A method and apparatus for codon sequence optimization of human albumin

Abstract

This application provides a method and apparatus for optimizing the codon sequence of human albumin, belonging to the field of data processing technology. Addressing the current problems of low synthesis efficiency and scarcity of human albumin drugs, it allows for the grouping of large datasets of human albumin amino acid sequences. An improved genetic optimization algorithm is then used to optimize the corresponding messenger RNA item by item, ultimately obtaining the optimal second target messenger RNA. When using this second target messenger RNA for protein expression to obtain the target recombinant human albumin, the expression efficiency of the host Pichia pastoris cells can be maximized, significantly improving the current synthesis efficiency of recombinant human albumin and greatly alleviating the problem of human albumin drug scarcity.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method and apparatus for optimizing the codon sequence of human albumin. Background Technology

[0002] Human serum albumin (HSA) is one of the most abundant proteins in human plasma, possessing multiple key physiological functions and widely used as a therapeutic agent in clinical medicine. HSA is not only a crucial protein for maintaining homeostasis but also an indispensable emergency drug in clinical practice. Its multifunctional properties enable it to play an important role in areas such as volume expansion, detoxification, and organ protection.

[0003] In related technologies, Pichia pastoris can be used as a host cell for expressing recombinant human albumin. Pichia pastoris containing the HSA expression element can be transcribed into mRNA after methanol induction, and then the mRNA is further translated into a polypeptide, which is finally secreted extracellularly to obtain recombinant human albumin. This can, to some extent, solve the problem of the scarcity of human albumin drugs.

[0004] However, due to the influence of DNA transcription efficiency and mRNA stability, the expression capacity of naturally formed mRNA is low in the above methods. This results in the current low efficiency of synthesizing recombinant human albumin, failing to completely solve the problem of human albumin drug scarcity. Summary of the Invention

[0005] In view of the above problems, embodiments of this application provide a method, apparatus, electronic device, and readable storage medium for optimizing the codon sequence of human albumin, so as to overcome the above problems or at least partially solve the above problems.

[0006] In a first aspect, embodiments of this application provide a method for optimizing the codon sequence of human albumin, the method comprising:

[0007] The first amino acid sequence of the target human albumin was grouped to obtain multiple first amino acid sub-sequences;

[0008] Determine the second amino acid subsequence from the first amino acid subsequence;

[0009] An improved genetic optimization algorithm is used to optimize the expression of the first codon sequence of the second amino acid subsequence in the first messenger RNA corresponding to the first amino acid sequence, thereby obtaining the first target messenger RNA;

[0010] From the first amino acid subsequence, determine the third amino acid subsequence other than the second amino acid subsequence;

[0011] The improved genetic optimization algorithm is used again to optimize the expression of the second codon sequence of the third amino acid subsequence in the first target messenger RNA corresponding to the first amino acid sequence, so as to obtain the second target messenger RNA.

[0012] Optionally, the step of employing an improved genetic optimization algorithm to optimize the expression of the second amino acid subsequence in the first amino acid sequence to obtain the first target messenger RNA includes:

[0013] Based on the second amino acid subsequence, a first codon population is generated; wherein, the first codon population includes multiple first codon individuals; each first codon individual is composed of codons corresponding to at least two different positions of amino acids in the second amino acid subsequence;

[0014] Multiple first parent codon individuals are obtained by randomly selecting from the first codon population;

[0015] Mutate at least one codon corresponding to the first amino acid in the first parent codon individual to obtain the first daughter codon individual;

[0016] Based on the codons corresponding to each second amino acid in the first progeny codon individual, the first messenger RNA corresponding to the first amino acid sequence is reconstructed to obtain the second messenger RNA;

[0017] Based on the second messenger RNA and the first messenger RNA, determine the first fitness corresponding to each first parent codon individual and each first daughter codon individual;

[0018] Based on the first fitness, a first target codon individual is determined from the first child codon individual and the first parent codon individual;

[0019] Based on the codons corresponding to the amino acids of each first target individual in the first target codon individual, the first messenger RNA is reconstructed to obtain the first target messenger RNA.

[0020] Optionally, the step of mutating the codon corresponding to at least one first amino acid in the first parent codon individual to obtain the first daughter codon individual includes:

[0021] Generate the first random number;

[0022] If the first random number is greater than or equal to the first threshold, the codon corresponding to at least one first amino acid in the first parent codon individual is mutated to obtain the first daughter codon individual.

[0023] Optionally, the step of mutating the codon corresponding to at least one first amino acid in the first parent codon individual to obtain the first daughter codon individual includes:

[0024] In the first parent codon individual, at least one first codon element is randomly selected;

[0025] Determine the element amino acid corresponding to the first codon element;

[0026] Determine the first codon pool corresponding to the amino acid of the element;

[0027] In the first codon pool, a second codon element is randomly selected, and the first codon element in the first parent codon individual is replaced with the second codon element to obtain the first child codon individual.

[0028] Optionally, determining the first fitness corresponding to each of the first parent codon individuals and each of the first daughter codon individuals based on the second messenger RNA and the first messenger RNA includes:

[0029] The second messenger RNA and the first messenger RNA were respectively transferred to Pichia pastoris cells, expressed in a preset culture environment, and the expression time was accumulated.

[0030] When the expression duration is greater than or equal to the first preset duration, the expression analysis images corresponding to each host Pichia pastoris cell are obtained by using the SDS-PAGE electrophoresis analysis method.

[0031] The number of purple pixels in the expression analysis image is determined as the first fitness corresponding to the second messenger RNA and the first messenger RNA, respectively.

[0032] Optionally, determining the first target codon individual from the first child codon individual and the first parent codon individual based on the first fitness includes:

[0033] From the first child codon individual and the first parent codon individual, determine the second codon individual corresponding to the first fitness value that is greater than or equal to the second threshold;

[0034] The second codon individual is added to the first codon population, and the third codon individual with the same number as the second codon individual is randomly deleted from the first codon population to obtain the iterated first codon population.

[0035] The step of randomly selecting from the first codon population to obtain multiple first parent codon individuals is repeated.

[0036] When the number of iterations of the first codon population reaches a preset number, the first target codon individual corresponding to the maximum value of the first fitness is determined from the first codon population.

[0037] Optionally, the preset number of times is at least five times the first number of sequence amino acids contained in the second amino acid subsequence.

[0038] Secondly, embodiments of this application provide a codon sequence optimization device for human albumin, the device comprising:

[0039] The grouping module is used to group the first amino acid sequence of the target human albumin to obtain multiple first amino acid sub-sequences;

[0040] A first determining module is configured to determine a second amino acid subsequence from the first amino acid subsequence;

[0041] The first expression optimization module is used to optimize the expression of the first codon sequence of the second amino acid subsequence in the first messenger RNA corresponding to the first amino acid sequence using an improved genetic optimization algorithm to obtain the first target messenger RNA.

[0042] The second determining module is used to determine a third amino acid subsequence other than the second amino acid subsequence from the first amino acid subsequence;

[0043] The second expression optimization module is used to further optimize the expression of the second codon sequence of the third amino acid subsequence in the first target messenger RNA corresponding to the first amino acid sequence by employing an improved genetic optimization algorithm to obtain the second target messenger RNA.

[0044] Optionally, the first expression optimization module includes:

[0045] A generation submodule is used to generate a first codon population based on the second amino acid subsequence; wherein the first codon population includes multiple first codon individuals; each first codon individual is composed of codons corresponding to at least two different positions of amino acids in the second amino acid subsequence;

[0046] The extraction submodule is used to randomly extract multiple first parent codon individuals from the first codon population;

[0047] The mutation module is used to mutate at least one codon corresponding to the first amino acid in the first parent codon individual to obtain the first daughter codon individual;

[0048] The first reconstruction submodule is used to reconstruct the first messenger RNA corresponding to the first amino acid sequence based on the codon corresponding to each second amino acid in the first daughter codon individual, to obtain the second messenger RNA;

[0049] The first determining submodule is used to determine the first fitness corresponding to each first parent codon individual and each first daughter codon individual based on the second messenger RNA and the first messenger RNA.

[0050] The second determining submodule is used to determine the first target codon individual from the first child codon individual and the first parent codon individual based on the first fitness.

[0051] The second reconstruction submodule is used to reconstruct the first messenger RNA based on the codon corresponding to each amino acid of the first target individual in the first target codon individual, so as to obtain the first target messenger RNA.

[0052] Optionally, the variant submodule includes:

[0053] A generation unit is used to generate the first random number;

[0054] The mutation unit is used to mutate at least one codon corresponding to the first amino acid in the first parent codon individual when the first random number is greater than or equal to the first threshold, so as to obtain the first daughter codon individual.

[0055] Optionally, the variant submodule includes:

[0056] The selection unit is used to randomly select at least one first codon element from the first parent codon individual;

[0057] The first determining unit is used to determine the element amino acid corresponding to the first codon element;

[0058] The second determining unit is used to determine the first codon pool corresponding to the element amino acid;

[0059] The replacement unit is used to randomly select a second codon element from the first codon pool and replace the first codon element in the first parent codon individual with the second codon element to obtain the first child codon individual.

[0060] Optionally, the first determining submodule includes:

[0061] The expression accumulation unit is used to transfer the second messenger RNA and the first messenger RNA into Pichia pastoris cells, express them in a preset culture environment, and accumulate the expression time.

[0062] The electrophoretic analysis unit is used to obtain expression analysis images corresponding to each host Pichia pastoris cell by using the SDS-PAGE electrophoretic analysis method when the expression duration is greater than or equal to a first preset duration.

[0063] The third determining unit is used to determine the number of purple pixels in the expression analysis image as the first fitness corresponding to the second messenger RNA and the first messenger RNA, respectively.

[0064] Optionally, the second determining submodule includes:

[0065] The fourth determining unit is used to determine, from the first child codon individual and the first parent codon individual, a second codon individual corresponding to a first fitness value greater than or equal to a second threshold.

[0066] An iterative unit is used to add the second codon individual to the first codon population and randomly delete the same number of third codon individuals as the second codon individuals from the first codon population to obtain the iterative first codon population.

[0067] An execution unit is configured to re-execute the step of randomly selecting multiple first parent codon individuals from the first codon population.

[0068] The fifth determining unit is used to determine the first target codon individual corresponding to the maximum value of the first fitness from the first codon population when the number of iterations of the first codon population reaches a preset number.

[0069] Optionally, the preset number of times is at least five times the first number of sequence amino acids contained in the second amino acid subsequence.

[0070] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the codon sequence optimization method for human albumin as described in any of the above claims.

[0071] Fourthly, embodiments of this application provide a readable storage medium storing a program or instructions that, when executed by a processor, implement the codon sequence optimization method for human albumin as described in any of the above claims.

[0072] The specific beneficial effects are as follows:

[0073] This application embodiment groups the first amino acid sequence of the target human albumin to obtain multiple first amino acid sub-sequences. A second amino acid sub-sequence is then determined from these sub-sequences. An improved genetic optimization algorithm is used to optimize the expression of the first codon sequence in the first messenger RNA corresponding to the second amino acid sub-sequence, resulting in a first target messenger RNA. From the first amino acid sub-sequences, a third amino acid sub-sequence (excluding the second amino acid sub-sequence) is determined. Again, an improved genetic optimization algorithm is used to optimize the expression of the second codon sequence in the first target messenger RNA corresponding to the third amino acid sub-sequence, resulting in a second target messenger RNA. This method can group the large amount of human albumin amino acid sequences and optimize the corresponding messenger RNA item by item using the improved genetic optimization algorithm, ultimately obtaining the optimal second target messenger RNA. When using the second target messenger RNA to express the target human albumin, the expression efficiency of the host Pichia pastoris cells can be maximized, greatly improving the current synthesis efficiency of human albumin and significantly addressing the problem of human albumin drug scarcity. Attached Figure Description

[0074] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 This is a flowchart illustrating a method for optimizing the codon sequence of human albumin according to an embodiment of this application;

[0076] Figure 2 This is a schematic diagram of an amino acid sequence data provided in an embodiment of this application;

[0077] Figure 3 This is a flowchart illustrating an improved genetic optimization algorithm provided in an embodiment of this application.

[0078] Figure 4 This is a schematic diagram of the analysis results of an SDS-PAGE electrophoresis analysis provided in an embodiment of this application.

[0079] Figure 5 This is a logic block diagram of a codon sequence optimization device for human albumin provided in an embodiment of this application;

[0080] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0081] Exemplary embodiments of this application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0082] Reference Figure 1 , Figure 1 This application provides a flowchart illustrating a method for optimizing the codon sequence of human albumin, which may include:

[0083] Step 101: The first amino acid sequence of the target human albumin is grouped to obtain multiple first amino acid sub-sequences.

[0084] In embodiments of this application, the first amino acid sequence of the target human albumin may not contain a start codon and a stop codon. For example... Figure 2 As shown, Figure 2 This is a schematic diagram of amino acid sequence data provided in an embodiment of this application. Each letter represents an amino acid. The correspondence between amino acids, single-letter symbols, and codons is shown in Table 1 below:

[0085] Table 1. Correspondence between amino acids, single-letter symbols, and codons

[0086] ;

[0087] Figure 2 The amino acid sequence contains 585 amino acids, which is a large amount of data. Therefore, the first amino acid sequence of the target human albumin can be grouped to obtain multiple first amino acid sub-sequences. To fully ensure the optimization effect, the embodiments of this application set the upper limit of the number of amino acids in each first amino acid sub-sequence to 20. Therefore, for Figure 2 The amino acid sequence in the middle can be divided into 30 amino acid subsequences.

[0088] Step 102: Determine the second amino acid subsequence from the first amino acid subsequence.

[0089] In embodiments of this application, a second amino acid subsequence can be extracted from a first amino acid subsequence. The extraction method can be random extraction without replacement or sequential extraction.

[0090] Step 103: Using an improved genetic optimization algorithm, the expression of the first codon sequence of the second amino acid subsequence in the first messenger RNA corresponding to the first amino acid sequence is optimized to obtain the first target messenger RNA.

[0091] In the embodiments of this application, a genetic optimization algorithm can be used in codon optimization, and the genetic optimization algorithm can be adaptively improved according to the characteristics of the codons to obtain an improved genetic optimization algorithm. The amino acids in the amino acid sequence of human albumin are non-substitutable, but the expression efficiency of the codons can vary. Therefore, in the embodiments of this application, the first messenger RNA corresponding to the first amino acid sequence is used as the optimization vector, the optimization position is the position of the second amino acid subsequence in the first amino acid sequence, and the expression optimization method is to equivalently replace the codons of the amino acids. For example, for threonine, if its original codon is ACU, it can be replaced with ACC, ACA, or ACG, and the optimization effect can be evaluated. After the optimization algorithm iteration is completed, the first target messenger RNA can be obtained.

[0092] Step 104: Determine a third amino acid subsequence from the first amino acid subsequence, excluding the second amino acid subsequence.

[0093] In the embodiments of this application, after the second amino acid subsequence is optimized, a third amino acid subsequence other than the second amino acid subsequence can be extracted from the first amino acid subsequence.

[0094] Step 105: The improved genetic optimization algorithm is used again to optimize the expression of the second codon sequence of the third amino acid subsequence in the first target messenger RNA corresponding to the first amino acid sequence, so as to obtain the second target messenger RNA.

[0095] In the embodiments of this application, an improved genetic optimization algorithm can be used again to optimize the expression of the second codon sequence in the first target messenger RNA corresponding to the third amino acid subsequence, thereby obtaining the second target messenger RNA. This completes the item-by-item optimization. After this optimization, the unoptimized first amino acid subsequence can be extracted again for optimization until all first amino acid subsequences have been optimized. Once all first amino acid subsequences have been optimized, the optimal target messenger RNA can be obtained.

[0096] In the embodiments of this application, the first amino acid sequence of the target human albumin is grouped to obtain multiple first amino acid sub-sequences. A second amino acid sub-sequence is determined from the first amino acid sub-sequences. An improved genetic optimization algorithm is used to optimize the expression of the first codon sequence in the first messenger RNA corresponding to the second amino acid sub-sequence, resulting in a first target messenger RNA. From the first amino acid sub-sequences, a third amino acid sub-sequence (excluding the second amino acid sub-sequence) is determined. Again, an improved genetic optimization algorithm is used to optimize the expression of the second codon sequence in the first target messenger RNA corresponding to the third amino acid sub-sequence, resulting in a second target messenger RNA. This method allows for grouping of large amounts of human albumin amino acid sequences and optimizing the corresponding messenger RNA item by item using the improved genetic optimization algorithm, ultimately obtaining the optimal second target messenger RNA. When using the second target messenger RNA to express protein and obtain the target human albumin, the expression efficiency of the host Pichia pastoris cells can be maximized, greatly improving the current synthesis efficiency of human albumin and significantly addressing the problem of human albumin drug scarcity.

[0097] Based on the above implementation method, refer to Figure 3 , Figure 3 A flowchart illustrating an improved genetic optimization algorithm provided in this application embodiment may include the following steps in step 102:

[0098] Step 201: Based on the second amino acid subsequence, generate a first codon population; wherein the first codon population includes multiple first codon individuals; the first codon individual is composed of codons corresponding to at least two different positions of amino acids in the second amino acid subsequence.

[0099] In the embodiments of this application, the extracted second amino acid subsequence can be randomly combined on a codon-by-codon basis for each amino acid, thereby obtaining multiple first codon individuals. Multiple first codon individuals can collectively constitute a first codon population. Thus, each first codon individual can include codons corresponding to at least two amino acids at different positions.

[0100] Step 202: Randomly select from the first codon population to obtain multiple first parent codon individuals.

[0101] In embodiments of this application, random sampling can be performed from the first codon population to obtain multiple first parent codon individuals. The number of first parent codon individuals will not exceed the number of individuals in the first codon population.

[0102] Step 203: Mutate the codon corresponding to at least one first amino acid in the first parent codon individual to obtain the first daughter codon individual.

[0103] In embodiments of this application, at least one codon corresponding to the first amino acid in the first parent codon individual can be mutated to obtain the first daughter codon individual. The mutation method can be a substitution mutation, where the current codon of a certain amino acid is replaced with another codon corresponding to that amino acid, or an addition mutation, where a codon containing an amino acid from the second amino acid subsequence is added to the first parent codon individual. When performing the mutation, at least one codon can be replaced or added in accordance with the above method.

[0104] Optionally, step 203 may include the following sub-steps:

[0105] Sub-step 2031: Generate the first random number.

[0106] Sub-step 2032: If the first random number is greater than or equal to the first threshold, mutate the codon corresponding to at least one first amino acid in the first parent codon individual to obtain the first daughter codon individual.

[0107] In the embodiments of this application, a first random number can be used to control the frequency of mutations. Considering that the initial codon sequence is a reference sequence for the optimization result, the first threshold can be set to a smaller value in the interval (0, 1) (e.g., 0.1) to ensure the frequency of mutations and avoid the problem of optimization effect rollback caused by endless mutations, thus ensuring the optimization effect.

[0108] Sub-step 2033: Randomly select at least one first codon element from the first parent codon individual.

[0109] Sub-step 2034: Determine the element amino acid corresponding to the first codon element.

[0110] Sub-step 2035: Determine the first codon pool corresponding to the element amino acid.

[0111] Sub-step 2036: In the first codon pool, a second codon element is randomly selected, and the first codon element in the first parent codon individual is replaced with the second codon element to obtain the first child codon individual.

[0112] In the embodiments of this application, the mutation can be performed according to the following steps: First, at least one first codon element (e.g., UUA) is selected from the first parent codon individual. Then, the elemental amino acid corresponding to the selected first codon element is determined (e.g., UUA corresponds to leucine). Next, the first codon pool corresponding to the elemental amino acid is determined (the codon pool for leucine is UUA, UUG, CUU, CUC, CUA, CUG). Then, a second codon element is selected from the first codon pool (e.g., UUG is selected). Finally, the first codon element in the first parent codon individual is replaced with the second codon element (e.g., UUA is replaced with UUG), thus obtaining the first child codon individual. Through sub-steps 2033 to 2036, the replacement mutation process can be precisely controlled, which can improve the data richness of the first child codon individual to a certain extent.

[0113] Step 204: Based on the codon corresponding to each second amino acid in the first progeny codon individual, reconstruct the first messenger RNA corresponding to the first amino acid sequence to obtain the second messenger RNA.

[0114] In the embodiments of this application, the first parent codon individual can correspond to the reconstructed first messenger RNA (the first messenger RNA is the original messenger RNA during the first optimization iteration). Therefore, here, it is only necessary to reconstruct the first messenger RNA corresponding to the first amino acid based on the codon corresponding to each second amino acid in the newly generated first daughter codon individual, thereby obtaining the second messenger RNA. The above process can be summarized as a "local optimization, global evaluation" approach. This is because the polypeptide chain obtained from local expression may lack biological activity and may be unanalyzable. Therefore, the entire messenger RNA is always reconstructed during the messenger RNA segmentation optimization process.

[0115] Step 205: Based on the second messenger RNA and the first messenger RNA, determine the first fitness corresponding to each of the first parent codon individuals and each of the first daughter codon individuals.

[0116] In the embodiments of this application, the first fitness corresponding to the first parent codon individual and the first daughter codon individual can be determined by the expression levels of the second messenger RNA and the first messenger RNA.

[0117] Optionally, step 205 may include the following sub-steps:

[0118] Sub-step 2051: The second messenger RNA and the first messenger RNA are respectively transferred to Pichia pastoris cells, expressed in a preset culture environment, and the expression time is accumulated.

[0119] Sub-step 2052: If the expression duration is greater than or equal to the first preset duration, the expression analysis images corresponding to each host Pichia pastoris cell are obtained by using the SDS-PAGE electrophoresis analysis method.

[0120] Sub-step 2053: The number of purple pixels in the expression analysis image is determined as the first fitness corresponding to the second messenger RNA and the first messenger RNA, respectively.

[0121] In the embodiments of this application, the host cells for the second messenger RNA and the first messenger RNA can be Pichia pastoris cells. DNA sequences are designed using the information from the second and first messenger RNAs, expression vectors are constructed, and these vectors are transferred to Pichia pastoris cells to form expression cells for gene expression, thereby obtaining the expressed target human albumin. The expression duration can be accumulated. If the expression duration is greater than or equal to a first preset duration, SDS-PAGE electrophoresis analysis can be used to analyze the expression analysis images corresponding to each host Pichia pastoris cell. Finally, the number of purple pixels in the expression analysis image can be determined as the first fitness corresponding to the second and first messenger RNAs, respectively. SDS-PAGE (sodium dodecyl sulfate-polyacrylamide gel electrophoresis) is a separation technique based on protein molecular weight differences. Sodium dodecyl sulfate (SDS) gives proteins a uniform negative charge, eliminating interference from molecular shape and intrinsic charge, and separating them solely based on molecular weight.

[0122] For example, such as Figure 4 As shown, Figure 4 This diagram illustrates the analysis results of an SDS-PAGE electrophoresis analysis provided in this application embodiment. The bright purple areas represent the expression analysis results of recombinant human albumin, while the blurred purple areas represent the expression analysis results of other proteins (which should be discarded). The expression efficiency is determined by the area of ​​the purple stripes (i.e., the number of purple pixels); a larger area indicates higher expression efficiency.

[0123] Step 206: Based on the first fitness, determine the first target codon individual from the first child codon individual and the first parent codon individual.

[0124] In embodiments of this application, a first target codon individual can be determined from a first child codon individual and a first parent codon individual based on a first fitness. Specifically, the individual corresponding to the highest first fitness (either the first child codon individual or the first parent codon individual) can be determined as the first target codon individual, or an individual with a first fitness greater than or equal to a preset threshold can be determined as the first target codon individual.

[0125] Optionally, step 206 may include the following sub-steps:

[0126] Sub-step 2061: Determine the second codon individual corresponding to the first fitness value that is greater than or equal to the second threshold from the first child codon individual and the first parent codon individual.

[0127] Sub-step 2062: Add the second codon individual to the first codon population, and randomly delete the third codon individual from the first codon population in the same number as the second codon individual, to obtain the iterated first codon population.

[0128] Sub-step 2063: Repeat the step of randomly selecting from the first codon population to obtain multiple first parent codon individuals.

[0129] Sub-step 2064: When the number of iterations of the first codon population reaches a preset number, determine the first target codon individual corresponding to the maximum value of the first fitness from the first codon population.

[0130] Optionally, the preset number of times is at least five times the first number of sequence amino acids contained in the second amino acid subsequence.

[0131] In the embodiments of this application, the first codon population can be iteratively updated to continuously execute the genetic optimization algorithm. First, a second codon individual with a first fitness greater than a second threshold can be selected from the first offspring codon individuals and the first parent codon individuals. Then, the second codon individual can be added to the first codon population, and a third codon individual is randomly deleted from the first codon population, resulting in an iterated first codon population. The number of deleted third codon individuals can be the same as the number of second codon individuals. Afterward, the step of randomly selecting multiple first parent codon individuals from the first codon population can be repeated to begin the next optimization. If the number of iterations of the first codon population reaches a preset number, the codon individual with the maximum first fitness can be selected as the first target codon individual. Through sub-steps 2061 to 2064, the first target codon individual with the highest first fitness can be obtained, which can improve the accuracy and reliability of the first target codon individual to some extent. Furthermore, the aforementioned preset number of iterations can be at least five times the first number of sequence amino acids contained in the second amino acid subsequence. For example, if the first quantity is 20, the preset number of times can be at least 100 to fully guarantee the optimization effect.

[0132] Step 207: Based on the codons corresponding to the amino acids of each first target individual in the first target codon individual, the first messenger RNA is reconstructed to obtain the first target messenger RNA.

[0133] In the embodiments of this application, the first messenger RNA can be reconstructed based on the codons corresponding to each amino acid in the first target codon individual to obtain the first target messenger RNA. During reconstruction, the reconstruction can be performed based on the position of the codons contained in the first target codon individual within the first amino acid subsequence to avoid altering the biological properties of the synthesized protein. Through steps 201 to 207, the first messenger RNA corresponding to the first amino acid subsequence can be optimized in one stage using an improved genetic optimization algorithm, which can improve the expression efficiency of the first target messenger RNA to a certain extent.

[0134] This application provides improved genetic optimization algorithm pseudocode as follows:

[0135] ;

[0136] Reference Figure 5 , Figure 5 This is a logic block diagram of a codon sequence optimization device for human albumin provided in an embodiment of this application. The device 500 may include:

[0137] Grouping module 501 is used to group the first amino acid sequence of the target human albumin to obtain multiple first amino acid sub-sequences;

[0138] The first determining module 502 is used to determine the second amino acid subsequence from the first amino acid subsequence;

[0139] The first expression optimization module 503 is used to optimize the expression of the first codon sequence of the second amino acid subsequence in the first messenger RNA corresponding to the first amino acid sequence using an improved genetic optimization algorithm to obtain the first target messenger RNA.

[0140] The second determining module 504 is used to determine a third amino acid subsequence other than the second amino acid subsequence from the first amino acid subsequence;

[0141] The second expression optimization module 505 is used to again employ an improved genetic optimization algorithm to optimize the expression of the second codon sequence of the third amino acid subsequence in the first target messenger RNA corresponding to the first amino acid sequence, thereby obtaining the second target messenger RNA.

[0142] Optionally, the first expression optimization module 503 includes:

[0143] A generation submodule is used to generate a first codon population based on the second amino acid subsequence; wherein the first codon population includes multiple first codon individuals; each first codon individual is composed of codons corresponding to at least two different positions of amino acids in the second amino acid subsequence;

[0144] The extraction submodule is used to randomly extract multiple first parent codon individuals from the first codon population;

[0145] The mutation module is used to mutate at least one codon corresponding to the first amino acid in the first parent codon individual to obtain the first daughter codon individual;

[0146] The first reconstruction submodule is used to reconstruct the first messenger RNA corresponding to the first amino acid sequence based on the codon corresponding to each second amino acid in the first daughter codon individual, to obtain the second messenger RNA;

[0147] The first determining submodule is used to determine the first fitness corresponding to each first parent codon individual and each first daughter codon individual based on the second messenger RNA and the first messenger RNA.

[0148] The second determining submodule is used to determine the first target codon individual from the first child codon individual and the first parent codon individual based on the first fitness.

[0149] The second reconstruction submodule is used to reconstruct the first messenger RNA based on the codon corresponding to each amino acid of the first target individual in the first target codon individual, so as to obtain the first target messenger RNA.

[0150] Optionally, the variant submodule includes:

[0151] A generation unit is used to generate the first random number;

[0152] The mutation unit is used to mutate at least one codon corresponding to the first amino acid in the first parent codon individual when the first random number is greater than or equal to the first threshold, so as to obtain the first daughter codon individual.

[0153] Optionally, the variant submodule includes:

[0154] The selection unit is used to randomly select at least one first codon element from the first parent codon individual;

[0155] The first determining unit is used to determine the element amino acid corresponding to the first codon element;

[0156] The second determining unit is used to determine the first codon pool corresponding to the element amino acid;

[0157] The replacement unit is used to randomly select a second codon element from the first codon pool and replace the first codon element in the first parent codon individual with the second codon element to obtain the first child codon individual.

[0158] Optionally, the first determining submodule includes:

[0159] The expression accumulation unit is used to transfer the second messenger RNA and the first messenger RNA into Pichia pastoris cells, express them in a preset culture environment, and accumulate the expression time.

[0160] The electrophoretic analysis unit is used to obtain expression analysis images corresponding to each host Pichia pastoris cell by using the SDS-PAGE electrophoretic analysis method when the expression duration is greater than or equal to a first preset duration.

[0161] The third determining unit is used to determine the number of purple pixels in the expression analysis image as the first fitness corresponding to the second messenger RNA and the first messenger RNA, respectively.

[0162] Optionally, the second determining submodule includes:

[0163] The fourth determining unit is used to determine, from the first child codon individual and the first parent codon individual, a second codon individual corresponding to a first fitness value greater than or equal to a second threshold.

[0164] An iterative unit is used to add the second codon individual to the first codon population and randomly delete the same number of third codon individuals as the second codon individuals from the first codon population to obtain the iterative first codon population.

[0165] An execution unit is configured to re-execute the step of randomly selecting multiple first parent codon individuals from the first codon population.

[0166] The fifth determining unit is used to determine the first target codon individual corresponding to the maximum value of the first fitness from the first codon population when the number of iterations of the first codon population reaches a preset number.

[0167] Optionally, the preset number of times is at least five times the first number of sequence amino acids contained in the second amino acid subsequence.

[0168] The codon sequence optimization device for human albumin in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a GPU BOX, mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the specific implementation.

[0169] The codon sequence optimization device for human albumin in this embodiment can be a device with an operating system. This operating system can be Android, Linux, Windows, or other possible operating systems; this embodiment does not specifically limit its use.

[0170] The codon sequence optimization device for human albumin provided in this application embodiment can achieve... Figure 1 and Figure 3 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0171] This application provides an electronic device, see [link to relevant documentation] Figure 6 The electronic device 60 includes a processor 601, a memory 602, and a computer program 6021 stored in the memory 602 and executable on the processor 601. When the processor 601 executes the program, it implements the codon sequence optimization method for human albumin of the foregoing embodiments.

[0172] This application also provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps in the human albumin codon sequence optimization method disclosed in this application.

[0173] This application also provides a computer program product that, when run on an electronic device, causes a processor to execute the steps in the human albumin codon sequence optimization method disclosed in this application.

[0174] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0175] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, electronic devices, and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0176] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0177] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0178] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0179] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0180] The codon sequence optimization method and apparatus for human albumin provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application areas based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for optimizing the codon sequence of human albumin, characterized in that, The method includes: The first amino acid sequence of the target human albumin was grouped to obtain multiple first amino acid sub-sequences; Determine the second amino acid subsequence from the first amino acid subsequence; An improved genetic optimization algorithm is used to optimize the expression of the first codon sequence of the second amino acid subsequence in the first messenger RNA corresponding to the first amino acid sequence, thereby obtaining the first target messenger RNA; From the first amino acid subsequence, a third amino acid subsequence other than the second amino acid subsequence is determined; the improved genetic optimization algorithm is used again to optimize the expression of the second codon sequence of the third amino acid subsequence in the first target messenger RNA corresponding to the first amino acid sequence, so as to obtain the second target messenger RNA. The improved genetic optimization algorithm is used to optimize the expression of the second amino acid subsequence in the first amino acid sequence to obtain the first target messenger RNA, including: Based on the second amino acid subsequence, a first codon population is generated; wherein, the first codon population includes multiple first codon individuals; each first codon individual is composed of codons corresponding to at least two different positions of amino acids in the second amino acid subsequence; Multiple first parent codon individuals are obtained by randomly selecting from the first codon population; Mutate at least one codon corresponding to the first amino acid in the first parent codon individual to obtain the first daughter codon individual; Based on the codons corresponding to each second amino acid in the first progeny codon individual, the first messenger RNA corresponding to the first amino acid sequence is reconstructed to obtain the second messenger RNA; Based on the second messenger RNA and the first messenger RNA, determine the first fitness corresponding to each first parent codon individual and each first daughter codon individual; Based on the first fitness, a first target codon individual is determined from the first child codon individual and the first parent codon individual; Based on the codons corresponding to the amino acids of each first target individual in the first target codon individual, the first messenger RNA is reconstructed to obtain the first target messenger RNA.

2. The method according to claim 1, characterized in that, The step of mutating the codon corresponding to at least one first amino acid in the first parent codon individual to obtain the first daughter codon individual includes: Generate the first random number; If the first random number is greater than or equal to the first threshold, the codon corresponding to at least one first amino acid in the first parent codon individual is mutated to obtain the first daughter codon individual.

3. The method according to claim 2, characterized in that, The step of mutating the codon corresponding to at least one first amino acid in the first parent codon individual to obtain the first daughter codon individual includes: In the first parent codon individual, at least one first codon element is randomly selected; Determine the element amino acid corresponding to the first codon element; Determine the first codon pool corresponding to the amino acid of the element; In the first codon pool, a second codon element is randomly selected, and the first codon element in the first parent codon individual is replaced with the second codon element to obtain the first child codon individual.

4. The method according to claim 3, characterized in that, The determination of the first fitness corresponding to each first parent codon individual and each first daughter codon individual based on the second messenger RNA and the first messenger RNA includes: The second messenger RNA and the first messenger RNA were respectively transferred to Pichia pastoris cells, expressed in a preset culture environment, and the expression time was accumulated. When the expression duration is greater than or equal to the first preset duration, the expression analysis images corresponding to each host Pichia pastoris cell are obtained by using the SDS-PAGE electrophoresis analysis method. The number of purple pixels in the expression analysis image is determined as the first fitness corresponding to the second messenger RNA and the first messenger RNA, respectively.

5. The method according to claim 4, characterized in that, The step of determining the first target codon individual from the first child codon individual and the first parent codon individual based on the first fitness includes: From the first child codon individual and the first parent codon individual, determine the second codon individual corresponding to the first fitness value that is greater than or equal to the second threshold; The second codon individual is added to the first codon population, and the third codon individual with the same number as the second codon individual is randomly deleted from the first codon population to obtain the iterated first codon population. The step of randomly selecting from the first codon population to obtain multiple first parent codon individuals is repeated. When the number of iterations of the first codon population reaches a preset number, the first target codon individual corresponding to the maximum value of the first fitness is determined from the first codon population.

6. The method according to claim 5, characterized in that, The preset number is at least five times the first number of sequence amino acids contained in the second amino acid subsequence.

7. A codon sequence optimization device for human albumin, characterized in that, The method is implemented using any one of claims 1-6, wherein the apparatus comprises: The grouping module is used to group the first amino acid sequence of the target human albumin to obtain multiple first amino acid sub-sequences; A first determining module is configured to determine a second amino acid subsequence from the first amino acid subsequence; The first expression optimization module is used to optimize the expression of the first codon sequence of the second amino acid subsequence in the first messenger RNA corresponding to the first amino acid sequence using an improved genetic optimization algorithm to obtain the first target messenger RNA. The second determining module is used to determine a third amino acid subsequence other than the second amino acid subsequence from the first amino acid subsequence; The second expression optimization module is used to further optimize the expression of the second codon sequence of the third amino acid subsequence in the first target messenger RNA corresponding to the first amino acid sequence by employing an improved genetic optimization algorithm to obtain the second target messenger RNA.

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