Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

This research aims to generate the structural models of proteins with antimicrobial functions. These models are used to perform specific analyses that help determine the usefulness of each protein as a treatment before in vitro testing. Currently, research in this field relies on in silico methods for the validation of design proteins. However, they have not established tools specifically developed to support this part of a process. Our future research will focus on the biological network inference and the microbiota interaction between the host and environment on multi-layered networks. This work will be supported by ML bionets and our package developed by colleagues in the laboratory.

[Narrator] To begin visit the I-TASSER server for protein structure and function prediction. Submit the molecular target or design sequence as a FASTA format file, a text file, or by pasting it directly into the input field. Assign a unique name to the sequence and click run I-TASSER to allow the program to analyze the sequence. To predict the 3D structure using trRosetta, access the trRosetta server. Enter the target receptor sequence as a FASTA format file, a text file, an MSA format file, or paste it directly into the input field on the server. After registering using an institutional email, assign a name to the structurally predicted protein. Ensure the option to exclude templates is selected and choose run trRosettaX-Single to exclude the use of any homologous sequences and templates. Click submit to initiate the protein structure prediction process. In the prediction result, verify that the TM score is a measure of model quality. Then examine contact maps, distance maps per amino acid, and rotation maps for alpha and beta carbons at angles omega, theta, and phi. Open a web browser and navigate to the HADDOCK web server. Click on submit a new job, then enter a job name and the number of molecules. Upload the PDB structures of the molecules for docking. Leave the default settings unchanged, and click on Next. Enter active and passive amino acid residues for both molecule one and molecule two, and click on next. In the docking parameters section, leave the default settings for all the parameters, such as distance restraints, sampling parameters, clustering parameters, et cetera, unchanged and click on submit to start the docking process. Open the results page and review the docking results. After downloading the ligand receptor docking file, open a structural visualization software such as UCSF Chimera or PyMOL. Upload the docking file and visualize the structure model. The trRosetta model of the sodium-hydrogen antiporter revealed a highly ordered tertiary structure composed primarily of alpha-helices forming a tightly packed transmembrane bundle. This suggests the protein's functional role as a membrane-embedded ion transporter. The model's reliability is supported by high local difference test values above 80% across the central region, with reduced confidence at both termini indicating possible flexibility. Contact and distance maps confirm stable folding with consistent diagonal and clustered patterns. Molecular docking of the designed antifungal peptide into the receptor's extracellular domain results in a stable complex, as evidenced by a favorable HADDOCK score of -73 and a low root mean square deviation of 0.7 Angstroms. Measured distances confirm close contacts between specific amino acids, with the shortest observed between tyrosine 407 and cysteine 13.