Methods and apparatus for evaluating peptide sequences

Abstract

This disclosure relates to a method and apparatus for evaluating peptide sequences. It pertains to the field of peptide detection technology. The method includes: detecting a set of detection peptide sequences and their content for each protein to be tested in a sample to be evaluated; determining a first similarity between each detection peptide sequence and a target peptide sequence based on a preset sequence similarity algorithm, the content of each detection peptide sequence, the content of the target peptide sequence in the target peptide sequence set corresponding to the reference sample, and the null distribution, thereby constructing an optimal match; determining a first proportion of significant detection peptide sequences in the sample to be evaluated based on the optimal match and a preset significance level; determining the total content of significant detection peptide sequences in the reference sample based on the content of the target peptide sequences corresponding to the significant detection peptide sequences; and evaluating the similarity between the sample to be evaluated and the reference sample at a preset significance level based on the first proportion and the total content. This method evaluates the similarity between different raw materials and breast milk from the perspectives of peptide content and sequence.

Description

Technical Field

[0001] This disclosure relates to the field of peptide detection technology, and in particular to a method and apparatus for evaluating peptide sequences. Background Technology

[0002] In related technologies, sequence similarity algorithms are commonly used to quantify the similarity between biological sequences (such as DNA and protein sequences) or text sequences, playing a crucial role in fields such as bioinformatics and natural language processing. While sequence similarity algorithms can assess the amino acid sequence similarity of peptide sequences, the content of peptides added to or introduced into the raw materials must also be considered. This renders the above algorithms unsuitable because peptides with high sequence similarity may exhibit significant content differences. Therefore, providing a method to assess both the similarity and content of peptides in milk powder is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] In view of this, this disclosure proposes a method and apparatus for evaluating polypeptide sequences.

[0004] According to one aspect of this disclosure, a method for evaluating a polypeptide sequence is provided, the method comprising:

[0005] The peptides in the sample to be evaluated are detected to obtain a set of detection peptide sequences corresponding to each protein to be tested and the content of each detection peptide sequence in the set of detection peptide sequences;

[0006] Based on a preset sequence similarity algorithm, the content of each of the detected peptide sequences, the content and zero distribution of the target peptide sequences in the target peptide sequence set corresponding to the reference sample, the first similarity between each of the detected peptide sequences in each of the target sources in the sample to be evaluated and the target peptide sequence is determined;

[0007] Based on the optimal matching between the set of detected polypeptide sequences and the set of target polypeptide sequences and the preset significance level, the first proportion of significant detected polypeptide sequences in the sample to be evaluated with a first similarity greater than or equal to a similarity threshold is determined.

[0008] Based on the content of the target polypeptide sequence corresponding to the significant detection polypeptide sequence, the total content of the significant detection polypeptide sequence in the reference sample is determined;

[0009] Based on the first proportion and the total content, the similarity between the sample to be evaluated and the reference sample is evaluated at the preset significance level.

[0010] In one possible implementation, each set of detection polypeptide sequences includes multiple detection polypeptide sequences derived from the same protein to be detected;

[0011] The zero distribution is constructed in advance based on random polypeptide sequences in the set of random polypeptide sequences of each protein to be tested corresponding to the sample to be evaluated, and target polypeptide sequences in the set of target polypeptide sequences corresponding to the reference sample.

[0012] In one possible implementation, the method further includes:

[0013] Based on the first similarity between the detected peptide sequence and the target peptide sequence, an optimal match is constructed between the set of detected peptide sequences and the set of target peptide sequences.

[0014] In one possible implementation, the method further includes:

[0015] For each protein to be tested in the sample to be evaluated, a set of random polypeptide sequences for each protein to be tested is constructed, wherein each set of random polypeptide sequences includes multiple random polypeptide sequences derived from the same protein to be tested.

[0016] Based on the preset sequence similarity algorithm, the zero distribution is constructed for the random polypeptide sequences in each set of random polypeptide sequences and the target polypeptide sequences in the set of target polypeptide sequences corresponding to the reference sample.

[0017] In one possible implementation, the method further includes:

[0018] The reference sample is tested for each target protein to determine the set of target polypeptide sequences corresponding to each target protein in the reference sample and the content of each target polypeptide sequence in the set of target polypeptide sequences;

[0019] The content of each target polypeptide sequence includes the abundance distribution of the target polypeptide sequences in the reference sample.

[0020] In one possible implementation, for each protein to be tested in the sample to be evaluated, a set of random polypeptide sequences for each protein to be tested is constructed, including:

[0021] The amino acid sequences of each of the proteins to be tested in the sample to be evaluated were determined;

[0022] Based on a preset length range and the amino acid sequence of each of the proteins to be tested, a random polypeptide sequence set of each of the proteins to be tested is generated by enumeration.

[0023] In one possible implementation, based on the preset sequence similarity algorithm, the null distribution is constructed for random polypeptide sequences in each of the random polypeptide sequence sets and target polypeptide sequences in the target polypeptide sequence set corresponding to the reference sample, including:

[0024] Based on a preset sequence similarity algorithm, the similarity between the random polypeptide sequence in each set of random polypeptide sequences and the target polypeptide sequence in the set of target polypeptide sequences corresponding to the target protein in the reference sample is calculated.

[0025] Based on the similarity between the random polypeptide sequence and the corresponding target polypeptide sequence, an optimal match is constructed between the set of random polypeptide sequences and the set of target polypeptide sequences;

[0026] A zero distribution for peptide sequences is constructed based on the optimal matching between the set of random peptide sequences and the set of target peptide sequences.

[0027] In one possible implementation, based on a preset sequence similarity algorithm, the content of each of the detected peptide sequences, the content and zero distribution of the target peptide sequences in the target peptide sequence set corresponding to the reference sample, a first similarity is determined between each of the detected peptide sequences in each of the target sources in the sample to be evaluated and the target peptide sequence, including:

[0028] Based on a preset sequence similarity algorithm, the original similarity between each detected polypeptide sequence and the target polypeptide sequence is calculated.

[0029] The filtering threshold is calculated based on the preset threshold and the zero distribution;

[0030] If the original similarity is greater than or equal to the filtering threshold, a first similarity between the detected polypeptide sequence and the target polypeptide sequence is calculated based on the original similarity, the zero distribution, the content of each target polypeptide sequence in the target polypeptide sequence set, and the content of each detected polypeptide sequence.

[0031] If the original similarity is less than the filtering threshold, zero is determined as the first similarity between the detected polypeptide sequence and the target polypeptide sequence.

[0032] In one possible implementation, when the original similarity is greater than or equal to the filtering threshold, a first similarity between the detected polypeptide sequence and the target polypeptide sequence is calculated based on the original similarity, the null distribution, the content of each target polypeptide sequence in the target polypeptide sequence set, and the content of each detected polypeptide sequence, including:

[0033] If the original similarity is greater than or equal to the filtering threshold, the original similarity is converted to the percentile in the zero distribution to obtain the normalized sequence similarity.

[0034] Based on the content of each target polypeptide sequence in the target polypeptide sequence set and the content of each detection polypeptide sequence, the content similarity between each detection polypeptide sequence and the target polypeptide sequence is calculated;

[0035] The original comprehensive score between each detected polypeptide sequence and the target polypeptide sequence is calculated based on the content similarity and the normalized sequence similarity.

[0036] Based on the target multipliers of each target polypeptide sequence and the original comprehensive score, the first similarity between the detected polypeptide sequence and the target polypeptide sequence is calculated; the target multipliers are calculated based on preset weight parameters.

[0037] In one possible implementation, based on the optimal match between the set of detected peptide sequences and the set of target peptide sequences and a preset significance level, a first proportion of significant detected peptide sequences in the sample to be evaluated with a first similarity greater than or equal to a similarity threshold is determined, including:

[0038] Based on the optimal match between the set of detected peptide sequences and the set of target peptide sequences, an original similarity set in the optimal match is determined, wherein the original similarity set includes the first similarity between the detected peptide sequences and the target peptide sequences matched in the optimal match;

[0039] A similarity threshold is determined from the null distribution based on a preset significance level.

[0040] Based on the original similarity set, a first number of significant detection polypeptide sequences with a first similarity greater than or equal to the similarity threshold is determined, and based on the first number, a first proportion of the significant detection polypeptide sequences in all detection polypeptide sequences is determined.

[0041] According to another aspect of this disclosure, an evaluation apparatus for a polypeptide sequence is provided, the apparatus comprising:

[0042] The peptide detection module is used to detect peptides in the sample to be evaluated, and to obtain a set of detection peptide sequences corresponding to each protein to be tested and the content of each detection peptide sequence in the set of detection peptide sequences.

[0043] The first similarity determination module is used to determine the first similarity between each of the detected peptide sequences in each of the target sources in the sample to be evaluated and the target peptide sequence based on a preset sequence similarity algorithm, the content of each of the detected peptide sequences, the content of the target peptide sequences in the target peptide sequence set corresponding to the reference sample, and the zero distribution.

[0044] The proportion determination module is used to determine the first proportion of significant detection peptide sequences in the sample to be evaluated whose first similarity is greater than or equal to a similarity threshold, based on the optimal matching between the detection peptide sequence set and the target peptide sequence set and a preset significance level.

[0045] The total content determination module is used to determine the total content of the significant detection peptide sequence in the reference sample based on the content of the target peptide sequence corresponding to the significant detection peptide sequence;

[0046] An evaluation module is used to evaluate the similarity between the sample to be evaluated and the reference sample at the preset significance level, based on the first proportion and the total content.

[0047] In one possible implementation, each set of detection polypeptide sequences includes multiple detection polypeptide sequences derived from the same protein to be detected;

[0048] The zero distribution is constructed in advance based on random polypeptide sequences in the set of random polypeptide sequences of each protein to be tested corresponding to the sample to be evaluated, and target polypeptide sequences in the set of target polypeptide sequences corresponding to the reference sample.

[0049] In one possible implementation, the device further includes:

[0050] A matching construction module is used to construct the optimal match between the set of detected peptide sequences and the set of target peptide sequences based on the first similarity between the detected peptide sequences and the target peptide sequences.

[0051] In one possible implementation, the device further includes:

[0052] The set construction module is used to construct a set of random polypeptide sequences for each protein to be tested in the sample to be evaluated. Each set of random polypeptide sequences includes multiple random polypeptide sequences from the same protein to be tested. The protein to be tested is a protein that has a common evolutionary origin and is functionally similar to the target protein in the reference sample.

[0053] The zero-distribution construction module is used to construct the zero distribution based on the preset sequence similarity algorithm for random polypeptide sequences in each of the random polypeptide sequence sets and target polypeptide sequences in the target polypeptide sequence set corresponding to the reference sample.

[0054] In one possible implementation, the device further includes:

[0055] The sample detection module is used to detect each target protein in the reference sample, and to determine the set of target polypeptide sequences corresponding to each target protein in the reference sample and the content of each target polypeptide sequence in the set of target polypeptide sequences.

[0056] The content of each target polypeptide sequence includes the abundance distribution of the target polypeptide sequences in the reference sample.

[0057] In one possible implementation, for each protein to be tested in the sample to be evaluated, a set of random polypeptide sequences for each protein to be tested is constructed, including:

[0058] The amino acid sequences of each of the proteins to be tested in the sample to be evaluated were determined;

[0059] Based on a preset length range and the amino acid sequence of each of the proteins to be tested, a random polypeptide sequence set of each of the proteins to be tested is generated by enumeration.

[0060] In one possible implementation, based on the preset sequence similarity algorithm, the null distribution is constructed for random polypeptide sequences in each of the random polypeptide sequence sets and target polypeptide sequences in the target polypeptide sequence set corresponding to the reference sample, including:

[0061] Based on a preset sequence similarity algorithm, the similarity between the random polypeptide sequence in each set of random polypeptide sequences and the target polypeptide sequence in the set of target polypeptide sequences corresponding to the target protein in the reference sample is calculated.

[0062] Based on the similarity between the random polypeptide sequence and the corresponding target polypeptide sequence, an optimal match is constructed between the set of random polypeptide sequences and the set of target polypeptide sequences;

[0063] A zero distribution for peptide sequences is constructed based on the optimal matching between the set of random peptide sequences and the set of target peptide sequences.

[0064] In one possible implementation, based on a preset sequence similarity algorithm, the content of each of the detected peptide sequences, the content and zero distribution of the target peptide sequences in the target peptide sequence set corresponding to the reference sample, a first similarity is determined between each of the detected peptide sequences in each of the target sources in the sample to be evaluated and the target peptide sequence, including:

[0065] Based on a preset sequence similarity algorithm, the original similarity between each detected polypeptide sequence and the target polypeptide sequence is calculated.

[0066] The filtering threshold is calculated based on the preset threshold and the zero distribution;

[0067] If the original similarity is greater than or equal to the filtering threshold, a first similarity between the detected polypeptide sequence and the target polypeptide sequence is calculated based on the original similarity, the zero distribution, the content of each target polypeptide sequence in the target polypeptide sequence set, and the content of each detected polypeptide sequence.

[0068] If the original similarity is less than the filtering threshold, zero is determined as the first similarity between the detected polypeptide sequence and the target polypeptide sequence.

[0069] In one possible implementation, when the original similarity is greater than or equal to the filtering threshold, a first similarity between the detected polypeptide sequence and the target polypeptide sequence is calculated based on the original similarity, the null distribution, the content of each target polypeptide sequence in the target polypeptide sequence set, and the content of each detected polypeptide sequence, including:

[0070] If the original similarity is greater than or equal to the filtering threshold, the original similarity is converted to the percentile in the zero distribution to obtain the normalized sequence similarity.

[0071] Based on the content of each target polypeptide sequence in the target polypeptide sequence set and the content of each detection polypeptide sequence, the content similarity between each detection polypeptide sequence and the target polypeptide sequence is calculated;

[0072] The original comprehensive score between each detected polypeptide sequence and the target polypeptide sequence is calculated based on the content similarity and the normalized sequence similarity.

[0073] Based on the target multipliers of each target polypeptide sequence and the original comprehensive score, the first similarity between the detected polypeptide sequence and the target polypeptide sequence is calculated; the target multipliers are calculated based on preset weight parameters.

[0074] In one possible implementation, based on the optimal match between the set of detected peptide sequences and the set of target peptide sequences and a preset significance level, a first proportion of significant detected peptide sequences in the sample to be evaluated with a first similarity greater than or equal to a similarity threshold is determined, including:

[0075] Based on the optimal match between the set of detected peptide sequences and the set of target peptide sequences, an original similarity set in the optimal match is determined, wherein the original similarity set includes the first similarity between the detected peptide sequences and the target peptide sequences matched in the optimal match;

[0076] A similarity threshold is determined from the null distribution based on a preset significance level.

[0077] Based on the original similarity set, a first number of significant detection polypeptide sequences with a first similarity greater than or equal to the similarity threshold is determined, and based on the first number, a first proportion of the significant detection polypeptide sequences in all detection polypeptide sequences is determined.

[0078] According to another aspect of this disclosure, an evaluation apparatus for a polypeptide sequence is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.

[0079] According to another aspect of this disclosure, a non-volatile computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described method.

[0080] According to another aspect of this disclosure, a computer program product is provided, including a computer program or a non-volatile computer-readable storage medium carrying the computer program, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0081] This disclosure provides a method and apparatus for evaluating peptide sequences. It can assess the similarity between different raw material peptides and breast milk peptides from the perspectives of content and sequence, guiding the development of protein raw materials and thereby developing products with peptide sequences and contents closer to those of breast milk.

[0082] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0083] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0084] Figure 1 A flowchart illustrating a method for evaluating a polypeptide sequence according to an embodiment of the present disclosure is shown.

[0085] Figure 2 A schematic flowchart of a method for evaluating a polypeptide sequence according to an embodiment of the present disclosure is shown.

[0086] Figure 3 The zero-distribution histogram constructed in Example 1 is shown.

[0087] Figure 4 The zero-distribution histogram constructed in Example 2 is shown.

[0088] Figure 5 This is a block diagram illustrating an apparatus 1900 for evaluating a polypeptide sequence according to an exemplary embodiment. Detailed Implementation

[0089] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0090] As used herein, the terms “comprising,” “including,” “having,” or variations thereof are open-ended and include one or more of the stated features, integrals, elements, steps, components, or functions, but do not exclude the presence or addition of one or more other features, integrals, elements, steps, components, functions, or groups thereof.

[0091] When an element is referred to as “connected,” “coupled,” “responding,” or a variation thereof relative to another element, it may be directly connected, coupled, or responding to another element, or there may be an intermediate element present.

[0092] Although the terms first, second, third, etc., may be used herein to describe various elements / operations, these elements / operations should not be limited by these terms. These terms are only used to distinguish one element / operation from another. Therefore, without departing from the teachings of the inventive concept, a first element / operation in some embodiments may be referred to as a second element / operation in other embodiments.

[0093] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0094] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0095] Endogenous free polypeptides in breast milk are a class of bioactive substances with important biological functions, mainly derived from breast milk proteins such as casein and whey protein. Casein, through specific enzymatic hydrolysis, releases various bioactive polypeptides such as casein phosphopeptides, while whey protein contains osteopontin and polyimmunoglobulin receptors, all of which are important sources of endogenous free polypeptides. In terms of efficacy, endogenous free polypeptides in breast milk exhibit multidimensional biological activities. On the one hand, some polypeptides possess antibacterial activity, inhibiting the growth of pathogens such as Escherichia coli and Staphylococcus aureus, thus building an immune barrier for infants against external pathogens. On the other hand, some polypeptides can regulate the structure of the infant's gut microbiota, promoting the proliferation of beneficial bacteria such as Bifidobacteria, improving the intestinal microecological environment, and thereby enhancing intestinal digestive and absorptive functions. Furthermore, endogenous free polypeptides also participate in regulating the infant's immune response, enhancing immune cell activity, promoting immunoglobulin synthesis, and contributing to the development and maturation of the infant's immune system. Simultaneously, some polypeptides can bind to receptors on the surface of intestinal cells, promoting the growth and repair of intestinal epithelial cells, which is significant for establishing the infant's intestinal mucosal barrier function.

[0096] Breast milk is the gold standard for infant formula design. Infant formula contains a certain amount of peptides through additives (such as directly adding casein phosphopeptides) and / or introduction from raw materials (such as hydrolyzed whey protein powder, whey protein powder, and raw cow's milk). However, because infant formula typically uses cow's milk or goat's milk as its basic raw material, and the protein sequences of cow's milk and goat's milk inherently differ from those of breast milk, differences in peptide sequences between infant formula and breast milk are unavoidable. This makes assessing the peptide similarity between breast milk and infant formula quite difficult.

[0097] In related technologies, sequence similarity algorithms are commonly used to quantify the similarity between biological sequences (such as DNA and protein sequences) or text sequences, playing a crucial role in fields such as bioinformatics and natural language processing. Among these, global matching algorithms, such as Needleman's algorithm, are particularly important. Represented by the Wunsch algorithm, based on the principle of dynamic programming, it constructs a two-dimensional score matrix to perform a globally optimal alignment of two complete sequences. This algorithm assigns corresponding scores to each position for base or amino acid matching, mismatch, insertion, and deletion. After traversing the matrix, it backtracks to obtain the globally optimal alignment result, making it suitable for sequence analysis with similar lengths and high overall similarity. Local matching algorithms, such as the Smith algorithm... The Waterman algorithm is a typical example. It also employs a dynamic programming strategy, but allows alignment to begin and end at any position in the sequence, focusing on finding the most similar local regions. This algorithm effectively identifies conserved domains or functional fragments in sequences by setting thresholds to control the start and end conditions of alignment. It is particularly suitable for analyzing sequences with local similarity, such as finding conserved motifs within protein families.

[0098] While the above algorithms can assess the amino acid sequence similarity of peptide sequences, the content of peptides added to or introduced into the raw materials must also be considered. This renders the algorithms unsuitable because peptides with high sequence similarity may exhibit significant content differences. Therefore, providing a method to assess both the similarity and content of peptides in milk powder is a pressing technical problem that needs to be solved.

[0099] To address the aforementioned technical problems, this disclosure provides a method and apparatus for evaluating peptide sequences. It can assess the similarity between different raw material peptides and breast milk peptides from both content and sequence perspectives, guiding the development of protein raw materials and thereby developing products with peptide sequences and contents more closely resembling those of breast milk.

[0100] like Figure 1 , Figure 2 As shown, the method for evaluating polypeptide sequences provided in this disclosure includes step S100. Step S108.

[0101] In step S100, the reference sample is tested for each target protein to determine the set of target polypeptide sequences corresponding to each target protein in the reference sample and the content of each target polypeptide sequence in the set of target polypeptide sequences. The number of reference samples can be one or more, and this disclosure does not limit this.

[0102] In the case where the sample to be evaluated is infant formula milk powder, the reference sample can be breast milk. The set of target polypeptide sequences for each target protein can then be represented as:

[0103]

[0104] in, This is a set of target polypeptide sequences derived from the target protein p in a reference sample (such as breast milk). For the first One derived from the target protein The target polypeptide sequence, the target polypeptide sequence It is composed of multiple amino acids. It is derived from the target protein in the reference sample. The number of target polypeptide sequences. Target protein. This could be proteins such as casein and whey protein in the reference sample that can serve as sources of bioactive substances, i.e. P1 and P2 represent various proteins in the reference sample that can provide active substances.

[0105] In some embodiments, the content of each target polypeptide sequence may include the abundance distribution of the target polypeptide sequences in the reference samples. When the number of reference samples is... In the case of, for Each target polypeptide sequence Target polypeptide sequence exist Abundance in a reference sample It can be represented as:

[0106]

[0107] Furthermore, the abundance distribution for each target polypeptide sequence can be calculated. :

[0108]

[0109] in, Indicates the target polypeptide sequence The lower limit of abundance, . Indicates the target polypeptide sequence The upper limit of abundance, . Indicates the target polypeptide sequence If the mean or median is then or .in, and It can be a percentile specified by the user, such as l=5 representing 5% and r=95 representing 95%.

[0110] In step S101, for each protein to be tested in the sample to be evaluated, a set of random polypeptide sequences is constructed for each protein to be tested. Each set of random polypeptide sequences includes multiple random polypeptide sequences derived from the same protein to be tested. There can be multiple proteins to be tested.

[0111] In some embodiments, the protein to be tested can be some or all of the various proteins present in the sample to be evaluated, to ensure a comprehensive and detailed evaluation of the sample. In some embodiments, the protein to be tested can also be various homologous proteins in the sample to be evaluated. Homologous proteins can be proteins that share a common evolutionary origin and are functionally similar to the target protein in the reference sample. This simplifies the evaluation process while improving its efficiency and speed.

[0112] In one possible implementation, if the proteins to be tested are all proteins present in the sample to be evaluated, step S101 may include: determining the amino acid sequence of each protein R to be tested in the sample to be evaluated; and then, based on a preset length range and the amino acid sequence of each protein R to be tested, generating a set of random polypeptide sequences of each protein R to be tested by enumeration. If the protein to be tested is a homologous protein, step S101 may include: based on the target protein in the reference sample. The corresponding target proteins in the sample to be evaluated were identified. Protein to be tested The amino acid sequence; then, based on a preset length range and each of the proteins to be detected; The amino acid sequence is used to generate each of the proteins to be tested by enumeration. A collection of random polypeptide sequences .

[0113] Each protein to be tested A collection of random polypeptide sequences It can be represented as:

[0114]

[0115] in, The protein to be tested in the sample to be evaluated (such as infant formula milk powder) The source of the target polypeptide sequence set. For the first One originating from the protein to be tested A random polypeptide sequence, the random polypeptide sequence Includes multiple amino acids, It is derived from the protein to be tested in the sample to be evaluated. The number of random polypeptide sequences. Protein to be tested. It can be the target protein in the reference sample. Proteins with a common evolutionary origin and similar functions. Proteins to be tested in the sample to be evaluated. It refers to proteins in the sample to be evaluated that can provide bioactive substances, that is... R1, R2... represent various proteins in the sample to be evaluated that can provide active substances.

[0116] In some embodiments, a preset length range can be set in advance based on the amino acid sequence length of the target peptide sequence in the reference sample, so that the length of the amino acid sequence of the random peptide sequence generated by enumeration is within the preset length range. For example, the preset length range can be 5 amino acids. 20, meaning each random polypeptide sequence consists of 5 amino acids. It is composed of 20 amino acids.

[0117] In step S102, based on a preset sequence similarity algorithm, for each of the random polypeptide sequence sets... random polypeptide sequences in The set of target polypeptide sequences corresponding to the reference sample The target polypeptide sequence Constructing a zero distribution .

[0118] In this embodiment, the preset sequence similarity algorithm can be set according to actual needs, such as Smith. This disclosure does not limit the use of algorithms such as Waterman, Needleman-Wunsch, and BLAST.

[0119] In one possible implementation, step S102 may include: calculating the similarity between random peptide sequences in each of the random peptide sequence sets and target peptide sequences in the target peptide sequence set corresponding to the target protein in the reference sample, based on a preset sequence similarity algorithm; and constructing an optimal match between the random peptide sequence sets and the target peptide sequence sets based on the similarity between the random peptide sequences and the corresponding target peptide sequences. Based on the optimal matching between the set of random polypeptide sequences and the set of target polypeptide sequences. Constructing a zero-distribution targeting the peptide sequence .

[0120] In some embodiments, the similarity calculated based on a preset sequence similarity algorithm can be represented by the following function:

[0121]

[0122] in, For calculation and A parameter representing the degree of amino acid sequence similarity between the two sequences. Among them, the parameter... This can include substitution matrices and empty space penalties to accurately quantify sequence similarity, resulting in... and The comparison score between them. That is, Indicates based on parameters Calculated target polypeptide sequence With random polypeptide sequences The similarity between them.

[0123] Optimal matching It can make and The optimal match between the random polypeptide sequence set and the target polypeptide sequence set, based on the highest similarity (i.e., the most closely matched sequence), can be determined using the following formula. :

[0124]

[0125] Then based on optimal matching Constructing a zero distribution targeting the peptide sequence .

[0126] Among them, zero distribution This can be expressed by the following formula:

[0127]

[0128] In step S103, the peptides in the sample to be evaluated are detected to obtain a set of detection peptide sequences corresponding to each of the proteins to be tested and the content of each detection peptide sequence in the set of detection peptide sequences. Each set of detection peptide sequences includes multiple detection peptide sequences derived from the same protein to be tested.

[0129] The set of detected polypeptide sequences can be represented as follows: .

[0130] in, For the first The target source k of the sample to be evaluated contains the proteins to be detected. The detection peptide sequence, the detection peptide sequence It is composed of multiple amino acids and actually exists in the sample to be evaluated. The protein to be tested in the sample to be evaluated originates from target source k. The number of detected peptide sequences, i.e. The sample to be evaluated may include at least one target source k.

[0131] For detecting polypeptide sequence sets Each detection peptide sequence can be identified through detection. content In some embodiments, the detection peptide sequence can be represented in the following manner. content , .in, express The content of.

[0132] In step S104, based on a preset sequence similarity algorithm, the content of each of the detected peptide sequences, the content and zero distribution of the target peptide sequences in the target peptide sequence set corresponding to the reference sample, the first similarity between each of the detected peptide sequences in each of the target sources in the sample to be evaluated and the target peptide sequence is determined.

[0133] In this embodiment, the first similarity may be each of the detection polypeptide sequences. With the target polypeptide sequence Weighted composite similarity between .in, , The first similarity can be determined through the following steps: 1 4. Calculations show that:

[0134] Step 1: Calculate the detection peptide sequence based on a preset sequence similarity algorithm. With the target polypeptide sequence Original similarity between , .

[0135] Step 2, based on the preset threshold Calculate the filtering threshold .in, quantiles ( ), that is yes of % quantile. For example, if Then take The 95th percentile, such as 80, is used as the filtering threshold.

[0136] Step 3: If the original similarity is greater than or equal to the filtering threshold, calculate the first similarity between the detected polypeptide sequence and the target polypeptide sequence based on the original similarity, the zero distribution, the content of each target polypeptide sequence in the target polypeptide sequence set, and the content of each detected polypeptide sequence.

[0137] In some embodiments, the calculation process in step 3 can be as follows:

[0138] In original similarity Greater than or equal to In the case of the original similarity Converting to percentiles in the zero distribution yields the normalized sequence similarity. Wherein, the normalized sequence similarity... Then, based on each target polypeptide sequence in the target polypeptide sequence set... The content of each of the aforementioned detection peptide sequences and the content of each detection peptide sequence are used to calculate the content of each detection peptide sequence. With the target polypeptide sequence The similarity in content between them.

[0139] Among them, in the target polypeptide sequence The content information is the content distribution. In this case, and The similarity in content between two samples is called abundance similarity. Therefore, abundance similarity is calculated. The process can be:

[0140] make Calculate abundance similarity :

[0141] if ,but .

[0142] if ,but .

[0143] if ,but .

[0144] if ,but .

[0145] Then, based on abundance similarity (i.e., content similarity) and normalized sequence similarity Calculate and The original composite score between ;Calculate the weighting parameter β based on the preset weighting parameter β for the above target multiplier Based on target multipliers The first similarity (i.e., weighted overall similarity) is calculated by combining the original comprehensive score with the original comprehensive score. .

[0146] Among them, the original comprehensive score Abundance similarity and normalized sequence similarity The product between them, that is, .

[0147] Target multiplier It can be calculated based on the following formula: .

[0148] Then the first similarity Can be the target multiplier Compared with the original composite score The product between them, that is, .

[0149] Step 4, based on the original similarity Less than In the case of zero, zero is determined as the first similarity between the detected polypeptide sequence and the target polypeptide sequence.

[0150] In step S105, based on the first similarity between the detected peptide sequence and the target peptide sequence, an optimal match is constructed between the set of detected peptide sequences and the set of target peptide sequences. .

[0151] Among them, the optimal match can be achieved. It can make and First similarity between The longest match, that is:

[0152]

[0153] In step S106, based on the optimal match between the detection peptide sequence set and the target peptide sequence set... and the preset significance level The first similarity in the samples to be evaluated is determined. Greater than or equal to the similarity threshold The first proportion of significant detection of peptide sequences In this way, the proportion of the target peptide sequences in the sample to be evaluated that are similar to those in the reference sample can be reflected by the first proportion. The similarity can be reflected by the degree of sequence similarity, and the higher the value of the first proportion, the stronger the conservation of the sequence.

[0154] In some embodiments, step S106 may include: determining the optimal match. The original similarity set in Based on the preset significance level Distribution from zero Calculate the similarity threshold Based on the original similarity set The first similarity is determined to be greater than or equal to the similarity threshold. The first significant detection of peptide sequences and according to the first quantity The significant detection peptide sequence was determined to have the highest proportion among all detection peptide sequences. .

[0155] Among them, the original similarity set It can be represented as: .

[0156] Among them, significance level This is a preset value, representing the salience level specified by the user. quantiles ( ), that is yes of Quantiles.

[0157] The significant detection of the polypeptide sequence corresponds to the first similarity. and The detection of polypeptide sequences.

[0158] and then,

[0159] in, for The number of matches in .

[0160] In step S107, based on the content of the target polypeptide sequence corresponding to the significant detection polypeptide sequence, the total content of the significant detection polypeptide sequence in the reference sample is determined. In this way, the total content reflects the overall abundance level of the detected peptide sequence in the reference sample, and the similarity is reflected by the similarity of the content. The higher the total content value, the more likely it is to match the main functional peptide in the reference sample.

[0161] Among them, total content It can be represented as:

[0162]

[0163] In step S108, based on the first proportion and the total content Evaluation at the preset significance level The similarity between the sample to be evaluated and the reference sample is described below.

[0164] To further illustrate the methods provided in the embodiments of this disclosure, the following examples 1 and 2 are provided for illustrative purposes.

[0165] First, both the reference sample and the sample to be evaluated need to undergo the following processing, hereinafter referred to as samples. Specifically,

[0166] The first step is sample defatting and peptide extraction and purification:

[0167] Take each sample Centrifuge at 4°C and 16,000 × g for 10 minutes. Aspirate the defatted emulsion layer located beneath the fat layer. Repeat the centrifugation and aspiration steps until no visible fat layer forms.

[0168] Step 2, protein precipitation:

[0169] Add to 200 μL of skim milk Trichloroacetic acid (TCA) solution. Mix thoroughly using a vortex mixer, then centrifuge at 4°C and 3,000 × g for 10 minutes. Collect the supernatant.

[0170] The third step is peptide adsorption, concentration, and impurity removal:

[0171] The supernatant (rich in peptides) was treated using a C18 solid-phase extraction (SPE) column with a bed volume of 200 mg to adsorb and concentrate the peptides and purify the column to remove impurities (mainly oligosaccharides and salts). The peptides were eluted from the SPE column using a solution containing 80% acetonitrile (ACN) and 0.1% trifluoroacetic acid (TFA). The eluent sample was collected and freeze-dried under vacuum. The sample was reconstituted before mass spectrometry analysis.

[0172] Step 4, liquid chromatography Mass spectrometry (LC) MS / MS analysis:

[0173] Liquid chromatography separation: using EASY The analysis was performed using an nLC 1200 high-performance liquid chromatography system, etc. The elution gradient was set as follows: within 50 minutes, mobile phase B (containing...) The proportion of formic acid in acetonitrile increased linearly from 5% to 30% (mobile phase A was a mixture of formic acid and acetonitrile). (An aqueous solution of formic acid). Subsequently, the proportion of mobile phase B was rapidly increased to 50% within 3 minutes for column washing (mobile phases A and B both contain 50% water and...). Formic acid).

[0174] Mass spectrometry detection: Data was acquired using a Thermo Scientific Orbitrap Fusion Lumos mass spectrometer in positive ion mode. Key parameters for the Thermo Scientific Orbitrap Fusion Lumos mass spectrometer were set as follows: electrospray voltage of 2400V and mass spectrometry scan range of 400 m / z. 1500 m / z, first-order mass spectrometry resolution of 120,000 (defined at m / z 200), automatic gain control (AGC) target value is The maximum injection time is 50 ms, the fragmentation mode is collision-induced dissociation (CID), the collision energy is 35%, the mass spectrometry cycle time is 3 seconds (data-dependent acquisition mode, automatic selection of precursor ions), the precursor ion exclusion time is 60 seconds after fragmentation (mass tolerance ±10 ppm), and the selection criterion for fragmentation precursor ions is the ion with the highest signal intensity; ion intensity threshold. ; Charge state to Fragment ion detection is performed using an automatic scanning range detection system with an ion trap.

[0175] Database retrieval and data analysis: The original spectra were searched using software such as Thermo Proteome Discoverer (v2.4). Specifically, the human protein database in uniprot was used to analyze breast milk samples, and the bovine milk protein database in uniprot was used to analyze formula milk powder and raw material samples. The search settings were as follows: "Min. Precursor Mass" was 300 Da, "Max. Precursor Mass" was 5000 Da, and the selected enzyme type was No. Enzyme (Unspecific) was used, with a minimum peptide length of 4 and a maximum of 144. The "Precursor Mass Tolerance" was set to 10 ppm, and the "fragment Mass Tolerance" to 0.8 Da. Methionine oxidation and phosphorylation of serine and threonine were considered variable modifications, with no fixed modifications. Only peptides with high confidence were included (P < 0.01). Peptide sequences with multiple modifications were grouped into a single peptide for counting. The numerical value measures the number of unique peptide sequences identified in a sample. Abundance was measured as the area under the elution peak (ion intensity). Peptide content was analyzed as a percentage of total abundance.

[0176] Example 1: Using the above method to test different samples with breast milk β Similarity assessment analysis of casein peptides

[0177] 1. Reference sample, sample to be evaluated

[0178] Two hundred breast milk samples were randomly selected from the breast milk sample bank (samples covering Beijing, Guangzhou, Weihai, Jinhua, Lanzhou, Chengdu, Wuhan, and Harbin) as reference samples. The content of free polypeptides in the breast milk was detected using the method described above, yielding the corresponding target protein (β). The target polypeptide sequence set of casein and the content of each target polypeptide sequence in the target polypeptide sequence set. Formula milk powder A (with added ordinary casein phosphopeptide), B (without added casein phosphopeptide), and casein phosphopeptide raw material C (β-casein) are selected. Casein phosphopeptide was used as the sample to be evaluated, and the free polypeptide content was detected using the same method to obtain a set of detectable polypeptide sequences and the content of each detectable polypeptide sequence in the set of detectable polypeptide sequences.

[0179] 2. Milk beta Casein random polypeptide sequence and breast milk β Construction of zero-distribution casein peptide similarity

[0180] Obtain the FASTA file of bovine (Bos taurus) protein amino acid sequence from uniprot.org, parse the FASTA file using the biopython library in Python, and write a script to enumerate bovine β-proteins according to a preset length range (5~20). Casein polypeptide sequences were obtained, yielding 3400 unique random polypeptide sequences. These sequences were then screened from 200 breast milk samples for β-cell protein. A total of 1464 unique target polypeptide sequences (breast milk β) were obtained from casein-derived polypeptides. Casein peptides). A similarity model was constructed using a self-written Python script, such as... Figure 3 The zero distribution D shown null (See step S102). This example uses Smith. The Waterman algorithm is used as the default sequence similarity algorithm to calculate sequence similarity (with the empty space penalty set as follows). 10. The penalty for open looks extended is set as follows: 0.5, amino acid substitution matrix set to BLOSUM62). Figure 3 As shown, when the preset length range is set to 5~20, the β of milk The random polypeptide sequence of casein is similar to that of breast milk β. In the null distribution of casein peptide similarity, the minimum value is 11, the maximum value is 85, the median is 39, and the 95% confidence interval is [21, 66].

[0181] 3. Calculation of weighted comprehensive similarity (i.e., first similarity)

[0182] Calculate each β in the breast milk sample The abundance of target polypeptide sequences derived from casein accounts for a significant portion of the total β-protein content. The percentage of casein abundance, and the 25th and 75th percentiles of that percentage, are used as the content range of the target polypeptide sequence. Calculate the β-carotene content in formula milk powders A and B and casein phosphopeptide ingredient C. The detection of casein-derived peptide sequences as a percentage of total β in the sample. The percentage of casein peptides. Then, based on the above step S104, a weighted comprehensive similarity is constructed. The weighted overall similarity is obtained by using the Hungarian algorithm. Maximum value of the sum and its optimal matching .

[0183] The above method was then used to evaluate the free polypeptide content in formula milk powders A, B, and C, and the β-protein content in the samples to be evaluated was obtained. Number of casein polypeptide sequences. β in different samples. The number of polypeptide sequences detected in casein varied considerably, with the highest number (428) in sample A, the lowest (116) in sample C, and a middle number (168) in sample B.

[0184] Furthermore, Set to 10, Set to 50, set different The given calculations were further performed using a self-written Python script. Below and The results are shown in Table 1 below. Referring to Table 1, it can be seen that regardless of... What is the value of , and what is the total content? C is significantly higher than both A and B, indicating that the β source of C is... The total casein peptide content was significantly higher than that of the two formula powders, a result consistent with the characteristics of C (C is β). Casein phosphopeptide raw material derived from casein hydrolysis). In addition, total content always This result is consistent with the formulations of both (A added regular casein phosphopeptides, B did not add casein phosphopeptides). First percentage have This result is consistent with the total content.

[0185] Table 1 Calculation Results of Example 1

[0186]

[0187] Example 2: Using the above method to perform similarity analysis between different samples and breast milk osteopontin (OPN) peptide.

[0188] 1. Reference sample, sample to be evaluated

[0189] Two hundred breast milk samples were randomly selected from the breast milk sample bank (samples covering Beijing, Guangzhou, Weihai, Jinhua, Lanzhou, Chengdu, Wuhan, and Harbin) as reference samples. The free polypeptide content in the breast milk was detected using the method described above, obtaining a set of target polypeptide sequences corresponding to the target protein OPN and the content of each target polypeptide sequence in the set. Formula milk powders D (whey protein with added osteopontin), E (whey protein without added osteopontin), regular raw milk (F), whey protein concentrate (WPC1), and whey protein concentrate (WPC2) were selected as samples to be evaluated. The free polypeptide content was detected using the same method, obtaining a set of detected polypeptide sequences and the content of each detected polypeptide sequence in the set.

[0190] 2. Construction of zero-distribution similarity between bovine milk OPN random peptide sequences and breast milk OPN peptides

[0191] The FASTA file of bovine (Bos taurus) protein amino acid sequences was obtained from uniprot.org. The FASTA file was parsed using the biopython library in Python. A script was written to enumerate bovine milk OPN peptide sequences within a preset length range (5-20), resulting in 4264 unique peptides (random peptide sequences). Peptides derived from OPN in 200 breast milk samples were screened, yielding 628 unique target peptide sequences (breast milk OPN peptides). A self-written Python script was used to construct... Figure 4 The zero distribution of similarity shown in the figure D null This example uses Smith. Waterman algorithm calculates sequence similarity (empty space open penalty set as follows) 10. The penalty for open looks extended is set as follows: 0.5, amino acid substitution matrix set to BLOSUM62). Figure 4 As shown, when the preset length range is set to 5~20, the minimum value of the zero distribution of similarity between the bovine milk OPN random polypeptide sequence and the breast milk OPN target polypeptide sequence is 16, the maximum value is 102, the median is 52, and the 95% confidence interval is [22, 91].

[0192] 3. Calculation of weighted comprehensive similarity (i.e., first similarity)

[0193] Calculate the percentage of the abundance of each OPN-derived target peptide sequence in the breast milk sample relative to the total OPN abundance. The 5th and 95th percentiles of this percentage are used as the content range for that target peptide sequence. Calculate the percentage of the detected OPN-derived peptide sequences relative to the total OPN peptides in formula milk powders D (whey protein with added osteopontin), E (whey protein without added osteopontin), regular raw milk, whey protein concentrate WPC1, and whey protein concentrate WPC2. Then, based on the above step S104, construct a weighted comprehensive similarity score. The weighted overall similarity is obtained by using the Hungarian algorithm. Maximum value of the sum and its optimal matching .

[0194] Furthermore, the above methods were used to evaluate the free polypeptide content in formula milk powders D (whey protein with added osteopontin), E (whey protein without added osteopontin), regular raw milk (F), whey protein concentrate (WPC1), and whey protein concentrate (WPC2). The number of detected polypeptide sequences in OPN varied significantly among different samples: 68 sequences in sample D, the fewest (46) in sample E, 55 in sample F, and 88 in sample WPC2.

[0195] Furthermore, Set to 1, Set to 50, set different The given calculations were further performed using a self-written Python script. Below and The results are shown in Table 2 below. Referring to Table 2, it can be seen that... When set to 0.25 (i.e., the sequence similarity is higher than the 75th percentile of the null distribution), WPC2 is the highest, F is the lowest. Formula milk powders fortified with osteopontin have varying total D content. The levels were higher than those in formula milk powder without osteopontin.

[0196] Table 2 Calculation Results (Example 2)

[0197]

[0198] This disclosure also provides an evaluation device for polypeptide sequences, the device comprising:

[0199] The peptide detection module is used to detect peptides in the sample to be evaluated, and to obtain a set of detection peptide sequences corresponding to each protein to be tested and the content of each detection peptide sequence in the set of detection peptide sequences.

[0200] The first similarity determination module is used to determine the first similarity between each of the detected peptide sequences in each of the target sources in the sample to be evaluated and the target peptide sequence based on a preset sequence similarity algorithm, the content of each of the detected peptide sequences, the content of the target peptide sequences in the target peptide sequence set corresponding to the reference sample, and the zero distribution.

[0201] The proportion determination module is used to determine the first proportion of significant detection peptide sequences in the sample to be evaluated whose first similarity is greater than or equal to a similarity threshold, based on the optimal matching between the detection peptide sequence set and the target peptide sequence set and a preset significance level.

[0202] The total content determination module is used to determine the total content of the significant detection peptide sequence in the reference sample based on the content of the target peptide sequence corresponding to the significant detection peptide sequence;

[0203] An evaluation module is used to evaluate the similarity between the sample to be evaluated and the reference sample at the preset significance level, based on the first proportion and the total content.

[0204] In one possible implementation, each set of detection polypeptide sequences includes multiple detection polypeptide sequences derived from the same protein to be detected;

[0205] The zero distribution is constructed in advance based on random polypeptide sequences in the set of random polypeptide sequences of each protein to be tested corresponding to the sample to be evaluated, and target polypeptide sequences in the set of target polypeptide sequences corresponding to the reference sample.

[0206] In one possible implementation, the device further includes:

[0207] A matching construction module is used to construct the optimal match between the set of detected peptide sequences and the set of target peptide sequences based on the first similarity between the detected peptide sequences and the target peptide sequences.

[0208] In one possible implementation, the device further includes:

[0209] The set construction module is used to construct a set of random polypeptide sequences for each protein to be tested in the sample to be evaluated. Each set of random polypeptide sequences includes multiple random polypeptide sequences from the same protein to be tested. The protein to be tested is a protein that has a common evolutionary origin and is functionally similar to the target protein in the reference sample.

[0210] The zero-distribution construction module is used to construct the zero distribution based on the preset sequence similarity algorithm for random polypeptide sequences in each of the random polypeptide sequence sets and target polypeptide sequences in the target polypeptide sequence set corresponding to the reference sample.

[0211] In one possible implementation, the device further includes:

[0212] The sample detection module is used to detect each target protein in the reference sample, and to determine the set of target polypeptide sequences corresponding to each target protein in the reference sample and the content of each target polypeptide sequence in the set of target polypeptide sequences.

[0213] The content of each target polypeptide sequence includes the abundance distribution of the target polypeptide sequences in the reference sample.

[0214] In one possible implementation, for each protein to be tested in the sample to be evaluated, a set of random polypeptide sequences for each protein to be tested is constructed, including:

[0215] The amino acid sequences of each of the proteins to be tested in the sample to be evaluated were determined;

[0216] Based on a preset length range and the amino acid sequence of each of the proteins to be tested, a random polypeptide sequence set of each of the proteins to be tested is generated by enumeration.

[0217] In one possible implementation, based on the preset sequence similarity algorithm, the null distribution is constructed for random polypeptide sequences in each of the random polypeptide sequence sets and target polypeptide sequences in the target polypeptide sequence set corresponding to the reference sample, including:

[0218] Based on a preset sequence similarity algorithm, the similarity between the random polypeptide sequence in each set of random polypeptide sequences and the target polypeptide sequence in the set of target polypeptide sequences corresponding to the target protein in the reference sample is calculated.

[0219] Based on the similarity between the random polypeptide sequence and the corresponding target polypeptide sequence, an optimal match is constructed between the set of random polypeptide sequences and the set of target polypeptide sequences;

[0220] A zero distribution for peptide sequences is constructed based on the optimal matching between the set of random peptide sequences and the set of target peptide sequences.

[0221] In one possible implementation, based on a preset sequence similarity algorithm, the content of each of the detected peptide sequences, the content and zero distribution of the target peptide sequences in the target peptide sequence set corresponding to the reference sample, a first similarity is determined between each of the detected peptide sequences in each of the target sources in the sample to be evaluated and the target peptide sequence, including:

[0222] Based on a preset sequence similarity algorithm, the original similarity between each detected polypeptide sequence and the target polypeptide sequence is calculated.

[0223] The filtering threshold is calculated based on the preset threshold and the zero distribution;

[0224] If the original similarity is greater than or equal to the filtering threshold, a first similarity between the detected polypeptide sequence and the target polypeptide sequence is calculated based on the original similarity, the zero distribution, the content of each target polypeptide sequence in the target polypeptide sequence set, and the content of each detected polypeptide sequence.

[0225] If the original similarity is less than the filtering threshold, zero is determined as the first similarity between the detected polypeptide sequence and the target polypeptide sequence.

[0226] In one possible implementation, when the original similarity is greater than or equal to the filtering threshold, a first similarity between the detected polypeptide sequence and the target polypeptide sequence is calculated based on the original similarity, the null distribution, the content of each target polypeptide sequence in the target polypeptide sequence set, and the content of each detected polypeptide sequence, including:

[0227] If the original similarity is greater than or equal to the filtering threshold, the original similarity is converted to the percentile in the zero distribution to obtain the normalized sequence similarity.

[0228] Based on the content of each target polypeptide sequence in the target polypeptide sequence set and the content of each detection polypeptide sequence, the content similarity between each detection polypeptide sequence and the target polypeptide sequence is calculated;

[0229] The original comprehensive score between each detected polypeptide sequence and the target polypeptide sequence is calculated based on the content similarity and the normalized sequence similarity.

[0230] Based on the target multipliers of each target polypeptide sequence and the original comprehensive score, the first similarity between the detected polypeptide sequence and the target polypeptide sequence is calculated; the target multipliers are calculated based on preset weight parameters.

[0231] In one possible implementation, based on the optimal match between the set of detected peptide sequences and the set of target peptide sequences and a preset significance level, a first proportion of significant detected peptide sequences in the sample to be evaluated with a first similarity greater than or equal to a similarity threshold is determined, including:

[0232] Based on the optimal match between the set of detected peptide sequences and the set of target peptide sequences, an original similarity set in the optimal match is determined, wherein the original similarity set includes the first similarity between the detected peptide sequences and the target peptide sequences matched in the optimal match;

[0233] A similarity threshold is determined from the null distribution based on a preset significance level.

[0234] Based on the original similarity set, a first number of significant detection polypeptide sequences with a first similarity greater than or equal to the similarity threshold is determined, and based on the first number, a first proportion of the significant detection polypeptide sequences in all detection polypeptide sequences is determined.

[0235] It should be noted that although the above embodiments are provided as examples of evaluation methods and apparatus for peptide sequences, those skilled in the art will understand that this disclosure is not limited thereto. In fact, users can flexibly set each step and module according to their personal preferences and / or actual application scenarios, as long as it conforms to the technical solution of this disclosure. For example, after raw material development engineers perform peptidomics detection on peptide samples (i.e., samples to be evaluated) obtained under different process conditions, they can use the above-mentioned evaluation methods and apparatus for peptide sequences to evaluate the similarity between different peptide samples and target source peptides (i.e., reference samples) such as breast milk, thereby guiding raw material development; furthermore, raw material development engineers can establish a predictive model of the similarity between peptide samples and breast milk peptides under different process conditions, accurately predicting the degree of closeness between peptide samples and breast milk under different processes, thereby improving the efficiency of raw material development. Product development engineers and raw material engineers can use this method to evaluate the closeness between peptide raw materials from different suppliers and breast milk peptides. If the raw material contains peptides from multiple protein sources, the similarity can be calculated separately and then the average value can be calculated for evaluation.

[0236] It is important to emphasize that the polypeptide sequence similarity function mentioned in this method... Users can freely choose according to their needs and research objectives, in addition to the commonly used Smith... The Waterman algorithm, Needleman-Wunsch algorithm, BLAST algorithm, and any other type of sequence similarity function based on pre-trained models (such as ProteinBert) are all acceptable, and this disclosure does not impose any restrictions on this. Furthermore, users can define their own similarity functions, such as adding information about peptide post-translational modifications (phosphorylation and glycosylation, etc.) to the function, and this disclosure does not impose any restrictions on this.

[0237] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0238] This disclosure also provides an evaluation apparatus for polypeptide sequences, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0239] This disclosure also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0240] This disclosure also provides a computer program product, including a computer program or a non-volatile computer-readable storage medium carrying the computer program, wherein the computer program, when executed by a processor, implements the steps of the above method.

[0241] Figure 5 This is a block diagram illustrating an apparatus 1900 for evaluating a peptide sequence according to an exemplary embodiment. For example, apparatus 1900 may be provided as a server or terminal device. (Refer to...) Figure 5 The apparatus 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0242] Device 1900 may also include a power supply component 1926 configured to perform power management of device 1900, a wired or wireless network interface 1950 configured to connect device 1900 to a network, and an input / output interface 1958 (I / O interface). Device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TM macOS XTM Unix TM Linux TM FreeBSD TM Or similar.

[0243] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of the device 1900 to perform the above-described method.

[0244] Computer-readable storage media can be tangible devices capable of holding and storing programs / instructions used by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), and portable compact disc read-only memory (CD). ROM, digital multifunction disc (DVD), memory stick, floppy disk, mechanical encoding device, such as a punch card or recessed protrusion structure storing instructions thereon, and any suitable combination thereof. The computer-readable storage medium used herein is not to be construed as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0245] The computer program (or computer-readable program instructions) described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage medium in the respective computing / processing device.

[0246] The computer program (or computer program instructions) used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions to implement various aspects of this disclosure.

[0247] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. 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-readable program instructions.

[0248] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0249] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0250] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0251] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method of evaluation for a polypeptide sequence, characterized by, The method comprises: detecting polypeptides in a sample to be evaluated to obtain a set of detected polypeptide sequences corresponding to each protein to be detected and the content of each detected polypeptide sequence in the set of detected polypeptide sequences; determining, based on a preset sequence similarity algorithm, the content of each detected polypeptide sequence, the content of a target polypeptide sequence in a set of target polypeptide sequences corresponding to a reference sample, and a zero distribution, a first similarity between each detected polypeptide sequence and the target polypeptide sequence in each target source in the sample to be evaluated; determining, according to an optimal matching between the set of detected polypeptide sequences and the set of target polypeptide sequences and a preset significance level, a first proportion of significant detected polypeptide sequences in the sample to be evaluated whose first similarity is greater than or equal to a similarity threshold; determining, based on the content of the target polypeptide sequence corresponding to the significant detected polypeptide sequence, a total content of the significant detected polypeptide sequence in the reference sample; evaluating, based on the first proportion and the total content, a similarity between the sample to be evaluated and the reference sample at the preset significance level; each set of detected polypeptide sequences includes a plurality of detected polypeptide sequences derived from the same protein to be detected; The zero distribution is constructed in advance based on a random polypeptide sequence in each set of random polypeptide sequences corresponding to each protein to be detected in the sample to be evaluated, and a target polypeptide sequence in a set of target polypeptide sequences corresponding to the reference sample.

2. The method of claim 1, wherein, The method further comprises: constructing, based on the first similarity between the detected polypeptide sequences and the target polypeptide sequences, an optimal matching between the set of detected polypeptide sequences and the set of target polypeptide sequences.

3. The method of claim 1, wherein, The method further comprises: constructing, for each protein to be detected in the sample to be evaluated, a set of random polypeptide sequences for each protein to be detected, each set of random polypeptide sequences including a plurality of random polypeptide sequences derived from the same protein to be detected; constructing, based on the preset sequence similarity algorithm, the zero distribution for a random polypeptide sequence in each set of random polypeptide sequences and a target polypeptide sequence in a set of target polypeptide sequences corresponding to the reference sample.

4. The method of claim 1, wherein, The method further comprises: detecting, for each target protein, a reference sample to determine a set of target polypeptide sequences corresponding to each target protein in the reference sample and the content of each target polypeptide sequence in the set of target polypeptide sequences; The content of each target polypeptide sequence includes an abundance distribution of the target polypeptide sequence in the reference sample.

5. The method of claim 1, wherein, For each protein to be detected in the sample to be evaluated, a set of random polypeptide sequences for each protein to be detected is constructed, including: determining the amino acid sequence of each protein to be detected in the sample to be evaluated; generating, based on a preset length range and the amino acid sequence of each protein to be detected, a set of random polypeptide sequences for each protein to be detected by enumeration.

6. The method of claim 3, wherein, Constructing, based on the preset sequence similarity algorithm, the zero distribution for a random polypeptide sequence in each set of random polypeptide sequences and a target polypeptide sequence in a set of target polypeptide sequences corresponding to the reference sample, includes: calculating, based on the preset sequence similarity algorithm, similarity of each random polypeptide sequence in the random polypeptide sequence set and a target polypeptide sequence corresponding to a target protein matched in the reference sample; constructing, based on the similarity of the random polypeptide sequence and the corresponding target polypeptide sequence, an optimal matching between the random polypeptide sequence set and the target polypeptide sequence set; constructing, based on the optimal matching between the random polypeptide sequence set and the target polypeptide sequence set, a zero distribution for polypeptide sequences.

7. The method of claim 1, wherein, determining, based on the preset sequence similarity algorithm, the content of each detection polypeptide sequence, the content of the target polypeptide sequence in the target polypeptide sequence set corresponding to the reference sample, and the zero distribution, a first similarity between each detection polypeptide sequence and the target polypeptide sequence in each target source in the sample to be evaluated, including: calculating, based on the preset sequence similarity algorithm, an original similarity between each detection polypeptide sequence and the target polypeptide sequence; calculating, based on a preset threshold and the zero distribution, a filtering threshold; in a case where the original similarity is greater than or equal to the filtering threshold, calculating, based on the original similarity, the zero distribution, the content of each target polypeptide sequence in the target polypeptide sequence set, and the content of each detection polypeptide sequence, a first similarity between the detection polypeptide sequence and the target polypeptide sequence; in a case where the original similarity is less than the filtering threshold, determining zero as the first similarity between the corresponding detection polypeptide sequence and the target polypeptide sequence.

8. The method of claim 7, wherein, in a case where the original similarity is greater than or equal to the filtering threshold, calculating, based on the original similarity, the zero distribution, the content of each target polypeptide sequence in the target polypeptide sequence set, and the content of each detection polypeptide sequence, a first similarity between the detection polypeptide sequence and the target polypeptide sequence, including: in a case where the original similarity is greater than or equal to the filtering threshold, converting the original similarity into a percentile in the zero distribution to obtain a normalized sequence similarity; calculating, based on the content of each target polypeptide sequence in the target polypeptide sequence set and the content of each detection polypeptide sequence, a content similarity between each detection polypeptide sequence and the target polypeptide sequence; calculating, based on the content similarity and the normalized sequence similarity, an original comprehensive score between each detection polypeptide sequence and the target polypeptide sequence; calculating, based on a target multiplier of each target polypeptide sequence and the original comprehensive score, a first similarity between the detection polypeptide sequence and the target polypeptide sequence; the target multiplier is calculated based on a preset weight parameter.

9. The method of claim 1, wherein, determining, according to the optimal matching between the detection polypeptide sequence set and the target polypeptide sequence set and a preset significance level, a first proportion of significant detection polypeptide sequences in the sample to be evaluated, the first similarity of which is greater than or equal to a similarity threshold, including: determine, according to the optimal matching between the set of detection polypeptide sequences and the set of target polypeptide sequences, a set of original similarities in the optimal matching, the set of original similarities comprising first similarities between matched detection polypeptide sequences and target polypeptide sequences in the optimal matching; determine, based on a preset significance level, a similarity threshold from the zero distribution; determine, based on the set of original similarities, a first number of significant detection polypeptide sequences with first similarities greater than or equal to the similarity threshold, and determine, based on the first number, a first proportion of the significant detection polypeptide sequences in all detection polypeptide sequences.

10. An evaluation device for a polypeptide sequence, characterized by The device comprises: a polypeptide detection module configured to detect polypeptides in a sample to be evaluated, to obtain a set of detection polypeptide sequences corresponding to each protein to be detected and a content of each detection polypeptide sequence in the set of detection polypeptide sequences; a first similarity determination module configured to determine, based on a preset sequence similarity algorithm, the content of each detection polypeptide sequence, the content of a target polypeptide sequence in a set of target polypeptide sequences corresponding to a reference sample and a zero distribution, a first similarity between each detection polypeptide sequence and the target polypeptide sequence in each target source in the sample to be evaluated; a proportion determination module configured to determine, according to an optimal matching between the set of detection polypeptide sequences and the set of target polypeptide sequences and a preset significance level, a first proportion of significant detection polypeptide sequences with first similarities greater than or equal to a similarity threshold in the sample to be evaluated; a total content determination module configured to determine, based on the content of a target polypeptide sequence corresponding to the significant detection polypeptide sequence, a total content of the significant detection polypeptide sequence in the reference sample; an evaluation module configured to evaluate, based on the first proportion and the total content, a similarity between the sample to be evaluated and the reference sample at the preset significance level. Each set of detection polypeptide sequences comprises a plurality of detection polypeptide sequences derived from the same protein to be detected. The zero distribution is constructed in advance based on a random polypeptide sequence in a set of random polypeptide sequences corresponding to each protein to be detected in the sample to be evaluated and a target polypeptide sequence in a set of target polypeptide sequences corresponding to the reference sample.

11. An evaluation device for a polypeptide sequence, comprising a memory, a processor and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the steps of the method of any one of claims 1 to 9.

12. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 9.

13. A computer program product comprising a computer program or a non-transitory computer readable storage medium bearing a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 9.

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