Evaluation system, evaluation method and program

Abstract

To provide a technology that mitigates a burden required for developing medicine that inhibits generation of a protein aggregate.SOLUTION: There is provided an evaluation system that includes an estimation unit for estimating, on the basis of first droplet information that indicates a size of a collective entity of protein in an evaluation object that includes a plurality of the collective entities of protein and second droplet information that indicates whether a protein aggregate nucleus is generated in each of the collective entities, a frequency J of generation of the protein aggregate nucleus in unit volume and / or a unit interfacial area of the collective entities and a unit time, using a nucleus generation probability equation that indicates a probability Pτ of one or more of the protein aggregate nucleus being generated in a prescribed space before a time τ.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an evaluation system, an evaluation method, and a program. [Background technology]

[0002] Protein aggregates, including amyloid fibrils, are known to be causative agents of various diseases, such as Alzheimer's disease, amyotrophic lateral sclerosis, and prion diseases. Therefore, there is a need to develop drugs that can inhibit the formation of protein aggregates. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] W. Michael Babinchak, et.al., “The role of liquid-liquid phase separation in aggregation of the TDP-43 low-complexity domain”, J. Biol. Chem. (2019) 294(16) 6306-6317 Summary of the Invention [Problem to be solved by the invention]

[0004] A model has been proposed in which nucleation is the rate-limiting step in the formation process of protein aggregates, such as amyloid fibrils. Although the mechanism of protein aggregate nucleation remains largely unknown, it has recently been suggested that protein aggregate nuclei are generated within transient aggregates formed by biological liquid-liquid phase separation within cells. Hereinafter, these aggregates will be referred to as droplets.

[0005] Therefore, a technique for quantitatively evaluating protein aggregate nucleation from droplets is important for developing drugs that inhibit the formation of protein aggregates. However, until now, there has been no technique for quantitatively evaluating the ease of protein aggregate nucleation from droplets. As a result, it has not been possible to obtain quantitative and accurate values ​​for the efficacy of drug candidate substances that inhibit the formation of protein aggregates, making large-scale screening and drug discovery using artificial intelligence and machine learning difficult. For example, Non-Patent Document 1 fails to quantitatively evaluate the nucleation of protein aggregates in droplets.

[0006] In view of the above circumstances, an object of the present invention is to provide a technology that reduces the burden required for developing drugs that suppress the formation of protein aggregates. [Means for solving the problem]

[0007] One aspect of the present invention is an evaluation system comprising an estimation unit that estimates either one of the unit volume or unit interfacial area of ​​an aggregate and the frequency J of generation of a protein aggregate nucleus per unit time using a nucleation probability equation that represents the probability Pτ that one or more protein aggregate nuclei will be generated in a specified space before time τ, based on first droplet information that indicates the size of the aggregates in an evaluation object containing multiple protein aggregates and second droplet information that indicates whether a protein aggregate nucleus has been generated in each of the aggregates.

[0008] One aspect of the present invention is an evaluation method comprising: an estimation step of estimating either one of the unit volume or unit interfacial area of ​​the aggregate and the frequency J of generation of the protein aggregate nuclei per unit time using a nucleation probability equation that represents the probability Pτ that one or more protein aggregate nuclei will be generated in a specified space before time τ, based on first droplet information that indicates the size of the aggregates in an evaluation object containing multiple protein aggregates, and second droplet information that indicates whether or not a protein aggregate nucleus has been generated in each of the aggregates.

[0009] One aspect of the present invention is a program for causing a computer to function as the above-described evaluation system. [Effects of the Invention]

[0010] The present invention makes it possible to provide a technology that reduces the burden required for developing drugs that suppress the formation of protein aggregates. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is an explanatory diagram illustrating an evaluation system according to an embodiment. [Figure 2] 10A and 10B are diagrams showing examples of photographing results of the photographing device. [Figure 3] FIG. 2 is an explanatory diagram illustrating an image captured by the imaging device. [Figure 4] 10A and 10B are diagrams showing an example of experimental results illustrating changes over time in the fluorescence intensity of droplets in an embodiment. [Figure 5] 10A and 10B are diagrams showing an example of experimental results showing how the ratio of droplets changes over time in the embodiment. [Figure 6] FIG. 4 is a diagram showing an example of non-normalized nucleation rate information in the embodiment. [Figure 7] FIG. 4 is a diagram showing an example of normalized nucleation rate information in the embodiment. [Figure 8] FIG. 2 is a diagram illustrating an example of a hardware configuration of an evaluation apparatus according to an embodiment. [Figure 9] FIG. 2 is a diagram showing an example of the configuration of a control unit in the embodiment. [Figure 10] 1 is a flowchart showing an example of a flow of processing executed by an evaluation device in an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] (Embodiment) FIG. 1 is an explanatory diagram illustrating an evaluation system 100 according to an embodiment. The evaluation system 100 estimates the frequency J of protein aggregate nuclei formation in an evaluation object 9. In this way, the evaluation system 100 reduces the burden required for developing drugs that inhibit the formation of protein aggregates. The evaluation object 9 includes a plurality of droplets. Note that the droplets may be any type of protein aggregate, and may be, for example, a liquid or gel-like protein aggregate.

[0013] For simplicity of explanation, the evaluation system 100 will be described below using an example in which the protein is an amyloid precursor protein. That is, for simplicity of explanation, the evaluation system 100 will be described below using an example in which the droplets in the evaluation object 9 are composed of an amyloid precursor protein.

[0014] When the protein is an amyloid precursor protein, the protein aggregate nucleus is an amyloid nucleus. That is, the amyloid nucleus is an example of a protein aggregate nucleus. When the protein is an amyloid precursor protein, the protein aggregate is an amyloid fiber. That is, the amyloid fiber is an example of a protein aggregate.

[0015] The evaluation object 9 in FIG. 1 is an example of a sample in which amyloid nuclei are generated, and is an example of an evaluation object of the evaluation system 100.

[0016] The evaluation system 100 includes an evaluation device 1 and a photographing device 2. The evaluation device 1 estimates the frequency of amyloid nuclei generation occurring in an evaluation subject 9 based on the photographing results of the photographing device 2.

[0017] The photographing device 2 photographs the evaluation object 9. The photographing device 2 is, for example, a camera. The photographing device 2 outputs image data of the photographed image to the evaluation device 1. The photographing result of the photographing device 2 is specifically the image data of the image obtained by the photographing device 2.

[0018] Fig. 2 is a diagram showing an example of the image captured by the image capture device 2. More specifically, Fig. 2 shows an example of the experimental results obtained by fluorescent observation of amyloid formation in droplets in agarose gel using a confocal microscope.

[0019] The conditions that the solution to be photographed met when the image in Figure 2 was taken are explained below. The solution to be photographed contained 39 μM of Sup35-NM domain, 60 mM of guanidine hydrochloride, 0.1 mM of Tris buffer (pH 8), 39 μM of , 10% of polyethylene glycol with a molecular weight of 2000, and 20 μM of thioflavin T. Furthermore, the solution to be photographed contained 3.8 mM of phosphate buffer (pH 6) and 2% of ultra-low melting point agarose. The solution to be photographed is an example of evaluation object 9.

[0020] The solution to be photographed contains thioflavin T, which emits fluorescence when it binds to the β-sheet structure of the protein. Amyloid has many cross-β structures in which β-sheet structures overlap, so the fluorescence of thioflavin T is more intense from droplets containing amyloid. The photographing device 2 photographs this fluorescence. In this way, photographing with the photographing device 2 obtains an image of the droplets containing amyloid in the evaluation object 9.

[0021] The images in Figure 2 were taken every 6 to 12 hours under the condition of Δz = 3 μm. Note that Figure 2 shows images taken 1 hour after droplet formation of the Sup35-NM domain.

[0022] FIG. 3 is an explanatory diagram illustrating an image captured by the imaging device 2. During imaging by the imaging device 2, amyloid nuclei are generated in the evaluation object 9. Image G101 in FIG. 3 illustrates the state of amyloid nuclei generation. Image G102 captured in image G101 is a diagram illustrating the state of the evaluation object 9 at the time origin (i.e., time t=0). Multiple droplets are formed on the evaluation object 9. In other words, the evaluation object 9 contains multiple droplets.

[0023] The Sup35-NM domain that constitutes the droplet also has a β-sheet structure within its molecule, and therefore emits fluorescence in a solution containing thioflavin T. Therefore, an image showing the droplet can be obtained by photographing with the imaging device 2.

[0024] As time passes from time t=0, amyloid nuclei begin to form within some of the droplets. Image G103 shows the state in which amyloid nuclei begin to form. Once amyloid nuclei are formed, amyloid fibers rapidly grow from the nuclei. Image G104 shows the state in which amyloid fibers have grown within the droplets. In this example, the agarose gel prevents droplets from coalescing, and the coefficient of variation in the volume of the droplets over time from 1 hour to 28 hours after preparation of the evaluation target was less than 50%.

[0025] The time from the formation of amyloid nuclei until the amyloid fibrils grow and the intensity of the Thioflavin T-derived fluorescence emitted from the droplets increases significantly is negligibly short compared to the time it takes for amyloid nuclei to form. Note that a single amyloid nucleus is not necessarily formed in a single droplet; multiple nuclei may also be formed.

[0026] The image capturing device 2 captures the fluorescence of the amyloid nuclei. The larger the amyloid nuclei, the stronger the intensity of the fluorescence of the amyloid nuclei. Therefore, when images of the evaluation object 9 captured by the image capturing device 2 at predetermined time intervals are arranged in chronological order, the fluorescence intensity of the droplets in which amyloid nuclei have formed increases over time. In this way, the images captured by the image capturing device 2 show the droplets in which amyloid nuclei have formed and the change in the size of the amyloid nuclei over time.

[0027] As mentioned above, the Sup35-NM domain that constitutes the droplets contains a β-sheet structure within the molecule, and therefore emits fluorescence in a solution containing thioflavin T even if amyloid nuclei have not formed. If amyloid nuclei have not formed, the intensity of the fluorescence does not change over time. Therefore, by using the images captured by the imaging device 2, droplets can be detected even if amyloid nuclei have not formed, and furthermore, it is possible to determine whether amyloid has formed in the droplets or not based on the change in fluorescence intensity over time.

[0028] Furthermore, since the droplets emit fluorescence in a solution containing Thioflavin T, the size of the fluorescent substance in the image captured by the image capture device 2 represents the size of the droplets. Therefore, it is possible to obtain information indicating the size of each droplet from the image captured by the image capture device 2.

[0029] In this way, the image data of the image captured by the photographing device 2 includes first droplet information, which is information indicating the size of the droplets in the evaluation object 9, which contains multiple droplets, and second droplet information, which is information indicating whether or not amyloid nuclei have been generated for each droplet.

[0030] When the evaluation device 1 acquires image data of an image captured by the imaging device 2, it performs image analysis on the image represented by the image data to acquire first droplet information and second droplet information. The first droplet information is obtained, for example, by edge detection. The second droplet information is obtained, for example, by processing to detect droplets whose brightness is equal to or greater than a predetermined value based on the first droplet information obtained as a result of edge detection.

[0031] FIG. 4 shows an example of experimental results illustrating the change in fluorescence intensity of a droplet over time in an embodiment. The horizontal axis of FIG. 4 represents the elapsed time since imaging began. The vertical axis of the graph in FIG. 4 represents the amount obtained by dividing the average fluorescence intensity in the droplet by the droplet diameter. This value is the amount of substance proportional to the molecular concentration in the droplet. Since no amyloid nuclei have been generated at t=0, the amount of substance is proportional to the concentration of Sup35-NM domain in the droplet, and is a constant value regardless of the droplet size. Hereinafter, the amount obtained by dividing the average fluorescence intensity in the droplet by the droplet diameter will be referred to as the normalized average intensity.

[0032] Each graph in Figure 4 shows the change over time in the normalized average intensity of one droplet. Figure 4 shows that abrupt changes in the normalized average intensity occur in some of the graphs. The droplets that experience the abrupt change are those in which amyloid nuclei have formed. The timing at which the abrupt change in the normalized average intensity occurs is the timing at which amyloid nuclei formation begins. In the example of Figure 4, droplets in which amyloid nuclei have not formed are those with a normalized average intensity of less than 190.

[0033] The results shown in Fig. 4 were obtained by the evaluation device 1 executing an average intensity information acquisition process. The average intensity information acquisition process is a process for acquiring information indicating the change over time in the normalized average intensity of each droplet captured in an image acquired by the photographing device 2 (hereinafter referred to as "normalized average intensity information") based on the image. An example of normalized average intensity information is the collection of multiple graphs shown in Fig. 4.

[0034] The second droplet information described above can be obtained, for example, by determining, based on the normalized average intensity information, that droplets having a normalized average intensity of 190 or greater are droplets in which amyloid has been generated. More specifically, the second droplet information can be obtained, for example, by determining whether amyloid nuclei have been generated for each droplet at each time.

[0035] In this way, the second droplet information can be obtained, for example, by executing a process at each time to determine droplets whose normalized average intensity is equal to or greater than a predetermined threshold based on normalized average intensity information obtained from image data of the image captured by the imaging device 2. Hereinafter, the process of obtaining second droplet information by executing a process at each time to determine droplets whose normalized average intensity is equal to or greater than a predetermined threshold based on the normalized average intensity information will be referred to as a second droplet extraction process. Furthermore, since the second droplet information is information extracted from the image data of the image captured by the imaging device 2, the image captured by the imaging device 2 includes the second droplet information.

[0036] The average intensity information acquisition process is obtained as a result of estimating the fluorescence intensity by image analysis of the image captured by the imaging device 2. Therefore, the second droplet information obtained using the result of the average intensity information acquisition process is information obtained by image analysis of the image captured by the imaging device 2.

[0037] FIG. 5 is a diagram showing an example of experimental results showing the change over time in the droplet ratio in an embodiment. More specifically, FIG. 5 is an example of results obtained by the evaluation device 1 based on the experimental results of FIG. 4. The horizontal axis of FIG. 5 indicates the time elapsed since imaging began. The vertical axis of FIG. 5 indicates the ratio of droplets in which one or more amyloid nuclei were generated. FIG. 5 shows the ratio of droplets in which one or more amyloid nuclei were generated for each category resulting from classification of droplets according to size. In the example of FIG. 5, the classification was performed, for example, according to average diameter.

[0038] The evaluation device 1 executes a nucleation rate acquisition process. The nucleation rate acquisition process is a process for acquiring nucleation rate information based on second droplet information obtained based on normalized average intensity information, etc. The nucleation rate information is information indicating the time change in the rate of droplets in which one or more amyloid nuclei have been generated, for each classification resulting from classification according to droplet size, as shown in FIG. 5, etc. The rate is the rate for each classification resulting from classification according to size of droplets indicated by the normalized average intensity information, and is the rate relative to the total number of droplets belonging to each classification in the images acquired at each time.

[0039] The determination of whether or not a droplet has generated one or more amyloid nuclei is based on whether or not there is a timing when the normalized average intensity is equal to or greater than a predetermined value. More specifically, among the graphs shown by the normalized average intensity information, droplets corresponding to a graph having a timing when the normalized average intensity is equal to or greater than a predetermined value are determined to have generated amyloid nuclei. On the other hand, among the graphs shown by the normalized average intensity information, droplets corresponding to a graph not having a timing when the normalized average intensity is equal to or greater than a predetermined value are determined to have not generated amyloid nuclei. The predetermined value is, for example, 190 in the example of FIG. 4.

[0040] In the example of Fig. 5, it is possible to read position information from the image. Therefore, in an analysis using image data of the image in the example of Fig. 5, each droplet may be identified using the position information in the image and the temporal change of each droplet may be analyzed. However, since the normalized average intensity is the same value regardless of the droplet, identifying droplets using position information is not necessarily a necessary process.

[0041] The evaluation device 1 performs a fitting process. The fitting process calculates the probability P that one or more amyloid nuclei will be generated before time τ in a predetermined space for each graph indicated by the nucleation rate information. τ This is a process of fitting using an equation (hereinafter referred to as the "nucleation probability equation") that expresses the following. The predetermined space may be, for example, a space of volume V, or a two-dimensional space (i.e., a two-dimensional plane) of area S. Each graph indicated by the nucleation rate information corresponds one-to-one to one of the classifications obtained by classifying the droplets indicated by the normalized average intensity information according to size. When the predetermined space is a space of volume V, the nucleation probability equation is expressed, for example, by the following equation (1).

[0042]

number

[0043] J is a quantity that indicates the nucleation rate, that is, the frequency of amyloid nuclei generation per unit volume or unit area and unit time. More specifically, if the specified space is the space of a droplet, the quantity J represents the frequency of amyloid nuclei generation per unit volume of the droplet and unit time, and if the specified space is the interface of the droplet, the quantity J represents the frequency of amyloid nuclei generation per unit interfacial area of ​​the droplet and unit time. Note that the unit interfacial area means the unit area of ​​the interface of the droplet. The quantity J can be expressed, for example, by the following formula (2):

[0044]

number

[0045] η indicates the molecular number density. ΔG indicates the free energy required to form a nucleus of critical size. k is Boltzmann's constant. T indicates absolute temperature. h is Planck's constant. Δg indicates the change in free energy when one molecule is added to an amyloid nucleus of critical size.

[0046] Equation (1) is an equation derived based on experimental results showing that the frequency of amyloid nuclei generation is low. Because the frequency of amyloid nuclei generation is low, the generation of amyloid nuclei follows a Poisson process. Therefore, the probability P of m amyloid nuclei being generated in a volume V within a time t according to the Poisson process is m is expressed, for example, by the following equation (3).

[0047]

number

[0048] The cumulative distribution function of equation (3) is equation (1).

[0049] By performing the fitting process, the evaluation device 1 obtains information indicating the relationship between the droplet size and the amount VJ (hereinafter referred to as "non-normalized nucleation rate information"). Note that the amount VJ means the product of the volume V and the amount J.

[0050] FIG. 6 is a diagram showing an example of non-normalized nucleation rate information in an embodiment. The horizontal axis of FIG. 6 represents droplet size. In the example of FIG. 6, the size is the average diameter. The vertical axis of FIG. 6 represents the quantity VJ. The results in FIG. 6 also show error bars. FIG. 6 shows that the value of the quantity VJ increases as the average diameter of the droplets increases.

[0051] The evaluation device 1 acquires information indicating the relationship between droplet size and quantity J (hereinafter referred to as "normalized nucleation rate information") based on the non-normalized nucleation rate information. That is, the evaluation device 1 acquires quantity J based on the non-normalized nucleation rate information. In this way, the evaluation device 1 estimates quantity J using a nucleation probability equation based on the first droplet information and the second droplet information. Estimating quantity J is equivalent to evaluating quantity J.

[0052] FIG. 7 is a diagram showing an example of normalized nucleation rate information in an embodiment. The horizontal axis of FIG. 7 represents droplet size. In the example of FIG. 7, the size is the average diameter. The vertical axis of FIG. 7 represents the quantity J. The results of FIG. 7 also show error bars. The results of FIG. 7 show that the frequency of amyloid nuclei generation per unit volume and unit time is approximately the same regardless of droplet size.

[0053] It is known that the frequency of amyloid nuclei generation per unit volume and unit time does not depend on the size of the droplets when the droplets are in the same environment. Therefore, the results in Figure 7 show qualitative properties that would be expected to emerge if the phenomenon of amyloid nuclei generation could be quantitatively evaluated. Therefore, the evaluation device 1 can quantitatively evaluate the phenomenon of amyloid nuclei generation.

[0054] <An example of how to create evaluation target 9> The evaluation target 9 is created, for example, by the following method.

[0055] 50 μL of 4% ultra-low melting point agarose gel solution melted at 70°C, 33.3 μL of an aqueous solution containing 30% 2000 molecular weight polyethylene glycol and adjusted to pH 6 with phosphate buffer, 2 μL of 1 mM thioflavin T solution, 3.8 μL of 76 mM phosphate buffer (pH 6), and 9.9 μL of water were mixed in a microtube. 1 μL of an aqueous solution containing 3.9 mM Sup35-NM domain, 6 M guanidine hydrochloride, and 10 mM Tris buffer (pH 8) was added, mixed, and then allowed to stand in a refrigerator for 5 minutes.

[0056] <Explanation of an example of hardware configuration> 8 is a diagram showing an example of the hardware configuration of the evaluation device 1 in the embodiment. The evaluation device 1 includes a control unit 11 having a processor 91 such as a CPU (Central Processing Unit) and a memory 92 connected by a bus, and executes a program. By executing the program, the evaluation device 1 functions as a device including the control unit 11, an input unit 12, a communication unit 13, a storage unit 14, and an output unit 15.

[0057] More specifically, the processor 91 reads out a program stored in the storage unit 14 and stores the read out program in the memory 92. When the processor 91 executes the program stored in the memory 92, the evaluation device 1 functions as a device including a control unit 11, an input unit 12, a communication unit 13, a storage unit 14, and an output unit 15.

[0058] The control unit 11 controls the operation of various functional units included in the evaluation device 1. The control unit 11 executes, for example, an average intensity information acquisition process and a second droplet extraction process. Second droplet information is obtained by executing the average intensity information acquisition process and the second droplet extraction process. The control unit 11 executes, for example, an estimation process. The estimation process is a process that uses a nucleation probability equation based on the first droplet information and the second droplet information to estimate the frequency J of amyloid nuclei generation per unit time and either one of the unit volume or unit interfacial area of ​​the droplet. Therefore, the estimation process includes a nucleation rate acquisition process, a fitting process, and a process of acquiring normalized nucleation rate information based on non-normalized nucleation rate information.

[0059] The control unit 11 controls, for example, the operation of the output unit 15. The control unit 11 records, for example, various pieces of information generated by the execution of the average intensity information acquisition process in the storage unit 14. The control unit 11 records, for example, various pieces of information generated by the execution of the estimation process in the storage unit 14.

[0060] The input unit 12 includes input devices such as a mouse, a keyboard, and a touch panel. The input unit 12 may be configured as an interface that connects these input devices to the evaluation device 1. The input unit 12 accepts input of various information to the evaluation device 1.

[0061] The communication unit 13 includes a communication interface for connecting the evaluation device 1 to an external device. The communication unit 13 communicates with the external device via wired or wireless communication. The external device is, for example, a device that transmits information including the first droplet information and the second droplet information, such as the imaging device 2. The communication unit 13 acquires information including the first droplet information and the second droplet information by communicating with the device that transmits the information including the first droplet information and the second droplet information.

[0062] Image data of an image captured by the imaging device 2 is an example of information including the first droplet information and the second droplet information. Note that the information including the first droplet information and the second droplet information does not necessarily have to be input via the communication unit 13, but may be input to the input unit 12.

[0063] The memory unit 14 is configured using a computer-readable storage medium device such as a magnetic hard disk drive or a semiconductor storage device. The memory unit 14 stores various information related to the evaluation device 1. The memory unit 14 stores information input via, for example, the input unit 12 or the communication unit 13. The memory unit 14 stores, for example, the results of the execution of an average intensity information acquisition process. The memory unit 14 stores, for example, the results of the execution of a second droplet extraction process. The memory unit 14 stores, for example, the results of the execution of an estimation process.

[0064] The output unit 15 outputs various types of information. The output unit 15 includes a display device such as a CRT (Cathode Ray Tube) display, a liquid crystal display, or an organic EL (Electro-Luminescence) display. The output unit 15 may be configured as an interface that connects these display devices to the evaluation device 1. The output unit 15 outputs, for example, information input to the input unit 12. The output unit 15 may display, for example, the execution result of an average intensity information acquisition process. The output unit 15 may display, for example, the execution result of an estimation process.

[0065] 9 is a diagram showing an example of the configuration of the control unit 11 in this embodiment. The control unit 11 includes a droplet information acquisition unit 110, an estimation unit 120, a memory control unit 130, a communication control unit 140, and an output control unit 150.

[0066] The droplet information acquisition unit 110 acquires first droplet information and second droplet information. For example, when information including the first droplet information and second droplet information is input to the communication unit 13, the droplet information acquisition unit 110 acquires the first droplet information and the second droplet information from the information including the first droplet information and the second droplet information acquired by the communication unit 13. The second droplet information is acquired, for example, by executing an average intensity information acquisition process and a second droplet extraction process. For example, when the first droplet information and the second droplet information are input to the communication unit 13, the droplet information acquisition unit 110 acquires the first droplet information and the second droplet information by, for example, acquiring the communication unit 13.

[0067] The estimation unit 120 executes the estimation process.

[0068] The storage control unit 130 records various information in the storage unit 14. The communication control unit 140 controls the operation of the communication unit 13. The output control unit 150 controls the operation of the output unit 15.

[0069] 10 is a flowchart showing an example of the flow of processing executed by the evaluation device 1 in this embodiment. The droplet information acquisition unit 110 acquires first droplet information and second droplet information (step S101). The estimation unit 120 executes an estimation process based on the first droplet information and second droplet information obtained in step S101, thereby estimating the frequency J of amyloid nuclei generation per unit time and either unit volume or unit interfacial area of ​​the droplet (step S102). The output control unit 150 controls the operation of the output unit 15 to output the obtained frequency J (step S103).

[0070] The evaluation device 1 in this embodiment configured as described above executes the estimation process to evaluate the frequency J. This reduces the burden required for developing a drug that suppresses the formation of amyloid fibers.

[0071] Furthermore, the evaluation system 100 in this embodiment configured as described above includes the evaluation device 1. This can reduce the burden required for developing drugs that suppress the formation of amyloid fibrils.

[0072] (Variation) In the explanation so far, the evaluation system 100 derives the amount J for droplets dispersed in agarose gel. That is, in the explanation so far, agarose was used as the dispersion medium for dispersing the droplets in the evaluation object 9. However, in the evaluation object 9, it is not necessary to disperse the droplets using agarose gel as the dispersion medium. The evaluation object 9 may be anything as long as it contains droplets, and may be, for example, a gel in which droplets are dispersed, a solution in which droplets are dispersed, or cells having droplets dispersed therein.

[0073] The evaluation system 100 may be implemented using a plurality of information processing devices communicably connected via a network. In this case, each functional unit of the evaluation system 100 may be distributed and implemented across the plurality of information processing devices. The evaluation device 1 may be implemented using a plurality of information processing devices communicably connected via a network. In this case, each functional unit of the evaluation device 1 may be distributed and implemented across the plurality of information processing devices.

[0074] If the predetermined space is a two-dimensional space with an area S, S is used instead of V in the above formula (1). That is, if the predetermined space is a two-dimensional space with an area S, for example, the following formula (4) is used as the nucleation probability equation instead of the above formula (1). Note that the two-dimensional space with an area S is, for example, an interface as described above. Nucleation that occurs at an interface is called heterogeneous nucleation.

[0075]

number

[0076] Note that image data of the image captured by the imaging device 2 may include information including the positions of droplets in the evaluation target 9, which contains multiple droplets. Therefore, the first droplet information may include information indicating the positions of droplets in the evaluation target 9, which contains multiple droplets. Therefore, the estimation unit 120 included in the evaluation system 100 calculates the probability P that one or more protein aggregate nuclei will be generated in a predetermined space before time τ, based on the first droplet information indicating the positions and sizes of droplets in the evaluation target, which contains multiple droplets that are protein aggregates, and the second droplet information indicating whether a protein aggregate nucleus has been generated in each droplet. τ The frequency J of protein aggregate nuclei formation per unit volume or unit area and unit time may be estimated using a nucleation probability equation, which is an equation showing:

[0077] The amyloid precursor protein may be, for example, any of the proteins listed below. That is, the amyloid precursor protein may be, for example, Aβ42, α-synuclein, tau, huntingtin, atrophin 1, ataxin (1,2,3,6,7,8,12,17), amylin, prion protein, (pro)calcitonin, atrial natriuretic factor, apolipoprotein AI, apolipoprotein AII, apolipoprotein AIV, serum amyloid, medin, (apo)serum AA, prolactin, transthyretin, lysozyme, β-2 microglobulin, fibrinogen α chain, gelsolin, ketone ... The protein may be any of latopterin, beta-amyloid, cystatin, ABriPP immunoglobulin light chain AL, immunoglobulin heavy chain, S-IBM, islet amyloid polypeptide, insulin, lactadherin, keratoepithelium, lactoferrin, tbn, leukocyte chemoattractant-2, AbriPP, ADanPP, pulmonary surfactant protein, galectin 7, corneodesmosin, lactadherin, keratoepithelium, odontogenic ameloblast-associated (ODAM) protein, semenogelin 1, and enfuvirtide.

[0078] To further reduce the error in the estimated value of the quantity J, it is preferable that the droplet volume change over time be small. Therefore, it is desirable that the rate of change per unit time of the average volume of droplets in which one or more protein aggregate nuclei have not formed be, for example, -99.99999% to 10,000,000%. It is more desirable that the rate of change per unit time of the average volume of droplets in which one or more protein aggregate nuclei have not formed be, for example, -99% to 10,000%. Therefore, it is desirable that the average droplet volume at time τ only fluctuates between -99.99999% and 10,000,000% of the average volume at the start of observation, time τ=0.

[0079] Amyloid fibrils are an example of protein aggregates.

[0080] All or part of the functions of the evaluation system 100 and the evaluation device 1 may be realized using hardware such as an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array). The program may be recorded on a computer-readable recording medium. Examples of computer-readable recording media include portable media such as flexible disks, magneto-optical disks, ROMs, and CD-ROMs, and storage devices such as hard disks built into computer systems. The program may be transmitted via a telecommunications line.

[0081] Although an embodiment of the present invention has been described in detail above with reference to the drawings, the specific configuration is not limited to this embodiment, and includes designs within the scope of the gist of the present invention. [Industrial Applicability]

[0082] According to the evaluation system, evaluation method, and program of the present invention, drug responses can be accurately evaluated at the screening stage in the development of drugs that inhibit the formation of protein aggregates such as amyloid fibrils. [Explanation of symbols]

[0083] 100...Evaluation system, 1...Evaluation device, 2...Photographing device, 11...Control unit, 12...Input unit, 13...Communication unit, 14...Memory unit, 15...Output unit, 110...Droplet information acquisition unit, 120...Estimation unit, 130...Memory control unit, 140...Communication control unit, 150...Output control unit, 91...Processor, 92...Memory

Claims

1. an estimation unit that acquires nucleation rate information based on a time series of image data including first droplet information indicating the size of a plurality of protein aggregates in an evaluation object containing the aggregates and second droplet information indicating whether a protein aggregate nucleus has been generated in each of the aggregates, and estimates the frequency J by fitting a nucleation probability equation to a graph indicated by the acquired nucleation rate information; Equipped with the nucleation rate information is information indicating a change over time in the rate of the assemblies in which protein aggregate nuclei have been generated, for each classification of the results of classifying the assemblies according to the size of the assemblies; The nucleation probability equation is the probability P τ is an equation showing The frequency J is the frequency of generation of the protein aggregate nuclei per unit volume or unit interfacial area of ​​the assembly and per unit time. Rating system.

2. the first droplet information and the second droplet information are obtained as a result of image analysis of an image obtained as a result of fluorescence observation of the assembly to be evaluated; The evaluation system of claim 1 .

3. an imaging device for performing the fluorescence observation; The evaluation system of claim 2 further comprising:

4. The nucleation probability equation is the probability P that m protein aggregate nuclei are generated in a given space within a time t according to a Poisson process. ｍ is the cumulative distribution function of The evaluation system according to any one of claims 1 to 3.

5. The predetermined space is a space of volume V, and the probability P τ The evaluation system according to claim 1 , wherein is expressed by the following formula (1): [Equation 1]

6. The predetermined space is a two-dimensional space with an area S, and the probability P τ is expressed by the following formula (2): The evaluation system according to any one of claims 1 to 4. [Equation 2]

7. The frequency J is expressed by the following formula (3), where η is the molecular number density, ΔG is the free energy required for nucleation of a critical size, k is the Boltzmann constant, T is the absolute temperature, h is the Planck constant, and Δg is the amount of change in free energy when one molecule is added to an amyloid nucleus of a critical size: The evaluation system according to any one of claims 1 to 6. [Equation 3]

8. In the evaluation object, the rate of change per unit time of the average volume of the aggregate is −99.99999% to 10,000,000%. The evaluation system according to any one of claims 1 to 7.

9. an estimation step of acquiring nucleation rate information based on a time series of image data including first droplet information indicating the size of a plurality of protein aggregates in an evaluation object including the aggregates and second droplet information indicating whether a protein aggregate nucleus has been generated in each of the aggregates, and estimating the frequency J by fitting a nucleation probability equation to a graph indicated by the acquired nucleation rate information; and the nucleation rate information is information indicating a change over time in the rate of the assemblies in which protein aggregate nuclei have been generated, for each classification of the results of classifying the assemblies according to the size of the assemblies; The nucleation probability equation is the probability P τ is an equation showing The frequency J is the frequency of generation of the protein aggregate nuclei per unit volume or unit interfacial area of ​​the assembly and per unit time. Evaluation method.

10. A program for causing a computer to function as the evaluation system according to any one of claims 1 to 8.

Images