HOMOLOGY MODELLING AND ANALYSIS OF PROTEIN DRUG TARGETS FOR ERYSIPELOTHRIX RHUSIOPATHIAE A BACTERIAL PATHOGEN CAUSING SWINE ERYSIPELAS

HOMOLOGY MODELLING AND ANALYSIS OF PROTEIN DRUG TARGETS FOR ERYSIPELOTHRIX RHUSIOPATHIAE A BACTERIAL PATHOGEN CAUSING SWINE ERYSIPELAS

Annapoorna Harikumar, C. Khushi, S. Manaswini, Medha M. Savkur, K. Rakshith Srivatsa, Sanmitha Narayan and Lokesh Ravi *

Department of Botany, St. Joseph’s College (Autonomous), Bengaluru, Karnataka, India.

ABSTRACT: Aim of this study was to generate 3D models of protein drug targets in Erysipelothrix rhusiopathiae by homology modelling. E. rhusiopathiae causes swine erysipelas disease that has great economic impact on the pork industry. Bioinformatic databases such as Uniprot KB, Drug Bank, PMDB and online tools such as BLASTp, SWISS Model and Ramachandran plot analysis and software such as Autodock4 and Pymol were used to perform this study. Among 4153 proteins reported from Erysipelothrix rhusiopathiae, 396 proteins were identified as potential drug targets. These 396 proteins were employed in homology modelling through the SWISS Model. Total of 131 homology structures with the Ramachandran favorable score above 95% were considered as reliable drug targets and were submitted to PMDB online database. The modelled proteins were subjected to protein-ligand docking analysis with standard antibiotics such as Ribostamycin, Cefalotin, Pefloxacin, Penicillamine, Artenimol, Cycloserine against their respective drug targets. Among these antibiotics Ribostamycin was identified as a potent drug against the Protein Disulphide Isomerase with a significant binding energy of -7.35Kcal/mol with formation of 5 hydrogen bonds. The docking results suggest that the infection of Erysipelothrix rhusiopathiae could be treated with Ribostamycin antibiotic. The homology model of the proteins generated in this study can be exploited in further research using computational drug discovery and design to accelerate the research on disease management and pathogen control of Erysipelothrix rhusiopathiae induced swine erysipelas.

Keywords: Homology modelling, Erysipelothrix rhusiopathiae, Protein Drug Targets, Swine erysipelas

INTRODUCTION: The pork sector has immensely contributed to the farming and agriculture economy. Development in the swine industry is mainly because of advancements in genetics and breeding, improved farming techniques and better management practices About one-third of meat consumed in the world is pork ¹. Pig meat production was around 117 million tonnes in 2015 and is expected to reach 127 million tonnes in 2025, growing at 1.4% ². Asia, Europe, and North America contribute 80% towards pig meat production ¹.

China is the largest pork producer and contributes about 50% of the world's pork production ¹. The second-largest producer of pork is the European Union, and it is the largest exporter ². Erysipelothrix rhusiopathiae is a rod-shaped Gram-positive bacterium.

It is responsible for causing erysipelas in a wide range of vertebrate animals. The bacteria is zoonotic and hence can cause severe outbreaks ³. Koch first isolated the bacteria in 1876, and he described the organism as ‘bacillus of mouse septicaemia’ and was named E. muriseptica as he inoculated it from a mouse’s putrefied blood. Loeffer was the one to provide a detailed report on the bacteria and the infection caused by it. He studied the cutaneous blood from a pig that died due to erysipelas in 1882. The disease erysipelas was first confused with anthrax, but with further studies, the causative organism was found to be a bacillus rather than a streptococcus. The presence of erysipelas was revealed in the United States due to the efforts of Theobold Smith while he was working for the Bureau of Animal Industry before the 1900s. In 1921 G.T Screech established the relationship between Diamond skin disease, a clinical form of erysipelas, and Erysipelothrix rhusiopathiae ⁴.

The organism Erysipelothrix rhusiopathiae causes infection in the absence of specific antibodies and by evading the phagocytotic cells. Even on being phagocytised this pathogen can replicate intracellularly within these cells. This property demonstrates the capacity of the bacteria to survive intracellularly ⁵.

The Erysipilothrix rhusiopathiae usually enters the host through contaminated food. It was found that approximately 30-50% of healthy swine carry these organisms. These organisms are usually found in the tonsils and other lymphoid tissues of the alimentary canal of the host organism ⁶.

Erysipelothrix rhusiopathiae is a Gram-positive, non-spore-forming, non-acid fast, rod-shaped bacterium. The bacteria have a capsule that contributes to their virulence. The capsule polysaccharide forms the major non-protein antigen. There are various enzymes involved in the pathogenicity of the bacteria; neuraminidase is one such enzyme produced by the organism that causes the release of terminal sialic acid residues from glycoproteins, glycolipid, oligosaccharides of the host cell. The organism also produces hyaluronidase, a spreading factor that contributes to the bacteria's spread into tissues ⁶. The spread of Erysipilothrix rhusiopathiae is very difficult to control as the bacteria are zoonotic and can spread to other animals and humans (occupational hazard). Even though the bacteria affects almost all the vertebrates, the study of the bacteria is not as widespread as estimated, and very little information is available on the bacteria, it's structure and its pathogenicity. Bioinformatics conceptualizes biology in terms of molecules and utilizes informatics techniques to understand and organize these molecules. The aim for bioinformatics is to (i) To organize data to make it easier for researchers to access information and submit new information (ii) To develop tools and resources that help in the analysis of data (iii) To analyze and interpret data in a biologically meaningful manner. Homology modelling allows valuable insights into the molecular basis of protein function; here we are using it to identify drug targets that will help in developing a potential cure for the disease. The SWISS-MODEL Automated is one of the homology modelling tools which develops automated protein models for a given template or amino acid sequence ^{7, 8}.

MATERIALS & METHODS:

NCBI Database: This website was looked upon to derive the known data regarding the organism of interest. A detailed search on the organism disclosed the available data from several databases. (https://www.ncbi.nlm.nih.gov/)

Sequence Retrieval: A consolidated, publicly accessible online website was referred to get the preferred amino acid sequences for further research ⁹. The name of the organism was used as the main criteria which could retrieve the best sequences, which were later sorted according to the length of the amino acid chain and were downloaded in a specific format called fasta. The sequences were checked and verified for duplicates. Duplicates were deleted to avoid confusion, and downloaded sequences were sorted along with their respective Uniprot IDs. (www.uniprot.org) ⁹.

Sequence Alignment: The amino acid sequences or query sequences procured from the UNIPROT (www.uniprot.org) were all collated with the BLASTp server, which contrasts the query sequence with the pre-existing protein sequences in the Protein Data Bank to obtain a likeness in percentages. The first BLAST search is performed to get resemblance values with pre-existing sequences. The second BLAST search follows this to get similar values with sequences present in the Protein Data Bank (www.rcsb.org). The similarity approximation obtained in percentages was noted down for further study ¹⁰.

Structural Prediction: The 3D structure of the desired drug target proteins are predicted through homology modelling technique, using the online tool Swiss-Model (https://swissmodel.expasy.org/). This tool utilizes respective amino acid sequences of the protein as well as the templates present in the protein databank to predict 3D protein structure. Sequences got from UNIPROT were uploaded in fasta format, and the predicted 3D models were saved in pdb file format. The developed model quality depended on the availability and percentage similarities of the templates. The Ramachandran plots were used to analyze the developed 3D models, which are the graphical plots of protein structures that confirm the precision of the predicted structure in terms of torsion angles. After completion of the analysis, the best models procured were saved in pdb file format ^11–13.

Model Analysis: The validity of the expected model was tested and executed through the Ramachandran plot given by the SWISS-MODEL. The degree angles of all the residues were anticipated to be within the Most-Favored regions of the Ramachandran plot establishing the quality of the predicted structure. The residues outside the favored regions were considered to be outliers or unfavored predictions ^{11, 13}.

Model Submission: The 3D models predicted with the preferred Ramachandran plot (favored region) were uploaded to the public database, i.e., Protein Model Data Base (PMDB) [https://bioinformatics.cineca.it/PMDB/] that keeps manually constructed protein models. The 3D models are put up in a specific format of a file called pdb, and the NR ids are generated through the BLAST search; if unavailable, the entries are manually updated, and conclusively, a unique PMDBID is generated. In the final step, interactions between protein and ligand are analyzed using PYMOL, followed by protein-ligand docking in Autodock 4, a molecular modeling simulation software ¹⁴.

RESULTS & DISCUSSION:

Pathogen and Drug Selection: The pathogen Erysipelothrix rhusiopathiae was selected for the homology modelling studies. There were no reported protein structures of the organism available in the NCBI database. However, 4153 protein sequences were reported in the UniProt KB database (www.uniprot.org). Therefore, the organism chosen was apt to build protein models. Among 4153 protein sequences, the sequences that contained non-enzymatic protein parts, such as ribosomal subunits 900 enzymatic protein sequences, were further analyzed. The protein was searched in the Drug Bank database to check if these 900 protein sequences were reported as drug targets (www.drungbank.ca). Among 900 proteins, 396 proteins were recognized as potential drug targets. These proteins were further subjected to homology modelling.

Building Homology Model: The selected 396 protein sequences were retrieved from the Uniprot database (www.uniprot.org) and were saved in fasta file format (fasta). These sequences were later subjected to known as Protein Blast (Blastp) (https://blast.ncbi.nlm.nih.gov/) to search within the Protein Data Bank (www.rcsb.org) to recognize the templates for Homology Modelling. Protein structures with more than 80% sequence similarity were selected as the ideal template. All 396 had significant sequence similarities, with more than 80% match with existing protein entries on the Protein Data Bank website. The 396 protein sequences were further subjected to building computational homology models using these identified similarity structures. Homology models were constructed using the online web tool SWISS Model (https://swissmodel.expasy.org/). The web tool could construct multiple models for each protein. Among the protein models, the best one was selected based on the sequence coverage (Sequence Identity), and Ramachandran plot analysis, and the Ramachandran favoured region was noted down.

Ramachandran Plot Analysis: The quality of the protein models was evaluated based on their Ramachandran plots.

This analysis was done for 396 drug targets. The models were built using the SwissModel tool. Of the models generated, the one with its highest number of residues lying in the most preferred regions on the Ramachandran plot was selected. This process was done for each protein. 131 sequences with Ramachandran scores above 95% were considered drug targets and submitted to PMDB. The plots of the proteins with the least confident score (80.79%) ATP-dependent DNA helicase RecG and the most confident score (100.00%) ATP synthase subunit b are graphically interpreted in Fig. 1.

Submission to PMDB: A total of 396 homology models were developed through the Swiss-Model interactive workspace out of which only 131 were found favourable by Ramachandran plot analysis i.e., with a Ramachandran score above 95%.

These favoured protein sequences were submitted to Protein Model DataBase (https://bioinformatics.cineca.it/ PMDB/) for cataloging and improvements to be made to the structures.

All models were uploaded in pdb file formats with relevant details and unique PMDB IDs for references and quick searching. Table 1 shows the list of proteins submitted with their Uniprot IDs, Ramachandran scores, and PMDB IDs.

FIG. 1: RAMACHANDRAN PLOT ANALYSIS OF; [A]: LEAST FAVOURED MODEL WITH LOWEST FAVOURABLE SCORE OF 80.79% (ATP-DEPENDENT DNA HELICASE RECG); [B]: MOST FAVOURED MODEL WITH HIGHEST FAVOURABLE SCORE OF 100.00% (ATP SYNTHASE SUBUNIT B)

TABLE 1: LIST OF PROTEIN DRUG TARGETS SUBMITTED TO PMDB DATABASE

Protein-Ligand Docking: The template proteins were selected from PDB website (www.rcsb.org). The template proteins and the proteins of Erysipelothrix rhusiopathiae that were modelled in this study were subjected to protein-ligand docking with respective antibiotics that are tabulated in Table 2. Results of the docking studies indicate that antibacterial drug Ribostamycin has greater binding energy of -7.35Kcal/mol with the protein disulphide isomerase of Erysipelothrix rhusiopathiae and forms 5 hydrogen bonds with ASP-31, GLU-206, ALA-210, ASN-209 and hydrophobic interactions with ASP-29, LYS30, GLN-211, LYS-212. Ribostamycin with template protein demonstrated lower binding energy of -6.17Kcal/mol and it formed 4 hydrogen bonds with ASP-346, GLU-345, LEU-343, GLU-342, and hydrophobic interactions with GLN-341, ARG-283, and PRO-344, suggesting that Ribostamycin could be used as a potent drug to control swine erysipelas caused by Erysipelothrix rhusiopathiae. The graphical representation of protein-ligand interactions between the drug target and the antibiotics is shown in Fig. 2 and 3. The list of test antibiotics, template proteins, modelled proteins, and the binding energies are tabulated in Table 2.

FIG. 2: DOCKING INTERACTION OF ANTIBIOTIC RIBOSTAMYCIN WITH MODELLED

FIG. 3: DOCKING INTERACTION OF ANTIBIOTIC RIBOSTAMYCIN WITH TEMPLATE PROTEINS

TABLE 2: PROTEIN-LIGAND DOCKING ANALYSIS OF HOMOLOGY MODELS AND TEMPLATE PROTEINS WITH STANDARD ANTIBIOTICS

CONCLUSIONS: This study was aimed to construct 3D models of drug targets of Erysipelothrix rhusiopathiae that causes swine erysipelas by computational methods.

The 6 modelled proteins and their templates were subjected to docking with 6 different antibiotics.

This analysis suggested that Ribostamycin is a better antibiotic when compared to other antibiotics, which could help curb the infection of Erysipelothrix rhusiopathiae. The modeled proteins in this study are accessible to the scientific community in the PMDB database. These structures can be utilized in advanced research using computational methods for drug discovery and design, which aids in hastening the process of in-vitro research of this pathogen and thereby improving the economy of the pork sector.

ACKNOWLEDGEMENT: The authors are thankful to the management of St. Josephs's College (Autonomous), Bengaluru, for supporting this research work.

Funding: No funding was availed for this study.

CONFLICTS OF INTEREST: No conflict of interest for this work.

REFERENCES:

1. McGlone JJ: The future of pork production in the world: Towards sustainable, welfare-positive systems. Animals 2013; 3(2): 401–415.
2. Szűcs I and Vida V: Global tendencies in pork meat - production, trade and consumption. Appl Stud Agribus Commer 2017; 11(3–4): 105–111.
3. Brooke CJ and Riley TV: Erysipelothrix rhusiopathiae: Bacteriology, epidemiology and clinical manifestations of an occupational pathogen. JMM 1999; 48(9): 789–99.
4. Ugochukwu ICI, Samuel F, Orakpoghenor O, Nwobi OC, Anyaoha CO, Majesty-Alukagberie LO, Ugochukwu MO, and Ugochukwu EI: Erysipelas, the opportunistic zoonotic disease: history, epidemiology, pathology, and diagnosis a review. Comp Clin Path 2019; 28(3): 853–859.
5. Eriksson J, Brännström H, Skarin S, & Chirico H: Characterization of Erysipelothrix rhusiopathiae isolates from laying hens and poultry red mites (Dermanyssus gallinae) from an outbreak of erysipelas. Avian Pathol 2010; 39(6): 505–509.
6. Shimoji Y: Pathogenicity of Erysipelothrix rhusiopathiae: Virulence factors and protective immunity. Microbes Infect 2000; 2(8): 965–972.
7. Luscombe NM, Greenbaum D and Gerstein M: Mim01-Luscombe-What-Is-Bioinf 2001; 346–358.
8. Schwede T, Kopp J, Guex N and Peitsch MC: Swiss-Model: An automated protein homology-modeling server. Nucleic Acids Res 2003; 31(13): 3381–3385.
9. Apweiler R: “UniProt: the Universal Protein knowledgebase. Nucleic Acids Res 2004; 32: 115–119.
10. McGinnis TL & Madden S: BLAST: at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res 2004; 32.
11. Arnold T, Bordoli K, Kopp L, & Schwede J: The Swiss-Model workspace: a web-based environment for protein structure homology modeling. Bioinf 2006; 22:  195–01.
12. Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, Kiefer F, Gallo Cassarino T, Bertoni M, Bordoli L and Schwede T: Swiss-Model: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res 2014; 42.
13. Kumar A & Arya P: Ramachandran plot- A simplified approach. Pathfinder Research and Training Foundation - India 2019; 1-6.
14. Castrignanò A, de Meo T, Cozzetto PDO, Talamo D & Tramontano IG: The PMDB protein model database. Nucleic Acids Res 2006; 34.
    

    ---

    How to cite this article:

    Harikumar A, Khushi C, Manaswini S, Savkar MM, Srivatsa KR, Narayan S and Ravi L: Homology modelling and analysis of protein drug targets for Erysipelothrix rhusiopathiae a bacterial pathogen causing swine erysipelas. Int J Pharm Sci & Res 2022; 13(11): 4560-67. doi: 10.13040/IJPSR.0975-8232.13(11).4560-67.

    ---

    All © 2022 are reserved by International Journal of Pharmaceutical Sciences and Research. This Journal licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.