A joint optimization method for mRNA sequences based on CAI and AUP

Abstract

The invention discloses a combined optimization method for mRNA sequences based on CAI and AUP, which relates to the field of mRNA sequence optimization and comprises the following steps: step 1, collecting target mRNA sequences and data expression of target species, and obtaining codon usage frequency and RNA structure information through analysis; step 2, calculating target CAI value and target AUP value of the target mRNA sequence; step 3, generating an initial mRNA sequence according to the amino acid sequence of the target mRNA sequence using codon preference and an optimization algorithm; step 4, optimizing the CAI value and AUP value of the initial mRNA sequence to obtain an optimized mRNA sequence; during the optimization process, finding an optimal balance point by balancing the CAI value and AUP value of the initial mRNA sequence; step 5, performing RNA structure prediction and simulation on the optimized mRNA sequence, and verifying the performance of the optimized mRNA sequence in terms of structural stability and expected translation efficiency; and step 6, continuing to iteratively optimize the verification result obtained in step 5, and optimizing the CAI value and AUP value of the optimized mRNA sequence by adjusting parameters and algorithms.

Description

Technical Field

[0001] The present invention relates to the field of mRNA sequence optimization, and in particular to a CAI and AUP-based mRNA sequence joint optimization method. Background Art

[0002] In 2020, Moderna and BioNTech's mRNA COVID-19 vaccine was approved for marketing, marking initial recognition of the safety and effectiveness of the mRNA COVID-19 vaccine, and also accelerated the development and application of mRNA technology.

[0003] mRNA technology offers advantages such as a short R&D cycle, simple production process, strong immunogenicity, and high safety, and is widely applicable in fields such as infectious disease vaccines, tumor immunity, and protein replacement. However, mRNA is susceptible to degradation and instability. Therefore, more appropriate mRNA sequence design and more efficient delivery systems require further exploration, and future equipment for large-scale production also needs to be developed.

[0004] The Codon Adaptation Index (CAI) is a metric used to describe the frequency of codon usage within an organism. In an organism's DNA or RNA sequence, a codon consists of three bases that code for an amino acid. The CAI measures the frequency of use of each codon in a specific genome, ranging from 0 to 1. During protein synthesis, codons on mRNA bind to tRNA molecules to incorporate specific amino acids into the synthesized protein chain. Different codons bind to different tRNAs, so codon selection can influence the efficiency and accuracy of protein synthesis. The CAI considers the frequency of codon usage in a specific species or environment, as well as the efficiency of tRNA binding. A common CAI is calculated based on the relative frequency of codon usage in a target species. In large amounts of genomic data, the number of codon occurrences can be counted and then the codon frequency calculated. A higher CAI indicates that the codon is more frequently used in the target species and binds more efficiently to the corresponding tRNA. Such codons are more easily recognized and translated, thereby improving protein synthesis efficiency. Conversely, a low codon adaptation index may lead to slower translation or incorrect tRNA selection, thereby reducing the efficiency of protein synthesis.

[0005] Average Unpaired Probability (AUP) is an indicator used in RNA sequence design and structure optimization to evaluate the degree of unpaired bases in RNA sequences. It is calculated as follows:

[0006]

[0007] Where N is the number of bases in the RNA sequence, p unpaired (i) is the unpaired frequency of the i-th base in the RNA sequence, and p(i:j) is the pairing probability of the i-th base and the j-th base in the RNA sequence.

[0008] Unpaired bases form variable structures in RNA molecules, including internal loops, external loops, and multi-stranded structures. These structures can affect the stability and function of RNA. The average unpaired probability is a statistical indicator that describes the degree of base unpairing in an RNA sequence, indicating how many bases on average do not form stable base pairs with other bases in a given RNA sequence. A lower average unpaired probability means that more bases form stable base pairs, which indicates that the structure of the RNA molecule is more stable. By reducing the average unpaired probability, the instability of RNA molecules can be reduced, and their stability and resistance to degradation in cells can be improved, which is very important for RNA vaccines, RNA interference technology, and other RNA gene regulation applications, because a stable RNA structure can enhance its functional expression in the body.

[0009] The codon adaptation index (CAI) in the existing technology has defects such as insufficient optimization data, species specificity, and failure to consider regulatory factors. The average unpaired probability (AUP) also has defects such as structural prediction errors, dynamics, and environmental dependence. Therefore, technicians in this field are committed to developing a new mRNA sequence optimization method to address the above-mentioned defects in the existing technology. Summary of the Invention

[0010] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is how to overcome the defects in CAI, such as insufficient optimization data, species specificity, and failure to consider regulatory factors, as well as the defects in AUP, such as structural prediction errors, dynamics, and environmental dependence, so as to better optimize the mRNA sequence.

[0011] To achieve the above object, the present invention provides a method for jointly optimizing the codon adaptation index (CAI) and the average unpaired probability (AUP) of an mRNA sequence based on a dynamic programming algorithm.

[0012] Specifically, the present invention provides a method for joint optimization of mRNA sequences based on CAI and AUP, comprising the following steps:

[0013] Step 1: Collect target mRNA sequences and expression data of target species, and obtain codon usage frequency and RNA structure information through analysis;

[0014] Step 2, calculating the target CAI value and target AUP value of the target mRNA sequence;

[0015] Step 3: generating an initial mRNA sequence based on the amino acid sequence of the target mRNA sequence using the codon preference and optimization algorithm;

[0016] Step 4: Optimizing the CAI value and AUP value of the initial mRNA sequence to obtain an optimized mRNA sequence; during the optimization process, finding the optimal balance point by balancing the CAI value and AUP value of the initial mRNA sequence;

[0017] Step 5: performing RNA structure prediction and simulation on the optimized mRNA sequence to verify the performance of the optimized mRNA sequence in terms of structural stability and expected translation efficiency;

[0018] Step 6: Continue to iteratively optimize the verification result obtained in step 5, and optimize the CAI value and AUP value of the optimized mRNA sequence by adjusting parameters and algorithms.

[0019] Furthermore, the step 1 includes:

[0020] Step 1.1, collecting the target mRNA sequence and the expression data of the target species;

[0021] Step 1.2, selecting genes in the target species, and calculating the usage frequencies of the codons in the target species based on the genes;

[0022] Step 1.3: Obtain the codon table of the target species from an Internet database, analyze the existing annotation and translation information of the gene, and establish a codon usage frequency table of the target species based on the tRNA abundance and RNA structure information of the target species.

[0023] Furthermore, the genes in step 1.2 are more than 1000 highly expressed protein genes.

[0024] Furthermore, in step 2, the target CAI value of the target mRNA sequence can be calculated by comparing the usage frequency of the codon and the abundance of the tRNA of the target species.

[0025] Furthermore, in step 2, the target AUP value of the target mRNA sequence can be approximately calculated using a prediction algorithm based on the RNA structure information of the target species.

[0026] Furthermore, step 3 includes the following sub-steps:

[0027] Step 3.1, clarifying the amino acid sequence of the protein to be synthesized, and determining the codons that can correspond to each amino acid in the amino acid sequence;

[0028] Step 3.2: Determine the codon usage rules based on the target organism or optimization target of the synthesized protein, select the codon whose CAI value meets the optimization target based on the usage frequency table of the codons of the synthesized protein, and generate the initial mRNA sequence.

[0029] Furthermore, step 4 includes the following sub-steps:

[0030] Step 4.1: Calculate the AUP value of the initial mRNA sequence based on a dynamic programming algorithm and compare it with the target AUP value; and obtain a pre-set optimized AUP value by adjusting the selection of the codons in the initial mRNA sequence;

[0031] Step 4.2: Using an optimization algorithm and adjusting the selection of the codons to improve the CAI value of the initial mRNA sequence, thereby obtaining the optimized mRNA sequence; during the optimization process, the optimal balance point is found by balancing the CAI value and AUP value of the initial mRNA sequence.

[0032] Furthermore, the step 4.1 includes the following sub-steps:

[0033] Step 4.1.1. Define the problem, find an mRNA sequence with a high CAI value and a high AUP value as the optimization target, and determine the state, where the state is defined as the partial mRNA sequence that has been generated;

[0034] Step 4.1.2: Determine the state transition equation. For each state, determine how to transition to the next state to achieve the optimal solution, taking into account the contributions of the CAI value and the AUP value.

[0035] Furthermore, the step 4.1 further includes the following sub-steps:

[0036] Step 4.1.3, initializing the first state, i.e., the initial mRNA sequence;

[0037] Step 4.1.4, using the state transition equation, recursively calculate the CAI value and AUP value corresponding to all possible states;

[0038] Step 4.1.5: During the recursive calculation process of step 4.1.4, record the score and transfer source of each state so as to facilitate backtracking after the optimal solution is finally found;

[0039] Step 4.1.6: Based on the backtracking results, find the mRNA sequence with the highest CAI value and AUP value, which is the optimal solution.

[0040] Furthermore, in step 5, experimental verification can also be performed, such as using a fluorescent reporter gene to perform an expression experiment to evaluate the expression effect of the optimized mRNA sequence.

[0041] The present invention provides a method for joint optimization of mRNA sequences based on CAI and AUP, which has at least the following technical effects:

[0042] 1. The technical solution provided by the present invention comprehensively considers codon adaptability CAI and RNA structure to optimize the expression efficiency and stability of mRNA sequences;

[0043] 2. The technical solution provided by the present invention can reduce the production of by-products and improve the efficiency of protein synthesis;

[0044] 3. The technical solution provided by the present invention enhances the accuracy and controllability of gene expression, providing strong support for biomedical research and bioengineering fields.

[0045] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a method provided by a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following describes several preferred embodiments of the present invention with reference to the accompanying drawings to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0048] Using the codon adaptation index (CAI) and the average unpaired probability (AUP) alone to optimize mRNA sequences can achieve certain optimization effects, but each has its own defects and limitations.

[0049] The main drawback of CAI lies in its neglect of contextual information: the codon adaptation index typically only considers the frequency of codon usage itself, ignoring the influence of its contextual environment. However, base interactions and structure within RNA sequences also have a significant impact on codon selection and translation efficiency. Therefore, relying solely on the codon adaptation index may not fully account for the impact of this contextual information on gene expression.

[0050] The main drawback of AUP lies in its context-dependence: the average unpaired probability is simply an average statistic of the degree of base unpairing in an RNA sequence, failing to account for variations across positions and contexts. However, RNA structural stability and base pairing can be influenced by the surrounding sequence and structural environment. Therefore, the average unpaired probability may not fully reflect the structural characteristics and stability differences across different regions.

[0051] To overcome the shortcomings of existing CAI optimization techniques, such as insufficient data, species specificity, failure to consider regulatory factors, and structural prediction errors, dynamics, and environmental dependence of AUPs, the present invention provides a method for the combined optimization of the codon adaptation index and the average unpaired probability, comprising the following steps:

[0052] 1. Collect codon usage frequency and related RNA structure information in the target species;

[0053] 2. Calculate the codon adaptation index (CAI) of each codon based on the codon usage frequency and the stability of RNA structure;

[0054] 3. Calculate the average unpaired probability AUP of each codon in the mRNA sequence;

[0055] 4. Combining CAI and AUP, assign a comprehensive score to each codon;

[0056] 5. Select the codon combination with the highest score as the optimized mRNA sequence.

[0057] Through this method, it is possible to optimize the adaptability of codons and the stability of RNA structure while maintaining an appropriate codon usage frequency, which helps to improve the translation efficiency and degradation stability of mRNA sequences and reduce the production of by-products.

[0058] Example 1

[0059] The present invention provides a method for joint optimization of mRNA sequences based on CAI and AUP, comprising the following steps:

[0060] Step 1: Collect target mRNA sequences and expression data of target species, and obtain codon usage frequency and RNA structure information through analysis;

[0061] Step 2, calculating the target CAI value and target AUP value of the target mRNA sequence;

[0062] Step 3: Generate an initial mRNA sequence based on the amino acid sequence of the target mRNA sequence using codon preference and optimization algorithms;

[0063] Step 4: Optimize the CAI value and AUP value of the initial mRNA sequence to obtain an optimized mRNA sequence; during the optimization process, find the optimal balance point by balancing the CAI value and AUP value of the initial mRNA sequence;

[0064] Step 5: Perform RNA structure prediction and simulation on the optimized mRNA sequence to verify the performance of the optimized mRNA sequence in terms of structural stability and expected translation efficiency;

[0065] Step 6: Continue iterative optimization of the verification results obtained in step 5, and optimize the CAI value and AUP value of the optimized mRNA sequence by adjusting parameters and algorithms (such as Figure 1 shown).

[0066] Example 2

[0067] On the basis of Example 1, further, step 1 includes:

[0068] Step 1.1, collect target mRNA sequences and expression data of target species;

[0069] Step 1.2: Select genes in the target species and calculate the codon usage frequency in the target species based on the genes;

[0070] Step 1.3: Obtain the codon table of the target species from the Internet database, analyze the existing annotation and translation information of the gene, and combine the tRNA abundance and RNA structure information of the target species to establish the codon usage frequency table of the target species.

[0071] Example 3

[0072] On the basis of Example 1 and Example 2, further, the genes in step 1.2 are more than 1000 highly expressed protein genes.

[0073] In step 2, the target CAI value of the target mRNA sequence can be calculated by comparing the codon usage frequency and the tRNA abundance of the target species.

[0074] In step 2, the target AUP value of the target mRNA sequence can be approximately calculated using a prediction algorithm based on the RNA structure information of the target species.

[0075] Example 4

[0076] On the basis of Example 1, Example 2 and Example 3, step 3 further includes the following sub-steps:

[0077] Step 3.1. Determine the amino acid sequence of the protein to be synthesized and determine the codons that correspond to each amino acid in the amino acid sequence;

[0078] Step 3.2: Determine the codon usage rules based on the target organism or optimization target for protein synthesis, select codons whose CAI values ​​meet the optimization target based on the codon usage frequency table for protein synthesis, and generate the initial mRNA sequence.

[0079] Example 5

[0080] On the basis of Example 1, Example 2, Example 3 and Example 4, step 4 further includes the following sub-steps:

[0081] Step 4.1, calculating the AUP value of the initial mRNA sequence based on a dynamic programming algorithm and comparing it with the target AUP value; obtaining a predetermined optimized AUP value by adjusting the selection of codons in the initial mRNA sequence;

[0082] Step 4.2: Using an optimization algorithm and adjusting the selection of codons to improve the CAI value of the initial mRNA sequence, thereby obtaining an optimized mRNA sequence; during the optimization process, the optimal balance point is found by balancing the CAI value and AUP value of the initial mRNA sequence.

[0083] Wherein, step 4.1 includes the following sub-steps:

[0084] Step 4.1.1. Define the problem, find an mRNA sequence with high CAI and AUP values ​​as the optimization target, and determine the state, which is defined as the part of the mRNA sequence that has been generated;

[0085] Step 4.1.2: Determine the state transition equation. For each state, determine how to transition to the next state to achieve the optimal solution, taking into account the contributions of the CAI value and the AUP value.

[0086] Step 4.1.3: Initialize the first state, i.e., the initial mRNA sequence;

[0087] Step 4.1.4: Use the state transition equation to recursively calculate the CAI and AUP values ​​corresponding to all possible states.

[0088] Step 4.1.5: During the recursive calculation process of step 4.1.4, record the score and transfer source of each state so that you can backtrack after finding the optimal solution.

[0089] Step 4.1.6: Based on the backtracking results, find the mRNA sequence with the highest CAI value and AUP value, which is the optimal solution.

[0090] In step 5, experimental verification can also be performed, such as using a fluorescent reporter gene to perform an expression experiment to evaluate the expression effect of the optimized mRNA sequence.

[0091] Example 6

[0092] Establish a human codon usage frequency table and optimize the following antigen sequences: SARS-CoV-2 spike protein sequence.

[0093] The original sequence is:

[0094]

[0095] The optimized mRNA sequence is:

[0096]

[0097] Computational verification showed that the codon adaptation index (CAI) of the mRNA sequence was 0.996, and the average unpaired probability (AUP) was 0.479. Both CAI and AUP were at excellent levels, indicating that the mRNA sequence has higher translation efficiency and stability, while also reducing the production of byproducts.

[0098] The preferred embodiments of the present invention have been described in detail above. It should be understood that numerous modifications and variations based on the concepts of the present invention are possible without inventive effort by those skilled in the art. Therefore, any technical solution that can be derived by one skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A method for joint optimization of mRNA sequences based on CAI and AUP, characterized in that: The method comprises the following steps: Step 1: Collect target mRNA sequences and expression data of target species, and obtain codon usage frequency and RNA structure information through analysis; The step 1 comprises: Step 1.1, collecting the target mRNA sequence and the expression data of the target species; Step 1.2, selecting genes in the target species, and calculating the usage frequencies of the codons in the target species based on the genes; The genes in step 1.2 are more than 1000 highly expressed protein genes; Step 1.3: Obtain the codon table of the target species from an internet database, analyze the existing annotation and translation information of the gene, and establish a codon usage frequency table for the target species based on the tRNA abundance and RNA structure information of the target species; Step 2, calculating the target CAI value and target AUP value of the target mRNA sequence; Step 3: generating an initial mRNA sequence based on the amino acid sequence of the target mRNA sequence using the codon preference and optimization algorithm; The step 3 includes: Step 3.1, clarifying the amino acid sequence of the protein to be synthesized, and determining the codons that can correspond to each amino acid in the amino acid sequence; Step 3.2, determining the codon usage rules according to the target organism or optimization target of the synthesized protein, selecting the codons whose CAI values ​​meet the optimization target according to the codon usage frequency table of the synthesized protein, and generating the initial mRNA sequence; Step 4: Optimizing the CAI value and AUP value of the initial mRNA sequence to obtain an optimized mRNA sequence; during the optimization process, finding the optimal balance point by balancing the CAI value and AUP value of the initial mRNA sequence; The step 4 comprises: Step 4.1: Calculate the AUP value of the initial mRNA sequence based on a dynamic programming algorithm and compare it with the target AUP value; and obtain a pre-set optimized AUP value by adjusting the selection of the codons in the initial mRNA sequence; Step 4.2: using an optimization algorithm and adjusting the codon selection to increase the CAI value of the initial mRNA sequence, thereby obtaining the optimized mRNA sequence; during the optimization process, finding the optimal balance point by balancing the CAI value and the AUP value of the initial mRNA sequence; Step 5: performing RNA structure prediction and simulation on the optimized mRNA sequence to verify the performance of the optimized mRNA sequence in terms of structural stability and expected translation efficiency; Step 6: Continue to iteratively optimize the verification result obtained in step 5, and optimize the CAI value and AUP value of the optimized mRNA sequence by adjusting parameters and algorithms.

2. The mRNA sequence joint optimization method based on CAI and AUP according to claim 1, wherein In step 2, the target CAI value of the target mRNA sequence is calculated by comparing the usage frequency of the codons and the abundance of the tRNA of the target species.

3. The mRNA sequence joint optimization method based on CAI and AUP according to claim 2, wherein: In step 2, the target AUP value of the target mRNA sequence is approximately calculated using a prediction algorithm based on the RNA structure information of the target species.

4. The mRNA sequence joint optimization method based on CAI and AUP according to claim 3, wherein The step 4.1 includes the following sub-steps: Step 4.1.

1. Define the problem, find an mRNA sequence with a high CAI value and a high AUP value as the optimization target, and determine the state, where the state is defined as the partial mRNA sequence that has been generated; Step 4.1.2: Determine the state transition equation. For each state, determine how to transition to the next state to achieve the optimal solution, taking into account the contributions of the CAI value and the AUP value.

5. The mRNA sequence joint optimization method based on CAI and AUP according to claim 4, wherein The step 4.1 further includes the following sub-steps: Step 4.1.3, initializing the first state, i.e., the initial mRNA sequence; Step 4.1.4, using the state transition equation, recursively calculate the CAI value and AUP value corresponding to all possible states; Step 4.1.5: During the recursive calculation process of step 4.1.4, record the score and transfer source of each state so as to facilitate backtracking after the optimal solution is finally found; Step 4.1.6: Based on the backtracking results, find the mRNA sequence with the highest CAI value and AUP value, which is the optimal solution.

6. The mRNA sequence joint optimization method based on CAI and AUP according to claim 1, wherein In step 5, a fluorescent reporter gene is used to perform an expression experiment, perform experimental verification, and evaluate the expression effect of the optimized mRNA sequence.

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