|Katherine1216 aa082d2e53||1 week ago|
|Machine Learning Data||1 week ago|
|System Biology Data||1 week ago|
|README||2 weeks ago|
|README.md||1 week ago|
College of Life Sciences and Biotechnology, The State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University;
Wuxi School of Medicine, Jiangnan University, Wuxi, China
The Colorectal cancer is considered as one of the leading causes of death that is linked with the KRAS harboring codon 13 and
codon 61 mutations. The drive of the current work is to search for clinically important codon 61 mutations and analyze how they affect the
protein structural dynamics. Additionally, Deep-learning and neural-network approaches are practiced to scan for potential compounds that
might have a comparatively better affinity. Public databases like Cancer Genome Atlas (TCGA) and Genomic Data Commons (GDC) were accessed
for obtaining the data regarding mutations that are associated with colon cancer. Multiple analysis such as genomic alteration landscape,
survival analysis, and systems-biology based kinetic simulations were carried out to predict dynamic changes for the chosen mutations.
A molecular dynamics simulation of 100 ns for all the seven shortlisted codon 61 mutations have been conducted that revealed noticeable
deviations. Finally, the predicted compounds via a machine learning-based approach were docked with the KRAS 3D conformer and it was
observed that the proposed compounds have better affinities and docking score as compared to the already existing recommended drugs.
Taking together the outcomes of systems biology and molecular dynamics, it is observed that the reported mutations in SII region are
highly deleterious as they have an immense impact the protein sensitive sites native conformation and overall stability. The drugs
reported in this study also has significant performance and are encouraged to be used for further evaluation regarding the situation
that ascends as a result of KRAS mutations.
The background data for the Systems Biology and Machine Learning Scripts are provided in separate folders.
The integrase.cv is a huge file and is thus made accessible at https://drive.google.com/open?id=1-lwMZqWrLVS9UNae7Qoe9VGMJ0HXpZis.
In case of any inconvinience, please feel free to reach us at firstname.lastname@example.org.
Run data set (as a demo)
Download integrase.csv file (availabe at https://drive.google.com/open?id=1-lwMZqWrLVS9UNae7Qoe9VGMJ0HXpZis)
In the Machine Learning Data folder,
Run the DML Training script (Python).py
Open CMD and input:
cd .\Machine Learning Data\Your_Folder
python -u Script.py > Example.out