Machine learning techniques in protein design for vaccine production

Abstract

A discrete data object is received, and the discrete data object may include a plurality of first discrete values, the discrete data object may include one or more amino acid sequences. The discrete data object is converted into a contiguous data object that may include a plurality of first contiguous values. A contiguous data algorithm is applied to the contiguous data object to generate a contiguous result object that may include a plurality of second contiguous values. The continuous result object is converted to a discrete result object that may include a plurality of second discrete values. In some embodiments, vaccines are manufactured that may include at least one of the group that may include i) a protein defined by discrete result objects, ii) a nucleic acid capable of producing a protein defined by discrete result objects, and iii) a delivery vehicle capable of producing a protein defined by discrete result objects.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 319,700 filed on March 14, 2022 and U.S. Provisional Application No. 63 / 319,692 filed on March 14, 2022, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the use of machine learning techniques in vaccine design. Background Art

[0003] Machine learning (ML) is the use of computer algorithms that can automatically improve through experience and use of data. It is considered a part of artificial intelligence. Machine learning algorithms build models based on sample data (called training data) in order to make predictions or decisions without being explicitly programmed. Machine learning algorithms are widely used in fields such as medicine, email filtering, speech recognition, and computer vision where it is difficult or infeasible to develop traditional algorithms to perform the required tasks.

[0004] A vaccine is a biological preparation that provides acquired immunity for a specific infectious disease. Vaccines typically contain a substance similar to the pathogenic microorganism, and are usually made of a weakened or killed form of the microorganism, its toxins, or one of its surface proteins. The substance stimulates the body's immune system to recognize the substance as a threat, destroy the substance, and further recognize and destroy any microorganisms associated with the substance that it may encounter in the future. Vaccines can be preventive (to prevent or improve the effects of future infections with natural or "wild" pathogens) or therapeutic (to combat diseases that have already occurred, such as cancer). Some vaccines provide complete sterilizing immunity, in which infection is completely prevented. Summary of the invention

[0005] The strains used for seasonal influenza vaccines are currently and almost universally selected by public health authorities. Based on the observation of immune responses in animal models and human studies, these selections are made every year. However, the H3N2 vaccine using the strains recommended by public health authorities is not enough to cause extensive protection in the general population, for example, in the past 5 years (2015-2020). In addition, during this time frame, public data show that immune correlation has split into different clades, wherein each clade is protective to itself, and the protection to other clades may be limited. The present disclosure provides a solution to this problem. The embodiment described in the present disclosure provides an algorithm that introduces mutations into a given starting strain and uses a differentiable machine learning method so that a separate model predicts that modified antigens will be highly protective for both homologous and heterologous clades. In an example experiment, the algorithm is used to optimize the HA1 sequence (position 16 to 345) of the H3 hemagglutinin, and then the wild-type signal peptide and HA2 region are transplanted to produce a complete hemagglutinin sequence. An exemplary modified antigen sequence starting with A / Singapore / INFIMH-16-0019 / 2016 is provided with the mutated residues indicated in bold: MKTIIALSYILCLVFAQKIPGNDNSTATLCLGHHAVPNGTIVKTITNDRIEV TNATELVQNSSIGEICDSPHQILDGENCTLIDALLGDPQCDGFQNKKWDL FVERSKAYSNCYPYDVPDYASLRSLVASSGTLEFNNESFNWTGVTQNGT SSACIRGSSSSFFSRLNWLTHLNYTYPALNVTMPNKEQFDKLYIWGVHHP GTDKDQISLYARSSGRITVSTKRSQQAVIPNIGSRPRIRDIPSRISIYWTIVK PGDILLINSTGNLIAPRGYFKIRSGKSSIMRSDAPIGKCKSECITPNGSIPND KPFQNVNRITYGACPRYVKHSTLKLATGMRNVPEKQTRGIFGAIAGFIEN GWEGMVDGWYGFRHQNSEGRGQAADLKSTQAAIDQINGKLNRLIGKTN EKFHQIEKEFSEVEGRVQDLEKYVEDTKIDLWSYNAELLVALENQHTIDL TDSEMNKLFEKTKKQLRENAEDMGNGCFKIYHKCDNACIGSIRNETYDH NVYRDEALNNRFQIKGVELKSGYKDWILWISFAISCFLLCVALLGFIMWACQKGNIRCNICI(SEQ IDNO:1)

[0006] A system of one or more computers may be configured to perform specific operations or actions by software, firmware, hardware or a combination thereof installed on the system, which software, firmware, hardware or a combination thereof causes the system to perform these actions when in operation. One or more computer programs may be configured to perform specific operations or actions by including instructions, which, when executed by a data processing device, cause the device to perform these actions. A general aspect includes a method for manufacturing a vaccine by using a continuous data algorithm. The method includes receiving a discrete data object that may include a plurality of first discrete values, and the discrete data object may include one or more amino acid sequences. The method also includes converting the discrete data object into a continuous data object, and the continuous data object may include a plurality of first continuous values. The method also includes applying a continuous data algorithm to the continuous data object to generate a continuous result object that may include a plurality of second continuous values. The method also includes converting the continuous result object into a discrete result object that may include a plurality of second discrete values. The method also includes manufacturing a vaccine, which may include at least one of the groups that may include the following: i) a protein defined by a discrete result object, ii) a nucleic acid capable of producing a protein defined by a discrete result object, and iii) a delivery vehicle capable of producing a protein defined by a discrete result object. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of these methods.

[0007] Implementations may include one or more of the following features. In the method, the one or more amino acid sequences may include: a first amino acid sequence and a second amino acid sequence, each of the first amino acid sequence and the second amino acid sequence including a corresponding single letter or a corresponding string of letters. Converting a discrete data object to a continuous data object may include: generating a weight vector of weight values ​​for each first discrete value, each weight value indicating the likelihood that the first discrete value represents a specific amino acid; generating a property vector of property values ​​for each weight value of each weight vector, each property value indicating the physicochemical property of the specific amino acid; and combining the weight vector and the property vector to create a first continuous value of the continuous data object. Each weight vector has twenty weight values, each weight value corresponding to one of the twenty possible amino acids. Converting a continuous result object to a discrete result object may include determining a corresponding single amino acid for each second continuous value, wherein the determined single amino acid forms a plurality of second discrete values. The method may further include: generating a plurality of candidate discrete result objects; and excluding at least one discrete result object specifying an amino acid that has not passed the manufacturability test from the plurality of candidate discrete result objects. Applying a continuous data algorithm to generate a continuous result object may include applying a gradient descent with a loss function that determines a loss value based on multiple loss criteria, the loss function may include: a first loss criterion based on an immune response given two amino acid sequences; a second loss criterion that modifies the loss value of a subsequence that is not found in a dataset of wild-type sequences or is not predicted to be correctly folded; and a third loss criterion that modifies the loss value for each weight vector based on the maximum of the second continuous values. The vaccine is directed against one of the group that may include: i) influenza, ii) human rhinovirus, iii) hiv and iiiv) coronavirus disease. Implementations of the described technology may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0008] A general aspect includes a system for generating an amino acid sequence, which may include a computer memory. The system also includes one or more processors. The system also includes a computer memory storing instructions, which when executed by the processor cause the processor to perform operations, which operations may include: receiving a discrete data object containing a plurality of first discrete values, the discrete data object containing one or more amino acid sequences; converting the discrete data object into a continuous data object containing a plurality of first continuous values; applying a continuous data algorithm to the continuous data object to generate a continuous result object containing a plurality of second continuous values; converting the continuous result object into a discrete result object containing a plurality of second discrete values; and manufacturing a vaccine, the vaccine comprising at least one of the group consisting of the following: i) a protein defined by a discrete result object, ii) a nucleic acid capable of producing a protein defined by a discrete result object, and iii) a delivery vehicle capable of producing a protein defined by a discrete result object. Other embodiments of this aspect include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices, each of which is configured to perform the actions of these methods.

[0009] Implementations may include one or more of the following features. In the system, the one or more amino acid sequences may include: a first amino acid sequence and a second amino acid sequence, each of the first amino acid sequence and the second amino acid sequence including a corresponding single letter or a corresponding string of letters. Converting a discrete data object to a continuous data object may include: generating a weight vector of weight values ​​for each first discrete value, each weight value indicating the possibility that the first discrete value represents a specific amino acid; generating a property vector of property values ​​for each weight value of each weight vector, each property value indicating the physicochemical property of the specific amino acid; and combining the weight vector and the property vector to create a first continuous value of the continuous data object. Each weight vector has twenty weight values, each weight value corresponding to one of the twenty possible amino acids. Converting a continuous result object to a discrete result object may include determining a corresponding single amino acid for each second continuous value, wherein the determined single amino acid forms a plurality of second discrete values. These operations may further include: generating a plurality of candidate discrete result objects; and excluding at least one discrete result object specifying an amino acid that has not passed the manufacturability test from the plurality of candidate discrete result objects. Applying the continuous data algorithm to generate a continuous result object may include applying gradient descent with a loss function that determines a loss value based on a plurality of loss criteria, the loss function may include: a first loss criterion based on an immune response given two amino acid sequences; a second loss criterion that modifies the loss value for subsequences not found in a dataset of wild-type sequences or not predicted to be correctly folded; and a third loss criterion that modifies the loss value for each weight vector based on a maximum value among the second continuous values. Implementations of the described technology may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0010] A general aspect includes a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations that may include: receiving a discrete data object comprising a plurality of first discrete values, the discrete data object comprising one or more amino acid sequences; converting the discrete data object into a continuous data object comprising a plurality of first continuous values; applying a continuous data algorithm to the continuous data object to generate a continuous result object comprising a plurality of second continuous values; converting the continuous result object into a discrete result object comprising a plurality of second discrete values; and manufacturing a vaccine comprising at least one of the group consisting of: i) a protein defined by the discrete result object, ii) a nucleic acid capable of producing the protein defined by the discrete result object, and iii) a delivery vehicle capable of producing the protein defined by the discrete result object. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of these methods.

[0011] Implementations may include one or more of the following features. In a medium, one or more amino acid sequences may include: a first amino acid sequence and a second amino acid sequence, each of the first amino acid sequence and the second amino acid sequence including a corresponding single letter or a corresponding string of letters. Converting a discrete data object to a continuous data object may include: generating a weight vector of weight values ​​for each first discrete value, each weight value indicating the possibility that the first discrete value represents a specific amino acid; generating a property vector of property values ​​for each weight value of each weight vector, each property value indicating the physicochemical property of the specific amino acid; and combining the weight vector and the property vector to create a first continuous value of the continuous data object. Each weight vector has twenty weight values, each weight value corresponding to one of the twenty possible amino acids. Converting a continuous result object to a discrete result object may include determining a corresponding single amino acid for each second continuous value, wherein the determined single amino acid forms a plurality of second discrete values. Implementations of the described technology may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0012] Also disclosed herein are vaccine compositions comprising any of the plurality of amino acid sequences generated by the methods described herein.

[0013] Also disclosed are vectors, fusion proteins, and cells comprising one or more peptides and / or proteins produced according to the methods described herein.

[0014] Also disclosed herein are methods of eliciting an immune response in a subject, comprising administering one or more of the isolated nucleic acids, peptides and / or proteins described herein, thereby eliciting an immune response in the subject.

[0015] In one aspect, disclosed herein are methods of inhibiting viral infection, comprising administering to a subject any of one or more of the isolated nucleic acids, peptides and / or proteins described herein, or administering any vaccine comprising any of the isolated nucleic acids, peptides and / or proteins described herein.

[0016] Also disclosed herein is a method for making a subject immune to influenza virus, the method comprising administering to the subject an immune effective amount of a vaccine composition as disclosed herein. Also disclosed herein is a vaccine composition as disclosed herein for use in a method for making a subject immune to a virus (e.g., influenza virus). Also disclosed herein is a vaccine composition as disclosed herein for the manufacture of a medicament for use in a method for making a subject immune to a virus (e.g., influenza virus). In certain embodiments, the method prevents a subject from viral infection (e.g., influenza virus infection), and in certain embodiments, the method causes a protective immune response (e.g., HA antibody response and / or NA antibody response) in the subject. In certain embodiments, the subject is a person, and in certain embodiments, the vaccine composition is administered intramuscularly, intradermally, subcutaneously, intravenously or intraperitoneally.

[0017] Another aspect of the disclosure relates to a method for alleviating one or more symptoms of a viral infection (e.g., influenza virus infection), the method comprising administering to a subject a preventive effective amount of a vaccine composition disclosed herein. Also disclosed herein is a vaccine composition as disclosed herein for use in a method for alleviating one or more symptoms of a viral infection (e.g., influenza virus infection). Also disclosed herein is a vaccine composition as disclosed herein for use in a method for alleviating one or more symptoms of an infection (e.g., influenza virus infection) for the manufacture of a medicament as disclosed herein for use in a method for alleviating one or more symptoms of an infection (e.g., influenza virus infection).

[0018] In various embodiments, the methods and compositions disclosed herein treat or prevent disease caused by one or both of a seasonal viral strain or a pandemic viral strain (eg, a seasonal influenza strain or a pandemic influenza strain).

[0019] In certain embodiments of the methods disclosed herein, wherein the subject is a human, the human is 6 months or older, less than 18 years old, at least 6 months and less than 18 years old, at least 18 years old and less than 65 years old, at least 6 months and less than 5 years old, at least 5 years old and less than 65 years old, at least 60 years old, or at least 65 years old. For example, the subject is 6 months, 8 months, 10 months, 12 months, 14 months, 16 months, 18 months, 20 months, 22 months, 24 months, 3 years old, 4 years old, 5 years old, 6 years old, 10 years old, 12 years old, 15 years old, 18 years old, 20 years old, 21 years old, 25 years old, 30 years old, 35 years old, 40 years old, 50 years old, 60 years old, 70 years old, 75 years old, 80 years old, 85 years old, or 90 years old. In certain embodiments, the methods disclosed herein comprise administering two doses of the vaccine composition to the subject at intervals of 2-6 weeks, such as 4 weeks.

[0020] Implementations may include any, all, or none of the following features.

[0021] The embodiments discussed in this disclosure may provide one or more of the following advantages. Embodiments may be used to generate hemagglutinin sequences that are likely to induce broad protection against influenza infection after vaccination. Notably, embodiments may be used to generate antigens that have a higher than expected cure rate for functional influenza viruses containing designed hemagglutinin sequences. These antigens are believed to have broad protection, greater than current standard of care antigens in animal models. Embodiments may be used to generate broadly protective hemagglutinin proteins for use as influenza vaccine antigens, or for defining nucleic acid sequences, or any other delivery vehicle (including viral or bacterial vectors), wherein such nucleic acids or delivery vehicles produce proteins for use as influenza vaccine antigens.

[0022] By converting only discrete domain data (e.g., amino acid sequences) into continuous data sets, algorithms designed for continuous data can be used for discrete data. For example, off-the-shelf solvers, computational maximizers, classifiers, etc. can be applied to amino acid sequences, while these tools are generally not able to operate directly on amino acid sequences. This can advantageously allow vaccine development using amino acid sequences and only continuous algorithms. Therefore, machine learning predictors can be used to predict the immune response of mammals to given two protein sequences. For example, algorithms such as gradient descent can be used for protein sequences with the goal of improving immune response, even if such gradient descent is generally not able to operate on such discrete data for representing protein sequences. Gradient descent can be used to optimize the predicted immune response, immunogenicity, and biophysical stability of candidate proteins. Candidate proteins generated by gradient descent can then be analyzed to determine their efficacy, such as the efficacy of vaccines for diseases caused by multiple or rapidly evolving pathogenic strains.

[0023] Another advantage of the techniques provided in the present disclosure is that it increases the likelihood of generating protein sequence data for proteins that can actually exist and be manufactured. As will be appreciated, protein sequences can be described that cannot exist due to geometry, physical forces, etc. The processes described in this document can advantageously be limited to processes that are known to be manufacturable or expected to be manufacturable.

[0024] Other features, aspects and potential advantages will be apparent from the accompanying description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a block diagram of an example system that can be used to make vaccines.

[0026] Figure 2 It is a schematic diagram of the data that can be used to create a vaccine.

[0027] Figures 3 to 6is a flow chart of an example process that can be used to apply a continuous algorithm to discrete data, such as can be used to make vaccines.

[0028] Figure 7 is a swim lane diagram of an example process for making a vaccine.

[0029] Figure 8 is a schematic diagram illustrating examples of computing devices and mobile computing devices.

[0030] Like reference numbers in the various drawings indicate like elements. DETAILED DESCRIPTION

[0031] This document describes the creation of vaccines through a machine learning process. The creation of vaccines uses candidate proteins generated through a computational process including machine learning. Machine learning techniques are used to modify the initial antigen sequence into one or more candidate sequences that can be used as a vaccine. However, many machine learning operations use continuous values, while antigen sequences are often characterized by discrete values. In order to perform continuous value operations on discrete value data sets, the present disclosure provides techniques for converting discrete values ​​to continuous values, operating on the continuous values, and then converting the continuous values ​​back to discrete values. In order to produce useful results, these operations can be constrained so that the output of the discrete values ​​does not define an antigen sequence that is known or expected to be physically impossible.

[0032] Influenza viruses are members of the Orthomyxoviridae family. There are three subtypes of influenza viruses: influenza A, influenza B, and influenza C. Influenza A viruses infect a wide variety of birds and mammals, including humans, chickens, ferrets, pigs, and horses. In mammals, most influenza A viruses cause mild localized infections of the respiratory tract and intestinal tract.

[0033] Influenza virions contain a negative sense RNA genome that encodes the following nine proteins: hemagglutinin (HA), matrix (M1), proton ion channel protein (M2), neuraminidase (NA), nonstructural protein 2 (NS2), nucleoprotein (NP), polymerase acidic protein (PA), polymerase basic protein 1 (PB1) and polymerase basic protein 2 (PB2). HA, M1, M2 and NA are membrane-associated proteins, while NP, NS2, PA, PB1 and PB2 are nucleocapsid-associated proteins. M1 protein is the most abundant protein in influenza particles. HA and NA proteins are envelope glycoproteins that are responsible for viral attachment and cell entry. HA and NA proteins are the source of the main immunodominant epitopes for virus neutralization and protective immunity. HA and NA proteins are considered to be the most important components of preventive influenza vaccines.

[0034] HA is a viral surface glycoprotein that typically contains about 560 amino acids and accounts for 25% of the total viral protein.

[0035] NA is a membrane glycoprotein of influenza virus. NA is 413 amino acids in length and is encoded by a gene of 1413 nucleotides. Nine different NA subtypes (N1, N2, N3, N4, N5, N6, N7, N8 and N9) have been identified in influenza virus, all of which are found in wild birds.

[0036] The ability of influenza viruses to cause widespread illness derives from their ability to evade the immune system by undergoing antigenic changes.

[0037] definition

[0038] In order to more easily understand the present disclosure, certain terms are first defined below. The following terms and other definitions of other terms can be set forth through the specification. If the definition of a term set forth below is inconsistent with the definition in the application or patent incorporated by reference, the definition set forth in the present application should be used to understand the meaning of the term.

[0039] As used in this specification and the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a method" includes one or more methods, and / or steps of the type described herein and / or which will become apparent to those skilled in the art upon reading this disclosure, etc.

[0040] Adjuvant: As used herein, the term "adjuvant" refers to a substance or combination of substances that can be used to enhance the immune response to the antigenic component of a vaccine.

[0041] Antigen: As used herein, the term "antigen" refers to a factor that elicits an immune response; and / or (ii) a factor that is bound by a T cell receptor (e.g., when presented by an MHC molecule) or bound to an antibody (e.g., produced by a B cell) when exposed or administered to an organism. In some embodiments, an antigen elicits a humoral response in an organism (e.g., including the production of antigen-specific antibodies); alternatively or additionally, in some embodiments, an antigen elicits a cellular response in an organism (e.g., involving a T cell whose receptor specifically interacts with the antigen). One skilled in the art will appreciate that a particular antigen may elicit an immune response in one or several members of a target organism (e.g., mouse, ferret, rabbit, primate, human), but not in all members of the target organism species. In some embodiments, the antigen triggers an immune response in at least about 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% of the members of the target organism species. In some embodiments, the antigen binds to an antibody and / or a T cell receptor and may or may not induce a specific physiological response in an organism. In some embodiments, for example, an antigen may bind to an antibody and / or a T cell receptor in vitro, regardless of whether such interaction occurs in vivo. In some embodiments, the antigen reacts with a product of a specific humoral or cellular immunity (including those induced by a heterologous immunogen). Antigens include NA and HA forms as described herein.

[0042] Carrier: As used herein, the term "carrier" refers to a diluent, adjuvant, excipient or vehicle with which the composition is administered. In some exemplary embodiments, the carrier may include sterile liquids such as, for example, water and oils, including oils of petroleum, animal, vegetable or synthetic origin, such as, for example, peanut oil, soybean oil, mineral oil, sesame oil, etc. In some embodiments, the carrier is or includes one or more solid components.

[0043] Epitope: As used herein, the term "epitope" includes any portion that is specifically recognized by an immunoglobulin (e.g., an antibody or receptor) binding component, in whole or in part. In some embodiments, an epitope is composed of a plurality of chemical atoms or groups on an antigen. In some embodiments, such chemical atoms or groups are surface exposed when the antigen adopts a related three-dimensional conformation. In some embodiments, such chemical atoms or groups are physically close to each other in space when the antigen adopts such a conformation. In some embodiments, when the antigen adopts an alternative conformation (e.g., linearized), at least some of such chemical atoms or groups are physically separated from each other.

[0044] Excipients: As used herein, the term "excipient" refers to non-therapeutic agents that may be included in a pharmaceutical composition, for example, to provide or contribute to a desired consistency or stabilization effect. Suitable pharmaceutical excipients include, for example, starch, glucose, lactose, sucrose, sorbitol, gelatin, malt, rice, flour, chalk, silica gel, sodium stearate, glyceryl monostearate, talc, sodium chloride, skim milk powder, glycerol, propylene, ethylene glycol, water, ethanol, and the like.

[0045] Immune response: As used herein, the term "immune response" refers to the response of cells of the immune system (such as B cells, T cells, dendritic cells, macrophages or polymorphonuclear cells) to stimuli (such as antigens, immunogens or vaccines). The immune response can include any cell of the body that participates in the host defense response, including, for example, epithelial cells that secrete interferons or cytokines. Immune responses include, but are not limited to, innate and / or adaptive immune responses. As used herein, a protective immune response refers to an immune response that protects a subject from infection (prevents infection or prevents the occurrence of diseases associated with infection) or alleviates symptoms of infection. Methods for measuring immune responses are well known in the art and include, for example, measuring the proliferation and / or activity of lymphocytes (such as B or T cells), the secretion of cytokines or chemokines, inflammation, antibody production, etc. Antibody response or humoral response is an immune response that produces antibodies. "Cellular immune response" is an immune response mediated by T cells and / or other white blood cells.

[0046] Immunogen: As used herein, the term "immunogen" or "immunogenic" refers to a compound, composition or substance that can stimulate an immune response (such as the production of antibodies or T cell responses) in an animal under appropriate conditions, including compositions injected or absorbed into an animal. As used herein, "immunization" means to protect a subject from an infectious disease.

[0047] Immunologically effective amount: As used herein, the term "immunologically effective amount" means an amount sufficient to immunize a subject.

[0048] Prevention: As used herein, the term "prevention" refers to preventing, avoiding disease manifestation, delaying the onset of one or more symptoms of a particular disease, disorder or condition (e.g., infection, such as influenza virus infection), and / or reducing the frequency and / or severity of the one or more symptoms. In some embodiments, prevention is assessed on a population basis, such that if a statistically significant reduction in the development, frequency and / or intensity of one or more symptoms of the disease, disorder or condition is observed in a population susceptible to a particular disease, disorder or condition, then the agent is considered to "prevent" the disease, disorder or condition.

[0049] Sequence identity: Similarity between amino acid or nucleic acid sequences is expressed as similarity between the sequences, also known as sequence identity. Sequence identity is often measured as a percentage identity (or similarity or homology); the higher the percentage, the more similar the two sequences are. "Sequence identity" between two nucleic acid sequences indicates the percentage of nucleotides that are identical between the sequences. "Sequence identity" between two amino acid sequences indicates the percentage of amino acids that are identical between the sequences. Homologs or variants of a given gene or protein will have a relatively high degree of sequence identity when aligned using standard methods.

[0050] The terms "% identical", "% identity" or similar terms are intended to refer in particular to the percentage of identical nucleotides or amino acids in an optimal alignment between the sequences to be compared. The percentages are purely statistical, and the differences between the two sequences may, but are not necessarily, randomly distributed over the entire length of the sequences to be compared. The comparison of two sequences is usually performed in the following manner: after optimal alignment, the sequences are compared with respect to segments or "comparison windows" to identify local regions of corresponding sequences. Optimal alignment for comparison can be performed manually or with the aid of the local homology algorithm of Smith and Waterman, 1981, Ads App. Math. 2, 482, with the aid of the local homology algorithm of Needleman and Wunsch, 1970, J. Mol. Biol. 48, 443, with the aid of the similarity search algorithm of Pearson and Lipman, 1988, Proc. Natl Acad. Sci. USA 88, 2444, or with the aid of computer programs that use such algorithms (GAP, BESTFIT, FASTA, BLAST P, BLAST N, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group, 575 Science Drive, Madison, Wis.).

[0051] The percent identity is obtained by determining the number of corresponding identical positions in the sequences being compared, dividing this number by the number of compared positions (eg, the number of positions in the reference sequence), and multiplying this result by 100.

[0052] In some embodiments, the degree of identity given for a region is at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90%, or about 100% of the entire length of the reference sequence. For example, if the reference nucleic acid sequence consists of 200 nucleotides, the degree of identity is given for at least about 100, at least about 120, at least about 140, at least about 160, at least about 180, or about 200 nucleotides (contiguous nucleotides in some embodiments). In some embodiments, the degree of identity is given for the entire length of the reference sequence.

[0053] A nucleic acid sequence or an amino acid sequence having a specific degree of identity to a given nucleic acid sequence or an amino acid sequence, respectively, may have at least one functional and / or structural property of the given sequence, for example and in some cases, functionally and / or structurally equivalent to the given sequence. In some embodiments, a nucleic acid sequence or an amino acid sequence having a specific degree of identity to a given nucleic acid sequence or an amino acid sequence is functionally and / or structurally equivalent to the given sequence.

[0054] Subject: As used herein, the term "subject" means any member of the animal kingdom. In some embodiments, "subject" refers to a human. In some embodiments, "subject" refers to a non-human animal. In some embodiments, subjects include, but are not limited to, mammals, birds, reptiles, amphibians, fish, insects, and / or worms. In some embodiments, non-human subjects are mammals (e.g., rodents, mice, rats, rabbits, ferrets, monkeys, dogs, cats, sheep, cattle, primates, and / or pigs). In some embodiments, the subject may be a transgenic animal, a genetically engineered animal, and / or a clone. In some embodiments, the subject is an adult, a teenager, or an infant. In some embodiments, the term "individual" or "patient" is used and is intended to be interchangeable with "subject."

[0055] Vaccination: As used herein, the term "vaccination" refers to the administration of a composition intended to generate an immune response, for example, to a pathogenic agent such as influenza virus. Vaccination can be administered before, during, and / or after exposure to a pathogenic agent and / or the appearance of one or more symptoms, and in some embodiments, before, during, and / or shortly after exposure to the pathogenic agent. Vaccines may induce prophylactic / preventative and therapeutic responses. The method of administration varies depending on the vaccine, but may include inoculation, ingestion, inhalation, or other forms of administration. Vaccination can be delivered by any of a variety of routes, including parenteral, such as intravenous, subcutaneous, intraperitoneal, intradermal, or intramuscular. The vaccine may be administered together with an adjuvant to enhance the immune response. In some embodiments, vaccination includes multiple administrations of the vaccination composition at appropriate intervals.

[0056] Vaccine efficacy: As used herein, the term "vaccine efficacy" or "vaccine effectiveness" refers to an indicator of the percentage of reduction in disease evidence in subjects to which a vaccine composition has been administered. For example, a vaccine efficacy of 50% indicates that the number of disease cases in the group of vaccinated subjects is reduced by 50% compared to a group of unvaccinated subjects or a group of subjects administered a different vaccine.

[0057] Wild-type (WT): As understood in the art, the term "wild-type" generally refers to the normal form of a protein or nucleic acid, as found in nature. For example, wild-type HA and NA polypeptides are found in natural isolates of influenza virus. A variety of different wild-type HA and NA sequences can be found in the NCBI influenza virus sequence database.

[0058] Measuring hemagglutinin activity

[0059] Hemagglutinin activity can be measured using techniques known in the art, including, for example, hemagglutinin inhibition assay (HAI).HAI uses the following hemagglutination process, wherein the sialic acid receptors on the surface of red blood cells (RBC) bind to the hemagglutinin glycoprotein found on the surface of influenza virus (and several other viruses), and form a network or lattice structure of interconnected RBC and virions, called hemagglutination, which occurs on virions in a concentration-dependent manner. This is a physical measurement, used as an indicator of the ability of similar sialic acid receptors on virus-binding pathogen targeting cells in vivo. The introduction of antiviral antibodies produced in human or animal immune responses (for another virus, which may be genetically similar or different from the virus used to bind RBC in the assay) interferes with virus-RBC interactions, and changes the virus concentration, which is enough to change the concentration when hemagglutination is observed in the assay. A target of HAI can be to characterize the concentration of antibodies in antiserum or other samples containing antibodies, which is related to their ability to induce hemagglutination in the assay. The highest dilution of antibodies that prevent hemagglutination is called HAI titer (i.e., the reaction measured).

[0060] Another method of measuring HA antibody response is to measure the potential larger group of antibodies caused by human or animal immune response, which may not necessarily affect the hemagglutination in HAI determination. For this reason, a common method is to utilize enzyme-linked immunosorbent assay (ELISA) technology, in which viral antigens (such as hemagglutinin) are fixed on a solid surface, and then the antibodies from the antiserum are allowed to bind to the antigen. The readout measures the catalysis of the exogenous enzyme substrates compounded with the antibodies from the antiserum or other antibodies that bind to the antibodies of the antiserum. The catalysis of the substrate produces a product that is easy to detect. There are many variants of this in vitro assay. One such variant is called antibody forensics (AF), which is a multiple bead array technology that allows single serum samples to be measured simultaneously for multiple antigens. Compared with HAI titer, these measurements characterize concentration and total antibody recognition, and HAI titer is considered to be more particularly related to the interference of hemagglutinin molecules and sialic acid binding. Thus, in some cases, the antibody measurements of an antiserum may be proportionally higher or lower than the corresponding HAI titer of the hemagglutinin molecule of one virus relative to the hemagglutinin molecule of another virus; in other words, the two measurements of AF and HAI may not be linearly related.

[0061] Another method for measuring HA antibody responses includes virus neutralization assays (e.g., microneutralization assays) in which antibody titers are measured by the reduction of plaques, foci, and / or fluorescent signal (depending on the specific neutralization assay technique) in permissive cultured cells following incubation of virus with serial dilutions of antibody / serum samples.

[0062] Measurement of neuraminidase activity

[0063] Neuraminidase activity can be measured using techniques known in the art, including, for example, the MUNANA assay, the ELLA assay, or Assay (ThermoFisher Scientific, Waltham, MA). In the MUNANA assay, 2'-(4-methylumbelliferyl)-α-DN-acetylneuraminic acid (MUNANA) is used as a substrate. Any enzymatically active neuraminidase contained in the sample will cleave the MUNANA substrate, releasing the fluorescent compound 4-methylumbelliferyl (4-MU). Therefore, the amount of neuraminidase activity in the test sample is related to the amount of 4-MU released, which can be measured using fluorescence intensity (RFU, relative fluorescence units).

[0064] To determine the neuraminidase activity of the soluble tetrameric NA disclosed herein, the MUNANA assay should be performed using the following conditions: the soluble tetrameric NA is mixed with a buffer [33.3 mM 2-(N-morpholino)ethanesulfonic acid (MES, pH 6.5), 4 mM CaCl2, 50 mM BSA] and a substrate (100 μM MUNANA), and incubated with shaking at 37°C for 1 hour; the reaction is terminated by adding an alkaline pH solution (0.2 M Na2CO3); the fluorescence intensity is measured using excitation and emission wavelengths of 355 and 460 nm, respectively; and the enzyme activity relative to the 4MU reference value is calculated. If necessary, an equivalent assay can be used to measure the neuraminidase enzyme activity.

[0065] Vaccine composition

[0066] In certain aspects, disclosed herein are vaccine compositions comprising a plurality of generated amino acid sequences.

[0067] Each amino acid sequence produced can be present in the compositions disclosed herein in an amount effective to induce an immune response in a subject to which the composition is administered. In certain embodiments, each amino acid sequence produced can be present in a vaccine composition disclosed herein in an amount ranging, for example, from about 0.1 g to about 500 g, such as from about 5 g to about 120 g, from about 1 g to about 60 g, from about 10 g to about 60 g, from about 15 g to about 60 g, from about 40 g to about 50 g, from about 42 g to about 47 g, from about 5 g to about 45 g, from about 15 g to about 45 g, from about 0.1 g to about 90 g, from about 5 g to about 90 g, from about 10 g to about 90 g, or from about 15 g to about 90 g. In certain embodiments, each recombinant HA may be present in the vaccine compositions disclosed herein in an amount of about 5 g, 10 g, 15 g, 20 g, 25 g, 30 g, 35 g, 40 g, 45 g, 50 g, 55 g, 60 g, 65 g, 70 g, 75 g, 80 g, 85 g or about 90 g.

[0068] The vaccine composition may further include an adjuvant. As used herein, the term "adjuvant" refers to a substance or vehicle that non-specifically enhances the immune response to an antigen. The adjuvant may include a suspension of a mineral (alum, aluminum salt, including, for example, aluminum hydroxide / aluminum hydroxide (AlOOH), aluminum phosphate (AlPO4), aluminum hydroxyphosphate sulfate (AAHS) and / or potassium aluminum sulfate) on which an antigen is adsorbed; or an oil-in-water emulsion, wherein the antigen solution is emulsified in mineral oil (e.g., Freund's incomplete adjuvant), sometimes including killed mycobacteria (Freund's complete adjuvant) to further enhance antigenicity. Immunostimulatory oligonucleotides (e.g., those including CpG motifs) may also be used as adjuvants (e.g., see U.S. Patent Nos. 6,194,388; 6,207,646; 6,214,806; 6,218,371; 6,239,116; 6,339,068; 6,406,705; and 6,429,199). Adjuvants also include biomolecules, such as lipids and co-stimulatory molecules. Exemplary biological adjuvants include AS04 (Didierlaurent, AM et al., AS04, an Aluminum Salt-and TLR4 Agonist-Based Adjuvant System, Induces a Transient Localized Innate Immune Response Leading to Enhanced Adaptive Immunity [AS04--an adjuvant system based on aluminum salts and TLR4 agonists, inducing transient local innate immune responses, thereby enhancing adaptive immunity], J. IMMUNOL. [Journal of Immunology] 2009, 183: 6186-6197), IL-2, RANTES, GM-CSF, TNF-?, IFN-?, G-CSF, LFA-3, CD72, B7-1, B7-2, OX-40L and 41BBL.

[0069] In certain embodiments, the adjuvant is a squalene-based adjuvant comprising an oil-in-water adjuvant emulsion comprising at least squalene, an aqueous solvent, a polyoxyethylene alkyl ether hydrophilic nonionic surfactant, and a hydrophobic nonionic surfactant. In certain embodiments, the emulsion is thermoreversible, optionally wherein 90% of the population by volume of the oil droplets is less than 200 nm in size.

[0070] In certain embodiments, the polyoxyethylene alkyl ether has the formula CH3-(CH2)x-(O-CH2-CH2)n-OH, wherein n is an integer from 10 to 60, and x is an integer from 11 to 17. In certain embodiments, the polyoxyethylene alkyl ether surfactant is polyoxyethylene (12) cetearyl ether.

[0071] In certain embodiments, 90% of the population by volume of the oil droplets has a size less than 160 nm. In certain embodiments, 90% of the population by volume of the oil droplets has a size less than 150 nm. In certain embodiments, 50% of the population by volume of the oil droplets has a size less than 100 nm. In certain embodiments, 50% of the population by volume of the oil droplets has a size less than 90 nm.

[0072] In certain embodiments, the adjuvant further comprises at least one alditol, including but not limited to glycerol, erythritol, xylitol, sorbitol, and mannitol.

[0073] In certain embodiments, the hydrophilic nonionic surfactant has a hydrophilic / lipophilic balance (HLB) greater than or equal to 10. In certain embodiments, the hydrophobic nonionic surfactant has an HLB less than 9. In certain embodiments, the hydrophilic nonionic surfactant has an HLB greater than or equal to 10, and the hydrophobic nonionic surfactant has an HLB less than 9.

[0074] In certain embodiments, the hydrophobic nonionic surfactant is a sorbitan ester (such as sorbitan monooleate) or a mannide ester surfactant. In certain embodiments, the amount of squalene is between 5% and 45%. In certain embodiments, the amount of polyoxyethylene alkyl ether surfactant is between 0.9% and 9%. In certain embodiments, the amount of hydrophobic nonionic surfactant is between 0.7% and 7%. In certain embodiments, the adjuvant comprises: i) 32.5% squalene, ii) 6.18% polyoxyethylene (12) hexadecyl ether, iii) 4.82% sorbitan monooleate, and iv) 6% mannitol.

[0075] In certain embodiments, the adjuvant further comprises an alkyl polyglycoside and / or a cryoprotectant, such as a sugar, in particular dodecyl maltoside and / or sucrose.

[0076] In certain embodiments, the adjuvant comprises AF03, as described in Klucker et al., AF03, an alternative squalene emulsion-based vaccine adjuvant prepared by a phase inversion temperature method, J. PHARM. SCI. 2012, 101(12):4490-4500, which is hereby incorporated by reference in its entirety. In certain embodiments, the adjuvant comprises a liposome-based adjuvant, such as SPA14. SPA14 is a liposome-based adjuvant (AS01-like) comprising a toll-like receptor 4 (TLR4) agonist (E6020) and a saponin (QS21).

[0077] In addition to recombinant HA, recombinant NA and optional adjuvants, the vaccine composition may further include one or more pharmaceutically acceptable excipients. Generally, the nature of the excipient will depend on the specific mode of administration used. For example, parenteral formulations generally include injections that include pharmaceutically and physiologically acceptable fluids, such as water, saline, balanced salt solutions, dextrose aqueous solutions, glycerol, etc. as vehicles. For solid compositions (e.g., powders, pills, tablets or capsule forms), conventional non-toxic solid carriers may include, for example, pharmaceutical grade mannitol, lactose, starch or magnesium stearate. In addition to biological neutral carriers, the vaccine composition to be administered may also contain a small amount of non-toxic auxiliary substances, such as wetting agents or emulsifiers, pharmaceutically acceptable salts (to adjust osmotic pressure), preservatives, stabilizers, buffers, sugars, amino acids and pH buffers, such as sodium acetate or sorbitan monolaurate.

[0078] Typically, the vaccine composition is a sterile liquid solution formulated for parenteral administration (e.g., intravenous, subcutaneous, intraperitoneal, intradermal, or intramuscular administration). The vaccine composition may also be formulated for intranasal or inhalation administration. The vaccine composition may also be formulated for any other intended route of administration.

[0079] In certain embodiments, vaccine compositions are formulated for intradermal injection, intranasal administration or intramuscular injection. In certain embodiments, injections are prepared in conventional forms (as liquid solutions or suspensions, suitable for solid forms dissolved or suspended in liquids before injection, or as emulsions). In certain embodiments, injection solutions and suspensions are prepared from sterile powders or particles. General considerations for the preparation and manufacture of medicaments used by these approaches can be found in, for example, Remington's Pharmaceutical Sciences, 19th edition, Mack Publishing Co., Easton, PA, 1995 (incorporated herein by reference). At present, oral or nasal sprays or aerosol approaches (e.g., by inhalation) are most commonly used for direct delivery of therapeutic agents to the lungs and respiratory system. In certain embodiments, vaccine compositions are administered using a device for delivering a metered dose of vaccine compositions. Suitable devices for use in delivering the intradermal pharmaceutical compositions described herein include short needle devices, such as those described in the following patents: U.S. Patent No. 4,886,499, U.S. Patent No. 5,190,521, U.S. Patent No. 5,328,483, U.S. Patent No. 5,527,288, U.S. Patent No. 4,270,537, U.S. Patent No. 5,015,235, U.S. Patent No. 5,141,496, U.S. Patent No. 5,417,662 (all of which are incorporated herein by reference). Intradermal compositions can also be applied by devices that limit the effective penetration length of the needle into the skin, such as those described in WO 1999 / 34850 (incorporated herein by reference) and their functional equivalents. In addition, jet injection devices are also suitable, which deliver liquid vaccines to the dermis via a liquid jet syringe or a needle that pierces the stratum corneum and produces a jet that reaches the dermis.Jet injection devices are described, for example, in U.S. Pat. No. 5,480,381, U.S. Pat. No. 5,599,302, U.S. Pat. No. 5,334,144, U.S. Pat. No. 5,993,412, U.S. Pat. No. 5,649,912, U.S. Pat. No. 5,569,189, U.S. Pat. No. 5,704,911, U.S. Pat. No. 5,383,851, U.S. Pat. No. 5,893,397, U.S. Pat. No. 5,466,220, U.S. Pat. No. 5,339,163, U.S. Pat. No. 5,312,335, U.S. Pat. No. 5,503,627, U.S. Pat. No. 5,064,413, U.S. Pat. No. 5,520,639, U.S. Pat. No. 4,596,556, U.S. Pat. No. 4,790,824, U.S. Pat. No. 4,941,880, U.S. Pat. No. 4,940,460, WO 1997 / 37705 and WO 1997 / 13537 (all of which are incorporated herein by reference). In addition, ballistic powder / particle delivery devices are also suitable, which use compressed gas to accelerate the vaccine in powder form through the outer layer of the skin to the dermis. In addition, conventional syringes can be used in the classic mantoux method of intradermal administration.

[0080] Preparations for parenteral administration typically include sterile aqueous or non-aqueous solutions, suspensions and emulsions. Examples of non-aqueous solvents are propylene glycol, polyethylene glycol, vegetable oils (such as olive oil) and injectable organic esters (such as ethyl oleate). Aqueous carriers include water, alcohol / water solutions, emulsions or suspensions, including saline and buffered media. Parenteral vehicles include sodium chloride solution, Ringer's dextrose, dextrose and sodium chloride, lactated Ringer's solution or fixed oils. Intravenous vehicles include fluids and nutritional supplements, electrolyte supplements (such as those based on Ringer's dextrose), etc. Preservatives and other additives may also be present, such as, for example, antimicrobial agents, antioxidants, chelating agents, and inert gases, etc.

[0081] Reagent test kit

[0082] Further disclosed herein are kits for the vaccine compositions as disclosed herein. The kit may include one suitable container comprising the vaccine composition or multiple containers comprising the different components of the vaccine composition, optionally with instructions for use.

[0083] In certain embodiments, a kit can include multiple containers, including, for example, a first container comprising one or more isolated nucleic acids, peptides, and / or proteins as disclosed herein.

[0084] Nucleic Acids, Cloning and Expression Systems

[0085] The present disclosure further provides artificial nucleic acid molecules. Nucleic acids may comprise DNA or RNA, and may be synthesized or recombinant in whole or in part. Unless the context otherwise requires, reference to a nucleotide sequence described herein encompasses a DNA molecule having a specified sequence, and encompasses an RNA molecule having a specified sequence in which U or a derivative thereof (such as pseudouridine) replaces T. Other nucleotide derivatives or modified nucleotides may be incorporated into the artificial nucleic acid molecule.

[0086] The present disclosure also provides constructs in the form of vectors (e.g., plasmids, phagemids, cosmids, transcription cassettes or expression cassettes, artificial chromosomes, etc.), which contain artificial nucleic acid molecules encoding the amino acid sequences produced as disclosed herein. The present disclosure further provides a host cell comprising one or more constructs as above.

[0087] Also provided are methods for preparing isolated peptides and / or proteins using recombinant techniques known in the art and as discussed above. The production and expression of recombinant proteins are well known in the art and can be performed using conventional procedures (such as those disclosed in Sambrook et al., Molecular Cloning: A Laboratory Manual (4th Edition 2012), Cold Spring Harbor Press). For example, the expression of HA or NA polypeptides can be achieved by culturing host cells containing artificial nucleic acid molecules encoding HA or NA as disclosed herein under appropriate conditions. For example, the expression of recombinant HA or NA polypeptides can be achieved by culturing host cells containing nucleic acid molecules encoding HA or NA as disclosed herein under appropriate conditions. After being produced by expression, HA or NA can be separated and / or purified using any suitable technique and then used as appropriate.

[0088] Systems for cloning and expressing polypeptides in a variety of different host cells are well known in the art.Any protein expression system (eg, stable or transient) compatible with the constructs disclosed in this application can be used to produce the amino acid sequences produced herein.

[0089] Appropriate vectors can be chosen or constructed so that they contain appropriate regulatory sequences, including promoter sequences, terminator sequences, polyadenylation sequences, enhancer sequences, marker genes and other appropriate sequences.

[0090] In order to express the amino acid sequence produced as disclosed herein, the nucleic acid encoding the amino acid sequence produced can be introduced into the host cell. The introduction can adopt any available technology. For eukaryotic cells, suitable techniques can include calcium phosphate transfection, DEAE-dextran, electroporation, liposome-mediated transfection and transduction using retrovirus or other viruses (such as vaccinia or baculovirus (for insect cells)). For bacterial cells, suitable techniques can include calcium chloride transformation, electroporation and transfection using bacteriophage. These technologies are well known in the art. (See, for example, "Current Protocols in Molecular Biology [Molecular Biology Laboratory Guide]," Ausubel et al., ed., John Wiley & Sons [John Wiley and Sons Publishers], 2010). After DNA is introduced, a selection method (e.g., antibiotic resistance) can be used to select cells containing the vector.

[0091] The host cell can be a plant cell, a yeast cell or an animal cell. Animal cells include invertebrates (e.g., insect cells), non-mammalian vertebrates (e.g., birds, reptiles, and amphibians) and mammalian cells. In one embodiment, the host cell is a mammalian cell. Examples of mammalian cells include, but are not limited to, COS-7 cells, HEK293 cells; baby hamster kidney (BHK) cells; Chinese hamster ovary (CHO) cells; mouse supporting cells; African green monkey kidney cells (VERO-76); human cervical cancer cells (e.g., HeLa); canine kidney cells (e.g., MDCK), etc. In one embodiment, the host cell is a CHO cell. In one embodiment, the host cell is an insect cell.

[0092] How to use

[0093] The present disclosure provides a method for administering a vaccine composition as described herein to a subject. These methods can be used to vaccinate a subject against a virus (e.g., influenza virus). In some embodiments, the vaccination method includes administering a vaccine composition to a subject in need, the vaccine composition comprising one or more separated nucleic acids, peptides and / or proteins encoding the amino acid sequence produced as described herein (e.g., recombinant influenza virus Has as described herein or recombinant influenza virus NA as described herein) and optional adjuvants, and the amount is effective for the virus (e.g., influenza virus) to vaccinate the subject. Similarly, the present disclosure provides a vaccine composition, the vaccine composition comprising one or more separated nucleic acids, peptides and / or proteins encoding the amino acid sequence produced as described herein (e.g., influenza virus Has or NA as described herein) and optional adjuvants, for vaccinating a subject against a virus (e.g., influenza virus) (or for the manufacture of a drug for use in vaccinating a subject against a virus (e.g., influenza virus)).

[0094] The present disclosure also provides methods of immunizing a subject against a virus (e.g., influenza virus), comprising administering to the subject an immunologically effective amount of a vaccine composition comprising one or more recombinant influenza virus HA or NA as described herein and optionally an adjuvant.

[0095] In some embodiments, the method or use prevents viral infection (e.g., influenza virus infection) or disease in a subject. In some embodiments, the method or use causes a protective immune response in a subject. In some embodiments, the protective immune response is an antibody response.

[0096] The method / uses of immunity provided herein can cause a broad neutralization immune response for one or more viruses (e.g., influenza virus).Therefore, in different embodiments, compositions described herein can provide a broad cross protection for different types of viruses (e.g., influenza virus).In certain embodiments, compositions provide cross protection for avian influenza virus, swine influenza virus, seasonal influenza virus and / or pandemic influenza virus.In certain embodiments, the method / uses of immunity can cause an improved immune response for one or more seasonal influenza strains (e.g., standard care strain).For example, the improved immune response can be an improved humoral immune response.In certain embodiments, the method / uses of immunity can cause an improved immune response for one or more pandemic influenza strains.In certain embodiments, immune methods can cause an improved immune response for one or more swine influenza strains.In certain embodiments, the method / uses of immunity can cause an improved immune response for one or more avian influenza strains.

[0097] In certain embodiments, provided herein is a method of enhancing or expanding a protective immune response in a subject, the method comprising administering to the subject an immunologically effective amount of a vaccine composition disclosed herein, wherein the vaccine composition increases the vaccine efficacy of a standard of care influenza virus vaccine composition by an incremental amount ranging from about 5% to about 100%, such as about 10% to about 25%, about 20% to about 100%, about 15% to about 75%, about 15% to about 50%, about 20% to about 75%, about 20% to about 50%, or about 40% to about 80%, such as about 40% to about 60% or about 60% to about 80%. In certain embodiments, the vaccine compositions disclosed herein have a vaccine efficacy that is at least 5% higher than the vaccine efficacy of a standard of care influenza virus vaccine, for example, a vaccine efficacy that is at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or at least 100% higher than the vaccine efficacy of a standard of care influenza virus vaccine. Similarly, the present disclosure provides any vaccine composition described herein for use in enhancing or expanding a protective immune response in a subject (or for use in the manufacture of a medicament for use in enhancing or expanding a protective immune response in a subject).

[0098] Also provided are methods for preventing viral diseases (e.g., influenza virus diseases) in subjects, comprising administering to the subject a vaccine composition comprising one or more isolated nucleic acids, peptides and / or proteins encoding the generated amino acid sequences (e.g., recombinant influenza virus HA or NA as described herein), and an optional adjuvant, in an amount effective to prevent viral diseases (e.g., influenza virus diseases) in subjects. Similarly, the present disclosure provides a vaccine composition comprising one or more recombinant influenza virus HA or NA as described herein, and an optional adjuvant, for use in preventing viral diseases (e.g., influenza virus diseases) in subjects (or for use in the manufacture of a medicament for use in preventing viral diseases (e.g., influenza virus diseases) in subjects).

[0099] Also provided are methods of inducing an immune response against influenza virus HA and influenza virus NA in a subject, the methods comprising administering to the subject a vaccine composition comprising one or more recombinant influenza virus HA as described herein, one or more recombinant influenza virus NA as described herein, and optionally an adjuvant.

[0100] Figure 1is a block diagram of an example system 100 that can be used to make vaccines. In system 100, the techniques described in this document are used to design and make novel vaccines 116. For example, for viruses that contain multiple strains, clades, serotypes, and / or rapidly mutating strains (such as influenza virus or COVID-19 or human rhinovirus, HIV, etc.) or new viruses that have never been encountered before, the techniques described herein can be used to quickly generate vaccine candidates that can be used for testing in humans or other subjects.

[0101] The system 100 receives as input virus strain data 102 and seed amino acid data 104. The virus strain data 102 includes data about one or more virus strains for which a vaccine is needed. The virus strain data 102 may include amino acid sequence data, as well as other types of data, such as metadata (e.g., unique identifiers, strain identification) or non-metadata attributes (e.g., records of physicochemical properties of amino acid sequences, such as molecular weight). The seed amino acid data 104 includes data about initial or seed amino acids that will be modified by the computer system 106 to generate one or more vaccine definitions for a candidate vaccine. The seed amino acid data 104 may include amino acid sequence data, as well as other types of data, such as metadata (e.g., unique identifiers, strain identification) or non-metadata attributes (e.g., records of physicochemical properties of amino acid sequences, such as molecular weight).

[0102] The system 100 includes a computer system 106 that can generate data 108 of candidate non-wild-type amino acid sequences by using the data 102 and 104. These non-wild-type amino acid sequences are sequences that are not found in the wild, or sequences that are not known to be found in the wild. As will be appreciated, it is possible that one or more candidate non-wild-type amino acid sequences 108 may actually exist in the wild, but are unknown to the operator of the system 100 or even to society at large. The candidate non-wild-type amino acid sequence data 108 may include amino acid sequence data, as well as other types of data, such as metadata (e.g., unique identifiers, strain identifiers) or non-metadata attributes (e.g., records of physicochemical properties of amino acid sequences (e.g., molecular weight)).

[0103] Computer system 106 verifies the manufacture of one or more candidates in data 108, thereby generating data 110. Data 110 may include amino acid sequence data, as well as other types of data, such as metadata (e.g., unique identifier, strain identification) or non-metadata attributes (e.g., a record of physicochemical properties of the amino acid sequence (e.g., molecular weight)). In some cases, data 102 / 104, 108, and 110 have the same data format, while in some cases, data 102 / 104, 108, and 110 have different data formats.

[0104] In some cases, the validation process for selecting a candidate may include determining whether an amino acid sequence can be synthesized, or whether it can be synthesized in an easy or economical manner. As will be appreciated, an amino acid sequence may define the structure of a molecule that cannot exist in the physical world due to the geometry and forces exhibited by the molecule. Therefore, these impossible sequences may be excluded from the validation process. In addition, some candidates may be excluded even if effective molecules are defined. For example, a computer 106 may maintain a data repository of previous candidates that have not actually been effectively used as vaccines after being studied in clinical trials, or are predicted to have low immunogenicity or low protectiveness to target virus strains, and the data repository may include virus strain data 102. In this case, the candidates in data 108 may be excluded from validation data 110. In some cases, candidates may be excluded or prioritized based on synthesis and manufacturing considerations. For example, candidates with specific synthesis or handling conditions (e.g., refrigeration, shock sensitivity) may be excluded from validation or have a lower priority than other candidates with less onerous synthesis or handling conditions.

[0105] The system 100 may also include vaccine manufacturing equipment 112, which can use vaccine precursors 114 and one or more verified non-wild type amino acid sequence data 110 to manufacture one or more vaccine doses or vaccine molecules 116. As will be understood, the scale of synthesis required for preliminary exploration and testing is much smaller than that for large-scale manufacturing of vaccines that have been tested, proven to be safe and effective and approved for use in humans or other subjects. Therefore, the details of the manufacturing equipment 112 can be changed as needed. Similarly, although the vaccine precursors 114 include those articles, chemicals, materials, etc. used to manufacture the vaccine 116, the precursors 114 will also change as needed.

[0106] Figure 2 is a schematic diagram of data that can be used to make a vaccine. For example, the data shown here can be used by computer system 106 or other computer systems.

[0107] Seed amino acid data 104 is shown here, wherein the sub-part of the sequence uses the single letter name recommended by the Joint Committee on Biochemical Nomenclature of the International Union of Pure and Applied Chemistry-International Union of Biochemistry and Molecular Biology (IUPAC-IUBMB) for ease of reading. Data 104 may include a data value vector (e.g., a single American Standard Code for Information Interchange (ASCII) character, an integer) to represent the amino acid in the sequence represented by data 104. As will be understood, longer sequences will have more indexes than those intuitively shown here. In addition, for the sake of clarity, the other parts of data 104 are not presented here. The data 104 is a discrete data object of one or more amino acid sequences. Each amino acid sequence can be recorded as a single letter or a string of letters. The string of letters can include multiple single letters. One or more amino acid sequences may include a first amino acid sequence and a second amino acid sequence, each of which includes a corresponding single letter and a corresponding string of letters. That is, each amino acid sequence may be stored in data that conforms to the same format while preserving different values. This may achieve interoperability and consistent handling of data.

[0108] For each discrete value in the data 104, a corresponding weight vector 202 may be created and maintained. For each possible amino acid in a particular index of the data 104, the weight vector 202 may be configured with an index, such as twenty weight values, each corresponding to one of the twenty possible amino acids 200. Initially, this probability may be set to either 0 or 1. As shown here, for the index with a value of "Y", each index of the weight vector 202 is set to zero, except for the second-to-last index position, because "Y" is the second-to-last possible amino acid label when arranged alphabetically.

[0109] For each weight value of the weight vector 202, a corresponding attribute vector 204 of an attribute value is used. In this example, each attribute vector 204 is a vector of length four, but other lengths are possible. Twenty attribute vectors 204 are shown, one for each of the twenty indices of the weight vector 202. The weight vector 202 and some subsequent vectors are not shown as having no values ​​because their format (e.g., real numbers) may be too large to be clearly presented in the space provided.

[0110] In the attribute vector 204, each attribute value represents the physicochemical attribute of a specific amino acid. For example, the attribute vector may record molecular weight, charge, hydrophobic tendency, isoelectric point, alpha helix tendency, beta fold tendency, molecular volume, octanol-water partition energy, etc.

[0111] Each value in the weight vector 202 may be combined with a corresponding attribute vector 204 to create a corresponding weighted probability vector 206. For example, each value in each attribute vector 204 may be multiplied by a corresponding weight value in the weight vector 202.

[0112] One or more optimization, solver, classifier, or other functions may be applied to each weighted probability vector 206 (the vectors associated with a single index of vector 104 shown, and all other vectors associated with other indexes of vector 104), or to a collection of weighted probability vectors 206, to generate an optimized vector 208. Such functions will be described later in this document, but in general, (these) functions may be configured to operate on continuous data (e.g., real numbers) to generate a second set of continuous data (e.g., real numbers) that more closely matches a target or attribute. As shown, the optimized vector 208 contains such continuous data, while the data 104 contains discrete data (e.g., specific ASCII characters representing specific amino acids). Figure 2 The intermediate data 202 to 206 shown are used to process the data 104 to implement the functions of operating on continuous data. Once these functions are completed, the optimization vector 208 can be converted into discrete data for our use in practical applications, such as specifying the amino acid sequence for making a vaccine. Although the term "optimization" is used here, it will be understood that this may or may not be optimization in the strictest mathematical sense.

[0113] In contrast to the described process, the optimization vector 208 is used to find weights in the continuous result vector 210. These weights are the values ​​that, when multiplied by the corresponding attribute vector 204, will produce the optimization vector 208. Also, while the weight vector 202 only contains values ​​of 0 and 1, the continuous result vector 210 is unlikely to have values ​​of 0 or 1, but rather has a continuous range of values ​​between 0 and 1. In other words, when the values ​​in vector 206 change to the values ​​in 208, the values ​​in vector 202 also change to the values ​​in vector 210.

[0114] By finding the best fitting discrete value for each index using the continuous result vector 210, a discrete result object 212 can be found. When applied to the amino acid sequence example, this involves finding the amino acid at that position in the discrete result object 212. This can include, for example, finding the maximum value in the continuous result vector 210 and selecting the amino acid corresponding to the index position, but other best fit processes are also possible. Therefore, data 202 to 210 can be created for each index position in the vectors 104 and 212, thereby starting from the discrete data 104, using one or more continuous functions only, and generating discrete data 212. This advantageously allows one amino acid sequence to be transformed into another amino acid sequence, thereby allowing the synthesis and / or manufacture of new vaccines. The manufacturing equipment (e.g., 112) can use the discrete data 212 to generate vaccine precursors.

[0115] Figure 3 is a flow chart of an example process 300 that can be used to apply a continuous algorithm to discrete data, such as can be used to make a vaccine. For example, process 300 can be used Figure 1 and Figure 2 204 to 206), so the elements of these figures will be used in the description. Possible embodiments of the various elements of process 300 will be described later in processes 400 to 700.

[0116] A discrete data object including a plurality of first discrete values ​​is received 302. For example, a vector 104 representing a seed amino acid sequence is received. The seed amino acid sequence can be, for example, a vaccine that has been shown to be safe and effective against a previously encountered virus, an amino acid sequence previously observed in nature, an amino acid sequence that has not been previously observed in nature but has been studied experimentally, or a definition of a hypothetical molecule that has a desired property but cannot or has not yet been synthesized or randomly generated.

[0117] The discrete data object includes one or more amino acid sequences. For example, the discrete data object can take the form of binary data (i.e., 1s and 0s) stored in a computer memory and / or transmitted to the discrete / continuous converter 702 via a data network. The binary data can be interpreted as a sequence of characters that specifies an amino acid sequence or a group of amino acid sequences. In addition, other payload data (e.g., the source of the sequence) and metadata (e.g., the creation date of the discrete data object) can also be included.

[0118] The discrete data object is converted to a continuous data object including a plurality of first continuous values ​​304. For example, vector 104 may be converted to vector 206. The conversion process may or may not involve the use of vectors 202 and 204, depending on the particular process used to perform the conversion.

[0119] A continuous data algorithm is applied to the continuous data object to generate a continuous result object 306 including a plurality of second continuous values. For example, one or more computer functions can be created based on a mathematical or logical model that are intended to make the amino acid sequence of the vector 104 more likely to have a certain property. One such example is modifying the amino acid sequence to create a new vaccine against a newly emerging virus or strain. This can include applying a gradient descent to the vector 206 using a loss function that considers, among other parameters, the score of a given amino acid sequence on a model that predicts an immune response given the strain data 102.

[0120] The continuous result object is converted to a discrete result object including a plurality of second discrete values ​​308. For example, vector 208 may be converted to vector 212. The conversion process may or may not involve the use of vector 210, depending on the particular process used to perform the conversion.

[0121] Figure 4 is a flow chart of an example process that can be used to apply a continuous algorithm to discrete data (such as can be used to make vaccines). For example, process 400 can use Figure 1 and Figure 2 4. Process 400 is performed with the data shown in , and therefore elements of these figures will be used in the description. Process 400 is a possible example of how operation 304 may be performed, but other processes may also be used.

[0122] A weight vector of weight values ​​is generated for each first discrete value, each weight value representing the likelihood that the first discrete value represents a particular amino acid 402. For example, for each value in array 104, a corresponding vector 202 is generated. In this example, each value in vector 104 is a particular amino acid, so one and only one value in vector 202 is 1, and all other values ​​are 0. However, another example may use a scheme where a position can have one or the other amino acid. In this case, the associated vector 202 may have, for example, two values ​​of 0.5.

[0123] For each weight value of each weight vector, an attribute vector of attribute values ​​is generated, each attribute value representing a physicochemical attribute of a particular amino acid 404. For example, the vectors 204 can be accessed from a data repository storing physicochemical attributes or other attributes of various amino acids. These attributes can be used in their original state or pre-processed (e.g., normalized to between 0 and 1, rounded to a given level of precision, converted to a different data format). As will be appreciated, these physicochemical attributes or other attributes can be recorded and maintained unchanged because they reflect observations and measurements of the amino acids, and these values ​​can be obtained from a third party.

[0124] The weight vector and the attribute vector are combined to form the weighted attribute vector 406. For example, each weight value of the vector 202 may be multiplied by each corresponding vector 204 to create the vector 206. These values ​​may be post-processed (e.g., normalized to between 0 and 1, rounded to a given level of precision, converted to a different data format).

[0125] Figure 5 is a flow chart of an example process that can be used to apply a continuous algorithm to discrete data (such as can be used to make vaccines). For example, process 500 can use Figure 1 and Figure 2 306 , and thus elements of these figures will be used in the description. Process 500 is one possible example of how operation 306 may be performed, but other processes may also be used.

[0126] Successive representations of the amino acid sequence are accessed 502. For example, for each index of vector 104, twenty vectors 206 are accessed, thereby generating a set of vectors having a size of twenty times the length of the amino acid sequence represented by vector 104.

[0127] Gradient descent is applied to the continuous representation 504. The gradient descent is configured with a loss function that determines a loss value based on a plurality of loss criteria. Generally speaking, the loss criteria can be divided into two categories - the first category is to change the amino acid sequence to make it tend to the desired predicted property, including but not limited to immune response; the second category is to reduce to a feasible amino acid sequence. If a larger change is required to the original sequence, the change type can be applied first and then the reduction type. If a smaller change is desired to the original sequence, a different order or mixed loss criteria can be used.

[0128] The first loss criterion is based on the immune response of a given two amino acid sequences. For example, the predictor function can be configured to accept two amino acid sequences as input. The function can be configured to return the predicted immune response of a subject (e.g., a human, an animal) as output. The output can take the form of a value between 0 and 1, for example, wherein the higher the value, the greater the predicted immune response. The predictor function can be operated using a machine learning model.

[0129] The second loss criterion modifies (e.g., penalizes) subsequences that are not found in the wild-type sequence data set. For example, a data repository of known wild-type subsequences of amino acid sequences can be stored. If a given subsequence generated by the process is also found in the wild-type amino acid sequence, then the given subsequence is likely a subsequence that can be synthesized. However, if a subsequence is not found in any known wild-type amino acid sequence, it may not be possible to synthesize, or it may be possible or economical to develop a new synthesis method. Therefore, a penalty can be imposed on subsequences that are not found in the wild-type amino acid sequence to avoid these problems. In general, the second loss criterion can include a score derived by a machine learning model.

[0130] The third loss criterion penalizes each weight vector based on the maximum value in the vector of weights. For example, if the weight in vector 210 is close to 1 (i.e., the maximum value), this is a high confidence rating or a high immune response indication for a given amino acid at a specific position in the sequence of vector 212. In this case, the third loss criterion may not apply a penalty or apply a small penalty. In another example, if the maximum weight value is much lower, this is a low confidence rating or a low immune response indication, so a larger penalty may be applied. In some cases, the penalty may be to multiply the score by a value of 1 minus the maximum weight, but other schemes may also be used.

[0131] As will be appreciated, some, all, or none of these standards may be used.

[0132] A continuous representation of the new amino acid sequence is generated 506. For example, for each index of the vector 104 containing a discrete representation of a single amino acid, twenty vectors 208 holding continuous values ​​may be created. As described elsewhere in this document, these twenty vectors 208 may be converted to a single discrete value in the vector 212 to represent a single amino acid.

[0133] Figure 6 is a flow chart of an example process that can be used to apply a continuous algorithm to discrete data (such as can be used to make vaccines). For example, process 600 can use Figure 1 and Figure 2 The present invention is performed with the data shown in FIG. 1 , and therefore elements of these figures will be used in the description.

[0134] A plurality of candidate discrete result objects 602 are generated. For example, for a single virus strain data object 102, a large collection of seed amino acid data objects 104 may be created. This may include collecting all known viable vaccines for a given virus and using them as seed data 104 for a new virus strain 102.

[0135] For each seed, the sequence-changing algorithm is applied 604. For example, process 300 may be performed using each seed to generate an equal number of candidate sequences.

[0136] Candidate outputs are collected 606 and some are excluded 608. For example, it may be found that at least one candidate specified amino acid sequence fails a manufacturability test. This test may involve determining that the sequence is unlikely to be synthesized, is too similar to another candidate, matches one of the seeds, etc. This may be used, for example, to prioritize the most likely candidates when testing resources are limited.

[0137] Figure 7 is a swim lane diagram of an example process for making a vaccine. For example, process 400 may use Figure 1 and Figure 2 , and therefore elements of these figures will be used in the description. Process 700 includes process 300, and therefore will be shown using elements of process 300. To perform process 700, computer system 106 may use discrete / continuous converter 702, optimizer 704, and immune response predictor 706, but different components may also be used.

[0138] Once the data defining the amino acid sequence to be manufactured are generated, the vaccine maker 116 will manufacture 708 a vaccine comprising a protein defined by a discrete result object (i.e., an amino acid sequence) and / or a vaccine comprising a nucleic acid or any other delivery vehicle (including a virus or bacterial vector), wherein such nucleic acid or delivery vehicle produces a protein defined by a discrete result object. This manufacture may be in small batches for preliminary testing, clinical trials, and / or general use. As will be appreciated, elements 308 and 708 may be separated by a large amount of time and gap operations. For example, if the manufacture in 708 is for general use in large quantities, it may only be carried out after clinical trials have proven that the vaccine is safe and effective for its intended purpose.

[0139] Figure 8 An example of a computing device 800 and an example of a mobile computing device that can be used to implement the techniques described herein are shown. Computing device 800 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Mobile computing devices are intended to represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, and other similar computing devices. The components shown here, their connections and relationships, and their functions are exemplary only and are not intended to limit the embodiments of the present invention described and / or claimed in this document.

[0140] The computing device 800 includes a processor 802, a memory 804, a storage device 806, a high-speed interface 808 connected to the memory 804 and a plurality of high-speed expansion ports 810, and a low-speed interface 812 connected to the low-speed expansion port 814 and the storage device 806. Each of the processor 802, the memory 804, the storage device 806, the high-speed interface 808, the high-speed expansion port 810, and the low-speed interface 812 is interconnected using various buses and can be installed on a common motherboard or in other appropriate ways. The processor 802 can process instructions for execution within the computing device 800, including instructions stored in the memory 804 or on the storage device 806, to display graphical information of a GUI on an external input / output device (such as a display 816 coupled to the high-speed interface 808). In other embodiments, multiple processors and / or multiple buses, as well as multiple memories and multiple types of memories, can be used as needed. In addition, multiple computing devices can be connected, wherein each device provides part of the necessary operations (for example, as a server group, a group of blade servers, or a multi-processor system).

[0141] The memory 804 stores information within the computing device 800. In some implementations, the memory 804 is one or more volatile memory units. In some implementations, the memory 804 is one or more non-volatile memory units. The memory 804 may also be other forms of computer-readable media, such as a magnetic disk or an optical disk.

[0142] The storage device 806 can provide mass storage for the computing device 800. In some embodiments, the storage device 806 can be or include a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a magnetic tape device, a flash memory or other similar solid-state memory device, or an array of devices, including devices in a storage area network or other configuration. A computer program product can be tangibly embodied in an information carrier. A computer program product can also include instructions that, when executed, perform one or more methods, such as the above-described methods. A computer program product can also be tangibly embodied in a computer or machine-readable medium, such as the memory 804, the storage device 806, or a memory on the processor 802.

[0143] The high-speed interface 808 manages bandwidth-intensive operations of the computing device 800, while the low-speed interface 812 manages less bandwidth-intensive operations. This allocation of functions is exemplary only. In some embodiments, the high-speed interface 808 is coupled to the memory 804, the display 816 (e.g., through a graphics processor or accelerator), and to a high-speed expansion port 810 that can accept various expansion cards (not shown). In an embodiment, the low-speed interface 812 is coupled to the storage device 806 and the low-speed expansion port 814. The low-speed expansion port 814 (which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet)) can be coupled to one or more input / output devices, such as a keyboard, a pointing device, a scanner, or a network device (such as a switch or router), for example, through a network adapter.

[0144] The computing device 800 can be implemented in a variety of different forms, as shown. For example, it can be implemented as a standard server 820, or multiple times in a group of such servers. In addition, it can also be implemented in a personal computer (such as a laptop computer 822). It can also be implemented as part of a rack server system 824. Alternatively, the components in the computing device 800 can be combined with other components in a mobile device (not shown) (such as a mobile computing device 850). Each such device can include one or more of the computing device 800 and the mobile computing device 850, and the entire system can be composed of multiple computing devices that communicate with each other.

[0145] The mobile computing device 850 includes a processor 852, a memory 864, an input / output device (such as a display 854), a communication interface 866, and a transceiver 868, among other components. The mobile computing device 850 may also be equipped with a storage device, such as a micro drive or other device, to provide additional storage. Each of the processor 852, the memory 864, the display 854, the communication interface 866, and the transceiver 868 are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other suitable ways.

[0146] The processor 852 can execute instructions within the mobile computing device 850, including instructions stored in the memory 864. The processor 852 can be implemented as a chipset including separate multiple analog and digital processors. The processor 852 can provide, for example, coordination of other components of the mobile computing device 850, such as control of a user interface, applications running on the mobile computing device 850, and wireless communications of the mobile computing device 850.

[0147] The processor 852 can communicate with the user through a control interface 858 and a display interface 856 coupled to the display 854. The display 854 can be, for example, a TFT (thin film transistor liquid crystal display) display or an OLED (organic light emitting diode) display, or other appropriate display technology. The display interface 856 may include appropriate circuits for driving the display 854 to present graphical information and other information to the user. The control interface 858 can receive commands from the user and convert them for submission to the processor 852. In addition, the external interface 862 can provide communication with the processor 852 to enable near-area communication of the mobile computing device 850 with other devices. The external interface 862 can, for example, provide wired communication in some embodiments, or provide wireless communication in other embodiments, and multiple interfaces can also be used.

[0148] The memory 864 stores information within the mobile computing device 850. The memory 864 may be implemented as one or more computer-readable media, one or more volatile memory units, or one or more non-volatile memory units. An expansion memory 874 may also be provided and connected to the mobile computing device 850 via an expansion interface 872, which may include, for example, a SIMM (single in-line memory module) card interface. The expansion memory 874 may provide additional storage space for the mobile computing device 850, and may also store applications or other information for the mobile computing device 850. Specifically, the expansion memory 874 may include instructions for executing or supplementing the above-mentioned processes, and may also include security information. Thus, for example, the expansion memory 874 may be provided as a security module for the mobile computing device 850, and may be programmed with instructions that allow the secure use of the mobile computing device 850. In addition, secure applications and other information may also be provided via a SIMM card, such as placing identification information on the SIMM card in an unbreakable manner.

[0149] The memory may include, for example, flash memory and / or NVRAM memory (non-volatile random access memory), as discussed below. In some embodiments, a computer program product is tangibly embodied in an information carrier. The computer program product includes instructions that, when executed, perform one or more methods, such as the methods described above. The computer program product may be a computer or machine readable medium, such as the memory 864, the expansion memory 874, or a memory on the processor 852. In some embodiments, the computer program product may be received, for example, via the transceiver 868 or the external interface 862 in the form of a propagated signal.

[0150] The mobile computing device 850 can communicate wirelessly via a communication interface 866, which may include digital signal processing circuitry when necessary. The communication interface 866 can provide communications in various modes or protocols, such as GSM voice calls (Global System for Mobile Communications), SMS (Short Message Service), EMS (Enhanced Short Message Service), or MMS messages (Multimedia Short Message Service), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), etc. Such communications can be performed, for example, using radio frequencies via a transceiver 868. In addition, short-range communications can also be performed, such as using Bluetooth, WiFi, or other such transceivers (not shown). In addition, a GPS (Global Positioning System) receiver module 870 can provide additional wireless data related to navigation and location to the mobile computing device 850, which can be used appropriately by applications running on the mobile computing device 850.

[0151] Mobile computing device 850 may also communicate audio using audio codec 860, which may receive spoken information from a user and convert it into usable digital information. Audio codec 860 may also generate audible sounds for the user, such as through a speaker (e.g., in a receiver of mobile computing device 850). Such sounds may include sounds from voice phone calls, may include recorded sounds (e.g., voice messages, music files, etc.), and may also include sounds generated by applications running on mobile computing device 850.

[0152] Mobile computing device 850 can be implemented in many different forms, as shown in the figure. For example, it can be implemented as a cellular phone 880. It can also be implemented as a part of a smart phone 882, a personal digital assistant or other similar mobile device.

[0153] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system that includes at least one programmable processor that can be special purpose or general purpose, coupled to receive data and instructions from a storage system, at least one input device, and at least one output device, and to transmit data and instructions to the storage system, at least one input device, and at least one output device.

[0154] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and may be implemented in high-level procedural and / or object-oriented programming languages ​​and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., magnetic disks, optical disks, memories, programmable logic devices (PLDs)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0155] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube), or LCD (liquid crystal display) monitor) for displaying information to the user, and a keyboard and pointing device (e.g., a mouse or trackball), through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including sound, voice, or tactile input.

[0156] The systems and techniques described herein can be implemented in a computing system that includes a back-end component (e.g., as a data server) or includes a middleware component (e.g., an application server), or includes a front-end component (e.g., a client computer with a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any digital data communication form or medium (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0157] A computing system may include clients and servers. Clients and servers are generally remote from each other and generally interact through a communication network. The relationship of client and server arises from computer programs running on the respective computers and having a client-server relationship to each other.

Claims

1. A method for manufacturing a vaccine by using a continuous data algorithm, the method comprising: receiving a discrete data object comprising a plurality of first discrete values, the discrete data object comprising one or more amino acid sequences; converting the discrete data object into a continuous data object comprising a plurality of first continuous values; applying a continuous data algorithm to the continuous data object to generate a continuous result object comprising a plurality of second continuous values; converting the continuous result object into a discrete result object containing a plurality of second discrete values; as well as A vaccine is manufactured, the vaccine comprising at least one of the group consisting of: i) a protein defined by the discrete outcome object, ii) a nucleic acid capable of producing the protein defined by the discrete outcome object, and iii) a delivery vehicle capable of producing the protein defined by the discrete outcome object.

2. The method of claim 1, wherein the one or more amino acid sequences comprise: A first amino acid sequence and a second amino acid sequence, each of the first amino acid sequence and the second amino acid sequence comprises a corresponding single letter or a corresponding letter string.

3. The method of any one of claims 1 to 2, wherein converting the discrete data object into the continuous data object comprises: generating a weight vector of weight values ​​for each first discrete value, each weight value representing the likelihood that the first discrete value represents a specific amino acid; generating, for each weight value of each weight vector, a property vector of property values, each property value representing a physicochemical property of a particular amino acid; and The weight vector and the attribute vector are combined to create a first continuous value of the continuous data object.

4. The method of claim 3, wherein each weight vector has twenty weight values, each weight value corresponding to one of the twenty possible amino acids.

5. The method of any one of claims 3 to 4, wherein converting the continuous result object to the discrete result object comprises determining a corresponding single amino acid for each second continuous value, wherein the determined single amino acids form the plurality of second discrete values.

6. The method according to any one of claims 3 to 5, wherein the method further comprises: Generate multiple candidate discrete result objects; as well as At least one discrete outcome object specifying an amino acid that failed the manufacturability test is excluded from the plurality of candidate discrete outcome objects.

7. The method of any one of claims 3 to 6, wherein applying the continuous data algorithm to generate the continuous result object comprises applying gradient descent with a loss function that determines a loss value based on a plurality of loss criteria, the loss function comprising: The first loss criterion based on the immune response given two amino acid sequences; a second loss criterion that modifies the loss value for subsequences that are not found in the dataset of wild-type sequences or that are not predicted to be correctly folded; as well as A third loss criterion, which modifies the loss value for each weight vector based on the maximum of these second continuous values.

8. The method of any one of claims 1 to 7, wherein the vaccine is against one of the group consisting of: i) influenza, ii) human rhinovirus, iii) HIV and iv) coronavirus disease.

9. A system for generating an amino acid sequence, the system comprising: one or more processors; as well as Computer memory storing instructions that, when executed by the processors, cause the processors to perform operations including: receiving a discrete data object comprising a plurality of first discrete values, the discrete data object comprising one or more amino acid sequences; converting the discrete data object into a continuous data object comprising a plurality of first continuous values; applying a continuous data algorithm to the continuous data object to generate a continuous result object comprising a plurality of second continuous values; converting the continuous result object into a discrete result object comprising a plurality of second discrete values; and A vaccine is manufactured, the vaccine comprising at least one of the group consisting of: i) a protein defined by the discrete outcome object, ii) a nucleic acid capable of producing the protein defined by the discrete outcome object, and iii) a delivery vehicle capable of producing the protein defined by the discrete outcome object.

10. The system of claim 9, wherein the one or more amino acid sequences comprise: A first amino acid sequence and a second amino acid sequence, each of the first amino acid sequence and the second amino acid sequence comprises a corresponding single letter or a corresponding letter string.

11. The system of any one of claims 9 to 10, wherein converting the discrete data object into the continuous data object comprises: generating a weight vector of weight values ​​for each first discrete value, each weight value representing the likelihood that the first discrete value represents a specific amino acid; generating, for each weight value of each weight vector, a property vector of property values, each property value representing a physicochemical property of a particular amino acid; and The weight vector and the attribute vector are combined to create a first continuous value of the continuous data object.

12. The system of claim 11, wherein each weight vector has twenty weight values, each weight value corresponding to one of twenty possible amino acids.

13. The system of any one of claims 11 to 12, wherein converting the continuous result object to the discrete result object comprises determining a corresponding single amino acid for each second continuous value, wherein the determined single amino acids form the plurality of second discrete values.

14. The system of any one of claims 11 to 13, wherein the operations further comprise: Generate multiple candidate discrete result objects; as well as At least one discrete outcome object specifying an amino acid that failed the manufacturability test is excluded from the plurality of candidate discrete outcome objects.

15. The system of any one of claims 11 to 14, wherein applying the continuous data algorithm to generate the continuous result object comprises applying gradient descent with a loss function that determines a loss value based on a plurality of loss criteria, the loss function comprising: The first loss criterion based on the immune response given two amino acid sequences; a second loss criterion that modifies the loss value for subsequences that are not found in the dataset of wild-type sequences or that are not predicted to be correctly folded; as well as A third loss criterion, which modifies the loss value for each weight vector based on the maximum of these second continuous values.

16. A non-transitory computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving a discrete data object comprising a plurality of first discrete values, the discrete data object comprising one or more amino acid sequences; converting the discrete data object into a continuous data object comprising a plurality of first continuous values; applying a continuous data algorithm to the continuous data object to generate a continuous result object comprising a plurality of second continuous values; converting the continuous result object into a discrete result object containing a plurality of second discrete values; as well as A vaccine is manufactured, the vaccine comprising at least one of the group consisting of: i) a protein defined by the discrete outcome object, ii) a nucleic acid capable of producing the protein defined by the discrete outcome object, and iii) a delivery vehicle capable of producing the protein defined by the discrete outcome object.

17. The medium of claim 16, wherein the one or more amino acid sequences comprise: A first amino acid sequence and a second amino acid sequence, each of the first amino acid sequence and the second amino acid sequence comprises a corresponding single letter or a corresponding letter string.

18. The medium of any one of claims 16 to 17, wherein converting the discrete data object to the continuous data object comprises: generating a weight vector of weight values ​​for each first discrete value, each weight value representing the likelihood that the first discrete value represents a specific amino acid; generating, for each weight value of each weight vector, a property vector of property values, each property value representing a physicochemical property of a particular amino acid; and The weight vector and the attribute vector are combined to create a first continuous value of the continuous data object.

19. The medium of claim 18, wherein each weight vector has twenty weight values, each weight value corresponding to one of twenty possible amino acids.

20. The medium of any one of claims 18-19, wherein converting the continuous result object to the discrete result object comprises determining a corresponding single amino acid for each second continuous value, wherein the determined single amino acids form the plurality of second discrete values.

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