Machine learning techniques in protein design for vaccine generation

Abstract

A discrete data object is received, 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 to a continuous data object, which may include a plurality of first continuous values. A continuous data algorithm is applied to the continuous data object to generate a continuous result object, which may include a plurality of second continuous values. The continuous result object is converted to a discrete result object, which may include a plurality of second discrete values. A vaccine is manufactured, which may include at least one of a group that may include 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.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 63 / 319,700, filed March 14, 2022, and U.S. Provisional Application No. 63 / 319,692, filed March 14, 2022, the entire contents of which are incorporated by reference herein.

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

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

[0004] A vaccine is a biological product that provides acquired immunity against a specific infectious disease. Vaccines usually contain an agent that resembles the disease-causing microorganism, and are often made from a weakened or killed form of the microorganism, its toxins, or one of its surface proteins. The agent stimulates the body's immune system to recognize the agent as a threat, destroy it, and further recognize and destroy any microorganisms related to the agent that may be encountered in the future. Vaccines can be prophylactic (to prevent or ameliorate the effects of future infections with natural or "wild" pathogens) or therapeutic (to combat diseases that have already developed, such as cancer). Some vaccines provide complete sterilizing immunity, in which infection is completely prevented. Summary of the Invention [Means for solving the problem]

[0005] Strains used in seasonal influenza vaccines are now almost universally selected by public health authorities. These selections are made annually based on observations of immune responses in animal models and human trials. However, H3N2 vaccines using strains recommended by public health authorities have not been sufficient to induce broad protection in the general population, for example, over the past 5 years (2015-2020). Furthermore, published data from this period show that immunological associations are separated into diverse clades, where each clade is protective to itself, while protection against other clades may be limited. The present disclosure provides a solution to this problem. The embodiments described in this disclosure provide an algorithm that uses a differentiable machine learning approach to introduce mutations into a given starting strain and another model predicts that the modified antigen is highly protective against both homologous and heterologous clades. In one example, the algorithm was used to optimize the HA1 sequence of H3 hemagglutinin (positions 16 to 345), and then graft the wild-type signal peptide and HA2 region to create the complete hemagglutinin sequence. Exemplary modified antigen sequences starting with A / Singapore / INFIMH-16-0019 / 2016 are provided with mutated residues shown in bold: [ka]

[0006] One or more computer systems may be configured to perform certain operations or actions by having software, firmware, hardware, or a combination thereof installed on the system that causes the system to perform the operation during operation. One or more computer programs may be configured to perform certain operations or actions by including instructions that, when executed by a data processing device, cause the device to perform the operation. One general aspect includes a method for producing 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, the discrete data object may include one or more amino acid sequences. The method also includes converting the discrete data object to a continuous data object that may include a plurality of first continuous values. The method also includes applying the 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 to a discrete result object that may include a plurality of second discrete values. The method also includes manufacturing a vaccine that may include at least one of the following groups: 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. Other embodiments of this aspect include corresponding computer systems, devices, and computer programs stored on one or more computer storage devices, each configured to perform the operations of the method.

[0007] Implementations may include one or more of the following features: 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 respective single character or a respective character string. The conversion of the discrete data object to the continuous data object may include generating, for each of the first discrete values, a weight vector of weight values, each weight value representing a likelihood that the first discrete value represents a particular 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 combining the weight vector and the property vector to generate the first continuous value of the continuous data object. Each weight vector has 20 weight values, each weight value corresponding to one of the 20 possible amino acids. The conversion of the continuous result object to the discrete result object may include determining, for each of the second continuous values, a respective single amino acid, the determined single amino acids forming a plurality of second discrete values. The method may further include generating a plurality of candidate discrete result objects, and filtering out at least one discrete result object from the plurality of candidate discrete result objects that identifies an amino acid that fails the manufacturability test. The application of the continuous data algorithm to generate the continuous result objects may include applying a gradient descent method 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 immunological response given two amino acid sequences; a second loss criterion that modifies the loss value of a subsequence not found in the dataset of wild-type sequences or subsequences that are predicted to not fold correctly; and a third loss criterion that modifies the loss value based on a maximum value in the second continuous value for each weight vector. The vaccine is against one of a group that may include i) influenza, ii) human rhinovirus, iii) HIV, and iiiv) coronavirus diseases. Implementations of the described technology may include hardware, methods or processes, or computer software on a computer accessible medium.

[0008] One general aspect includes a system for generating amino acid sequences, which may include a computer memory. The system also includes one or more processors. The system also includes a computer memory that stores instructions that, when executed by the processor, cause the processor to perform operations, which may include receiving a discrete data object including a plurality of first discrete values, the discrete data object including one or more amino acid sequences, converting the discrete data object into a continuous data object including a plurality of first continuous values, applying a continuous data algorithm to the continuous data object to generate a continuous result object including a plurality of second continuous values, converting the continuous result object into a discrete result object including a plurality of second discrete values, and manufacturing a vaccine that includes at least one of the group consisting of i) a protein defined by the discrete result object, ii) a nucleic acid capable of generating the protein defined by the discrete result object, and iii) a delivery vehicle capable of generating the protein defined by the discrete result object. Other embodiments of this aspect include corresponding computer systems, devices, and computer programs stored on one or more computer storage devices, each configured to perform the operations of the method.

[0009] Implementations may include one or more of the following features: The system, wherein 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 respective single character or a respective character string. The conversion of the discrete data object to the continuous data object may include generating, for each of the first discrete values, a weight vector of weight values, each weight value representing a likelihood that the first discrete value represents a particular 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 combining the weight vector and the property vector to generate the first continuous value of the continuous data object. Each weight vector has 20 weight values, each weight value corresponding to one of the 20 possible amino acids. The conversion of the continuous result object to the discrete result object may include determining, for each of the second continuous values, a respective single amino acid, the determined single amino acids forming a plurality of second discrete values. The operations may further include: generating a plurality of candidate discrete result objects and filtering out from the plurality of candidate discrete result objects at least one discrete result object that identifies an amino acid that fails the manufacturability test. Applying the continuous data algorithm to generate the continuous result objects may include applying a gradient descent method 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 immunological reaction given the two amino acid sequences; a second loss criterion that modifies the loss value of a subsequence not found in the dataset of wild-type sequences or subsequences that are predicted to not fold correctly; and a third loss criterion that modifies the loss value based on the maximum value in the second continuous value for each weight vector. Implementations of the described techniques may include hardware, methods or processes, or computer software on a computer accessible medium.

[0010] One 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 including a plurality of first discrete values, the discrete data object including one or more amino acid sequences, converting the discrete data object into a continuous data object including a plurality of first continuous values, applying a continuous data algorithm to the continuous data object to generate a continuous result object including a plurality of second continuous values, converting the continuous result object into a discrete result object including 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, devices, and computer programs stored on one or more computer storage devices, each configured to perform the operations of the method.

[0011] Implementations may include one or more of the following features: The medium 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 respective single character or a respective character string. The conversion of the discrete data object to the continuous data object may include generating, for each of the first discrete values, a weight vector of weight values, each weight value representing a likelihood that the first discrete value represents a particular 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 combining the weight vector and the property vector to generate a first continuous value of the continuous data object. Each weight vector has 20 weight values, each weight value corresponding to one of the 20 possible amino acids. The conversion of the continuous result object to the discrete result object may include determining, for each of the second continuous values, a respective single amino acid, the determined single amino acids forming a plurality of second discrete values. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer accessible medium.

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

[0013] Also disclosed are vectors, fusion proteins, and cells that include one or more of the 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 a subject.

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

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

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

[0018] In various embodiments, the methods and compositions disclosed herein treat or prevent disease caused by either or both seasonal or pandemic virus strains (e.g., either or both seasonal and pandemic influenza strains).

[0019] In embodiments of the methods disclosed herein where the subject is a human, the human is at least 6 months old, under 18 years old, at least 6 months old and under 18 years old, at least 18 years old and under 65 years old, at least 6 months old and under 5 years old, at least 5 years old and under 65 years old, at least 60 years old, or at least 65 years old. For example, the subject is 6 months old, 8 months old, 10 months old, 12 months old, 14 months old, 16 months old, 18 months old, 20 months old, 22 months old, 24 months old, 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 to a subject two doses of a vaccine composition, spaced 2-6 weeks apart, for example, 4 weeks apart.

[0020] An implementation may include any, all, or any of the following features:

[0021] The embodiments discussed in this disclosure may provide one or more of the following advantages: The embodiments may be used to generate hemagglutinin sequences that may induce broad protection from influenza infection after vaccination. In particular, the embodiments may be used to produce antigens with higher than expected recovery rates of functional influenza viruses with designed hemagglutinin sequences. These antigens are believed to have broader protection in animal models than current standard of care antigens. The embodiments may be used to generate broadly protective hemagglutinin proteins for use as influenza vaccine antigens, or may define sequences of nucleic acids, or any other delivery vehicles, including viral or bacterial vectors, that generate proteins for use as influenza vaccine antigens.

[0022] By converting discrete-only domain data (e.g., amino acid sequences) into continuous data sets, algorithms designed for continuous data can be used with discrete data. For example, commercially available solvers, computational maximizers, classifiers, etc. can be applied to amino acid sequences that would not normally operate directly on amino acid sequences. This can advantageously enable vaccine development using amino acid sequences and continuous-only algorithms. Thus, machine learning predictors can be used to predict immune responses in mammals given two protein sequences. For example, algorithms such as gradient descent can be used to target protein sequences for increased immune responses, even though such gradient descent methods are typically unable to operate on the kind of discrete data used to represent protein sequences. Gradient descent can be used to optimize predicted immune responses, immunogenicity, and biophysical stability of candidate proteins. The gradient descent-generated candidate proteins can then be analyzed to determine their effectiveness as vaccines against diseases caused by diverse or rapidly evolving pathogen strains, for example.

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

[0024] Other features, aspects and potential advantages will be apparent from the accompanying description and figures. [Brief description of the drawings]

[0025] [Figure 1] FIG. 1 is a block diagram of an example system that can be used to produce a vaccine. [Diagram 2] FIG. 1 is a schematic representation of data that can be used in the manufacture of a vaccine. [Diagram 3] 1 is a flowchart of an example process that can be used to apply continuous algorithms to discrete data, such as may be used in the manufacture of a vaccine. [Figure 4] 1 is a flowchart of an example process that can be used to apply continuous algorithms to discrete data, such as may be used in the manufacture of a vaccine. [Diagram 5] 1 is a flowchart of an example process that can be used to apply continuous algorithms to discrete data, such as may be used in the manufacture of a vaccine. [Figure 6] 1 is a flowchart of an example process that can be used to apply continuous algorithms to discrete data, such as may be used in the manufacture of a vaccine. [Figure 7] FIG. 1 is a swim lane diagram of an example process for producing a vaccine. [Figure 8] FIG. 1 is a schematic diagram illustrating an example of a computing device and a mobile computing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] Reference characters in the various drawings indicate like elements.

[0027] Vaccine creation by machine learning processes is described herein. Vaccine creation uses candidate proteins generated by computational processes including machine learning. An initial antigen sequence is modified using machine learning techniques into one or more candidate sequences that can be used as a vaccine. However, while many machine learning operations use continuous values, antigen sequences are often characterized by discrete values. To perform continuous value operations on a dataset of discrete values, the present disclosure provides techniques to convert the discrete values ​​to continuous values, manipulate the continuous values, and then convert the continuous values ​​to discrete values. To generate useful results, these operations can be constrained so that the output of the discrete values ​​does not define antigen sequences that are known or expected to be physically impossible.

[0028] 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 and intestinal tracts.

[0029] Influenza virions contain a negative-sense RNA genome that encodes 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 involved in virus attachment and cell entry. HA and NA proteins are the source of the major immunodominant epitopes for virus neutralization and protective immunity. HA and NA proteins are considered the most important components of prophylactic influenza vaccines.

[0030] HA is a viral surface glycoprotein that generally contains approximately 560 amino acids and accounts for 25% of the total viral protein.

[0031] NA is a membrane glycoprotein of influenza viruses. NA is 413 amino acids in length and is encoded by a gene of 1413 nucleotides. Nine different NA subtypes have been identified in influenza viruses (N1, N2, N3, N4, N5, N6, N7, N8, and N9), all of which are found in wild birds.

[0032] The ability of influenza viruses to cause a wide range of diseases derives from their ability to evade the immune system by undergoing antigenic change.

[0033] definition In order that this disclosure may be more readily understood, certain terms are first defined below. Throughout the specification, additional definitions of the following terms and other terms may be set forth. In the event that a definition of a term set forth below conflicts with a definition in an application or patent incorporated by reference, the meaning of the term shall be understood using the definition set forth in this application.

[0034] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. So, for example, reference to "a method" includes one or more methods and / or steps of the type described herein and / or that will become apparent to those skilled in the art after reading this disclosure and so forth.

[0035] 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.

[0036] Antigen: As used herein, the term "antigen" refers to an agent that elicits an immune response when exposed to or administered to an organism, and / or (ii) an agent that binds to a T cell receptor (e.g., when presented by an MHC molecule) or an antibody (e.g., produced by a B cell). In some embodiments, the antigen elicits a humoral response in the organism (e.g., including production of antigen-specific antibodies); alternatively or additionally, in some embodiments, the antigen elicits a cellular response in the organism (e.g., involving T cells whose receptors specifically interact with the antigen). One of skill in the art will appreciate that a particular antigen may elicit an immune response in one or more members of a target organism (e.g., mice, ferrets, rabbits, primates, humans), but not in all members of the target organism's species. In some embodiments, the antigen elicits 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 members of the target species. In some embodiments, the antigen binds to an antibody and / or a T cell receptor and may or may not elicit a specific physiological response in the organism. In some embodiments, for example, the antigen may bind to an antibody and / or a T cell receptor in vitro, regardless of whether such interactions occur in vivo. In some embodiments, the antigen reacts with the products of specific humoral or cellular immunity, including those induced by heterologous immunogens. Antigens include forms of NA and HA as described herein.

[0037] 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 water and oils, including those of petroleum, animal, vegetable, or synthetic origin, such as peanut oil, soybean oil, mineral oil, sesame oil, and the like. In some embodiments, the carrier is or includes one or more solid ingredients.

[0038] Epitope: As used herein, the term "epitope" includes any moiety that is specifically recognized, in whole or in part, by an immunoglobulin (e.g., antibody or receptor) binding component. In some embodiments, an epitope is composed of multiple chemical atoms or groups on an antigen. In some embodiments, such chemical atoms or groups are surface exposed when the antigen adopts a relevant three-dimensional structure. In some embodiments, such chemical atoms or groups are physically close to each other in space when the antigen adopts such a structure. In some embodiments, at least some of such chemical atoms or groups are physically separated from each other when the antigen adopts an alternative structure (e.g., linearized).

[0039] Excipient: As used herein, the term "excipient" refers to a non-therapeutic agent that may be included in a pharmaceutical composition, for example, to impart or contribute to a desired consistency or stabilizing effect. Suitable pharmaceutical excipients include, for example, starch, glucose, lactose, sucrose, gelatin, malt, rice, flour, chalk, silica gel, sodium stearate, glycerol monostearate, talc, sodium chloride, dried skim milk, glycerol, propylene, glycol, water, ethanol, and the like.

[0040] Immune response: As used herein, the term "immune response" refers to a reaction of a cell of the immune system, such as a B cell, T cell, dendritic cell, macrophage, or polymorphonuclear cell, to a stimulus, such as an antigen, immunogen, or vaccine. An immune response can include any cell of the body involved in a 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 development of a disease associated with an infection) or reduces the symptoms of an 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 cells or T cells), measuring the secretion of cytokines or chemokines, measuring inflammation, measuring antibody production, and the like. An antibody response or humoral response is an immune response in which antibodies are produced. A "cellular immune response" is one that is mediated by T cells and / or other white blood cells.

[0041] Immunogen: As used herein, the term "immunogen" or "immunogenic" refers to a compound, composition, or substance that, under appropriate conditions, is capable of stimulating an immune response, such as the production of antibodies or a T cell response, in an animal, including compositions that are injected or absorbed into an animal. As used herein, "immunizing" means making a subject protected from an infectious disease.

[0042] Immunologically effective amount: As used herein, the term "immunologically effective amount" means an amount sufficient to immunize a subject.

[0043] Prevention: The term "prevention," as used herein, refers to preventing, avoiding disease onset, delaying the onset, and / or reducing the frequency and / or severity of one or more symptoms of a particular disease, disorder, or condition (e.g., infection with an influenza virus). In some embodiments, prevention is assessed on a population basis, and an agent is considered to "prevent" a particular disease, disorder, or condition if a statistically significant reduction in the occurrence, frequency, and / or intensity of one or more symptoms of the disease, disorder, or condition is observed in a population susceptible to the disease, disorder, or condition.

[0044] Sequence identity: The similarity between amino acid or nucleic acid sequences is expressed in terms of the similarity between the sequences, otherwise referred to as sequence identity. Sequence identity is often measured in terms of the percentage of 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 have a relatively high degree of sequence identity when aligned using standard methods.

[0045] The terms "% identical", "% identity" or similar terms are intended to refer in particular to the percentage of nucleotides or amino acids that are identical in optimal alignment between the sequences to be compared. Said percentage is purely statistical, and the differences between the two sequences may, but do not necessarily, be randomly distributed over the entire length of the sequences to be compared. Comparison of two sequences is usually performed by comparing said sequences over segments or "windows of comparison" after optimal alignment in order to identify local regions of corresponding sequences. Optimal alignment for comparison can be performed manually or using the local homology algorithm of Smith and Waterman, 1981, Ads App. Math. 2, 482, using the local homology algorithm of Needleman and Wunsch, 1970, J. Mol. Biol. 48, 443, using the similarity search algorithm of Pearson and Lipman, 1988, Proc. Natl Acad. Sci. USA 88, 2444, or using computer programs which use the above algorithms (GAP, BESTFIT, FASTA, BLAST P, BLAST N and TFASTA from the Wisconsin Genetics Software Package, Genetics Computer Group, 575 Science Drive, Madison, Wis.).

[0046] The percentage of identity is obtained by determining the number of corresponding identical positions in the sequences to be compared, dividing this number by the number of positions being compared (e.g., the number of positions in the reference sequence) and multiplying this result by 100.

[0047] In some embodiments, the degree of identity is given for a region that 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, in some embodiments, in consecutive nucleotides. In some embodiments, the degree of identity is given for the entire length of the reference sequence.

[0048] A nucleic acid sequence or amino acid sequence that has a particular degree of identity to a given nucleic acid sequence or amino acid sequence, respectively, may have at least one functional and / or structural characteristic of the given sequence, e.g., in some instances, is functionally and / or structurally equivalent to the given sequence. In some embodiments, a nucleic acid sequence or amino acid sequence that has a particular degree of identity to a given nucleic acid sequence or amino acid sequence is functionally and / or structurally equivalent to the given sequence.

[0049] Subject: As used herein, the term "subject" refers to 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, the non-human subject is a mammal (e.g., a rodent, mouse, rat, rabbit, ferret, monkey, dog, cat, sheep, cow, primate, and / or pig). 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, an adolescent, or an infant. In some embodiments, the terms "individual" or "patient" are used and are intended to be interchangeable with "subject."

[0050] Vaccination: As used herein, the term "vaccination" or "vaccinate" refers to the administration of a composition intended to generate an immune response against a disease-causing agent, such as, for example, an influenza virus. Vaccination can be administered before, during, and / or after exposure to the disease-causing agent, and / or before, during, and / or after the onset of one or more symptoms, in some embodiments before, during, and / or immediately after exposure to the agent. Vaccines can induce both prophylactic (preventative) and therapeutic responses. Methods of administration vary depending on the vaccine, but may include inoculation, ingestion, inhalation, or other forms of administration. Inoculation may be delivered by any of a number of routes, including parenterally, such as intravenously, subcutaneously, intraperitoneally, intradermally, or intramuscularly. Vaccines may be administered with adjuvants to enhance the immune response. In some embodiments, vaccination includes multiple administrations of the vaccination composition at appropriate time intervals.

[0051] Vaccine efficacy: As used herein, the term "vaccine efficacy" or "vaccine efficacy" refers to a measure of the percentage reduction in evidence of disease among subjects administered a vaccine composition. For example, 50% vaccine efficacy indicates a 50% reduction in the number of disease cases in a group of vaccinated subjects compared to a group of unvaccinated subjects or a group of subjects administered a different vaccine.

[0052] 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 viruses. A variety of different wild-type HA and NA sequences can be found in the NCBI influenza virus sequence database.

[0053] Measurement of hemagglutinin activity Hemagglutinin activity may be measured using techniques known in the art, such as the hemagglutinin inhibition assay (HAI). HAI applies a process of hemagglutination, called hemagglutination, in which sialic acid receptors on the surface of red blood cells (RBCs) bind to the hemagglutinin glycoprotein found on the surface of influenza viruses (and some other viruses), creating a network or lattice structure of interconnected RBCs and virus particles that occurs in a concentration-dependent manner on the virus particles. This is a physical measurement taken as a proxy for the ability of the virus to bind to similar sialic acid receptors on cells that target pathogens in the body. The introduction of anti-viral antibodies, generated in a human or animal immune response to another virus (which may be genetically similar or different from the virus used to bind to the RBCs in the assay), alters the concentration of the virus enough to disrupt the virus-RBC interaction and change the concentration at which hemagglutination is observed in the assay. One goal of HAI may be to characterize the concentration of antibodies in an antiserum or other sample that contains the antibodies, relative to their ability to inhibit hemagglutination in the assay. The highest dilution of antibody that prevents hemagglutination is called the HAI titer (ie, the measured response).

[0054] Another approach to measure HA antibody responses is to measure a potentially larger set of antibodies elicited by human or animal immune responses, which may not necessarily affect hemagglutination in HAI assays. A common approach for this is to utilize enzyme-linked immunosorbent assay (ELISA) technology, in which a viral antigen (e.g., hemagglutinin) is immobilized on a solid surface and then antibodies from antisera are allowed to bind to the antigen. The readout measures the catalysis of a substrate for an exogenous enzyme conjugated to an antibody from the antisera, or to another antibody that itself binds to the antibody of the antisera. The catalysis of the substrate produces a readily detectable product. There are many variations of this type of in vitro assay. One such variation is called antibody forensics (AF), which is a multiplex bead array technology that allows a single serum sample to be measured simultaneously against many antigens. These measurements characterize concentration and total antibody recognition when compared to HAI titers, which are believed to be more specifically related to interference with sialic acid binding by the hemagglutinin molecule. Thus, antisera antibodies may, in some cases, have a proportionally higher or lower measurement than the corresponding HAI titer for the hemagglutinin molecule of one virus compared to the hemagglutinin molecule of another virus; in other words, these two measurements, AF and HAI, are not linearly correlated.

[0055] Another method for measuring HA antibody responses includes virus neutralization assays (e.g., microneutralization assays), in which antibody titers are measured in permissive cell cultures after incubation of virus with serial dilutions of antibody / serum samples by the reduction in plaques, foci, and / or fluorescent signal, depending on the specific neutralization assay technique.

[0056] Measurement of neuraminidase activity Neuraminidase activity may be measured using techniques known in the art, including, for example, the MUNANA assay, the ELLA assay, or the NA-Star® assay (ThermoFisher Scientific, Waltham, Mass.). The MUNANA assay uses 2'-(4-methylumbelliferyl)-alpha-DN-acetylneuraminic acid (MUNANA) as a substrate. Any enzymatically active neuraminidase in the sample will cleave the MUNANA substrate, releasing the fluorescent compound 4-methylumbelliferone (4-MU). Thus, the amount of neuraminidase activity in the test sample correlates with the amount of 4-MU released, which may be measured using fluorescence intensity (RFU, relative fluorescence units).

[0057] For the purpose of determining the neuraminidase activity of the soluble tetrameric NA of the present disclosure, the MUNANA assay should be performed using the following conditions: mix the soluble tetrameric NA with buffer [33.3 mM 2-(N-morpholino)ethanesulfonic acid (MES, pH 6.5), 4 mM CaCl2, 50 mM BSA] and substrate (100 μM MUNANA) and incubate at 37 ° C for 1 hour with shaking; stop the reaction by adding alkaline pH solution (0.2 M Na2CO3); measure the fluorescence intensity using an excitation wavelength of 355 nm and an emission wavelength of 460 nm, respectively; calculate the enzyme activity against a 4MU reference. If necessary, an equivalent assay can be used to measure neuraminidase enzyme activity.

[0058] Vaccine composition: In certain aspects, disclosed herein are vaccine compositions comprising a plurality of engineered amino acid sequences.

[0059] Each generated amino acid sequence may be present in the composition disclosed herein in an amount effective to induce an immune response in a subject to which the composition is administered. In a specific embodiment, each generated amino acid sequence may be present in the vaccine composition disclosed herein in an amount ranging from about 0.1 g to about 500 g, such as about 5 g to about 120 g, about 1 g to about 60 g, about 10 g to about 60 g, about 15 g to about 60 g, about 40 g to about 50 g, about 42 g to about 47 g, about 5 g to about 45 g, about 15 g to about 45 g, about 0.1 g to about 90 g, about 5 g to about 90 g, about 10 g to about 90 g, or about 15 g to about 90 g. In certain embodiments, each recombinant HA may be present in a vaccine composition 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.

[0060] The vaccine composition may also further comprise an adjuvant. As used herein, the term "adjuvant" refers to a substance or vehicle that non-specifically enhances the immune response to an antigen. Adjuvants may include inorganic substances (alum, aluminum salts (e.g., aluminum hydroxide / oxyhydroxide (AlOOH), aluminum phosphate (AlPO4), aluminum hydroxyphosphate sulfate (AAHS), and / or aluminum potassium sulfate)) suspensions to which antigens are adsorbed; or water-in-oil emulsions in which antigen solutions are emulsified in mineral oil (e.g., Freund's incomplete adjuvant), which may also include killed mycobacteria (Freund's complete adjuvant) to further enhance antigenicity. Immunostimulatory oligonucleotides (e.g., those that contain CpG motifs) can also be used as adjuvants (see, for example, 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 biological molecules such as lipids and costimulatory 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, J. Immunol. 2009, 183:6186-6197), IL-2, RANTES, GM-CSF, TNF-?, IFN-?, G-CSF, LFA-3, CD72, B7-1, B7-2, OX-40L, and 41 BBL.

[0061] 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 non-ionic surfactant, and a hydrophobic non-ionic surfactant. In certain embodiments, the emulsion is thermoreversible, and optionally has 90% of the population by volume of oil droplets having a size of less than 200 nm.

[0062] In certain embodiments, the polyoxyethylene alkyl ether is of the formula CH3-(CH2)x-(O-CH2-CH2)n-OH, where 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) cetostearyl ether.

[0063] In certain embodiments, 90% of the population by volume of the oil droplets have a size of less than 160 nm. In certain embodiments, 90% of the population by volume of the oil droplets have a size of less than 150 nm. In certain embodiments, 50% of the population by volume of the oil droplets have a size of less than 100 nm. In certain embodiments, 50% of the population by volume of the oil droplets have a size of less than 90 nm.

[0064] In certain embodiments, the adjuvant further comprises at least one alditol, including, but not limited to, glycerol, erythritol, xylitol, sorbitol, and mannitol.

[0065] In certain embodiments, the hydrophilic nonionic surfactant has a hydrophilic lipophilic balance (HLB) of 10 or more. In certain embodiments, the hydrophobic nonionic surfactant has an HLB of less than 9. In certain embodiments, the hydrophilic nonionic surfactant has an HLB of 10 or more and the hydrophobic nonionic surfactant has an HLB of less than 9.

[0066] In certain embodiments, the hydrophobic non-ionic surfactant is a sorbitan ester, such as sorbitan monooleate, or a mannide ester surfactant. In certain embodiments, the amount of squalene is 5-45%. In certain embodiments, the amount of polyoxyethylene alkyl ether surfactant is 0.9-9%. In certain embodiments, the amount of hydrophobic non-ionic surfactant is 0.7-7%. In certain embodiments, the adjuvant comprises i) 32.5% squalene, ii) 6.18% polyoxyethylene (12) cetostearyl ether, iii) 4.82% sorbitan monooleate, and iv) 6% mannitol.

[0067] 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.

[0068] 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 incorporated herein by reference in its entirety. In certain embodiments, the adjuvant comprises a liposome-based adjuvant such as SPA14, which is a liposome-based adjuvant (AS01-like) that comprises a Toll-like receptor 4 (TLR4) agonist (E6020) and a saponin (QS21).

[0069] In addition to the recombinant HA, recombinant NA, and optional adjuvants, the vaccine composition may also further comprise one or more pharma- ceutically acceptable excipients. In general, the nature of the excipient will depend on the particular mode of administration used. For example, parenteral formulations usually contain an injectable fluid, which contains pharma- ceutically and physiologically acceptable fluids, such as water, physiological saline, balanced salt solutions, aqueous dextrose, glycerol, and the like, as a vehicle. In the case of solid compositions (e.g., powder, pill, tablet, or capsule forms), conventional non-toxic solid carriers may include, for example, pharmaceutical grades of mannitol, lactose, starch, or magnesium stearate. In addition to biologically neutral carriers, the vaccine composition to be administered may contain minor amounts of non-toxic auxiliary substances, such as wetting or emulsifying agents, pharma- ceutically acceptable salts for adjusting osmotic pressure, preservatives, stabilizers, buffers, sugars, amino acids, pH buffers, and the like (e.g., sodium acetate or sorbitan monolaurate).

[0070] Typically, the vaccine composition is a sterile liquid solution formulated for parenteral administration, such as intravenous, subcutaneous, intraperitoneal, intradermal, or intramuscular. 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.

[0071] In some embodiments, the vaccine composition is formulated for intradermal injection, intranasal administration, or intramuscular injection. In some embodiments, the injectables are prepared in conventional forms, either as liquid solutions or suspensions, as solid forms suitable for solution or suspension in liquid prior to injection, or as emulsions. In some embodiments, the injectable solutions and suspensions are prepared from sterile powders or granules. General considerations in the formulation and manufacture of pharmaceuticals for administration by these routes can be found, for example, in Remington's Pharmaceutical Sciences, 19th ed., Mack Publishing Co., Easton, PA, 1995, which is incorporated herein by reference. Currently, oral or nasal spray or aerosol routes (e.g., by inhalation) are most commonly used to deliver therapeutics directly to the lungs and respiratory system. In some embodiments, the vaccine composition is administered using a device that delivers a metered amount of the vaccine composition. Suitable devices for use in delivering the intradermal pharmaceutical compositions described herein include short needle devices, such as those described in U.S. Patent Nos. 4,886,499, 5,190,521, 5,328,483, 5,527,288, 4,270,537, 5,015,235, 5,141,496, and 5,417,662 (all of which are incorporated herein by reference).Intradermal compositions can also be administered by devices that limit the effective penetration length of the needle into the skin, such as those described in WO 1999 / 34850 (which are incorporated herein by reference) and their functional equivalents. Also suitable are jet injection devices that deliver liquid vaccines to the dermis by means of a liquid jet injector or by means of 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. Nos. 5,480,381, 5,599,302, 5,334,144, 5,993,412, 5,649,912, 5,569,189, 5,704,911, 5,383,851, 5,893,397, 5,466,220, 5,339,163, and 5,31 The method is described in WO 2006 / 02335, WO 2006 / 02335, WO 2005 ...

[0072] Preparations for parenteral administration typically include sterile aqueous or non-aqueous solutions, suspensions, and emulsions. Examples of non-aqueous solvents include propylene glycol, polyethylene glycol, vegetable oils such as olive oil, and injectable organic esters such as ethyl oleate. Aqueous carriers include water, alcoholic / aqueous solutions, emulsions, or suspensions (e.g., saline and buffered media). Parenteral vehicles include sodium chloride solution, Ringer's dextrose, dextrose, and sodium chloride, lactated Ringer's, or fixed oils. Intravenous vehicles include fluid and nutrient replenishers, electrolyte replenishers (e.g., those based on Ringer's dextrose), and the like. Preservatives and other additives may also be present, such as, for example, antimicrobials, antioxidants, chelating agents, and inert gases, and the like.

[0073] kit Further disclosed herein are kits for the vaccine compositions disclosed herein, which may include a suitable container containing the vaccine composition or multiple containers containing different components of the vaccine composition, optionally together with instructions for use.

[0074] In certain embodiments, the kit may include multiple containers, for example, a first container that includes one or more isolated nucleic acids, peptides, and / or proteins disclosed herein.

[0075] Nucleic acids, cloning and expression systems The present disclosure further provides artificial nucleic acid molecules. Nucleic acids can include DNA or RNA, and can be wholly or partially synthetic or recombinant. Reference to a nucleotide sequence described herein includes DNA molecules having the specified sequence, and includes RNA molecules having the specified sequence in which U is replaced with T or its derivative, such as pseudouridine, unless the context requires otherwise. Other nucleotide derivatives or modified nucleotides can be incorporated into artificial nucleic acid molecules.

[0076] The present disclosure also provides constructs in the form of vectors (e.g., plasmids, phagemids, cosmids, transcription or expression cassettes, artificial chromosomes, etc.) that contain artificial nucleic acid molecules encoding nucleic acid sequences generated as disclosed herein. The present disclosure further provides host cells that contain one or more constructs as described above.

[0077] Also provided are methods of producing isolated peptides and / or proteins using recombinant techniques known in the art and described above. Recombinant protein production and expression 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 Ed. 2012), Cold Spring Harbor Press. For example, 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, 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 production by expression, HA or NA can be isolated and / or purified using any suitable technique and then used as appropriate.

[0078] Systems for cloning and polypeptide expression in a variety of different host cells are well known in the art. Any protein expression system (e.g., stable or transient) compatible with the constructs disclosed in this application may be used to produce the generated amino acid sequences described herein.

[0079] Suitable vectors can be selected or constructed to contain appropriate regulatory sequences, such as promoter sequences, terminator sequences, polyadenylation sequences, enhancer sequences, marker genes, and other sequences as appropriate.

[0080] To express the generated amino acid sequence as disclosed herein, the nucleic acid encoding the generated amino acid sequence can be introduced into a host cell. The introduction can use any available technique. For eukaryotic cells, suitable techniques can include calcium phosphate gene transfer, DEAE-dextran, electroporation, liposome-mediated gene transfer, and transduction using retroviruses or other viruses, such as vaccinia, or baculovirus in the case of insect cells. For bacterial cells, suitable techniques can include calcium chloride transformation, electroporation, and gene transfer using bacteriophage. These techniques are well known in the art. (See, for example, "Current Protocols in Molecular Biology", Ausubel et al. eds., John Wiley & Sons, 2010). DNA transfer can be followed by a selection method (e.g., antibiotic resistance) to select cells containing the vector.

[0081] The host cell may be a plant cell, a yeast cell, or an animal cell. Animal cells include invertebrate cells (e.g., insect cells), non-mammalian vertebrate cells (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 Sertoli cells; African green monkey kidney cells (VERO-76); human cervical carcinoma cells (e.g., HeLa); canine kidney cells (e.g., MDCK), and the like. In one embodiment, the host cell is a CHO cell. In one embodiment, the host cell is an insect cell.

[0082] How to use The present disclosure provides a method of administering a vaccine composition described herein to a subject. The method may be used to vaccinate a subject against a virus (e.g., influenza virus). In some embodiments, the vaccination method comprises administering to a subject in need of vaccination a vaccine composition comprising one or more isolated nucleic acids, peptides, and / or proteins encoding the generated amino acid sequences as described herein (e.g., a recombinant influenza virus HA described herein or a recombinant influenza virus NA described herein) and an optional adjuvant in an amount effective to vaccinate the subject against the virus (e.g., influenza virus). Similarly, the present disclosure provides a vaccine composition comprising one or more isolated nucleic acids, peptides, and / or proteins encoding the generated amino acid sequences as described herein (e.g., influenza virus HA or NA) and an optional adjuvant for use in vaccinating a subject against a virus (e.g., influenza virus) (or for the manufacture of a medicament for use in vaccination).

[0083] The present disclosure also provides a method of immunizing a subject against a virus (e.g., an 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 described herein, and an optional adjuvant.

[0084] In some embodiments, the method or use prevents infection or disease by a virus (e.g., influenza virus) in a subject. In some embodiments, the method or use induces a protective immune response in the subject. In some embodiments, the protective immune response is an antibody response.

[0085] The immunization methods / uses provided herein may induce a broad neutralizing immune response against one or more viruses (e.g., influenza viruses). Thus, in various embodiments, the compositions described herein may provide broad cross-protection against various types of viruses (e.g., influenza viruses). In some embodiments, the compositions provide cross-protection against avian, swine, seasonal and / or pandemic influenza viruses. In some embodiments, the immunization methods / uses may induce an improved immune response against one or more seasonal influenza strains (e.g., standard of care strains). For example, the improved immune response may be an improved humoral immune response. In some embodiments, the immunization methods / uses may induce an improved immune response against one or more pandemic influenza strains. In some embodiments, the immunization methods / uses may induce an improved immune response against one or more swine influenza strains. In some embodiments, the immunization methods / uses may induce an improved immune response against one or more avian influenza strains.

[0086] In certain embodiments, provided herein are methods of enhancing or expanding a protective immune response, the methods comprising administering to a subject an immunologically effective amount of a vaccine composition disclosed herein, where the vaccine composition increases the vaccine efficacy of a standard of care influenza virus vaccine composition by an amount ranging from about 5% to about 100%, e.g., 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%, e.g., 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% greater than the vaccine efficacy of a standard of care influenza virus vaccine, e.g., 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% greater than the vaccine efficacy of a standard of care influenza virus vaccine. Similarly, the disclosure provides any of the vaccine compositions described herein for use (or for the manufacture of a medicament for use in enhancing or expanding a protective immune response in a subject).

[0087] Also provided are methods of preventing a viral disease (e.g., influenza virus disease) in a subject, comprising administering a vaccine composition comprising one or more isolated nucleic acids encoding generated amino acid sequences, peptides, and / or proteins (e.g., a recombinant influenza virus HA or NA as described herein), and an optional adjuvant, in an amount effective to prevent the viral disease (e.g., influenza virus disease) in the subject. Similarly, the disclosure provides vaccine compositions comprising one or more recombinant influenza virus HA or NA, and an optional adjuvant, for use (or for the manufacture of a medicament for use therein) in preventing a viral disease (e.g., influenza virus disease) in a subject.

[0088] Also provided is a method of inducing an immune response to influenza virus HA and influenza virus NA in a subject, comprising administering to the subject a vaccine composition comprising one or more recombinant influenza virus HAs described herein, one or more recombinant influenza virus NAs described herein, and an optional adjuvant.

[0089] 1 is a block diagram of an example system 100 that can be used to produce a vaccine. In the system 100, new vaccines 116 are designed and produced using the techniques described herein. For example, in the case of viruses with many strains, clades, serotypes, and / or rapidly mutating strains, such as influenza or coronavirus disease 2019 or human rhinovirus, HIV, etc., or in the case of new viruses never before encountered, the techniques described herein can be used to rapidly generate vaccine candidates that can be tested for use in humans or other subjects.

[0090] As inputs, the system 100 receives virus strain data 102 and seed amino acid data 104. The virus strain data 102 includes data regarding one or more virus strains for which a vaccine is desired. This 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 characteristics (e.g., records of physicochemical properties of the amino acid sequence, such as molecular weight). The seed amino acid data 104 includes data regarding initial or seed amino acids that are modified by the computer system 106 to generate one or more vaccine definitions for a candidate vaccine. This 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 characteristics (e.g., records of physicochemical properties of the amino acid sequence, such as molecular weight).

[0091] The system 100 includes a computer system 106 that can use the data 102 and 104 to generate data 108 of candidate non-wild-type amino acid sequences. These non-wild-type amino acid sequences are sequences that are not found in the wild or are not known to be found in the wild. As will be appreciated, one or more of the candidate non-wild-type amino acid sequences 108 may in fact exist in the wild, but may be unknown to the operators of the system 100, or even to the community 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 identification) or non-metadata characteristics (e.g., records of physicochemical properties of the amino acid sequence, such as molecular weight).

[0092] The computer system 106 validates one or more of the candidates in the data 108 for production, resulting in data 110. The data 110 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 characteristics (e.g., records of physicochemical properties of the amino acid sequence, such as molecular weight). In some cases, the data 102 / 104, 108, and 110 are in the same data format, and in some cases, the data 102 / 104, 108, and 110 are in different data formats.

[0093] In some cases, the validation process used to select candidates may include determining whether the amino acid sequence can be synthesized at all, or whether it can be easily or economically synthesized. As will be appreciated, an amino acid sequence may define the structure of such a molecule that cannot exist in the physical world due to the geometry and forces that the molecule exhibits. Thus, such impossible sequences may be excluded from the validation process. In addition, some of the candidates may be excluded despite defining effective molecules. For example, the computer 106 may maintain a data store of previous candidates that were not actually effective as a vaccine once investigated in clinical trials, or were predicted to be less immunogenic or less protective against the virus strain of interest, which may include the virus strain data 102. In such cases, the candidates in the data 108 may be excluded from the validated data 110. In some cases, candidates may be excluded or prioritized based on synthesis and manufacturing considerations. For example, candidates with particular synthesis or processing conditions (e.g., cold storage, shock sensitivity) may be excluded from validation or may be de-prioritized compared to other candidates with less demanding synthesis or processing conditions.

[0094] The system 100 may also include a vaccine production device 112 capable of using the vaccine precursor 114 and one or more verified non-wild type amino acid sequence data 110 to produce one or more vaccine doses or vaccine molecules 116. As will be appreciated, early exploration and testing will require much smaller scale synthesis than large scale production of a vaccine that has been tested, found to be safe and effective, and approved for use in humans or other subjects. Thus, the details of the production device 112 may vary according to needs. Similarly, the vaccine precursor 114 includes the items, chemicals, materials, etc. for the production of the vaccine 116, but the precursor 114 will likewise vary according to needs.

[0095] Figure 2 is a schematic diagram of data that can be used in the manufacture of a vaccine. For example, the data shown here can be used by computer system 106 or other computer systems.

[0096] The seed amino acid data 104 is shown here with a subsection of the sequence rendered for readability using single-letter designations recommended by the International Union of Pure and Applied Chemistry-International Union of Biochemistry and Molecular Biology (IUPAC-IUBMB) Joint Commission on Biochemical Nomenclature. The data 104 may include vectors of data values ​​(e.g., single American Standard Code for Information Interchange (ASCII) characters, integers) to represent the amino acids in the sequence represented by the data 104. As will be appreciated, longer sequences will have more indices than are visually shown here. Additionally, for clarity, other portions of the data 104 are not shown here. This data 104 is a discrete data object of one or more amino acid sequences. Each amino acid sequence may be recorded as either a single character or a string of characters. A string of characters may include multiple single characters. 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 respective single character or a respective string of characters. That is, each amino acid sequence may be stored in data conforming to the same format while retaining different values. This allows for interoperability and consistent processing of data.

[0097] For each discrete value in the data 104, a corresponding weight vector 202 may be created and maintained. The weight vector 202 may be comprised of an index of each possible amino acid at a particular index of the data 104, e.g., 20 weight values, each weight value corresponding to one of the 20 possible amino acids 200. Initially, this probability may be set to either 0 or 1. As shown here, for an index with a value of "Y", each index of the weight vector 202 is set to zero except for the penultimate index position due to the fact that "Y" is the penultimate possible amino acid label when ordered alphabetically.

[0098] For each weight value in weight vector 202, a corresponding feature vector 204 of property values ​​is used. In this example, each feature vector 204 is a vector of length 4, although other lengths are possible. Twenty feature vectors 204 are shown, one for each of the 20 indices in weight vector 202. Weight vector 202 and some of the vectors that follow are shown without values ​​because they may be in a format (e.g., real numbers) that is too large to legibly render in the given space.

[0099] In the feature vector 204, each feature value represents a physicochemical property of a particular amino acid. For example, the feature vector may record molecular weight, charge, hydrophobicity tendency, isoelectric point, alpha-helical tendency, beta-sheet tendency, molecular volume, octanol-water partitioning energy, etc.

[0100] Each value of the weight vector 202 may be combined with a corresponding feature vector 204 to create a corresponding weighted probability vector 206. For example, each value of each feature vector 204 may be multiplied by a corresponding weight value in the weight vector 202.

[0101] One or more optimization, solver, classifier, or other functions may be applied to each weighted probability vector 206 (both those shown associated with a single index of the vector 104 and those associated with other indexes of the vector 104), or to a set of weighted probability vectors 206, to generate an optimization vector 208. Such functions are described later in this specification, but in general, the 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 characteristic. As shown, the optimization vector 208 includes such continuous data, and the data 104 instead includes discrete data (e.g., specific ASCII characters representing specific amino acids). The intermediate data 202-206 shown in FIG. 2 are used to process the data 104 for functions that operate on the continuous data. Once these functions are completed, the optimization vector 208 can be converted into discrete data for use in practical applications, such as specifying amino acid sequences used to create a vaccine. Although the term optimization is used here, it should be understood that this may or may not be optimization in the strictest mathematical sense.

[0102] In the reverse of the described process, the optimization vector 208 is used to find weights in the continuous results vector 210. These weights are the values ​​that, when multiplied by the corresponding feature vector 204, produce the optimization vector 208. And while the weight vector 202 contains only values ​​of 0 and 1, the continuous results vector 210 is unlikely to have either the value 0 or 1, but instead is more likely to have a continuous value between 0 and 1. In other words, when the value of vector 206 changes to the value of 208, the value of vector 202 also changes to the value of vector 210.

[0103] A discrete result object 212 is found by finding, for each index, a best fit discrete value using the continuous result vector 210. When applied to the amino acid sequence example, this involves finding the amino acid at that position in the discrete result object 212. This may involve, for example, finding the maximum value in the continuous result vector 210 and selecting the amino acid corresponding to that index position, although other best fit processes are possible. Thus, data 202-210 may be created for each index position in the vectors 104 and 212, such that one or more continuous-only functions may be used to generate the discrete data 212, starting from the discrete data 104. This advantageously allows one amino acid sequence to be converted to another, allowing the synthesis and / or manufacture of novel vaccines. A manufacturing device, such as 112, may use the discrete data 212 to generate vaccine precursors.

[0104] Figure 3 is a flow chart of an example process 300 that can be used to apply continuous algorithms to discrete data, such as may be used in the manufacture of a vaccine. For example, process 300 can be performed using the data shown in Figures 1 and 2, e.g., 102 / 104, 204-206, and therefore will use elements of those figures in the description. Possible embodiments of various elements of process 300 are described below in processes 400-700.

[0105] 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. This seed amino acid sequence may be, for example, a vaccine shown to be safe and effective against a previously encountered virus, an amino acid sequence previously observed in nature, an amino acid sequence not previously observed in nature but that has been experimentally studied, a definition of a hypothetical molecule that would have desired properties but that cannot or has not yet been synthesized, or that has not been randomly generated.

[0106] A discrete data object includes one or more amino acid sequences. For example, a discrete data object may take the form of binary data (i.e., 1's and 0's) stored in a computer memory and / or transmitted over a data network to the discrete to continuous converter 702. This binary data may be interpreted as a sequence of characters that specify an amino acid sequence or a group of amino acid sequences. In addition, other payload data (e.g., source of the sequence) and metadata (e.g., creation date of the discrete data object) may also be included.

[0107] The discrete data object is converted into a continuous data object that includes a plurality of first continuous values ​​304. For example, vector 104 may be converted into vector 206. This conversion process may or may not include the use of vectors 202 and 204, depending on the particular process used to perform this conversion.

[0108] A continuous data algorithm is applied to the continuous data object to generate a continuous result object that includes a plurality of second continuous values ​​306. For example, one or more computer functions may be created based on a mathematical or logical model designed to drive the amino acid sequences of the vector 104 into a state that is more likely to have a certain property. One such example is modifying amino acid sequences to create new vaccines against newly emerged viruses or virus strains. This may involve applying gradient descent to the vector 206 using a loss function that takes into account, among other parameters, how well a given amino acid sequence scores in a model that predicts an immune response given the virus strain data 102.

[0109] The continuous result object is converted to a discrete result object that includes a plurality of second discrete values ​​308. For example, vector 208 may be converted to vector 212. This conversion process may or may not include the use of vector 210, depending on the particular process used to perform this conversion.

[0110] 4 is a flow chart of an example of a process that can be used to apply a continuous algorithm to discrete data, such as may be used in the manufacture of a vaccine. For example, process 400 can be performed using the data shown in Figures 1 and 2, and therefore the description will use elements of those figures. Process 400 is one possible example of how operation 304 may be performed, although other processes may be used.

[0111] For each first discrete value, a weight vector of weight values ​​is generated, each weight value representing the likelihood that the first discrete value represents a particular amino acid 402. For example, for each value in the array 104, a corresponding vector 202 is generated. In this example, each value in the vector 104 is one particular amino acid, and therefore only one value in the vector 202 has a value of 1, and all other values ​​are 0. However, another example may use a scheme in which a position may have one amino acid. In such a case, the associated vector 202 may have two values, for example 0.5.

[0112] For each weight value of each weight vector, a property vector of property values ​​is generated, with each property value representing a physicochemical property of a particular amino acid 404. For example, the vectors 204 may be accessed from a data store that stores physicochemical or other properties of various amino acids. These properties may be used in their original state or may be pre-processed (e.g., normalized to 0-1, rounded to a given level of precision, converted to a different data format). As will be appreciated, these physicochemical or other properties may be recorded and held constant to reflect observations and measurements of amino acids, and these values ​​may be available from a third party.

[0113] The weight vector and feature vector are combined to create a weighted feature vector 406. For example, each weight value of vector 202 may be multiplied by each corresponding vector 204 to create vector 206. These values ​​may be post-processed (e.g., normalized between 0 and 1, rounded to a given level of precision, converted to a different data format, etc.).

[0114] 5 is a flow chart of one example of a process that can be used to apply a continuous algorithm to discrete data, such as may be used in the manufacture of a vaccine. For example, process 500 can be performed using the data shown in Figures 1 and 2, and therefore the description will use elements of those figures. Process 500 is one possible example of how operation 306 may be performed, although other processes may be used.

[0115] A continuous representation of the amino acid sequence is accessed 502. For example, for each index of vector 104, 20 vectors 206 are accessed, resulting in a collection of vectors whose size is 20 times the length of the amino acid sequence represented by vector 104.

[0116] A gradient descent method is applied to the continuous representation (504). The gradient descent method comprises a loss function that determines a loss value based on multiple loss criteria. In general, the loss criteria can be conceptualized into two categories: a first category that changes the amino acid sequence toward a desired predicted property, including but not limited to immunological reactivity, and a second category that scales back to a feasible amino acid sequence. If more changes to the original sequence are desired, a modification type can be applied first, followed by a scale-back type. If less changes to the original sequence are desired, a different order or incorporation of loss criteria can be used.

[0117] The first loss criterion is based on the immunological response given two amino acid sequences. For example, a prediction function may be configured to accept two amino acid sequences as input. The function may be configured to return a predicted immune response of a subject (e.g., human, animal) as output. The output may take the form of, for example, a value between 0 and 1, with higher values ​​indicating a higher predicted immune response. The prediction function may operate using a machine learning model.

[0118] The second loss criterion modifies (e.g., penalizes) subsequences that are not found in a dataset of wild-type sequences. For example, a data store of known wild-type subsequences of amino acid sequences may be stored. If a given subsequence generated by this process is also found in wild-type amino acid sequences, it is likely to be a synthesizable subsequence. However, there are cases where a subsequence that is not found in any known wild-type amino acid sequence may not be synthesizable or may require the development of a new synthesis to be synthesizable or economical. Thus, subsequences that are not found in wild-type amino acid sequences may be penalized to avoid these problems. In general, the second loss criterion may include a score obtained from a machine learning model.

[0119] The third loss criterion penalizes the weight vector based on the maximum value of the weight vector for each weight vector. For example, if the weight of vector 210 is close to 1 (i.e., the maximum value), this is an indication of a high confidence rating or high immune response for a given amino acid at a particular position in the sequence of vector 212. In this case, the third loss criterion may apply no penalty or a small penalty. In another example, if the maximum weight value is much lower, this is an indication of a low confidence rating or low immune response, and therefore a larger penalty may be applied. In some cases, the penalty may be to multiply the score by 1 minus the maximum weight, although other schemes may be used.

[0120] It will be appreciated that some, all, or none of these criteria may be used.

[0121] A continuous representation of the new amino acid sequence is generated 506. For example, for each index of the vector 104 that contains the discrete representation of a single amino acid, 20 vectors 208 may be created that hold the continuous values. As described elsewhere herein, these 20 vectors 208 may be converted to a single discrete value in vector 212 to represent the single amino acid.

[0122] 6 is a flow chart of one example of a process that can be used to apply continuous algorithms to discrete data, such as may be used in the manufacture of a vaccine. For example, process 600 can be performed using the data shown in Figures 1 and 2, and therefore the description will use elements from those figures.

[0123] A plurality of candidate discrete result objects are generated 602. For example, for a single virus strain data object 102, a large collection of seed amino acid data objects 104 can be created. This may include collecting all known viable vaccines for a given virus and using them as seed data 104 for a new strain of the virus 102.

[0124] For each seed, an algorithm for altering the sequence is applied 604. For example, process 300 can be performed with each seed to generate an equal number of candidate sequences.

[0125] The candidate outputs are collected (606) and some are filtered out (608). For example, at least one candidate may be found to specify an amino acid sequence that fails manufacturability testing, which may include determining that the sequence is unsynthesizable, that the sequence is too similar to other candidates, a match to one of the seeds, etc. This allows for prioritization of the most likely candidates, for example, when testing resources are limited.

[0126] 7 is a swim-lane diagram of an example process for producing a vaccine. For example, process 400 can be performed using the data shown in Figures 1 and 2, and therefore the description uses elements of those figures. Process 700 incorporates process 300, and therefore is shown with elements of process 300. To perform process 700, computer system 106 can use a discrete-to-continuous converter 702, an optimizer 704, and an immune response predictor 706, although different components may be used.

[0127] Once the data defining the amino acid sequence to be produced is generated, the vaccine manufacturer 116 manufactures (708) the vaccine comprising the protein defined by the discrete result object (i.e., amino acid sequence) and / or the vaccine comprising a nucleic acid, or any other delivery vehicle, including a viral or bacterial vector, whereby such nucleic acid or delivery vehicle produces the protein defined by the discrete result object. This production may be small batches for purposes of initial testing, clinical trials, and / or general use. As will be appreciated, elements 308 and 708 may be separated by a significant amount of time and intervening operations. For example, if the production at 708 is a large-scale production for general use, this may occur only after clinical trials have demonstrated that the vaccine is safe and effective for its intended purpose.

[0128] 8 illustrates 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. The computing device 800 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The mobile computing device is intended to represent various forms of mobile devices, such as personal digital assistants, mobile phones, smartphones, and other similar computing devices. The components, their connections and relationships, and their functions illustrated herein are intended to be merely exemplary and are not intended to limit the embodiments of the invention described and / or claimed herein.

[0129] The computing device 800 includes a processor 802, a memory 804, a storage device 806, a high-speed interface 808 connecting to the memory 804 and a number of high-speed expansion ports 810, and a low-speed interface 812 connecting to a 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 are interconnected using various buses and may be mounted on a common motherboard or in any other manner as appropriate. The processor 802 may process instructions, including those stored in the memory 804 or the storage device 806, for execution within the computing device 800 to display graphical information in a GUI on an external input / output device, such as a display 816 coupled to the high-speed interface 808. In other implementations, multiple processors and / or multiple buses may be used, along with multiple memories and types of memories, as appropriate. Also, multiple computing devices may be connected, with each device providing a portion of the required operations (e.g., as a server bank, a group of blade servers, or a multiprocessor system).

[0130] 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 another form of computer-readable medium, such as a magnetic disk or optical disk.

[0131] The storage device 806 can provide mass storage for the computing device 800. In some implementations, 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 an array of devices including a tape device, a flash memory or other similar solid-state memory device, or a storage area network or other configuration of devices. The computer program product can be tangibly embodied in an information carrier. The computer program product can also include instructions that, when executed, perform one or more methods, such as those described above. The 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.

[0132] The high-speed interface 808 manages the bandwidth-intensive operations of the computing device 800, while the low-speed interface 812 manages the less bandwidth-intensive operations. Such an allocation of functions is merely exemplary. In some implementations, the high-speed interface 808 is coupled to the memory 804, the display 816 (e.g., via a graphics processor or accelerator), and a high-speed expansion port 810 that can accept various expansion cards (not shown). In this implementation, 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 may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet) and can be coupled, for example, via a network adapter, to one or more input / output devices such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router.

[0133] Computing device 800 may be implemented in a number of different forms, as shown. For example, it may be implemented as a standard server 820 or multiple times in a group of such servers. It may also be implemented in a personal computer, such as a laptop computer 822. It may also be implemented as part of a rack server system 824. Alternatively, components from computing device 800 may be combined with other components in a mobile device (not shown), such as mobile computing device 850. Each such device may include one or more of computing device 800 and mobile computing device 850, and the entire system may be composed of multiple computing devices in communication with each other.

[0134] The mobile computing device 850 includes a processor 852, a memory 864, input / output devices such as a display 854, a communication interface 866, and a transceiver 868, among other components. The mobile computing device 850 may also be provided with a storage device, such as a microdrive or other device, to provide additional storage. Each of the processor 852, memory 864, display 854, communication interface 866, and transceiver 868 may be interconnected using various buses, or some of the components may be mounted on a common motherboard or in other manners as appropriate.

[0135] 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 of chips including separate analog and digital processors. The processor 852 can provide for coordination of other components of the mobile computing device 850, such as control of a user interface, applications run by the mobile computing device 850, and wireless communication by the mobile computing device 850.

[0136] The processor 852 can communicate with a user via a control interface 858 and a display interface 856 coupled to a display 854. The display 854 can be, for example, a TFT (thin film transistor liquid crystal display) display, an OLED (organic light emitting diode) display, or other suitable display technology. The display interface 856 can include appropriate circuitry for driving the display 854 to present graphical and other information to a user. The control interface 858 can receive commands from a user and translate them for submission to the processor 852. Additionally, an external interface 862 can provide communication with the processor 852 to enable short-range area communication between the mobile computing device 850 and other devices. The external interface 862 can be provided, for example, for wired communication in some implementations or for wireless communication in other implementations, and multiple interfaces can also be used.

[0137] The memory 864 stores information within the mobile computing device 850. The memory 864 may be implemented as one or more of a computer readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or 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 Single In Line Memory Module (SIMM) card interface. The expansion memory 874 may provide additional storage space for the mobile computing device 850 or may store applications or other information for the mobile computing device 850. In particular, the expansion memory 874 may include instructions for performing or supplementing the above processes, and may also include secure 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 enable secure use of the mobile computing device 850. Additionally, secure applications may be provided via a SIMM card, such as placing identifying information on the SIMM card in an unhackable manner, along with additional information.

[0138] The memory may include, for example, flash memory and / or NVRAM memory (non-volatile random access memory), as described below. In some implementations, the 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 those described above. The computer program product may be a computer-readable or machine-readable medium, such as memory 864, expansion memory 874, or memory on processor 852. In some implementations, the computer program product may be received in a propagated signal, for example, via transceiver 868 or external interface 862.

[0139] The mobile computing device 850 can communicate wirelessly through a communication interface 866, which may include digital signal processing circuitry as needed. The communication interface 866 may provide for communication under various modes or protocols, such as GSM voice (Global System for Mobile Communications), SMS (Short Message Service), EMS (Enhanced Message Service), or MMS messaging (Multimedia Messaging 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). Such communication may occur, for example, via a transceiver 868 using radio frequencies. In addition, short-range communication may occur, such as using Bluetooth, WiFi, or other such transceivers (not shown). In addition, a GPS (Global Positioning System) receiving module 870 may provide additional navigation and location related wireless data to the mobile computing device 850, which may be used as appropriate by applications executing on the mobile computing device 850.

[0140] The mobile computing device 850 can also communicate voice using an audio codec 860 that can receive voice information from a user and convert it into usable digital information. The audio codec 860 can also generate audible sounds to the user, for example, via a speaker in a handset of the mobile computing device 850. Such sounds can include sounds from a voice call, can include recorded sounds (e.g., voice messages, music files, etc.), and can include sounds generated by applications running on the mobile computing device 850.

[0141] The mobile computing device 850 can be implemented in a number of different forms, as shown in the figure: For example, it can be implemented as a mobile phone 880. It can also be implemented as part of a smartphone 882, personal digital assistant, or other similar mobile device.

[0142] Various implementations of the systems and methods described herein may be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementation in one or more computer programs executable and / or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to transmit and receive data and instructions from a memory device, at least one input device, and at least one output device.

[0143] 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 disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives the machine instructions as a machine-readable signal. The term machine-readable signal refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0144] To provide for user interaction, 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 a user, and a keyboard and pointing device (e.g., a mouse or trackball) by which the user can provide input to the computer. Other types of devices may be used to provide for user interaction as well; for example, 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 acoustic, speech, or tactile input.

[0145] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), includes middleware components (e.g., an application server), or includes front-end components (e.g., a client computer having a graphical user interface or a web browser that allows a user to interact with an embodiment of the systems and techniques described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0146] A computing system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

Claims

1. 1. A method of producing a vaccine using a continuous data algorithm, 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 first plurality of 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; producing a vaccine comprising at least one of: i) a protein defined by said discrete result object; ii) a nucleic acid capable of producing said protein defined by said discrete result object; and iii) a delivery vehicle capable of producing said protein defined by said discrete result object; The method includes:

2. 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, and each of the first amino acid sequence and the second amino acid sequence comprises a respective single character or a respective string of characters.

3. Transforming the discrete data object into the continuous data object comprises: generating, for each of the first discrete values, a weight vector of weight values, each weight value representing a likelihood that the first discrete value represents a particular 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; combining the weight vector and the feature vector to generate a first continuous value of the continuous data object; The method of claim 1 or 2, comprising:

4. 4. The method of claim 3, wherein each weight vector has 20 weight values, each weight value corresponding to one of 20 possible amino acids.

5. 5. The method of claim 3 or 4, wherein converting the continuous result object into the discrete result object comprises determining a respective single amino acid for each of the second continuous values, the determined single amino acids forming the plurality of second discrete values.

6. The method comprises: generating a plurality of candidate discrete result objects; removing at least one discrete result object from the plurality of candidate discrete result objects that designates an amino acid that fails a manufacturability test; The method of any one of claims 3 to 5, further comprising:

7. Applying the continuous data algorithm to generate the continuous result object includes applying a gradient descent method with a loss function that determines a loss value based on a plurality of loss criteria, the loss function comprising: a first loss criterion based on immunological reactions given two amino acid sequences; a second loss criterion that modifies the loss value for subsequences not found in the data set of wild-type sequences or subsequences that are predicted to not fold correctly; a third loss criterion for each weight vector that modifies the loss value based on the maximum of the second continuous values; The method according to any one of claims 3 to 6, comprising:

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

9. 1. A system for generating an amino acid sequence, comprising: one or more processors; a computer memory storing instructions that, when executed by the processor, cause the processor to perform operations; and the operation includes: 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 first plurality of 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; producing a vaccine comprising at least one of: i) a protein defined by said discrete result object; ii) a nucleic acid capable of producing said protein defined by said discrete result object; and iii) a delivery vehicle capable of producing said protein defined by said discrete result object; Including, the system.

10. The one or more amino acid sequences are 10. The system of claim 9, comprising a first amino acid sequence and a second amino acid sequence, each of the first amino acid sequence and the second amino acid sequence comprising a respective single character or a respective string of characters.

11. Transforming the discrete data object into the continuous data object comprises: generating, for each of the first discrete values, a weight vector of weight values, each weight value representing a likelihood that the first discrete value represents a particular 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; combining the weight vector and the feature vector to generate a first continuous value of the continuous data object; The system according to claim 9 or 10, comprising:

12. 12. The system of claim 11, wherein each weight vector has 20 weight values, each weight value corresponding to one of 20 possible amino acids.

13. 13. The system of claim 11 or 12, wherein converting the continuous result object into the discrete result object comprises determining a respective single amino acid for each of each second continuous value, the determined single amino acids forming the plurality of second discrete values.

14. The operation is generating a plurality of candidate discrete result objects; removing at least one discrete result object from the plurality of candidate discrete result objects that designates an amino acid that fails a manufacturability test; The system according to any one of claims 11 to 13, further comprising:

15. Applying the continuous data algorithm to generate the continuous result object includes applying a gradient descent method with a loss function that determines a loss value based on a plurality of loss criteria, the loss function comprising: a first loss criterion based on immunological reactions given two amino acid sequences; a second loss criterion that modifies the loss value for subsequences not found in the data set of wild-type sequences or subsequences that are predicted to not fold correctly; a third loss criterion for each weight vector that modifies the loss value based on the maximum of the second continuous values; The system according to any one of claims 11 to 14, comprising:

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, the 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 first plurality of 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; producing a vaccine comprising at least one of: i) a protein defined by said discrete result object; ii) a nucleic acid capable of producing said protein defined by said discrete result object; and iii) a delivery vehicle capable of producing said protein defined by said discrete result object; Including, media.

17. The one or more amino acid sequences are 17. The medium of claim 16, comprising a first amino acid sequence and a second amino acid sequence, each of the first amino acid sequence and the second amino acid sequence comprising a respective single character or a respective string of characters.

18. Transforming the discrete data object into the continuous data object comprises: generating, for each of the first discrete values, a weight vector of weight values, each weight value representing a likelihood that the first discrete value represents a particular 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; combining the weight vector and the feature vector to generate a first continuous value of the continuous data object; 18. The medium according to claim 16 or 17, comprising:

19. 20. The medium of claim 18, wherein each weight vector has 20 weight values, each weight value corresponding to one of 20 possible amino acids.

20. 20. The medium of claim 18 or 19, wherein converting the continuous result object into the discrete result object comprises determining a respective single amino acid for each of every two continuous values, the determined single amino acids forming the plurality of second discrete values.

Images