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https://github.com/ayax537/protein-folding-simulation-using-genetic-algorithms

The provided Python script simulates protein folding using genetic algorithms. It models the process of finding an optimal protein structure based on a predefined amino acid sequence. By employing evolutionary strategies, the script aims to minimize the energy of the protein structure.
https://github.com/ayax537/protein-folding-simulation-using-genetic-algorithms

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The provided Python script simulates protein folding using genetic algorithms. It models the process of finding an optimal protein structure based on a predefined amino acid sequence. By employing evolutionary strategies, the script aims to minimize the energy of the protein structure.

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# Protein-Folding-Simulation-Using-Genetic-Algorithms

Protein Folding Simulation Using Genetic Algorithms

# Description

The provided Python script simulates protein folding using genetic algorithms. It models the process of finding an optimal protein structure based on a predefined amino acid sequence. By employing evolutionary strategies, the script aims to minimize the energy of the protein structure, which is crucial for understanding protein behavior and function.



# Key Features

1. Amino Acid Sequence Generation

The script randomly generates a protein sequence composed of 20 amino acids selected from a predefined list. This allows for the exploration of various protein configurations.

2. Chromosome Representation

Each protein structure is represented as a chromosome, consisting of angles (phi, psi, omega) that define the conformation of each amino acid in the sequence. The chromosome length is three times the protein length, accommodating the three angles per amino acid.

3. Fitness Function

The fitness of each chromosome is evaluated using a fitness function that calculates the energy of the corresponding protein structure. The energy is determined using a scoring function based on trigonometric calculations, where lower energy values indicate more stable protein configurations.

4. Genetic Operators

Selection: Two individuals from the population are selected randomly, with the fitter individual (lower energy) chosen for reproduction.

Crossover: A single-point crossover is implemented to create offspring from two parent chromosomes, promoting genetic diversity.

Mutation: Each gene in the chromosome has a 10% chance of mutation, introducing variability and aiding in the exploration of the solution space.

5. Population Evolution

The algorithm maintains a population of candidate solutions. Over a series of generations, it applies selection, crossover, and mutation to evolve the population. The best-performing individuals are retained for subsequent generations.

6. Visualization

The script records the best fitness value (lowest energy) at each generation and plots this data using Matplotlib. This visualization helps track the progress of the optimization process and demonstrates how the population converges towards better solutions over time.

7. Final Output

After the specified number of generations, the script decodes the fittest chromosome to obtain the corresponding protein structure and displays the best fitness values achieved throughout the simulation.

# Conclusion

This project provides a practical application of genetic algorithms in computational biology, specifically in protein folding. It highlights the potential of evolutionary strategies to solve complex optimization problems in biological systems, making it a valuable tool for researchers and students interested in bioinformatics and computational modeling.