A quantum computing-based system and method for predicting protein structure from RNA sequences.

Abstract

This invention discloses a quantum computing-based RNA sequence prediction protein structure system, method, and electronic device. The quantum computing-based RNA sequence prediction protein structure system includes: a user interaction terminal layer for acquiring the target RNA sequence input by the user; a classical computing layer, connected to the user interaction terminal layer and the quantum computing cloud platform layer, for processing and analyzing the target RNA sequence, allocating computing tasks based on the analysis results, and generating predicted protein structures based on the obtained computing results; and a quantum computing cloud platform layer for performing the quantum computing tasks allocated by the classical computing layer; the computing results include classical computing results and / or quantum computing results. The system of this invention can more effectively traverse energy barriers in complex energy landscapes and find the global optimum with a higher probability. This solves the problem that traditional classical algorithms often get trapped in local minima when searching for the lowest energy conformation of proteins, leading to inaccurate predicted structures.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a quantum computing-based RNA sequence prediction protein structure system, a quantum computing-based RNA sequence prediction protein structure method, and an electronic device. Background Technology

[0002] The three-dimensional structure of proteins directly determines their biochemical functions. Structural analysis methods primarily rely on X-ray crystallography, cryo-electron microscopy (Cryo-EM), and nuclear magnetic resonance (NMR). While providing precise results, these methods are extremely costly, time-consuming, and ill-suited for unstable structures such as membrane proteins.

[0003] In recent years, computational biology methods based on deep learning, exemplified by AlphaFold, have learned from massive databases of known protein structures and utilized multiple sequence alignment and evolutionary constraints to predict unknown structures. However, this approach suffers from the following drawbacks: 1. Deep learning models are highly dependent on evolutionary information. For novel artificial proteins not found in nature, proteins expressed by orphan genes, or sequences with rare mutations, the prediction accuracy drops sharply due to a lack of homologous sequence data. 2. De novo prediction methods based on physical principles (such as molecular dynamics simulations) do not rely on databases and determine structures by finding the lowest energy state. However, the protein folding problem is mathematically an NP-hard combinatorial optimization problem. As the amino acid chain length increases, the conformational space explodes exponentially, and classical computers cannot traverse all conformations in polynomial time to find the global optimum, often getting trapped in local minima. 3. Although quantum computing has a significant theoretical advantage in solving combinatorial optimization problems, current noisy mesoscale quantum computers have a limited number of qubits (usually less than 100), and entanglement fidelity decreases with increasing circuit depth. A medium-sized protein may require thousands of logical qubits to simulate, and it is impractical to directly put the full-length sequence into an existing quantum computer for computation. Summary of the Invention

[0004] The present invention is proposed to address at least one of the aforementioned problems. According to a first aspect of the invention, a quantum computing-based system for predicting protein structure from RNA sequences is provided, comprising: a user interaction terminal layer, a classical computing layer, and a quantum computing cloud platform layer.

[0005] The user interaction terminal layer is used to obtain the target RNA sequence input by the user.

[0006] The classical computing layer is connected to the user interaction terminal layer and the quantum computing cloud platform layer. It is used to process and analyze the target RNA sequence, allocate computing tasks according to the analysis results, and generate predicted protein structures based on the obtained computing results.

[0007] The quantum computing cloud platform layer is used to perform quantum computing tasks assigned by the classical computing layer.

[0008] The calculation results include classical calculation results and / or quantum calculation results.

[0009] In one embodiment of the present invention, the classical computing layer includes: a sequence preprocessing module, a quantum mapping bridging module, a hybrid computing power scheduling module, and a structure reconstruction module.

[0010] The sequence preprocessing module is connected to the user interaction terminal layer and is used to translate, segment, and label the target RNA sequence.

[0011] The quantum mapping bridging module is connected to the sequence preprocessing module and is used to generate a crystalline polypeptide and its corresponding control instructions based on the amino acid polypeptide sequence obtained after processing by the sequence preprocessing module.

[0012] The hybrid computing power scheduling module is connected to the quantum mapping bridging module and the quantum computing cloud platform layer, and is also connected to the classical computing power module. It is used to analyze the computational task difficulty based on the lattice-shaped polypeptide, send the control command to the classical computing power module and / or the quantum computing cloud platform layer based on the difficulty analysis result, and obtain the three-dimensional coordinates of the amino acid in the lattice based on the classical computation result and / or the quantum computation result fed back by the classical computing power module and / or the quantum computing cloud platform layer.

[0013] The structure reconstruction module is connected to the hybrid computing scheduling module and is used to generate predicted protein structures based on the three-dimensional coordinates in the lattice.

[0014] In one embodiment of the present invention, the sequence preprocessing module includes a translation unit and an intelligent segmentation and labeling unit.

[0015] The translation unit is connected to the user interaction terminal layer and is used to translate the target RNA sequence into an amino acid polypeptide chain.

[0016] The intelligent segmentation and labeling unit is connected to the translation unit and the quantum computing cloud platform layer. It is used to obtain the qubit threshold in the quantum computing cloud platform layer and determine whether the length of the amino acid polypeptide chain exceeds the qubit threshold. If it exceeds the threshold, the amino acid polypeptide chain is segmented into multiple subdomain segments with overlapping parts, and topological tags are marked on the overlapping regions. If it does not exceed the threshold, the amino acid polypeptide chain is kept as a single domain.

[0017] In one embodiment of the present invention, the quantum mapping bridging module includes: a lattice modeling unit and a parameter encoding unit.

[0018] The lattice modeling unit is connected to the intelligent segmentation and labeling unit, and is used to map each of the subdomain fragments or amino acid polypeptide chains that remain as a single domain to a three-dimensional spatial lattice to obtain the corresponding lattice-based polypeptide.

[0019] The parameter encoding unit is connected to the lattice modeling unit and is used to convert the general interaction parameters between amino acid residues in each of the lattice-based polypeptides into corresponding control instructions.

[0020] The general interaction parameters include interaction forces.

[0021] In one embodiment of the present invention, the hybrid computing power scheduling module includes: a computing power routing unit and a result decoding unit.

[0022] The computing power routing unit is connected to the quantum mapping bridging module and the quantum computing cloud platform layer, and is also connected to the classical computing power module. It is used to determine the difficulty of the computing task based on the lattice-shaped peptide. If the lattice-shaped peptide is a short fragment with a regular structure, the control command corresponding to the lattice-shaped peptide is sent to the classical computing power module. If the lattice-shaped peptide is not a short fragment with a regular structure, the control command corresponding to the lattice-shaped peptide is sent to the quantum computing cloud platform layer.

[0023] The result decoding unit is connected to the quantum computing cloud platform layer and to the classical computing module, and is used to restore the classical computing results and / or the quantum computing results to the three-dimensional coordinates of amino acids in the crystal lattice.

[0024] The classical computing module performs classical calculations according to the control instructions and obtains the classical calculation results.

[0025] In one embodiment of the present invention, the structure reconstruction module includes: a topology splicing unit and a global refinement unit.

[0026] The topology splicing unit is connected to the result decoding unit and is used to assemble multiple locally optimal structures into a complete full-length chain based on the three-dimensional coordinates in the lattice and using the topology tags through a rigid body rotation and translation algorithm.

[0027] The global refinement unit is connected to the topology splicing unit and is used to perform molecular dynamics relaxation on the spliced ​​full-length chain to eliminate atomic conflicts generated at the splicing point and obtain the predicted protein structure.

[0028] In one embodiment of the present invention, the user interaction terminal layer includes: an RNA sequence input interface module and a 3D structure visualization display module.

[0029] The RNA sequence input interface module is used to receive the target RNA sequence.

[0030] The 3D structure visualization module is connected to the classical computing layer and is used to perform 3D visualization of the predicted protein structure.

[0031] In one embodiment of the present invention, the quantum computing cloud platform layer includes: a cloud API interface module and a quantum processing module.

[0032] The cloud API interface module is connected to the classic computing layer and is used to receive the control commands.

[0033] The quantum processing module is connected to the cloud API interface module and is used to perform quantum calculations according to the control instructions to obtain the quantum calculation results.

[0034] According to a second aspect of the present invention, a method for predicting protein structure based on RNA sequence using quantum computing is provided, comprising:

[0035] Obtain the target RNA sequence input by the user.

[0036] The target RNA sequence is processed and analyzed, and computational tasks are assigned based on the analysis results.

[0037] If the computation task includes a classical computation task, then classical computation is performed based on the classical computation task to obtain the classical computation result.

[0038] If the computational task includes a quantum computation task, then quantum computation is performed based on the quantum computation task to obtain the quantum computation result.

[0039] Based on the obtained classical calculation results and / or quantum calculation results, a predicted protein structure is generated.

[0040] According to a third aspect of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory, wherein when the computer program is executed by the processor, it implements the above-described method for predicting protein structure based on RNA sequence using quantum computing.

[0041] This application presents a quantum computing-based RNA sequence prediction system for protein structure. Leveraging the unique quantum tunneling effect and superposition characteristics of quantum computing, it can more effectively traverse energy barriers in complex energy landscapes, finding the global optimum with a higher probability. This solves the problem that traditional classical algorithms often get trapped in local minima when searching for the lowest-energy protein conformation, leading to inaccurate structure predictions. Furthermore, the hybrid architecture of classical and quantum computing delegates simple conformational search to classical computing power, avoiding the resource waste of full quantum simulation and achieving an optimal balance between computational cost and prediction accuracy. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A schematic diagram of the structure of a quantum computing-based RNA sequence prediction protein structure system provided in an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of a quantum computing-based RNA sequence prediction protein structure system provided in another embodiment of the present invention;

[0045] Figure 3 This is a schematic diagram of the structure of a classic computing layer according to an embodiment of the present invention;

[0046] Figure 4 This is a schematic flowchart of a quantum computing-based method for predicting protein structure from RNA sequences, provided in an embodiment of the present invention.

[0047] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of the present invention.

[0049] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0050] It should be understood that the invention can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0051] To fully understand this invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by this invention. Optional embodiments of the invention are described in detail below; however, in addition to these detailed descriptions, the invention may have other embodiments.

[0052] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0053] To improve the accuracy, efficiency, and computational resource utilization of protein structure prediction results, the first aspect of this invention provides a quantum computing-based RNA sequence prediction protein structure system, such as... Figure 1 As shown, the quantum computing-based RNA sequence prediction protein structure system includes: a user interaction terminal layer 10, a classical computing layer 20, and a quantum computing cloud platform layer 30.

[0054] User interaction terminal layer 10 is used to obtain the target RNA sequence input by the user.

[0055] As an example, the user interaction terminal layer 10 can also visualize the predicted protein structure generated by the classical computing layer 20.

[0056] The classical computing layer 20 is connected to the user interaction terminal layer 10 and the quantum computing cloud platform layer 30. It is used to process and analyze the target RNA sequence, allocate computing tasks according to the analysis results, and generate predicted protein structures based on the obtained computing results.

[0057] The calculation results include classical calculation results and / or quantum calculation results.

[0058] The quantum computing cloud platform layer 30 is used to perform quantum computing tasks assigned to the classical computing layer 20.

[0059] It should be noted that the quantum computing cloud platform layer 30 can solve problems such as high-dimensional optimization, multi-body quantum effect calculation, and global conformation sampling that classical computing cannot efficiently handle through dedicated quantum hardware and protein structure prediction quantum algorithms, thus breaking through the performance and accuracy limits of classical algorithms.

[0060] The quantum computing-based RNA sequence prediction protein structure system of this invention utilizes the unique quantum tunneling effect and superposition characteristics of quantum computing to more effectively traverse energy barriers in complex energy landscapes, finding the global optimum with a higher probability. This solves the problem that traditional classical algorithms often get trapped in local minima when searching for the lowest-energy protein conformation, leading to inaccurate structure predictions. Furthermore, the hybrid architecture of classical and quantum computing delegates simple conformation search to classical computing power, avoiding the resource waste of full quantum simulation and achieving an optimal balance between computational cost and prediction accuracy.

[0061] In some embodiments, such as Figure 2 As shown, the classical computing layer 20 includes: a sequence preprocessing module 201, a quantum mapping bridging module 202, a hybrid computing power scheduling module 203, and a structure reconstruction module 204.

[0062] The sequence preprocessing module 201 is connected to the user interaction terminal layer 10 and is used to translate, segment, and label the target RNA sequence.

[0063] Specifically, the sequence preprocessing module 201 is connected to the RNA sequence input interface module 101.

[0064] As an example, the sequence preprocessing module 201 first translates the target RNA sequence into an amino acid polypeptide chain using a built-in standard genetic code table. Next, it segments the amino acid polypeptide chain according to the maximum available qubits of the quantum computing cloud platform layer 30, obtaining one or more amino acid fragments. Finally, topological tags are applied to the overlapping regions after segmentation to record the relative positions between fragments, facilitating subsequent splicing.

[0065] The quantum mapping bridging module 202 is connected to the sequence preprocessing module 201 and is used to generate a lattice-shaped polypeptide and its corresponding control instructions based on the amino acid polypeptide sequence obtained after processing by the sequence preprocessing module 201.

[0066] As an example, the quantum mapping bridging module 202 maps amino acid fragments to a three-dimensional spatial lattice. Each amino acid fragment is placed on a node of the lattice, transforming the continuous protein conformation space into a discrete lattice model. It can also generate corresponding control commands based on the interaction force information between amino acid fragments.

[0067] The hybrid computing power scheduling module 203 is connected to the quantum mapping bridging module 202 and the quantum computing cloud platform layer 30, and is also connected to the classical computing power module. It is used to analyze the difficulty of the computing task based on the lattice-shaped peptide, send control commands to the classical computing power module and / or the quantum computing cloud platform layer 30 based on the difficulty analysis results, and obtain the three-dimensional coordinates of the amino acid in the lattice based on the classical computing power module and / or the quantum computing cloud platform layer 30 feedback from the classical computing power module and / or the quantum computing cloud platform layer 30.

[0068] The hybrid computing power scheduling module 203 is connected to the cloud API interface module 301 in the quantum computing cloud platform layer 30.

[0069] It should be noted that the computational task difficulty can be divided according to the length and structure of the amino acid fragment. Specifically, short chain fragments (e.g., < 50 amino acids) and regular secondary structures (e.g., simple α-helices, β-sheets) are classified as low-complexity tasks; long chain fragments or those containing irregularly coiled hydrophobic core regions are classified as high-complexity tasks.

[0070] As an example, for low-complexity tasks, control commands are sent to the classical computing module for computation using classical algorithms such as molecular dynamics simulations, which is efficient and low-cost. For high-complexity tasks, control commands are sent to the quantum computing cloud platform layer 30, which utilizes quantum superposition and tunneling effects to find the globally optimal conformation.

[0071] The structure reconstruction module 204 is connected to the hybrid computing scheduling module 203 and is used to generate predicted protein structures based on the three-dimensional coordinates in the lattice.

[0072] As an example, the structure reconstruction module 204 reads the topological tags left by the sequence preprocessing module 201 and uses the coordinate overlap of the overlapping region as the "alignment anchor" to assemble the three-dimensional structure of multiple sub-fragments into a complete full-length amino acid chain.

[0073] In this embodiment, by combining the complementary advantages of classical and quantum computing, the computational challenges of predicting long-chain proteins are solved, and the accuracy and efficiency of structural prediction in various complex scenarios are improved.

[0074] In some embodiments, such as Figure 3 As shown, the sequence preprocessing module 201 includes a translation unit 2011 and an intelligent segmentation and labeling unit 2012.

[0075] The translation unit 2011 is connected to the user interaction terminal layer 10 and is used to translate the target RNA sequence into an amino acid polypeptide chain.

[0076] The intelligent segmentation and labeling unit 2012 is connected to the translation unit 2011 and the quantum computing cloud platform layer 30. It is used to obtain the qubit threshold in the quantum computing cloud platform layer 30 and determine whether the length of the amino acid polypeptide chain exceeds the qubit threshold. If it exceeds the threshold, the amino acid polypeptide chain is divided into multiple subdomain segments with overlapping parts, and topological tags are marked on the overlapping areas of the segments. If it does not exceed the threshold, the amino acid polypeptide chain is kept as a single domain.

[0077] As an example, a sliding window algorithm can be used to divide a long-chain polypeptide into several overlapping subdomain fragments, and topological tags can be added to the overlapping regions of the segments as "alignment anchors" for subsequent splicing.

[0078] Preferably, each amino acid requires 3-5 qubits, so for example, 50 qubits can carry a fragment of about 10-15 amino acids.

[0079] In this embodiment, the sequence preprocessing module 201 not only achieves precise biological transformation of RNA into amino acid chains, but also overcomes the limitation of the number of qubits through intelligent segmentation and topological labeling adapted to quantum hardware thresholds. It supports quantum-assisted computation of long-chain proteins and provides a core basis for subsequent precise assembly of structures, thereby improving computational feasibility and result accuracy.

[0080] In some embodiments, such as Figure 3 As shown, the quantum mapping bridging module 202 includes a lattice modeling unit 2021 and a parameter encoding unit 2022.

[0081] The lattice modeling unit 2021 is connected to the intelligent segmentation and labeling unit 2012 to map each subdomain fragment or amino acid polypeptide chain that remains as a single domain to a three-dimensional spatial lattice to obtain the corresponding lattice-based polypeptide.

[0082] The parameter encoding unit 2022 is connected to the lattice modeling unit 2021 and is used to convert the general interaction parameters between amino acid residues in each lattice peptide into corresponding control instructions.

[0083] Among them, the universal interaction parameters include the interaction forces.

[0084] It should be noted that the interaction forces include van der Waals forces, hydrogen bonds, hydrophobic interactions, electrostatic interactions, etc.

[0085] As an example, the extracted interaction forces can be converted into Hamiltonian parameters, which describe the total energy of the system in quantum mechanics. Based on the Hamiltonian parameters, a standardized quantum control instruction set adapted to the hardware characteristics of the quantum computing cloud platform layer 30 can be generated. Furthermore, based on the extracted interaction forces, a classical computing power control instruction set adapted to the classical computing power module can be generated.

[0086] In this embodiment, the quantum mapping bridging module 202 realizes the discretization transformation of biological polypeptide structure into quantum computing adaptation through lattice modeling, and then transforms the biophysical interaction between amino acid residues into standardized quantum control instructions through parameter encoding, providing a unified and executable computational input basis for hybrid computing power scheduling.

[0087] In some embodiments, such as Figure 3 As shown, the hybrid computing power scheduling module 203 includes a computing power routing unit 2031 and a result decoding unit 2032.

[0088] The computing power routing unit 2031 is connected to the quantum mapping bridging module 202 and the quantum computing cloud platform layer 30, and is also connected to the classical computing power module. It is used to determine the difficulty of the computing task based on the lattice-shaped peptide. If the lattice-shaped peptide is a short fragment with a regular structure, the control command corresponding to the lattice-shaped peptide is sent to the classical computing power module. If the lattice-shaped peptide is not a short fragment with a regular structure, the control command corresponding to the lattice-shaped peptide is sent to the quantum computing cloud platform layer 30.

[0089] Specifically, the computing power routing unit 2031 is connected to the parameter encoding unit 2022 in the quantum mapping bridging module 202.

[0090] It should be noted that regular structural short segments refer to lattice-shaped polypeptides (such as simple α-helices, β-sheets, and other secondary structure segments) whose length does not exceed the qubit threshold and whose structure has a clear regularity. The conformation search of these segments is easy and computationally inexpensive. Irregular structural short segments include complex sub-domains after long chain splitting, hydrophobic core segments containing irregular curls, etc. The conformation space of these segments is complex, which is a high-difficulty task that classical algorithms cannot efficiently process.

[0091] As an example, multiple computation requests for each segment can be sent simultaneously to the quantum computing cloud platform layer 30 via a concurrency manager, achieving parallel acceleration.

[0092] The result decoding unit 2032 is connected to the quantum computing cloud platform layer 30 and to the classical computing module, and is used to restore the classical computing results and / or quantum computing results to the three-dimensional coordinates of amino acids in the crystal lattice.

[0093] The classical computing module performs classical calculations according to control instructions and obtains the classical calculation results.

[0094] As an example, the result decoding unit 2032 restores the measurement results (0 / 1 bit stream) returned by the quantum computing cloud platform layer 30 to the three-dimensional coordinates of the amino acid in the lattice.

[0095] In this embodiment, the hybrid computing power scheduling module 203 realizes intelligent and precise distribution of classical and quantum computing power through the computing power routing unit 2031, so that low-difficulty computing tasks are efficiently completed by classical computing power and high-difficulty tasks are handled by quantum computing power. Then, the result decoding unit 2032 restores the different types of computing results into standardized lattice three-dimensional coordinates, thus realizing the optimal allocation of computing power resources.

[0096] In some embodiments, such as Figure 3 As shown, the structure reconstruction module 204 includes: a topology splicing unit 2041 and a global refinement unit 2042.

[0097] The topology splicing unit 2041 is connected to the result decoding unit 2032 and is used to assemble multiple local optimal structures into a complete full-length chain based on the three-dimensional coordinates in the lattice and using topological tags through rigid body rotation and translation algorithms.

[0098] The global refinement unit 2042 is connected to the topology splicing unit 2041 to perform molecular dynamics relaxation on the spliced ​​full-length chain, eliminate atomic conflicts generated at the splicing point, and obtain the predicted protein structure.

[0099] Specifically, a molecular dynamics relaxation algorithm is employed to globally refine the full-chain structure based on classical biophysical force fields. This simulates the molecular motion of amino acids in their natural state, allowing the structure to spontaneously adjust atomic positions, bond lengths, bond angles, and spatial conformation under the influence of physical forces. This gradually eliminates atomic conflicts at splicing points, bringing the entire structure closer to a thermodynamically stable state. After relaxation optimization, the structure with the lowest energy and most stable conformation is selected as the final predicted protein structure. Simultaneously, complete atomic coordinates, domain distribution, and chemical bond information of this structure are generated to meet the needs of subsequent visualization and biological function analysis.

[0100] In this embodiment, the structure reconstruction module 204 uses the topology splicing unit 2041 to accurately and seamlessly assemble the locally optimal structure calculated in segments into a complete full-length chain based on the coordinates of the overlapping region and the rigid body algorithm. Then, the global refinement unit 2042 eliminates splicing defects and optimizes the thermodynamic stability of the structure through molecular dynamics relaxation, and finally generates a complete, high-precision predicted protein structure that conforms to biophysical rules, ensuring the spatial continuity and biological reliability of the prediction results.

[0101] In some embodiments, such as Figure 2 As shown, the user interaction terminal layer 10 includes: an RNA sequence input interface module 101 and a 3D structure visualization display module 102.

[0102] RNA sequence input interface module 101 is used to receive target RNA sequences.

[0103] The 3D structure visualization module 102 is connected to the classical computing layer 20 and is used to perform 3D visualization of the predicted protein structure.

[0104] Specifically, the 3D structure visualization module 102 is connected to the structure reconstruction module 204.

[0105] In this embodiment, the user interaction terminal layer 10 provides users with a low-threshold, standardized channel for sequence input and task initiation through the RNA sequence input interface module, and at the same time, it transforms abstract protein structure data into intuitive and interactive 3D models through the 3D structure visualization display module.

[0106] In some embodiments, such as Figure 2 As shown, the quantum computing cloud platform layer 30 includes: a cloud API interface module 301 and a quantum processing module 302.

[0107] The cloud API interface module 301 is connected to the classic computing layer 20 to receive control commands.

[0108] It should be noted that quantum control instructions issued by the classical computing layer 20 can be received through a common cloud API communication protocol.

[0109] Specifically, the cloud API interface module 301 also sends the quantum computing results back to the hybrid computing scheduling module 203 in the classical computing layer.

[0110] The quantum processing module 302 is connected to the cloud API interface module 301 and is used to complete quantum calculations and obtain quantum calculation results according to control instructions.

[0111] It should be noted that the quantum processing module 302 is compatible with quantum hardware implemented using different physical methods, such as superconductivity, ion traps, and photonic quantum computing. By running quantum optimization algorithms, it utilizes quantum superposition (simultaneously exploring multiple conformations) and quantum tunneling effect (crossing local minimum energy barriers) to efficiently find the "globally optimal conformation" (i.e., the naturally stable structure) of a protein with the lowest energy.

[0112] In addition, this invention also provides a method for predicting protein structure from RNA sequences based on quantum computing, such as... Figure 4 As shown, methods for predicting protein structure from RNA sequences based on quantum computing include:

[0113] S1, Obtain the target RNA sequence input by the user.

[0114] S2 processes and analyzes the target RNA sequence and assigns computational tasks based on the analysis results.

[0115] S3. If the computation task includes a classical computation task, then perform classical computation based on the classical computation task to obtain the classical computation result.

[0116] S4. If the computation task includes a quantum computation task, then perform quantum computation based on the quantum computation task to obtain the quantum computation result.

[0117] S5 generates a predicted protein structure based on the obtained classical and / or quantum computation results.

[0118] Other specific embodiments of the quantum computing-based RNA sequence prediction protein structure method of the present invention can be found in the specific embodiments of the quantum computing-based RNA sequence prediction protein structure system of the above embodiments of the present invention.

[0119] In addition, the present invention also provides an electronic device, such as Figure 5 As shown, the controller 500 includes a processor 501 and a memory 503. The processor 501 and the memory 503 are connected, for example, via a bus 502.

[0120] Optionally, the controller 500 may also include a transceiver 504. It should be noted that in practical applications, the transceiver 504 is not limited to one, and the structure of the controller 500 does not constitute a limitation on the embodiments of the present invention.

[0121] Processor 501 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 501 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0122] Bus 502 may include a pathway for transmitting information between the aforementioned components. Bus 502 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 502 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0123] The memory 503 stores a computer program corresponding to the quantum computing-based RNA sequence prediction protein structure method of the above embodiments of the present invention. This computer program is controlled and executed by the processor 501. The processor 501 executes the computer program stored in the memory 503 to implement the content shown in the foregoing method embodiments.

[0124] The controller 500 includes, but is not limited to, mobile terminals such as laptops, PDAs (personal digital assistants), and PADs (tablet computers), as well as fixed terminals such as desktop computers. Figure 5 The controller 500 shown is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.

[0125] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

[0126] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0127] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0128] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0129] Similarly, it should be understood that, in order to streamline this application and aid in understanding one or more of the various inventive aspects, features of this application may sometimes be grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of this application. However, this approach should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with features fewer than all features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0130] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or elements of any method or apparatus so disclosed may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0131] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0132] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules according to the embodiments of this application. This application can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0133] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0134] The above description is merely a specific embodiment or illustration of the embodiments of this application. The scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A quantum computing-based system for predicting protein structure from RNA sequences, characterized in that, The system includes: The user interaction terminal layer is used to obtain the target RNA sequence input by the user. The classical computing layer, connected to the user interaction terminal layer and the quantum computing cloud platform layer, is used to process and analyze the target RNA sequence, allocate computing tasks based on the analysis results, and generate predicted protein structures based on the obtained computing results. The quantum computing cloud platform layer is used to perform quantum computing tasks assigned by the classical computing layer; The classical computing layer includes: A sequence preprocessing module, connected to the user interaction terminal layer, is used to translate, segment, and label the target RNA sequence; A quantum mapping bridging module, connected to the sequence preprocessing module, is used to generate a lattice-shaped polypeptide and its corresponding control instructions based on the amino acid polypeptide sequence obtained after processing by the sequence preprocessing module. A hybrid computing power scheduling module is connected to the quantum mapping bridging module and the quantum computing cloud platform layer, and also connected to the classical computing power module. It is used to analyze the computational task difficulty based on the lattice-shaped polypeptide, send the control command to the classical computing power module and / or the quantum computing cloud platform layer based on the difficulty analysis result, and obtain the three-dimensional coordinates of the amino acid in the lattice based on the classical computation result and / or the quantum computation result fed back by the classical computing power module and / or the quantum computing cloud platform layer. The structure reconstruction module, connected to the hybrid computing scheduling module, is used to generate predicted protein structures based on the three-dimensional coordinates in the lattice. The calculation results include classical calculation results and / or quantum calculation results.

2. The RNA sequence prediction protein structure system based on quantum computing according to claim 1, characterized in that, The sequence preprocessing module includes: A translation unit, connected to the user interaction terminal layer, is used to translate the target RNA sequence into an amino acid polypeptide chain. The intelligent segmentation and labeling unit, connected to the translation unit and the quantum computing cloud platform layer, is used to obtain the qubit threshold in the quantum computing cloud platform layer, determine whether the length of the amino acid polypeptide chain exceeds the qubit threshold, and if it does, the amino acid polypeptide chain is segmented into multiple subdomain segments with overlapping regions, and topological tags are marked on the segmented overlapping regions; if it does not exceed the threshold, the amino acid polypeptide chain is kept as a single domain.

3. The RNA sequence prediction protein structure system based on quantum computing according to claim 2, characterized in that, The quantum mapping bridging module includes: A lattice modeling unit, connected to the intelligent segmentation and labeling unit, is used to map each of the subdomain fragments or amino acid polypeptide chains that remain as a single domain onto a three-dimensional spatial lattice to obtain the corresponding lattice-based polypeptide. The parameter encoding unit, connected to the lattice modeling unit, is used to convert the general interaction parameters between amino acid residues in each of the lattice-based polypeptides into corresponding control commands. The general interaction parameters include interaction forces.

4. The RNA sequence prediction protein structure system based on quantum computing according to claim 2, characterized in that, The hybrid computing power scheduling module includes: The computing power routing unit is connected to the quantum mapping bridging module and the quantum computing cloud platform layer, and is also connected to the classical computing power module. It is used to determine the difficulty of the computing task based on the lattice-shaped peptide. If the lattice-shaped peptide is a short fragment with a regular structure, the control command corresponding to the lattice-shaped peptide is sent to the classical computing power module. If the lattice-shaped peptide is not a short fragment with a regular structure, the control command corresponding to the lattice-shaped peptide is sent to the quantum computing cloud platform layer. The result decoding unit is connected to the quantum computing cloud platform layer and the classical computing module, and is used to restore the classical computing results and / or the quantum computing results to the three-dimensional coordinates of amino acids in the crystal lattice. The classical computing module performs classical calculations according to the control instructions and obtains the classical calculation results.

5. The RNA sequence prediction protein structure system based on quantum computing according to claim 4, characterized in that, The structural reconstruction module includes: The topology splicing unit, connected to the result decoding unit, is used to assemble multiple locally optimal structures into a complete full-length chain based on the three-dimensional coordinates in the lattice and using the topology tags through a rigid body rotation and translation algorithm. The global refinement unit, connected to the topology splicing unit, is used to perform molecular dynamics relaxation on the spliced ​​full-length chain, eliminate atomic conflicts generated at the splicing points, and obtain the predicted protein structure.

6. The RNA sequence prediction protein structure system based on quantum computing according to claim 1, characterized in that, The user interaction terminal layer includes: An RNA sequence input interface module is used to receive the target RNA sequence; The 3D structure visualization module is connected to the classical computing layer and is used to perform 3D visualization of the predicted protein structure.

7. The RNA sequence prediction protein structure system based on quantum computing according to claim 1, characterized in that, The quantum computing cloud platform layer includes: The cloud API interface module is connected to the classic computing layer and is used to receive the control commands; The quantum processing module is connected to the cloud API interface module and is used to perform quantum calculations according to the control instructions to obtain the quantum calculation results.

8. A method for predicting protein structure from RNA sequences based on quantum computing, characterized in that, The method, applied to the quantum computing-based RNA sequence prediction protein structure system as described in any one of claims 1-7, comprises: Obtain the target RNA sequence input by the user; The target RNA sequence is processed and analyzed, and computational tasks are assigned based on the analysis results; If the computation task includes a classical computation task, then classical computation is performed based on the classical computation task to obtain the classical computation result; If the computational task includes a quantum computation task, then quantum computation is performed based on the quantum computation task to obtain the quantum computation result; Based on the obtained classical calculation results and / or quantum calculation results, a predicted protein structure is generated.

9. An electronic device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory, which, when executed by the processor, implements the quantum computing-based method for predicting protein structure from RNA sequences as described in claim 8.

Images