KR102598390B1 - Method for identifying a binding site having selectivity of a target protein - Google Patents

Abstract

According to an embodiment of the present disclosure, a method for identifying a binding site having selectivity for a target protein and a computer program stored in a computer-readable storage medium using the same are disclosed. Specifically, according to the present disclosure, a computing device determines one or more binding sites of a homologous protein that correspond to one or more binding sites of a target protein, and determines one or more binding sites of the determined target protein and one or more binding sites of the homologous protein. Compare, and based on the comparison, identify a binding site having selectivity in relationship with one or more binding sites of the homologous protein among the one or more binding sites of the target protein, and selectivity included in the identified binding site. Identify the amino acid residues that contribute to .

Description

Method for identifying a binding site with selectivity for a target protein {METHOD FOR IDENTIFYING A BINDING SITE HAVING SELECTIVITY OF A TARGET PROTEIN}

The present invention relates to a method for identifying a binding site with selectivity for a target protein. More specifically, the similarity of each binding site between the target protein and the homologous protein is analyzed and the binding site where the compound can selectively bind to the target protein is identified. It's about identifying and visualizing it.

In the development of new drugs, the process of determining selectivity, that is, the property that a drug binds to a specific protein it targets but does not bind to a homologous protein with a similar sequence and structure to the target protein, is very important. In order to determine selectivity, the amino acid sequence and three-dimensional structure of the target protein and the homologous protein must be compared and analyzed, and amino acid sequences and structures that differ from each other at the site where the drug is expected to bind must be found.

In this process, methods have been developed to compare the amino acid sequence and three-dimensional structure of the target protein and the homologous protein by aligning them. However, the data must be collected after the user individually analyzes the amino acid sequence and three-dimensional structure of each protein. There is an inconvenience in having to collect and determine selectivity. Therefore, there is a need in the art to automate each step to determine selectivity and to visualize the amino acid sequence and structural comparison information obtained through analysis.

In addition, with the development of allosteric drugs and the development of PROTAC (Proteolysis-Targeting Chimera) technology, there are increasing attempts to develop substances that bind to binding sites other than the active site of a protein, thereby providing selectivity for target proteins and homologous proteins in other binding sites as well. There is a need in the art to determine the part that has.

Domestic registered patent No. 0839600 (June 12, 2008) discloses a ligand search device and method using automatic extraction of specific binding sites.

The present disclosure has been made in response to the above-mentioned background technology, and identifies a binding site that has selectivity for a drug compared to the corresponding binding site of a homologous protein among one or more binding sites of a target protein, and constitutes the binding site. The goal is to create a method to visualize information about amino acid residues.

According to an embodiment of the present disclosure for realizing the above-described problem, a method for identifying a binding site with selectivity is disclosed. The method is a method of identifying a selective binding site of a target protein, performed by one or more processors of a computing device, comprising: determining one or more binding sites of a homologous protein corresponding to one or more binding sites of the target protein; Comparing one or more binding sites of the target protein and one or more binding sites of the homologous protein; Based on the comparison, identifying a binding site that has selectivity in relation to one or more binding sites of the homologous protein among the one or more binding sites of the target protein; And it may include identifying amino acid residues that contribute to selectivity contained in the identified binding site.

In an alternative embodiment, prior to determining one or more binding sites of a homologous protein that correspond to one or more binding sites of the target protein, the step includes: aligning three-dimensional structures of the target protein and the homologous protein; And at least one operation of predicting one or more binding sites capable of binding from the three-dimensional structure of the target protein, or specifying a binding site to be analyzed in the three-dimensional structure of the target protein based on a user's input. It may further include steps of performing.

In an alternative embodiment, based on the comparison, identifying a binding site among the one or more binding sites of the target protein that has selectivity in relation to one or more binding sites of the homologous protein comprises: using an evaluation function. Evaluating the similarity between one or more binding sites of the target protein and one or more binding sites of the homologous protein; And it may include the step of identifying a binding site of the target protein whose similarity is below a certain standard.

In alternative embodiments, the similarity may include similarity in amino acid sequence, similarity in protein structure, similarity in volume of the binding site, similarity in shape of the binding site, or similarity in hydrophobicity, similarity in electrostatics properties of the binding site. ) may include at least one of the similarities.

In alternative embodiments, the selectivity may decrease as the degree of similarity between one or more binding sites of the target protein and one or more binding sites of the homologous protein increases.

Identifying amino acid residues contributing to selectivity contained in the identified binding site may include: analyzing the identified binding site to derive amino acid residues contained in the identified binding site; Measuring the conservation score of amino acid residues included in the identified binding site; And it may include identifying amino acid residues that contribute to selectivity based on the value of the conservation score.

In an alternative embodiment, the method may further include visualizing amino acid residues contributing to the selectivity in the sequence or three-dimensional structure of the target protein, based on the measured value of the conservation score.

According to an embodiment of the present disclosure for realizing the above-described problem, a computer program for identifying a binding site having selectivity for a target protein is disclosed. The program may include determining one or more binding sites of a homologous protein corresponding to one or more binding sites of a target protein; Comparing one or more binding sites of the target protein and one or more binding sites of the homologous protein; Based on the comparison, identifying a binding site that has selectivity in relation to one or more binding sites of the homologous protein among the one or more binding sites of the target protein; And it may include an operation of identifying amino acid residues contributing to selectivity contained in the identified binding site.

According to an embodiment of the present disclosure for realizing the above-described problem, a computing device for identifying a binding site having selectivity for a target protein is disclosed. The computing device includes at least one processor and a memory, wherein the at least one processor determines one or more binding sites of a homologous protein corresponding to one or more binding sites of the target protein, and determines one or more binding sites of the target protein. And comparing one or more binding sites of the homologous protein, and based on the comparison, identifying a binding site that has selectivity in relation to one or more binding sites of the homologous protein among the one or more binding sites of the target protein, And amino acid residues contributing to selectivity contained in the identified binding site can be identified.

The present disclosure can identify a binding site with selectivity for a target protein and provide a visualization result.

The following drawings attached to be used in explaining the embodiments of the present disclosure are only some of the embodiments of the present disclosure, and for those skilled in the art (hereinafter referred to as “ordinary skilled in the art”), Other drawings can be obtained based on these drawings without effort leading to new inventions.
1 is a block diagram of a computing device that performs an operation to identify a binding site with selectivity for a target protein according to an embodiment of the present disclosure.
2 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.
Figure 3 is a flowchart of a method for identifying and visualizing a binding site with selectivity for a target protein according to an embodiment of the present disclosure.
Figure 4 is a conceptual diagram visualizing amino acids in a binding site with selectivity according to an embodiment of the present disclosure.

The present disclosure identifies a binding site that has selectivity for the corresponding binding site of a homologous protein among the binding sites of a target protein, identifies amino acid residues that have a significant effect on selectivity among the amino acid residues constituting the binding site, and identifies these amino acid residues of the protein. A method for visualizing sequences or 3D structures is disclosed.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the disclosure. However, it is clear that these embodiments may be practiced without these specific descriptions.

As used herein, the terms “component,” “module,” “system,” and the like refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an implementation of software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and the computing device can be a component. One or more components may reside within a processor and/or thread of execution. A component may be localized within one computer. A component may be distributed between two or more computers. Additionally, these components can execute from various computer-readable media having various data structures stored thereon. Components can transmit signals, for example, with one or more data packets (e.g., data and/or signals from one component interacting with other components in a local system, a distributed system, to other systems and over a network such as the Internet). Depending on the data being transmitted, they may communicate through local and/or remote processes.

Additionally, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified or clear from context, “X utilizes A or B” is intended to mean one of the natural implicit substitutions. That is, either X uses A; X uses B; Or, if X uses both A and B, “X uses A or B” can apply to either of these cases. Additionally, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the related listed items.

Additionally, the terms “comprise” and/or “comprising” should be understood to mean that the corresponding feature and/or element is present. However, the terms “comprise” and/or “comprising” should be understood as not excluding the presence or addition of one or more other features, elements and/or groups thereof. Additionally, unless otherwise specified or the context is clear to indicate a singular form, the singular terms herein and in the claims should generally be construed to mean “one or more.”

And, the term “at least one of A or B” should be interpreted to mean “when it contains only A,” “when it contains only B,” or “when it is a combination of A and B.”

Those skilled in the art will additionally recognize that the various illustrative logical blocks, components, modules, circuits, means, logic, and algorithm steps described in connection with the embodiments disclosed herein may be implemented using electronic hardware, computer software, or a combination of both. It must be recognized that it can be implemented with To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or software will depend on the specific application and design constraints imposed on the overall system. A skilled technician can implement the described functionality in a variety of ways for each specific application. However, such implementation decisions should not be construed as causing a departure from the scope of the present disclosure.

The description of the presented embodiments is provided to enable anyone skilled in the art to use or practice the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Therefore, the present disclosure is not limited to the embodiments presented herein. This disclosure is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

In this disclosure, network function, artificial neural network, and neural network may be used interchangeably.

Meanwhile, the term “binding site” used throughout the detailed description and claims of the present disclosure is a region on a polymer such as a protein to which a ligand can bind. Here, the ligand may include other proteins, reactants, second messengers, hormones, and allosteric substances, but the present disclosure is not limited thereto. Binding sites can be classified into active sites and allosteric sites to which ligands can bind in addition to the active site.

Meanwhile, the term "conservation score" used throughout the detailed description and claims of the present disclosure refers to a score used to analyze which parts of the target protein are conserved by comparing the target protein with the homologous protein of the target protein. It means assigned. The method of deriving the retention score may vary depending on the purpose of use and what is being compared. Based on the derived conservation score, the selectivity of the binding site can be determined or which amino acid residue among the amino acid residues constituting the binding site contributes to selectivity.

1 is a block diagram of a computing device that performs an operation to identify a binding site with selectivity for a target protein according to an embodiment of the present disclosure.

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present disclosure, the computing device 100 may include different configurations for performing the computing environment of the computing device 100, and only some of the disclosed configurations may configure the computing device 100.

The computing device 100 may include a processor 110, a memory 130, and a network unit 150.

The processor 110 may be composed of one or more cores, and may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. unit) may include a processor for data analysis and deep learning. The processor 110 may read a computer program stored in the memory 130 and perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning a neural network. The processor 110 is used for learning neural networks, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating the weights of the neural network using backpropagation. Calculations can be performed.

At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the network function. For example, CPU and GPGPU can work together to process learning of network functions and data classification using network functions. Additionally, in one embodiment of the present disclosure, the processors of a plurality of computing devices can be used together to process learning of network functions and data classification using network functions. Additionally, a computer program executed in a computing device according to an embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

According to one embodiment of the present disclosure, the processor 110 may align the amino acid sequence and three-dimensional structure of the target protein and homologs of the target protein. Thereafter, the processor 110 may identify the binding site to be analyzed from the three-dimensional structure of the target protein. The process of identifying a binding site from the three-dimensional structure of a target protein may be in the form of receiving information about the binding site from an already created database, or may be the result of the processor directly aligning the three-dimensional structure of the protein.

The processor 110 may determine the binding site of the homologous protein corresponding to one or more binding sites of the target protein from the amino acid sequence and three-dimensional structure of the aligned target protein and the homologous protein. If there is a user input to select the binding site of the target protein, the processor determines the binding site of the homologous protein corresponding to the binding site, and if there is no user input, the processor predicts one or more binding sites of the target protein and then identifies the binding site. Determine the binding site of the homologous protein corresponding to the binding site. At this time, the number of binding sites of the homologous protein corresponding to a single binding site of the target protein may include both cases where there is no binding site and cases where there are two or more cases.

In one embodiment of the present disclosure, the processor 110 may evaluate similarity by comparing one or more binding sites of the target protein and one or more binding sites of the homologous protein using a created evaluation function. The similarity of the binding site can be evaluated by measuring the conservation score of the binding site, but the present disclosure is not limited to this. In evaluating similarity, similarity of amino acid sequence, similarity of protein structure, similarity of binding site, similarity of shape of binding site, similarity of hydrophobicity of binding site, and electrostatics properties of binding site. Parameters suitable for the purpose, such as similarity, can be appropriately selected. The method for calculating the hydrophobicity of the binding site and the electrostatic properties of the binding site may be a molecular mechanical calculation method based on a force field or a quantum mechanical (QM) calculation method, but the quantum mechanical calculation method is preferred. , but is not limited to this.

The processor 110 may identify a binding site of the target protein whose evaluated similarity is below a certain standard. At this time, the lower the similarity between the two binding sites, the greater the possibility that any ligand will bind to the binding site of the target protein but not the binding site of the corresponding homologous protein. Therefore, the lower the similarity is evaluated, the more likely it is that the binding site of the target protein will bind. The likelihood that a site will be identified as having selectivity increases.

The processor 110 may identify amino acid residues that contribute to selectivity among amino acid residues included in the binding site identified as having the selectivity. Amino acid residues contributing to selectivity can be identified by measuring the conservation score of the amino acid residues, but the present disclosure is not limited to this. In identifying amino acid residues that contribute to the selectivity, parameters suitable for the purpose, such as amino acid sequence similarity, protein structure similarity, hydrophobicity similarity, or electrostatic property similarity, may be appropriately selected.

The processor 110 may visualize amino acid residues contributing to the selectivity of the binding site having the selectivity in the sequence or three-dimensional structure of the target protein. At this time, the visualization method may be a method of emphasizing display by varying saturation, brightness, and hue based on the preservation score, but the present disclosure is not limited to this.

According to an embodiment of the present disclosure, the memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150.

According to an embodiment of the present disclosure, the memory 130 is a flash memory type, hard disk type, multimedia card micro type, or card type memory (e.g. (e.g. SD or -Only Memory), and may include at least one type of storage medium among magnetic memory, magnetic disk, and optical disk. The computing device 100 may operate in connection with web storage that performs a storage function of the memory 130 on the Internet. The description of the memory described above is merely an example, and the present disclosure is not limited thereto.

The network unit 150 according to an embodiment of the present disclosure includes Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and VDSL ( A variety of wired communication systems can be used, such as Very High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and Local Area Network (LAN).

In addition, the network unit 150 presented in this specification includes Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), and SC-FDMA ( A variety of wireless communication systems can be used, such as Single Carrier-FDMA) and other systems.

In the present disclosure, the network unit 150 may use any type of wired or wireless communication system.

The techniques described herein can be used in the networks mentioned above, as well as other networks.

Figure 3 is a flowchart of a method for identifying and visualizing a binding site with selectivity for a target protein according to an embodiment of the present disclosure.

In step S310, the processor 110 of the computing device 100 aligns the amino acid sequence and three-dimensional structure of the target protein and the homologous protein. A protein with a similar structure to the target protein to be analyzed may be selected as a homologous protein. As another example, the processor 110 may access an online database to receive a list of proteins homologous to the target protein, but the present disclosure is not limited to these methods.

In step S320, the processor 110 goes through step S331 to predict a binding site in the three-dimensional structure of the target protein depending on whether there is an input from the user specifying the binding site, or the user analyzes the three-dimensional structure of the target protein. You can go through step S332, where you receive an input specifying the desired binding site.

In step S331, if there is no user input specifying the binding site, the processor 110 may predict a binding site where the ligand can bind by analyzing the three-dimensional structure of the target protein.

In step S332, if there is no input from the user specifying the binding site, the processor 110 immediately analyzes the selectivity of the binding site selected by the user without analyzing the three-dimensional structure of the target protein to predict the binding site. You can start.

Methods for identifying the binding site that is the target of analysis can be accomplished through various routes. For example, the processor 110 may use an artificial neural network to predict one or more binding sites where a ligand can bind from the three-dimensional structure of the protein. In another embodiment, the processor 110 aligns the amino acid sequence and three-dimensional structure of the target protein and the homologous protein, provides visualization of the amino acid sequence and three-dimensional structure of the protein to the user, and allows the user to specify a specific binding site. You can receive the action. Other embodiments may exist in addition to the above examples, but the present disclosure is not limited thereto.

In step S340, the similarity between the binding site of the target protein and the binding site of the homologous protein may be evaluated.

In the present disclosure, several embodiments may be used as a method for evaluating the similarity of binding sites. For example, the processor 110 may measure the conservation score of the binding site of the target protein and the binding site of the homologous protein and use the similarity of the amino acid sequence as an index for evaluating the similarity between the binding sites. In another embodiment, the similarity of the structure of the binding site may be reflected in the evaluation using a method such as RMSD, which measures the distance between overlapping atoms that make up the protein. In addition, various indicators, including similarity in volume between binding sites, similarity in shape between binding sites, similarity in hydrophobicity between binding sites, and similarity in electrostatic properties of binding sites, can be used as indicators to evaluate the similarity between binding sites. , this disclosure is not limited thereto.

At this time, the evaluation function used to evaluate similarity may be a rule-based function or a model using an artificial neural network capable of supervised learning, unsupervised learning, and reinforcement learning, but the present disclosure is not limited to this.

In step S350, a binding site with selectivity compared to a homologous protein among the binding sites of the target protein may be identified. Selectivity can be determined based on the similarity between the binding site of the target protein and the binding site of the homologous protein, and the lower the similarity, the higher the likelihood of being identified as having selectivity.

In step S360, the processor may identify amino acid residues that contribute to selectivity in the binding site identified as having selectivity. Since there may be multiple amino acids constituting the binding site of a polymer material, including proteins, even if a binding site with selectivity is found, it is important to identify amino acid residues within it that contribute to the selectivity of the binding site. The conservation score of the protein may be included in the criteria for identifying amino acid residues contributing to selectivity, but the present disclosure is not limited to this.

In step S370, processor 110 may visualize the amino acid residues contributing to the selectivity identified in step S360. For example, the processor maps the measured conservation score to the three-dimensional structure of the protein and displays binding sites with selectivity and amino acid residues that contribute to selectivity based on the conservation score on the three-dimensional structure of the protein, allowing the user to The three-dimensional structure can help determine which binding site has selectivity and which amino acid residues contribute to selectivity.

Through the steps above, the user specifies the target protein or inputs the binding site of the target protein to determine which site has selectivity among the binding sites of the target protein and which amino acid residues contribute to selectivity, an automated process. It can be identified through . This can be easily used in new drug research to develop substances that selectively act on target proteins and in the development of allosteric drugs or protac binding modules.

Figure 4 is a conceptual diagram visualizing amino acids in a binding site with selectivity according to an embodiment of the present disclosure. The protein structure visualized in the 3D model shows the selective binding pocket and the amino acid residues that contribute to selectivity. The sequence of each protein is color-coded with a conservation score from 4 to 11.

2 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has generally been described above as being capable of being implemented by a computing device, those skilled in the art will understand that the present disclosure can be implemented in combination with computer-executable instructions and/or other program modules that can be executed on one or more computers and/or in hardware and software. It will be well known that it can be implemented as a combination.

Typically, program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Additionally, those skilled in the art will understand that the methods of the present disclosure are applicable to single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. It will be appreciated that each of these may be implemented in other computer system configurations, including those capable of operating in conjunction with one or more associated devices.

The described embodiments of the disclosure can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Computer-readable media can be any medium that can be accessed by a computer, and such computer-readable media includes volatile and non-volatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media refers to volatile and non-volatile media, transient and non-transitory media, removable and non-removable, implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Includes media. Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage. This includes, but is not limited to, a device, or any other medium that can be accessed by a computer and used to store desired information.

A computer-readable transmission medium typically implements computer-readable instructions, data structures, program modules, or other data on a modulated data signal, such as a carrier wave or other transport mechanism. Includes all information delivery media. The term modulated data signal refers to a signal in which one or more of the characteristics of the signal have been set or changed to encode information within the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

An example environment 1100 is shown that implements various aspects of the present disclosure, including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 couples system components, including but not limited to system memory 1106, to processing unit 1104. Processing unit 1104 may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be used as processing unit 1104.

System bus 1108 may be any of several types of bus structures that may further be interconnected to a memory bus, peripheral bus, and local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112. The basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, and EEPROM, and is a basic input/output system that helps transfer information between components within the computer 1102, such as during startup. Contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

Computer 1102 may also include an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA)—the internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes - a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from or writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., a CD-ROM for reading the disk 1122 or reading from or writing to other high-capacity optical media such as DVDs). Hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to system bus 1108 by hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to. The interface 1124 for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. For computer 1102, drive and media correspond to storing any data in a suitable digital format. Although the description of computer-readable media above refers to removable optical media such as HDDs, removable magnetic disks, and CDs or DVDs, those skilled in the art will also recognize removable optical media such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other types of computer-readable media, such as the like, may also be used in the example operating environment and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored in drives and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or portions of the operating system, applications, modules and/or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented on various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as mouse 1140. Other input devices (not shown) may include microphones, IR remote controls, joysticks, game pads, stylus pens, touch screens, etc. These and other input devices are connected to the processing unit 1104 through an input device interface 1142, which is often connected to the system bus 1108, but may also include a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, It can be connected by other interfaces, etc.

A monitor 1144 or other type of display device is also connected to system bus 1108 through an interface, such as a video adapter 1146. In addition to monitor 1144, computers typically include other peripheral output devices (not shown) such as speakers, printers, etc.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148, via wired and/or wireless communications. Remote computer(s) 1148 may be a workstation, computing device computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, and is generally connected to computer 1102. For simplicity, only memory storage device 1150 is shown, although it includes many or all of the components described. The logical connections depicted include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154. These LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, such as the Internet.

When used in a LAN networking environment, computer 1102 is connected to local network 1152 through wired and/or wireless communication network interfaces or adapters 1156. Adapter 1156 may facilitate wired or wireless communication to LAN 1152, which also includes a wireless access point installed thereon for communicating with wireless adapter 1156. When used in a WAN networking environment, the computer 1102 may include a modem 1158 or be connected to a communicating computing device on the WAN 1154 or to establish communications over the WAN 1154, such as via the Internet. Have other means. Modem 1158, which may be internal or external and a wired or wireless device, is coupled to system bus 1108 via serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored in remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and that other means of establishing a communications link between computers may be used.

Computer 1102 may be associated with any wireless device or object deployed and operating in wireless communications, such as a printer, scanner, desktop and/or portable computer, portable data assistant (PDA), communications satellite, wirelessly detectable tag. Performs actions to communicate with any device or location and telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, communication may be a predefined structure as in a conventional network or may simply be ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) allows connection to the Internet, etc. without wires. Wi-Fi is a wireless technology, like cell phones, that allows these devices, such as computers, to send and receive data indoors and outdoors, anywhere within the coverage area of a base station. Wi-Fi networks use wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, the Internet, and wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band). .

Meanwhile, according to an embodiment of the present disclosure, a computer-readable medium storing a data structure is disclosed.

Data structure can refer to the organization, management, and storage of data to enable efficient access and modification of data. Data structure can refer to the organization of data to solve a specific problem (e.g., retrieving data, storing data, or modifying data in the shortest possible time). A data structure may be defined as a physical or logical relationship between data elements designed to support a specific data processing function. Logical relationships between data elements may include connection relationships between user-defined data elements. Physical relationships between data elements may include actual relationships between data elements that are physically stored in a computer-readable storage medium (e.g., a persistent storage device). A data structure may specifically include a set of data, relationships between data, and functions or instructions applicable to the data. Effectively designed data structures allow computing devices to perform computations while minimizing the use of the computing device's resources. Specifically, computing devices can increase the efficiency of operations, reading, insertion, deletion, comparison, exchange, and search through effectively designed data structures.

Data structures can be divided into linear data structures and non-linear data structures depending on the type of data structure. A linear data structure may be a structure in which only one piece of data is connected to another piece of data. Linear data structures may include List, Stack, Queue, and Deque. A list can refer to a set of data that has an internal order. The list may include a linked list. A linked list may be a data structure in which data is connected in such a way that each data is connected in a single line with a pointer. In a linked list, a pointer may contain connection information to the next or previous data. Depending on its form, a linked list can be expressed as a singly linked list, a doubly linked list, or a circularly linked list. A stack may be a data listing structure that allows limited access to data. A stack can be a linear data structure in which data can be processed (for example, inserted or deleted) at only one end of the data structure. Data stored in the stack may have a data structure (LIFO-Last in First Out) where the later it enters, the sooner it comes out. A queue is a data listing structure that allows limited access to data. Unlike the stack, it can be a data structure (FIFO-First in First Out) where data stored later is released later. A deck can be a data structure that can process data at both ends of the data structure.

A non-linear data structure may be a structure in which multiple pieces of data are connected behind one piece of data. Nonlinear data structures may include graph data structures. A graph data structure can be defined by vertices and edges, and an edge can include a line connecting two different vertices. Graph data structure may include a tree data structure. A tree data structure may be a data structure in which there is only one path connecting two different vertices among a plurality of vertices included in the tree. In other words, it may be a data structure that does not form a loop in the graph data structure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. Below, it is described in a unified manner as a neural network. Data structures may include neural networks. And the data structure including the neural network may be stored in a computer-readable medium. Data structures including neural networks also include data preprocessed for processing by a neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, activation functions associated with each node or layer of the neural network, neural network It may include a loss function for learning. A data structure containing a neural network may include any of the components disclosed above. In other words, the data structure including the neural network includes preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, activation functions associated with each node or layer of the neural network, neural network It may be composed of all or any combination of loss functions for learning. In addition to the configurations described above, a data structure containing a neural network may include any other information that determines the characteristics of the neural network. Additionally, the data structure may include all types of data used or generated in the computational process of a neural network and is not limited to the above. Computer-readable media may include computer-readable recording media and/or computer-readable transmission media. A neural network can generally consist of a set of interconnected computational units, which can be referred to as nodes. These nodes may also be referred to as neurons. A neural network consists of at least one node.

The data structure may include data input to the neural network. A data structure containing data input to a neural network may be stored in a computer-readable medium. Data input to the neural network may include learning data input during the neural network learning process and/or input data input to the neural network on which training has been completed. Data input to the neural network may include data that has undergone pre-processing and/or data subject to pre-processing. Preprocessing may include a data processing process to input data into a neural network. Therefore, the data structure may include data subject to preprocessing and data generated by preprocessing. The above-described data structure is only an example and the present disclosure is not limited thereto.

The data structure may include the weights of the neural network. (In this specification, weights and parameters may be used with the same meaning.) And the data structure including the weights of the neural network may be stored in a computer-readable medium. A neural network may include multiple weights. Weights may be variable and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. Based on the weight, the data value output from the output node can be determined. The above-described data structure is only an example and the present disclosure is not limited thereto.

As an example and not a limitation, the weights may include weights that are changed during the neural network learning process and/or weights for which neural network learning has been completed. Weights that change during the neural network learning process may include weights that change at the start of the learning cycle and/or weights that change during the learning cycle. Weights for which neural network training has been completed may include weights for which a learning cycle has been completed. Therefore, the data structure including the weights of the neural network may include weights that are changed during the neural network learning process and/or the data structure including the weights for which neural network learning has been completed. Therefore, the above-mentioned weights and/or combinations of each weight are included in the data structure including the weights of the neural network. The above-described data structure is only an example and the present disclosure is not limited thereto.

The data structure including the weights of the neural network may be stored in a computer-readable storage medium (e.g., memory, hard disk) after going through a serialization process. Serialization can be the process of converting a data structure into a form that can be stored on the same or a different computing device and later reorganized and used. Computing devices can transmit and receive data over a network by serializing data structures. Data structures containing the weights of a serialized neural network can be reconstructed on the same computing device or on a different computing device through deserialization. The data structure including the weights of the neural network is not limited to serialization. Furthermore, the data structure including the weights of the neural network is a data structure to increase computational efficiency while minimizing the use of computing device resources (e.g., in non-linear data structures, B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree) may be included. The foregoing is merely an example and the present disclosure is not limited thereto.

The data structure may include hyper-parameters of a neural network. And the data structure including the hyperparameters of the neural network can be stored in a computer-readable medium. A hyperparameter may be a variable that can be changed by the user. Hyperparameters include, for example, learning rate, cost function, number of learning cycle repetitions, weight initialization (e.g., setting the range of weight values subject to weight initialization), Hidden Unit. It may include a number (e.g., number of hidden layers, number of nodes in hidden layers). The above-described data structure is only an example and the present disclosure is not limited thereto.

Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols and chips that may be referenced in the above description include voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields. It can be expressed by particles or particles, or any combination thereof.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein may be used in electronic hardware, (for convenience) It will be understood that it may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interoperability of hardware and software, various illustrative components, blocks, modules, circuits and steps have been described above generally with respect to their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the overall system. A person skilled in the art of this disclosure may implement the described functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of this disclosure.

The various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash. Includes, but is not limited to, memory devices (e.g., EEPROM, cards, sticks, key drives, etc.). Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of illustrative approaches. It is to be understood that the specific order or hierarchy of steps in processes may be rearranged within the scope of the present disclosure, based on design priorities. The appended method claims present elements of the various steps in a sample order but are not meant to be limited to the particular order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Thus, the present disclosure is not limited to the embodiments presented herein but is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

Claims (9)

1. A method of identifying a binding site with selectivity for a target protein, performed by one or more processors of a computing device, comprising:
determining a plurality of binding sites of a homologous protein corresponding to a plurality of binding sites of the target protein;
Comparing a plurality of binding sites of the target protein and a plurality of binding sites of the homologous protein;
Based on the comparison, identifying a binding site that has selectivity in relation to a plurality of binding sites of the homologous protein among the plurality of binding sites of the target protein; and
identifying amino acid residues that contribute to selectivity contained in the identified binding site;
Including,
method.
According to claim 1,
Before determining the plurality of binding sites of the homologous protein corresponding to the plurality of binding sites of the target protein:
Aligning the three-dimensional structures of the target protein and the homologous protein; and
At least one of predicting a plurality of binding sites capable of binding from the three-dimensional structure of the target protein, or specifying a binding site to be analyzed in the three-dimensional structure of the target protein based on a user's input. Steps to perform;
Containing more,
method. According to claim 1,
Based on the comparison, the step of identifying a binding site that has selectivity in relation to a plurality of binding sites of the homologous protein among the plurality of binding sites of the target protein is:
Evaluating the similarity between the plurality of binding sites of the target protein and the plurality of binding sites of the homologous protein using an evaluation function; and
Identifying a binding site of the target protein whose similarity is below a certain standard;
Including,
method. According to claim 3,
The similarity is,
Similarity of amino acid sequence,
protein structure similarity,
Similarity of binding site volumes,
Similarity in shape of the binding site,
Similarity of hydrophobicity, or
Similarity of electrostatics properties,
Containing at least one of
method. According to claim 1,
The selectivity is,
The similarity between one or more binding sites of the target protein and one or more binding sites of the homologous protein decreases as the similarity increases,
method, According to claim 1,
Identifying amino acid residues that contribute to selectivity contained in the identified binding site include:
Analyzing the identified binding site to derive amino acid residues included in the identified binding site;
Measuring the conservation score of amino acid residues included in the identified binding site; and
identifying amino acid residues contributing to selectivity based on the value of the conservation score;
Including,
method. According to claim 6,
Visualizing amino acid residues contributing to the selectivity in the sequence or three-dimensional structure of the target protein, based on the measured value of the conservation score;
Containing more,
method. A computer program stored on a computer-readable storage medium containing instructions that cause a computing device to perform operations, the operations comprising:
An operation of determining a plurality of binding sites of a homologous protein corresponding to a plurality of binding sites of the target protein;
Comparing a plurality of binding sites of the target protein and a plurality of binding sites of the homologous protein;
Based on the comparison, identifying a binding site that has selectivity in relation to a plurality of binding sites of the homologous protein among the plurality of binding sites of the target protein; and
Identifying amino acid residues that contribute to selectivity contained in the identified binding site;
Including,
A computer program stored on a computer-readable storage medium. As a computing device,
at least one processor; and
contains memory,
The at least one processor,
Determining a plurality of binding sites of the homologous protein corresponding to a plurality of binding sites of the target protein,
Comparing a plurality of binding sites of the target protein and a plurality of binding sites of the homologous protein,
Based on the comparison, among the plurality of binding sites of the target protein, identify a binding site that has selectivity in relationship with the plurality of binding sites of the homologous protein, and
Identifying amino acid residues that contribute to selectivity contained in the identified binding site,
Computing device.

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