Capstone
CHAPTER 3
Research Methodology
Research Design
The researchers will be utilizing a pre-experimental design, specifically a static group comparison, in conducting this study. According to George Choueiry (n.d.), in a static group comparison design, the outcome of interest is measured only once; and after exposing the experimental group to treatment, it will then be compared to the control group. In this study, static group comparison is fitted as a design considering that the researchers will establish the phytochemicals that are known to target the disease proteins within the disease pathway of psoriasis as a control group, which will be a basis for comparison with the phytochemicals of Lagundi that interacts with psoriasis. The results of the study can be used for true experiments, such as in vitro or in vivo studies in the future.
Materials & Methods
This study will utilize the 5-step general workflow adapted from the paper titled “Visualizing Phytochemical-Protein Interaction Networks: Momordica charantia and Colorectal Cancer” of Briones et al. (2021) with the focus on Vitex negundo or Chinese chaste tree, most commonly known locally as Lagundi, known for its efficacy as an anti-inflammatory and anti-respiratory medicine against psoriasis as the targeted disease.
1 / Phytochemical Compilation
The phytochemicals used in this study will be sourced from IMPPAT 2.0, a database containing 17,967 phytochemicals and 1,095 medicinal applications of Indian Medicinal Plants. Positive controls or existing drugs for the targeted condition will be obtained from KEGG, a database that connects gene functions with contextual information. The phytochemicals and controls will be categorized based on their classes using ChEMBL or MeSH, as compiled by PubChem and listed in the IMPPAT profile of each phytochemical. This list of phytochemicals will be referred to as the PC list.
2 / Reverse Screening
The phytochemicals and positive controls from the "PC list" will be entered into reverse screening to obtain Phytochemical-Protein Interaction (PCPI) using SwissTargetPrediction. Simplified Molecular Input Line Entry System (SMILES) is a chemical notation that allows a user to represent a chemical structure in a way that can be used by the computer. Using the SMILES code, the "PC list" will be entered into the program. The program will then run and create a CSV file as the output. The file included the probability scores, the target protein, the target class, and more pieces of information like the ChEMBL and UniProt IDs of each target. The likelihood scores range from 0 to 1, with 1 indicating that the phytochemical tested is also a known ligand for the protein in the ChEMBL database. Only nonzero probability scores will be considered for the "PCPI list".
3 / Network Building
This study focuses on exploring the protein interactions related to psoriasis. It begins by selecting protein targets from the "Complete PCPIs" list and conducting pathway enrichment analysis. After choosing a specific disease, the protein-to-protein interactions are further examined using the g:Profiler and KEGG database. To analyze the disease network, certain interactions are processed through the PCPI-SIGNOR database. The subcellular locations of the proteins are determined using the HeLa spatial proteome database, with UniProt used as an alternative if HeLa data is unavailable. Phytochemicals and controls with target proteins in specific subcellular locations are identified. The pharmacokinetic and physicochemical properties of these compounds are evaluated using the SwissADME program. The target proteins in the PCPI list undergo overrepresentation analysis using g:Profiler's g:GOSt tool with a 0.05 threshold. The results are then compiled into a CSV file, which includes information such as query size, effective domain size, and intersections. The proteins found in the intersection column are used to create the "Disease-specific PCPI list." Each entry in this list is annotated with specific protein-protein interactions from the SIGNOR 2.0 database. The source and target nodes are appropriately labeled, resulting in the creation of the "PCPI-SIGNOR list." The subcellular locations of the entries in this list are determined using the HeLa spatial protome database and UniProt. Phytochemicals and controls are assigned to specific subcellular sites based on their interactions with target proteins. This final version is referred to as the "Annotated PCPI-SIGNOR list."
4 / Network Visualization
Phytochemical and protein attributes are visualized. Networks are mapped and connected using Cytoscape for visualization. The "Annotated PCPI-SIGNOR list" will then be visualized in Cytoscape. Each of the qualities and properties specified in the file will determine the specifications of the nodes and edges.
5 / Evaluation
The “PCPI-SIGNOR disease network visualization” is analyzed and notable PCPIs are evaluated in silico. The AutoDock Vina will be used for the evaluation of the results through ChimeraX.
Workflow Process
Vitex negundo
Data Analysis
Since the researchers aim to determine the significant difference between the established control group, which will be assessed during the duration of data collection, and the experimental group in terms of:
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Docking scores
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Bioavailability
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Gastrointestinal absorption
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Lipophilicity
The researchers will be using the Student’s t-test for the comparison of data. The Student's t-test is frequently used in statistical procedures to compare the means of groups for a certain variable (Al Achi., 2019). In this study, the researchers will analyze the data using Student’s t-test since the means of both the control group and experimental group are statistically independent. The researchers would like to indicate that the in silico methodology for the whole study requires analysis for data interpretation that would come after doing the 5th step, evaluation, on the 5-step general workflow adapted from the paper titled “Visualizing Phytochemical-Protein Interaction Networks: Momordica charantia and Colorectal Cancer” of Briones et al. (2021). The researchers will set a p-value of 0.05 as a means to determine if the established hypothesis is statistically significant.