclear magnetic resonance spectroscopy we assess both efficiency of the modification as well as the structural consequences of the glucose attachment with a single sample. This approach can readily be adapted to study the myriad glycosylated proteins found in nature.

Targeting Cysteine Thiols for in Vitro Site-specific Glycosylation of Recombinant Proteins

Biochemical and structural analyses of glycosylated proteins require relatively large amounts of homogeneous samples. Here, we present an efficient chemical method for site-specific glycosylation of recombinant proteins purified from bacteria by targeting reactive Cys thiols.

Stromal interaction molecule-1 (STIM1) is a type-I transmembrane protein located on the endoplasmic reticulum (ER) and plasma membranes (PM). ER-resident ...

Immunology and Infection

Residue-Specific Exchange of Proline by Proline Analogs in Fluorescent Proteins: How "Molecular Surgery" of the Backbone Affects Folding and Stability

Replacement of proline (Pro) residues in proteins by the traditional site-directed mutagenesis by any of the remaining 19 canonical amino acids is often detrimental to protein folding and, in particular, chromophore maturation in green fluorescent proteins and related variants. A reasonable alternative is to manipulate the translation of the protein so that all Pro residues are replaced residue-specifically by analogs, a method known as selective pressure incorporation (SPI). The built-in chemical modifications can be used as a kind of &#34;molecular surgery&#34; to finely dissect measurable changes or even rationally manipulate different protein properties. Here, the study demonstrates the usefulness of the SPI method to study the role of prolines in the organization of the typical &#946;-barrel structure of spectral variants of the green fluorescent protein (GFP) with 10-15 prolines in their sequence: enhanced green fluorescent protein (EGFP), NowGFP, and KillerOrange. Pro residues are present in connecting sections between individual &#946;-strands and constitute the closing lids of the barrel scaffold, thus being responsible for insulation of the chromophore from water, i.e., fluorescence properties. Selective pressure incorporation experiments with (4R)-fluoroproline (R-Flp), (4S)-fluoroproline (S-Flp), 4,4-difluoroproline (Dfp), and 3,4-dehydroproline (Dhp) were performed using a proline-auxotrophic E. coli strain as expression host. We found that fluorescent proteins with S-Flp and Dhp are active (i.e., fluorescent), while the other two analogs (Dfp and R-Flp) produced dysfunctional, misfolded proteins. Inspection of UV-Vis absorption and fluorescence emission profiles showed few characteristic alterations in the proteins containing Pro analogs. Examination of the folding kinetic profiles in EGFP variants showed an accelerated refolding process in the presence of S-Flp, while the process was similar to wild-type in the protein containing Dhp. This study showcases the capacity of the SPI method to produce subtle modifications of protein residues at an atomic level (&#34;molecular surgery&#34;), which can be adopted for the study of other proteins of interest. It illustrates the outcomes of proline replacements with close chemical analogs on the folding and spectroscopic properties in the class of &#946;-barrel fluorescent proteins.

To overcome the limitations of classical site-directed mutagenesis, proline analogs with specific modifications were incorporated into several fluorescent proteins. We show how the replacement of hydrogen by fluorine or of the single by double bonds in proline residues ("molecular surgery") affects fundamental protein properties, including their folding and interaction with light.

Replacement of proline (Pro) residues in proteins by the traditional site-directed mutagenesis by any of the remaining 19 canonical amino acids is often ...

Bioengineering

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity.
To disseminate these methods for broader use we present Protein WISDOM (<a href="http://www.proteinwisdom.org" target="_blank">http://www.proteinwisdom.org</a>), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

We developed computational de novo protein design methods capable of tackling several important areas of protein design. To disseminate these methods we present Protein WISDOM, an online tool for protein design (<a href="http://www.proteinwisdom.org" target="_blank">http://www.proteinwisdom.org</a>). Starting from a structural template, design of monomeric proteins for increased stability and complexes for increased binding affinity can be performed.

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific ...

Biology

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Apoptosis is a type of programmed cell death that eliminates damaged cells and controls the development and tissue homeostasis of multicellular organisms. Caspases, a family of cysteine proteases, play a key role in apoptosis initiation and execution. The maturation of caspases and their activity is fine-tuned by post-translational modifications in a highly dynamic fashion. To assess the effect of post-translational changes, potential sites are routinely mutated with residues persistent to any modifications. For example, the serine residue is replaced with alanine or aspartic acid. However, such substitutions could alter the caspase active site's conformation, leading to disturbances in catalytic activity and cellular functions. Moreover, mutations of other amino acid residues located in critical positions could also break the structure and functions of caspases and lead to apoptosis perturbation. To avoid the difficulties of employing mutated residues, molecular modeling approaches can be readily applied to estimate the potential effect of amino acid substitutions on caspase structure. The present protocol allows the modeling of both the wild-type caspase and its mutant forms with the biomolecular simulation package (Amber) and supercomputer facilities to test the effect of mutations on the protein structure and function.

The present protocol uses a biomolecular simulation package and describes the molecular dynamics (MD) approach for modeling the wild-type caspase and its mutant forms. The MD method allows for assessing the dynamic evolution of the caspase structure and the potential effect of mutations or post-translational modifications.

Apoptosis is a type of programmed cell death that eliminates damaged cells and controls the development and tissue homeostasis of multicellular organisms. ...

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

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