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The Ras Gene

The Ras Gene

The Ras-gene-encoded proteins are regulators of signaling pathways controlling cell proliferation, differentiation, or cell survival. The Ras-gene family in humans constitutes three primary members—the HRas, NRas, and KRas. These genes code for four functionally distinct yet closely related proteins—the HRas, NRas, KRas4A, and KRas4B. The involvement of mutant Ras genes in human cancer was first discovered in 1982 and is among the most common causes of human tumorigenesis.
Ras is a...

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Related Experiment Video

Updated: Nov 19, 2025

Fully Processed Recombinant KRAS4b: Isolating and Characterizing the Farnesylated and Methylated Protein

Fully Processed Recombinant KRAS4b: Isolating and Characterizing the Farnesylated and Methylated Protein

Published on: January 16, 2020

Bringing Structural Implications and Deep Learning-Based Drug Identification for KRAS Mutants.

Aamir Mehmood^1,2, Aman Chandra Kaushik³, Qiankun Wang¹

¹Department of Bioinformatics and Biostatistics, State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.

Journal of Chemical Information and Modeling

|January 29, 2021

Summary

This summary is machine-generated.

KRAS codon 61 mutations significantly impact colorectal cancer protein stability. This study identifies detrimental mutations and proposes novel compounds with improved affinity for potential therapeutic strategies.

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Area of Science:

Oncology
Molecular Biology
Computational Chemistry

Background:

Colorectal cancer (CRC) is a leading cause of cancer death.
Kirsten Rat Sarcoma (KRAS) mutations, particularly at codons 13 and 61, are frequently observed in CRC.
Understanding the structural and dynamic impact of KRAS mutations is crucial for developing targeted therapies.

Purpose of the Study:

To identify clinically significant KRAS codon 61 mutations in colorectal cancer.
To analyze the effects of these mutations on KRAS protein structural dynamics.
To discover novel compounds with enhanced binding affinity to mutated KRAS using a deep-learning approach.

Main Methods:

Utilized public databases (The Cancer Genome Atlas, Genomic Data Commons) for mutation data acquisition.
Performed genomic alteration landscape analysis, survival analysis, and systems biology-based kinetic simulations.
Conducted 100 ns molecular dynamics simulations for seven shortlisted codon 61 mutations.
Employed a deep-learning approach for compound similarity search and docking studies.

Main Results:

Identified seven clinically relevant KRAS codon 61 mutations.
Molecular dynamics simulations revealed significant deviations in protein structure and stability for these mutations.
Deep learning predicted compounds demonstrated superior affinity and docking scores compared to existing drugs.
Mutations in the SII region were found to be highly detrimental to KRAS conformation and stability.

Conclusions:

KRAS codon 61 mutations, especially in the SII region, critically affect protein stability and function in colorectal cancer.
The identified novel compounds show promise for improved therapeutic efficacy against KRAS-mutated CRC.
Further evaluation of these compounds is recommended for clinical application in treating KRAS-driven colorectal cancers.