The disclosed subject matter relates to methods, systems, and media for predicting functions of molecular sequences.
Most approaches to relating the covalent structure of molecules in libraries to their function rely on the concept that the molecules can be described as a series of component pieces and those component pieces act more or less independently to give rise to function. A common example in the application of nucleic acid and peptide libraries is the derivation of a consensus motif, a description of a sequence of nucleotides or amino acids that assigns a position dependent functional significance to each. However, many of the interactions in biology cannot be described by such simple models and methods and higher order interactions between multiple components of a library molecule must be considered, both adjacent in the structure and distributed within the structure, with the ligand or functional activity in question. These higher order interactions are information rich processes, and thus to identify them requires the analysis of a large number of examples of interactions between the functional activity and many different library molecules.
The difficulty in designing models that do this accurately is that the models need to include high order interactions while at the same time not creating so many free parameters in the system so as to cause the problem to be under-determined.
Accordingly, it is desirable to provide new methods, systems, and media for predicting functions of molecular sequences.
Methods, systems, and media for predicting functions of molecular sequences are provided. In some embodiments, methods for predicting functions of molecular sequences are provided, the methods comprising: generating an array that represents a sequence of molecules; determining a projection of the sequence of molecules, wherein the determining comprises multiplying a representation of the array that represents the sequence of the molecules by a first hidden layer matrix that represents a number of possible sequence dependent functions, wherein the first hidden layer matrix is determined during training of a neural network; and determining a function of the sequence of molecules by applying a plurality of weights to a representation of the projection of the sequence of molecules, wherein the plurality of weights is determined during the training of the neural network.
In some embodiments, systems for predicting functions of molecular sequences are provided, the systems comprising: a memory; and a hardware processor coupled to the memory and configured to: generate an array that represents a sequence of molecules; determine a projection of the sequence of molecules, wherein the determining comprises multiplying a representation of the array that represents the sequence of the molecules by a first hidden layer matrix that represents a number of possible sequence dependent functions, wherein the first hidden layer matrix is determined during training of a neural network; and determine a function of the sequence of molecules by applying a plurality of weights to a representation of the projection of the sequence of molecules, wherein the plurality of weights is determined during the training of the neural network.
Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
In accordance with various embodiments, mechanisms (which can include methods, systems, and media) for predicting functions of molecular sequences are provided.
In some embodiments, the mechanisms described herein can be used to take data associated with chemical structure information, such as a sequence of monomers in a polymer, and create a neural network that allows the prediction of the function of sequences not in the original library.
In some embodiments, the mechanisms described herein can use a single set of specific molecular components and a single connecting reactionary chemistry for a large number of potential applications (examples given below), and a single manufacturing process and instrument can be used to create the molecules for any of these applications.
For example, in some embodiments, a molecular library can be created (as described in more detail below), which can include information for any suitable number of molecules (e.g., thousands of molecules, millions of molecules, billions of molecules, trillions of molecules, and/or any other suitable number), and the functional attributes of some or all of the molecules in the molecular library can be measured for any suitable function (e.g., binding, and/or any other suitable function as described below in more detail). Therefore, the large number of molecules described in the molecular library can provide diversity to create a quantitative relationship between structure and function that can then be used to design an optimized arrangement of the same molecular components used to create the library such that the new arrangement gives rise to enhanced function. For example, in some embodiments, the library can provide information between a molecular structure and a desired function, which can be used to design a new molecule that is not included in the library. Additionally, in instances where the molecular components and the number of components linked together by one or a small number of chemical bonds covers a sufficiently diverse functional space, the library can be used for any suitable purpose, as described below in more detail. Additionally, in instances where the molecular components are linked together by one common type of chemical bond (e.g., a peptide bond linking amino acids, and/or any other suitable type of chemical bond), any functional molecule designed in this way can be made with the same solid-state synthetic approach and using the same molecular components, thereby rendering manufacturing common to all compounds designed in this way. Therefore, the mechanisms described herein can be used to facilitate the equitable distribution of drugs globally and to play a positive role in personalized medicine applications, where an increasingly large number of different drugs, or combinations of drugs, may need to be generated for person-specific applications.
As a more particular example, in some embodiments, a molecular recognition profile of a target of a drug can be measured without the drug (e.g., a binding of the target to each of the molecules in a molecular library). Additionally, the molecular recognition profile of the target with the drug can be measured. The mechanisms described herein can then be used to design molecules that bind in the same place as the drug to identify molecules that can potentially replace the drug. In some such embodiments, each identified drug can then be synthesized using a single process and/or using a single manufacturing line changing only the sequence of a set of molecular components.
In some embodiments, the mechanisms described herein can be used for any suitable applications. For example, in some embodiments, the mechanisms can be used to: design new molecular libraries with specific functions; screen complex molecular systems of known structure for functional prediction; predict potential lead compounds with desirable functions; develop and implement diagnostic methods; develop therapeutics and vaccines; and/or for any other suitable applications. More particular examples of applications of the techniques described herein can include: the discovery/design of lead compounds to be used in the development of therapeutics; the discovery/design of potential targets of therapeutic treatment; the characterization of specific antibodies, such as monoclonal antibodies used as therapeutics, to determine what peptide and protein sequences they are expected to bind; the discovery/design of protein antigens that could be used in the development of vaccines; the discovery/design of ligands appropriate for developing specific binding complexes; the discovery/design of ligands that can be used to modify enzyme reactions; the discovery/design of ligands that can be used in the construction of artificial antibodies; the discovery/design of ligands that specifically interfere with binding between two targets; the discovery/design of binding partners (natural or man-made) to a particular target; the discovery/design of drugs such as antimicrobial drugs and/or any other suitable type of drugs; the design of peptide arrays that bind to specific antibodies or to serum with specific properties (e.g., the presence of antibodies expressed during a disease state); the enhancement and amplification of the diagnostic and prognostic signals provided by peptide arrays for use in analyzing the profile of antibodies in the blood produced in response to a disease, condition, or treatment; the discovery/design of protein antigens or polypeptide sequences that are responsible for the response to a disease, condition, or treatment (e.g., discovery of antigens for a vaccine); the discovery/design of protein antigens or polypeptide sequences that are responsible for adverse reactions resulting from a disease, condition, or treatment (e.g., autoimmune reactions); the design of coatings; the design of catalytic modifiers; the design of molecules for neutralization of toxic or unwanted chemical species; the design of adjuvants; the design of media for chromatography or purification; and/or for any other suitable applications.
As a more particular example, in some embodiments, pharmokinetics and solubility can be measured for a representative sample of molecular component combinations and used to predict pharmokinetic and solubility properties for all possible combinations. As a specific example, in the field of drug development, all drugs derived from this approach can have the same manufacturing system and many aspects of the drugs' action (e.g., toxicity, pharmokinetics, solubility, and/or any other suitable properties) can be accurately predicted based on previously known data about a molecular library (rather than about the specific application of the drug). Therefore, a drug specific to a particular application can be designed from simple, molecular-array-based measurements.
Note that, the techniques described herein describe use of a molecular library. In some embodiments, any suitable technique or combination of techniques can be used to prepare a molecular library, such as phage display, RNA display, synthetic bead-based libraries, other library techniques using synthesized molecules, and/or any other suitable technique(s). The techniques described herein are applicable to any molecular library system in which the function in question can be measured for enough of the unique molecular species in the library to allow the fitting routine (described below in more detail in connection with
Additionally, note that the mechanisms described herein are generally described as implemented using large peptide arrays. However, in some embodiments, any other suitable type of molecular library for which the structure of some or all of the molecules in the library can be described in terms of a common set of structural features, and a measured response associated with that structure, can be used. Other examples of molecular libraries which can be used include peptides, peptoids, peptide nucleic acids, nucleic acids, proteins, sugars and sugar polymers, any of the former with non-natural components (e.g., non-natural amino acids or nucleic acids), molecular polymers of known covalent structure, branched molecular structures and polymers, circular molecular structures and polymers, molecular systems of known composition created in part through self-assembly (e.g., structures created through hybridization to DNA or structures created via metal ion binding to molecular systems), and/or any other suitable type of molecular library. In some embodiments, the measured response can include binding, chemical reactivity, catalytic activity, hydrophobicity, acidity, conductivity, electromagnetic absorbance, electromagnetic diffraction, fluorescence, magnetic properties, capacitance, dielectric properties, flexibility, toxicity to cells, inhibition of catalysis, inhibition of viral function, index of refraction, thermal conductivity, optical harmonic generation, resistance to corrosion, resistance to or ease of hydrolysis, and/or any other suitable type of measurable response.
In some embodiments, the mechanisms described herein can use a neural network with any suitable type of architecture. In some embodiments, an input to the neural network can include information regarding a sequence of a heteropolymer, such as a peptide. In some embodiments, an output of the neural network can include an indication of a measurable function (e.g., binding, modification, structure, and/or any other suitable function). In some embodiments, information regarding a sequence that is used as an input to the neural network can be represented in any suitable format. For example, in some embodiments, as described below in more detail in connection with
Turning to
Process 100 can begin by generating a peptide array 102 (e.g., array A as shown in
At 103, process 100 can generate a binary representation 104 for the peptide array (e.g., array B as shown in
Note that, although array A has generally been described herein as dividing peptides into amino acids, in some embodiments, process 100 can divide the peptides in any suitable manner. For example, in some embodiments, peptides can be divided based on connectivity of substituent groups (carboxylic acids, amines, phenyl rings) and/or in terms of individual atoms. As a more particular example, in some embodiments, a structure can be encoded within a vector hierarchically by following covalent bonding lines. Additionally or alternatively, in some embodiments, peptides can be divided based on amino acid pairs. Continuing with this example, in some embodiments, a binary vector can have M2 bits for each residue. Furthermore, in some embodiments, molecular libraries do not have to be represented as arrays. For example, in some embodiments, bead libraries or other library approaches can be used.
At 105, process 100 can linearize array B 104 (that is, the binary representation of the molecular library) to produce linear/binary representation N×(M*R)B* 106. For example, in some embodiments, the matrix representation can be linearized such that binary descriptions of each amino acid in that peptide are concatenated end-to-end which can have size N×(M*R) (e.g., array B* as shown in
At 107, process 100 can multiply the linearized matrix representation array B* 106 by an eigensequence matrix 108 (e.g., array E as shown in
In some embodiments, eigensequence projection matrix F 110 (e.g., matrix F as shown in
At 111, process 100 can apply an activation function to eigensequence projection matrix F 110 to generate a rectified matrix 112 (e.g., matrix F* as shown in
At 113, process 100 can multiply the rectified matrix F* 112 by a final weighting function 114 (e.g., vector W as shown in
In some embodiments, any suitable technique or combination of techniques can be used to train the neural network described above. For example, in some embodiments, matrices E and W can be determined using any suitable nonlinear optimization technique(s) (e.g., gradient descent, stochastic descent, conjugated gradient descent, and/or any other suitable technique(s)). Additionally, note that, in some embodiments, any suitable training set of any suitable size can be used, as described in more detail below in connection with
Turning to
In some embodiments, process 200 can begin similarly to what is described above in connection with
At 209, process 200 can then linearize matrix C 208 to generate matrix D 210 using the techniques described above in connection with 105 of
Note that, in some embodiments, process 200 can add a nonlinear step after the linearization at 209. For example, in some embodiments, process 200 can apply an activation function to matrix D. As a more particular example, process 200 can apply an activation function (e.g., a rectifier, and/or any other suitable activation function) to matrix D to generate matrix D*, as shown in
At 213, matrix D or D* 212 (which matrix 212, when matrix D, can be the same as matrix D 210) can be multiplied by 1st eigensequence matrix 214 (e.g., matrix E as shown in
At 216, matrix F′ 215 can be multiplied by 2nd eigensequence matrix 217 (e.g., matrix E′ as shown in
In some embodiments, process 200 can then apply any suitable activation function 111 to provide matrix F* 112 and then apply weights 114 at 113 to matrix F* 112 to generate a predicted output 116 (e.g., vector P as shown in
In some embodiments, process 200 can determine matrices T, E, E′, and W using any suitable nonlinear optimization techniques, similarly to what was described above in connection with
Note that, in some embodiments, any other suitable number of hidden layers can be added to the neural network.
While eigensequence matrices 108, 214, and 217 are described herein as being used as hidden layers of the neural networks, any other suitable form of hidden layer can be used in some embodiments.
In some embodiments, an eigensequence (e.g., matrices E and E as shown in
In some embodiments, process 400 can add or subtract any column in matrix E or E from any other column in matrix E or E to create a new set of eigensequences describing the same space but having rotated vectors. In some embodiments, process 400 can identify a transformation matrix V (as shown in
In some embodiments, in instances where process 400 is used in conjunction with processes 100 and/or 200, processes 100 and/or 200 can iterate between process 400 and adjusting matrix E (e.g., using a nonlinear optimization algorithm as described above in connection with
In some embodiments, processes 100, 200, and/or 400 can be used for any suitable applications. For example, in some embodiments, a neural network as described above can be trained and/or optimized to predict the binding of peptide sequences to a particular protein of interest. In some such embodiments, the processes described above can be used to search for potential protein partners in the human proteome. For example, in some embodiments, the sequences of the proteome can be tiled into appropriately sized sequence fragments and can be used to form matrix A as shown above in connection with
Note that, in some embodiments, a molecular library can be assayed for function. For example, a function can be binding to a specific target (small molecule, protein, material, cells, pathogens, etc.), chemical reactivity, solubility, dynamic properties, electrical properties, optical properties, toxicity, pharmacokinetics, effects on enzymes, effects on cells (e.g., changes in gene expression or metabolism), effects on pathogens or any other effect that can be measured on the whole, or a large fraction, of the library resulting in a quantitative value or a qualitative result that can be represented as one of two or more alternatives. In some embodiments, in the case of binding interactions, targets can be labeled directly or indirectly or label free approaches can be used to detect binding. In some embodiments, isolated target binding can be considered alone or can be compared to target binding in the presence of a known binding ligand or other biomolecule. In the latter case, an aspect of the binding pattern due to the interaction with the ligand or the biomolecule can be identified, thereby allowing a known drug and its known target and to be used to generate a new ordered molecular component arrangement that mimics the binding of the drug.
Additionally, note that, in some embodiments, analyzing a function, such as binding, for a sparse sampling of particular ordered combinations of molecular components to form larger structures can be used to predict the function for all ordered combinations with similar structural characteristics (same set of molecular components, same kind or kinds of bonds, same kind of overall structure such as linear sequence, circular sequence, branched sequence, etc.). In some embodiments, a general quantitative relationship between the arrangement and identity of molecular components and the function can be derived using any suitable approach(es), including using a basis set of substructures and appropriate coefficients or using machine learning approaches.
In some embodiments, after a parameterized fit that describes a function of ordered combinations of molecular components in terms of some function(s) has been generated, the resulting parameterized fit can be used to optimize the ordered combination around the function(s) such that one or more new ordered combinations can be generated that are not in the original molecular library and are predicted to have functions that are more appropriate for the application of interest than any molecules in the original molecular library. In some embodiments, one or more optimized molecules (e.g., identified based on the one or more new ordered combinations) can be synthesized, and the function can be verified, as described below in more detail in connection with
Specific applications and their results are described below in connection with
Note that, in the specific applications described below in connection with
It should be noted that the arrays used in these applications have been extensively employed not only for antibody and protein binding but for binding to small molecules, whole viruses, whole bacteria and eukaryotic cells as well. See, e.g., Johnston, Stephen & Domenyuk, Valeriy & Gupta, Nidhi & Tavares Batista, Milene & C. Lainson, John & Zhao, Zhan-Gong & Lusk, Joel & Loskutov, Andrey & Cichacz, Zbigniew & Stafford, Phillip & Barten Legutki, Joseph & Diehnelt, Chris, “A Simple Platform for the Rapid Development of Antimicrobials,” Scientific Reports, 7, Article No. 17610 (2017), which is hereby incorporated by reference herein in its entirety. In some embodiments, functions other than binding such as chemical modification (e.g., phosphorylation, ubiquination, adenylation, acetylation, etc.), hydrophobicity, structure response to environmental change, thermal conductivity, electrical conductivity, polarity, polarizability, optical properties (e.g., absorbance, fluorescence, harmonic generation, refractive index, scattering properties, etc.) can be measured and modeled. The analysis described applies to all of these cases. Array synthesis and binding assays in the examples given below were performed as has been described in the literature. See, e.g., Legutki J B, Zhao Z G, Greying M, Woodbury N, Johnston S A, Stafford P, “Scalable High-Density Peptide Arrays for Comprehensive Health Monitoring,” Nature Communications, 5, 4785. PMID: 25183057 (2014), which is hereby incorporated by reference herein in its entirety. For some of the studies, the arrays were synthesized and or assays performed by the company HealthTell, Inc., of San Ramon, Calif., (www.healthtell.com). For other studies the arrays were synthesized and/or assays performed in the Peptide Array Core (www.peptidearraycore.com) at Arizona State University.
Turning to
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Note that, although the examples described herein generally relate to measuring binding, in some embodiments, any other suitable type of function can be used. For example, in some embodiments, any suitable function can be used for which the function can be measured for each type of molecule in the molecular library. Specific examples of functions can include chemical reactivity (e.g., acid cleavage, base cleavage, oxidation, reduction, hydrolysis, modification with nucleophiles, etc.), enzymatic modification (for peptides, that could be phosphorylation, ubiquination, acetylation, formyl group addition, adenylation, glycosylation, proteolysis, etc.; for DNA, it could be methylation, removal of cross-linked dimers, strand repair, strand cleavage, etc.), physical properties (e.g., electrical conductivity, thermal conductivity, hydrophobicity, polarity, polarizability, refraction, second harmonic generation, absorbance, fluorescence, phosphorescence, etc.), and/or biological activity (e.g., cell adhesion, cell toxicity, modification of cell activity or metabolism, etc.).
Molecular recognition between a specific target and molecules in a molecular library that includes sequences of molecular components linked together can be comprehensively predicted from a very sparse sampling of the total combinatorial space (e.g., as described above in connection with
Note that, in the examples shown in and described below in connection with
Next, at 2006, the process can assay the members of the molecular library for a specific function of interest. In some embodiments, the members can be assayed in any suitable manner and for any suitable function of interest in some embodiments.
Then, at 2008, the process can derive a quantitative relationship between the organization or sequence of the particular combination of molecular components for each member of the library to function(s) or characteristic(s) of that combination using a parameterized fit(s). In some embodiments, any suitable quantitative relationship can be derived and the quantitative relationship can be derived in any suitable manner.
At 2010, process 2000 can then determine combinations of sequences likely to provide optimized function(s). The combinations of sequences can be determined in any suitable manner in some embodiments. For example, in some embodiments, process 2000 can use the parameterized fit(s) to determine, from a larger set of all possible combinations of molecular components linked together, combinations of sequences likely to provide optimized function(s).
Then, at 2012, process 2000 can synthesize and empirically validate the function(s). In some embodiments, the functions can be synthesized and empirically validated in any suitable manner.
Finally, process 2000 can end at 2014.
Turning to
In the examples of
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In conjunction with
Micromolar concentrations of three different fluorescently labeled proteins, diaphorase, ferredoxin and ferredoxin-NADP reductase, were incubated with separate arrays of ˜125000 peptides in standard phosphate saline buffer. The fluorescence due to binding of each protein to every peptide in each array was recorded. The experiment was performed in triplicate and the values averaged. The Pearson correlation coefficient between replicates for each protein was 0.98 or greater. 90% of the peptide/binding value pairs from each protein were used to train a neural network similar to that in
Turning to
Server(s) 802 can be any suitable server(s) for predicting functions of molecular sequences. For example, in some embodiments, server(s) 802 can store any suitable information used to train a neural network to predict functions of molecular sequences. As a more particular example, in some embodiments, server(s) 802 can store sequence information (e.g., amino acid sequences of peptides, and/or any other suitable sequence information). As another more particular example, in some embodiments, server(s) 802 can store data and/or programs used to implement a neural network. In some embodiments, server(s) 802 can implement any of the techniques described above in connection with
Communication network 804 can be any suitable combination of one or more wired and/or wireless networks in some embodiments. For example, communication network 804 can include any one or more of the Internet, a mobile data network, a satellite network, a local area network, a wide area network, a telephone network, a cable television network, a WiFi network, a WiMax network, and/or any other suitable communication network.
In some embodiments, user device 806 can include one or more computing devices suitable for predicting functions of molecular sequences, and/or performing any other suitable functions. For example, in some embodiments, user device 806 can store any suitable data or information for implementing and/or using a neural network to predict functions of molecular sequences. As a more particular example, in some embodiments, user device 806 can store and/or use sequence information (e.g., sequences of amino acids in peptides, and/or any other suitable information), data and/or programs for implementing a neural network, and/or any other suitable information. In some embodiments, user device 806 can implement any of the techniques described above in connection with
Although only one each of server(s) 802 and user device 806 are shown in
Server(s) 802 and/or user device 806 can be implemented using any suitable hardware in some embodiments. For example, in some embodiments, devices 802 and 806 can be implemented using any suitable general purpose computer or special purpose computer. For example, a server may be implemented using a special purpose computer. Any such general purpose computer or special purpose computer can include any suitable hardware. For example, as illustrated in example hardware 900 of
Hardware processor 902 can include any suitable hardware processor, such as a microprocessor, a micro-controller, digital signal processor(s), dedicated logic, and/or any other suitable circuitry for controlling the functioning of a general purpose computer or a special purpose computer in some embodiments.
Memory and/or storage 904 can be any suitable memory and/or storage for storing programs, data, and/or any other suitable information in some embodiments. For example, memory and/or storage 904 can include random access memory, read-only memory, flash memory, hard disk storage, optical media, and/or any other suitable memory.
Input device controller 906 can be any suitable circuitry for controlling and receiving input from a device in some embodiments. For example, input device controller 906 can be circuitry for receiving input from a touch screen, from one or more buttons, from a voice recognition circuit, from a microphone, from a camera, from an optical sensor, from an accelerometer, from a temperature sensor, from a near field sensor, and/or any other type of input device.
Display/audio drivers 910 can be any suitable circuitry for controlling and driving output to one or more display/audio output circuitries 912 in some embodiments. For example, display/audio drivers 910 can be circuitry for driving an LCD display, a speaker, an LED, or any other type of output device.
Communication interface(s) 914 can be any suitable circuitry for interfacing with one or more communication networks, such as network 804 as shown in
Antenna 916 can be any suitable one or more antennas for wirelessly communicating with a communication network in some embodiments. In some embodiments, antenna 916 can be omitted when not needed.
Bus 918 can be any suitable mechanism for communicating between two or more components 902, 904, 906, 910, and 914 in some embodiments.
Any other suitable components can be included in hardware 900 in accordance with some embodiments.
It should be understood that at least some of the above described blocks of the processes of
In some embodiments, any suitable computer readable media can be used for storing instructions for performing the functions and/or processes herein. For example, in some embodiments, computer readable media can be transitory or non-transitory. For example, non-transitory computer readable media can include media such as non-transitory magnetic media (such as hard disks, floppy disks, and/or any other suitable magnetic media), non-transitory optical media (such as compact discs, digital video discs, Blu-ray discs, and/or any other suitable optical media), non-transitory semiconductor media (such as flash memory, electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and/or any other suitable semiconductor media), any suitable media that is not fleeting or devoid of any semblance of permanence during transmission, and/or any suitable tangible media. As another example, transitory computer readable media can include signals on networks, in wires, conductors, optical fibers, circuits, any suitable media that is fleeting and devoid of any semblance of permanence during transmission, and/or any suitable intangible media.
Accordingly, methods, systems, and media for predicting functions of molecular sequences are provided.
Although the invention has been described and illustrated in the foregoing illustrative embodiments, it is understood that the present disclosure has been made only by way of example, and that numerous changes in the details of implementation of the invention can be made without departing from the spirit and scope of the invention, which is limited only by the claims that follow. Features of the disclosed embodiments can be combined and rearranged in various ways.
This application claims the benefit of U.S. Provisional Patent Application No. 62/625,867, filed Feb. 2, 2018, and U.S. Provisional Patent Application No. 62/650,342, filed Mar. 30, 2018, each of which is hereby incorporated by reference herein in its entirety.
This invention was made with government support under Grant No. HSHQDC-15-C-B0008 awarded by the Department of Homeland Security. The government has certain rights in the invention.
Filing Document | Filing Date | Country | Kind |
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PCT/US19/16540 | 2/4/2019 | WO | 00 |
Number | Date | Country | |
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62625867 | Feb 2018 | US | |
62650342 | Mar 2018 | US |