Structural bioinformatics is a field that combines computer science, mathematics, and biology to analyze and predict the 3D structures of biological molecules, such as proteins and RNA to understand their functions, interactions, and roles in diseases.

Key Concepts:

• Protein Structure Prediction
• Homology Modeling
• Molecular Docking
• Structural Alignment

Importance:

• Drug Discovery and Development: Structural bioinformatics helps predict how a potential drug will bind to a target protein, aiding in the design of effective drugs.
• Understanding Disease Mechanisms and Protein Function: By analyzing the structure of a protein, researchers can predict its function and role in various biological processes, and understand how genetic mutations affect protein structure and function, leading to disease.
• Synthetic Biology and Biotechnology: By designing new proteins with specific functions, researchers can create novel biological pathways and organisms, and develop new bioproducts, such as biofuels, and improve existing ones, such as vaccines and gene therapies.
• Gene Therapy, Vaccine Development, and Biocatalysis: Structural bioinformatics aids in the design of effective vaccines, gene therapies, and biocatalysts by predicting the structure and function of proteins involved in these processes.

Note:

Software's Used: For modeling and analyzing protein structures, I used I-TASSER for predicting 3D structures from amino acid sequences, ChimeraX1.8 for visualization, structural analysis, and final designing, and RasMol for identifying and highlighting mutation positions. Each software played a distinct role in ensuring accurate and comprehensive protein analysis.

Data Sources: The sequence data was downloaded in FASTA (.fasta) format from UniProt and PDB, while mutation information for KRAS and TP53 was sourced from The Cancer Genome Atlas (TCGA). These reliable databases provided high-quality sequences and mutation data essential for precise modeling and analysis.

Preparation Process: In the preparation phase, the downloaded sequences were first submitted to I-TASSER to generate predicted models with secondary and tertiary structure information, along with confidence scores. These models were then imported into ChimeraX1.8 for detailed analysis, focusing on highlighting ligands, secondary and tertiary structures, and ions, as well as identifying amino acid numbers and the total number of atoms. For the mutation landscape, sequences were submitted to I-TASSER, and mutation positions derived from TCGA data were identified and mapped in RasMol using distinct color codes to effectively visualize potential mutation sites. This integrated workflow ensured accurate modeling, detailed analysis, and clear representation of mutation patterns across the selected proteins.

Section 1: Cancer-Related Proteins