Bioinformatics Approaches for Molecular Characterization of CT670 Hypothetical Protein of Chlamydia pneumoniae ^†

Abstract

Researchers have linked Chlamydia pneumoniae (C. pneumoniae), a type of bacteria that cannot survive outside of cells and is resistant to Gram staining, to many autoimmune diseases. People hypothesized that C. pneumoniae had a harmful function due to its tendency to inhabit human endothelium and epithelial tissue. This study implemented multiple bioinformatics tools and databases to understand the possible function of the CT670 hypothetical protein of C. pneumoniae. The physicochemical parameters showed the protein’s half-life in different media. These parameters also displayed the protein’s theoretical isoelectric point, aliphatic index, GRAVY value, extinction coefficient, instability index, and the amino acids and atoms that it comprises. Amino acid composition measured the percentage of amino acids present in the selected protein, with glutamate demonstrated as the greatest proportion. Moreover, hydrogen was the most abundant ratio in terms of the atomic composition of the protein, followed by carbon, oxygen, nitrogen, and sulfur. The PPI networks reveal its potential primary and secondary interactions with other proteins. We modeled and assessed the secondary and tertiary structures to understand the nature of the selected protein. Computational functional analysis predicted that the protein would be a chaperone effector. By designing and developing drugs and vaccines, we can use this protein as a target for further analysis to combat diseases caused by C. pneumoniae.

1. Introduction

C. pneumoniae, a kind of bacteria that cannot survive outside of cells and has a negative reaction to the Gram stain, is a common respiratory infection. It is often responsible for respiratory disorders in humans, such as pneumonia [1]. Despite the complete sequencing of the C. pneumoniae genome, there is still a lack of knowledge about the processes of acute infection, target cell activation, and the discovery of possible chlamydial virulence factors. Interestingly, several groups of patients with atherosclerosis have shown that the existing antibiotic treatments for acute chlamydial infection are ineffective in achieving positive clinical outcomes [2].

However, in addition to respiratory infections, C. pneumoniae also plays a role in the development of various inflammatory conditions, including asthma, COPD, lung cancer, neurological disorders like multiple sclerosis, Alzheimer’s disease, and schizophrenia, as well as arthritis and atherosclerosis. Therefore, the healthcare professional must possess the ability to immediately identify, assess, and manage this disease in order to prevent any associated problems [3,4,5].

2. Methods

2.1. Sequence Retrieval

The amino acid sequence of the CT670 protein of C. pneumoniae was collected from the NCBI protein database with the accession number BAA88656 (version number: BAA88656.1) [6].

2.2. Physicochemical Properties

The ProtParam web-based tool was used for the determination of the physicochemical properties of the CT670 protein with default parameters [7].

2.3. Protein–Protein Interaction (PPI) and Functional Analysis

The PPI was determined by utilizing the STRING database (v.12) [8] and visualized by Cytoscape software (v.3.10.2) [9]. Functional analysis was performed using the CD-Search tool of the NCBI [10].

2.4. Secondary and Tertiary Structural Assessment

SOPMA web-based software was used with default options (width for output: 70; similarity threshold: 8; and window width: 17) to determine the secondary structural parameters [11]. The 3D structure was predicted using the Swiss-Model server [12]. In addition, the Swiss-Model’s assessment tool and the ProSA-Web were implemented to verify the modeled structure of the protein [12,13,14].

3. Results and Discussion

3.1. Protein Sequence Retrieval and Physicochemical Properties Determination

Protein sequence analysis is a fundamental and essential approach for evaluating biological information. Given the significance of proteins, protein sequence benchmarks play a particularly vital role [15,16]. The obtained sequence is consisted of 168 amino acids retrieved in FASTA format from the NCBI database (GenBank accession: BAA88656.1). It located to the locus BAA88656 of C. pneumoniae [6].

The chemical composition of the constituent amino acids determines the physicochemical characteristics of proteins [15]. The ProtParam anticipated the protein’s molecular weight of 19,882.89 Dalton and the isoelectric point (pI) of 8.84. There was an increased amount of protein solubility and electrophoretic separation, as measured by the isoelectric point (pI), which is greater than 7.0 [17,18]. Moreover, the amino acid composition (Figure 1a) and the atomic composition (Figure 1b) were also measured. The negatively charged residues including Asp and Glu were 38, while the positively charged ones (Arg and Lys) were 41. One way to quantify the degree to which a protein absorbs light at a given wavelength is by looking at its extinction coefficient, which is another name for its molar absorption or absorption coefficient. For every given protein molecule, there is an individual extinction coefficient [19]. The extinction coefficient of the selected protein was anticipated as 9970 M⁻¹ cm⁻¹. Additionally, the estimated half-lives were 30 h (mammalian reticulocytes, in vitro), >20 h (yeast, in vivo), and >10 h (Escherichia coli, in vivo). In a laboratory setting, one can evaluate proteins for their potential stability using the instability index [20]. The predicted instability index of the targeted protein was 62.24. The aliphatic index refers to the percentage of a protein’s total volume that its aliphatic side chains account for [21,22]. Computational methods measured the aliphatic index at 83.57. The GRAVY value is one way to quantify the overall hydrophobicity of a protein or peptide [23,24]. The GRAVY value was measured at −1.151 of the selected hypothetical protein.

3.2. Protein–Protein Interaction (PPI) and Functional Analysis

Electrostatic forces, hydrogen bonds, and hydrophobic influence are some of the interactions that make PPIs possible. PPIs are specific physical contacts between two or more protein molecules, while interactions influence metabolic processes [25,26]. While performing the PPI network (Figure 2), the STRING program demonstrated 21 nodes, 128 edges, an average node degree of 12.2, a median clustering coefficient value of 0.777, and an enrichment p-value of less than 1.0 × 10⁻¹⁶. Moreover, the CD-Search tool predicted that the protein was a YscO-like protein. Some proteins, including Chlamydia trachomatis’s CT670, have unknown cellular roles despite having genes in a type III secretion gene cluster. The structures and/or functionalities of these proteins are still unknown; however, CT670 has many similarities with the Yersinia pestis YscO protein, such as the adjacent genes, size, charge, and secondary structure. CT670 interacts with CT671, which is a potential YscP homolog. It is possible that CT670 and CT671 function together as a chaperone–effector pair [27].

3.3. Secondary and Tertiary Structural Assessment

The sequence of amino acids is the main building block. The term ‘secondary structure’ refers to the local interactions between parts of a polypeptide chain. It includes both α-helix and β-pleated sheet structures (Figure 3). Interactions between R groups are the primary drivers of tertiary structure, which is the overall three-dimensional folding [28,29]. The 3D structure was predicted based on the most suitable template following GMQE (0.95) and identity (84.43%) compared to the selected hypothetical protein. Moreover, the predicted structure was assessed by Z-score and Ramachandran plots (Figure 3).

4. Conclusions

Researchers have linked C. pneumoniae, which cannot survive outside of cells and is resistant to Gram staining. Researchers postulated that C. pneumoniae’s propensity to reside in human endothelium and epithelial tissue played a detrimental role. This study utilized various bioinformatics methods and databases to elucidate the potential functionality of the CT670 putative protein of C. pneumoniae. The physicochemical properties indicated the protein’s half-life in various media. These metrics also indicated the protein’s theoretical isoelectric point, aliphatic index, GRAVY value, extinction coefficient, instability index, and the amino acids and atoms that make up the protein. The amino acid composition quantified the relative abundance of different amino acids in the chosen protein, with glutamate shown to be the most prevalent. Furthermore, hydrogen had the highest proportion in the atomic makeup of the protein, with carbon, oxygen, nitrogen, and sulfur following in descending order. The PPI networks demonstrated the possibility for both primary and secondary interactions with other proteins. We employed computational modeling techniques to analyze and evaluate the secondary and tertiary structures of the chosen protein in order to gain insights into its characteristics. Computational functional analysis anticipated that the protein would serve as part of a chaperone–effector pair. Through the process of designing and developing medications and vaccines, we can utilize this protein as a specific target for in-depth examination in order to treat diseases caused by C. pneumoniae.