Codon Optimization via Immune and Genetic Algorithms
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Solution Overview
Problem
Existing codon optimization methods face challenges with long cycles and poor expression accuracy, limiting their ability to efficiently search the codon optimization space within a limited time.
Innovation Solution
A computer-implemented codon optimization method using an immune algorithm for local and global multi-objective optimization, followed by exhaustive fine adjustment, to identify the optimal DNA sequence for protein expression, combining the strengths of immune and genetic algorithms to prevent premature convergence and maintain diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional heuristic algorithms (particle swarm, genetic algorithm) are used for codon optimization, then the optimization process can be completed, but the cycle is long and expression accuracy is poor
Solution Approach 1:
The patent divides the protein sequence into nonoverlapping sequence fragments (e.g., 5-10 amino acids per fragment) and performs local optimization on each fragment independently using immune algorithm. This segmentation allows parallel processing of multiple fragments, significantly reducing the overall optimization cycle while maintaining comprehensive search coverage of the codon space.
Solution Approach 2:
The patent introduces an immune algorithm as an intermediary optimization method between traditional heuristic algorithms and exhaustive search. The immune algorithm acts as a mediator that combines the advantages of both approaches: it provides faster convergence than traditional heuristics while avoiding the computational burden of exhaustive search, achieving both speed and accuracy.
2Manufacturing precision
If exhaustive search is performed on the entire protein coding sequence, then the optimal expression sequence can be found, but the computational complexity and time required become prohibitively large
Solution Approach 1:
By segmenting the protein sequence into smaller nonoverlapping fragments, the patent reduces the search space from the entire protein length to manageable fragment sizes. Each fragment's codon optimization space is searched exhaustively or with immune algorithm, but the total computational complexity is divided into independent sub-problems that can be solved in parallel, making the overall process feasible.
Solution Approach 2:
The patent applies different optimization strategies to different parts of the sequence: immune algorithm for local fragment optimization and genetic algorithm for global sequence optimization. This local quality approach allows intensive optimization where needed (local fragments) while using less computationally intensive methods for the overall sequence, balancing accuracy and complexity.
3Productivity
If local optimization is performed on protein fragments using immune algorithm, then the search speed increases, but global optimization may be compromised
Solution Approach 1:
The patent merges two different optimization algorithms: immune algorithm for local fragment optimization and genetic algorithm for global sequence optimization. The local optimizations from immune algorithm provide high-quality initial solutions, which are then refined through global optimization using genetic algorithm. This combination ensures both fast local search and comprehensive global optimization.
Solution Approach 2:
The patent performs preliminary local optimization on protein fragments using immune algorithm before conducting global optimization. This preliminary action creates high-quality initial populations for the subsequent global optimization step, accelerating convergence and improving the quality of the final global optimum without compromising the comprehensiveness of global search.
Data Source
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AI summary
A codon optimization method based on an immune algorithm is characterized in that an immune algorithm and a genetic algorithm are successively used to respectively perform local multi-objective optimization and global multi-objective optimization on a protein coding sequence, and then an exhaustive method is used to perform fine adjustment and optimization on the sequence, so as to search the optimal expression sequence to the greatest extent. The present invention not only retains the characteristic of random global parallel search of the genetic algorithm, but also avoids premature convergence to a comparatively great extent to ensure rapid convergence to the global optimal solution. The present invention is the first to combine the advantages of the immune algorithm and the genetic algorithm in accuracy and efficiency to carry out codon optimization through a step-by-step process (local optimization, global optimization, and fine adjustment and optimization respectively in sequence), and proves the high efficiency of the algorithm in codon optimization through example tests.