Patent AU-2021103292-A4 (AU)
Title: Computational biology approaches towards development of substrate based drug for colerectal cancer
Inventor: ELENGOE ASITA
IPC: G16B5/00
Filed: 2021-06-11
Granted: 2022-03-24
Abstract: COMPUTATIONAL BIOLOGY APPROACHES 
TOWARDS DEVELOPMENT OF SUBSTRATE BASED 
DRUG FOR COLERECTAL CANCER 
ABSTRACT 
Worldwide, colorectal cancer is one of the leading causes of cancer-related deaths. In Malaysia, 
colorectal cancer was the most common cancer in males and the second most common cancer in 
females. The exact reason for what causes colorectal cancer is not known. Thus, there is a need to 
developing new approaches for colon cancer treatment. Recent developments in high-throughput 
biotechnologies for genomes, proteomes and transcriptomes require the advancement of novel 
computational approaches for a better understanding of the mechanisms of cancer. The main 
computational method used in cancer treatment is known as structure-based drug design (SBDD).  
The three-dimensional (3-D) models of the target proteins or colorectal cancer cell proteins 
(adenomatous polyposis coli (APC), epidermal growth factor receptor (EGFR) and MSH2) were 
built, stimulated and their novel interaction with plant compounds (allicin, gingerol, catechin, 
ascorbate, quercetin, pentamethyl ether, tyrosol (HT), indicaxanthin and naringenin) was studied.  
Swiss model was used to generate the 3-D structure of protein models which were not available 
in Protein Data Bank (PDB). Then, physiochemical characterisation of the proteins was 
determined by the ExPASy's ProtParam Proteomics server while the secondary structure analysis 
was carried out using the Self-Optimized Prediction Method from Alignment (SOPMA). After 
that, the target protein models were validated using PROCHECK, ProQ, ERRAT and Verify 3D 
programs. Lastly, the protein was docked successfully with allicin, gingerol, catechin, ascorbate, 
quercetin, pentamethyl ether, tyrosol (HT), indicaxanthin and naringenin) using the SwissDock 
server and visualised with PyMol software. The best binding affinity was selected based on the 
lowest binding energy and highest number of hydrogen bonds between the target protein and 
phyto-component. The results revealed that the generated target protein models were stable after 
the validation process. The target protein-phyto-compound complexes (allicin-APC, ascorbate
EGFR and ascorbate-MSH2) had the best binding energy among all the protein-phyto-compound 
complexes which were -7.56, -7.83 and -7.86 respectively. The novel binding interaction between 
the target protein and phyto-component was identified. This current study of the target protein
phyto-compound-complex interaction will aid in designing new clinical medications for 
colorectal cancer patients.  
Keywords: Adenomatous polyposis coli (APC); Epidermal growth factor receptor (EGFR); 
MSH2); Homology modelling; Docking 
1
Table 1: Docking results of target protein and phyto-component 
Target protein Phyto-component Binding energy 
Allicin -7.56 
Gingerol -7.54 
Catechin -7.29 
Ascorbate -7.43 
Quercetrin Pentamethyl Ether -6.98 
Tyrosol (HT) -6.44 
Indicaxanthin -7.05 
Naringenin -7.11 
Allicin -6.64 
Gingerol -7.37 
Catechin -7.20 
Ascorbate -7.83 
Quercetrin Pentamethyl Ether -7.53 
Tyrosol (HT) -6.38 
Indicaxanthin -7.55 
Naringenin -6.86 
Allicin -6.55 
Gingerol -7.50 
Catechin -7.21 
Ascorbate -7.86 
Quercetrin Pentamethyl Ether -7.62 
Tyrosol (HT) -6.54 
Indicaxanthin -6.12 
Naringenin -5.98 
2