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

We propose a protocol for the computational prediction of amino acid preferences in more specific protein-protein interactions. This protocol can be considered as a first step in the design of mediators of these interactions. We are interested in using these mediators as potential inhibitors of specific interactions, in immunology technologist.

Our implementer used for characterizing amino acid preferences among the specific binding sites are expensive and cumbersome. Our protocol is a bio-backed technology based on efficiency and series operations. This strategy has the potential to process a high number of line sequences, providing complete and consistent difference of amino acid preferences.

Our protocol offer a professional cost-effective by processing a large number of DNA sequences preferred over a more complete from the typically limited number of sequences, processes in web lab approaches. In addition to the development of inhibitors of immunological products, we would like to compare the performance of this methodology with other product families. This will allow us to better understand the structural basis for multi specificity, not only in immunological context, but also in other cellular functions, such as signaling and communication.

Begin by downloading the structure of the protein-protein complex. For this, navigate to the protein data bank homepage and enter the PDB ID in the main search box. On the main page for the structure, click on Download Files, and then on Biological Assembly 1 to download the files in PDB gz format.

Open the downloaded structure in UCSF Chimera. Navigate to Tools, then Structure Editing, and click on Change Chain IDs. Rename the second chain initially labeled as A, to B.Then, click on Favorites, followed by Model Panel.

Select the model with the two chains and click on the group/ungroup button to separate each chain into a different model. Next, select the two models and click on the copy/combine button. Enter a new name for the combined model.

Check Close source models, and click OK.Click on Select, then Chain and confirm that the chains in the dimer are now identified as A and B.Click on File and save PDB to save the edited structure as a new PDB file. For identifying the target segment in the ligand protein, navigate to the BUDE Alanine Scan server. Click on the Choose File button under Structure Upload and upload the saved PDB file.

On the next page, check that the structure was correctly loaded, and enter a name for the job in the server. Set chains A as a Receptor, and B as the Ligand, and click on the Start Scan button to submit the job. Once the job is finished, click on Show Results to open the Results page.

From the Residue list, select the stretch of residues predicted to better interact with the target binding surface. Using this protocol and amino acid segment, interacting with IRF5 binding surface, was predicted. Using computational alanine scanning mutagenesis, a 13 amino acid segment was predicted from positions 424 to 436, with the p L x IS motif starting at arginine 432.

To begin, prepare the protein peptide interface for sequence diversification. Open the PDB file in Chimera and ensure the structure of the target subunits is intact with no missing atoms or bonds. For removing all non-essential molecules from the structure click on Select then Residues, and then select all molecules other than standard amino acids.

Then click on Actions followed by Adams/Bonds, and delete. Then, click on Favorites, in Sequence, and then click on the chain considered as the ligand. Crop the ligand chain to the identified interacting segment by deleting all the residues except those between the selected positions.

Click on File and save PDB to save the edited structure to a different PDB file. Copy this file to a Linux location accessible by the Rosetta applications. Use Rosetta's fixedbb application to perform a repack of all the amino acid side chains of the base structure before sequence diversification, by running this command.

Then rename the repack PDB file with a _ repack suffix using the following command. Next, run pepspec in design mode to perform sequence diversification using this command. Then, generate a pwm using the gen pepspec pwm.

py script included in the Rosetta suite. To run this script, use the following command. To create a sequence logo, open the file with the peptide sequences generated in the previous step with a preferred text editor and copy all the sequences.

Navigate to the WebLogo server and paste the sequences in the Multiple Sequence Alignment text box. Choose a desired format and size of the logo according to the input length, and click on Create Logo. Using this protocol, the amino acid preferences were predicted for the conserved p L x IS motif in IRF5 binding surface.

Position, weight matrix, and sequence logo generated upon sequence diversification showed a preference for glutamate at position 432, and for leucine and isoleucine at positions 433 and 435. Positions 427, 429, and 436, typically occupied by Serine showed a higher preference for aspartate and glutamate, highlighting the role of phosphorylation and IRF5 dimerization. Position 425 showed a high preference for Serine, suggesting its involvement in protein-protein interaction in its unphosphorylated form.