Methods for quantifying the interactions between components of biomolecular systems

Abstract

According to a first aspect of this disclosure, a computer-based method is provided for quantifying the interactions between components of a biomolecular system exhibiting phase separation into two phases, first and second phases, under specific conditions, the method comprising receiving input data, the input data comprising data describing a biomolecular system under a plurality of different conditions that differ with respect to at least two parameters, the parameters comprising the concentration of at least one constituent biomolecule in the biomolecular system, and for each of the different conditions, the input data comprising data describing at least two parameters and the concentration of at least one component of at least one component in the first phase and / or second phase, and based on the input data, the first component in the first phase The method comprises determining a concentration boundary between a first dataset substantially containing data where the concentration of a component is above a threshold and a second dataset substantially containing data where the concentration of a first component in a first phase is below a threshold; determining a gradient of the concentration boundary with respect to the concentration of the first component with respect to at least one of at least two other parameters, wherein the gradient corresponds to a portion of the concentration boundary corresponding to a subset of the first dataset and the second dataset that substantially contains data for conditions in which both the first and second phases exist; and determining stoichiometric parameters that quantify the interactions between components of a biomolecular system based on the gradient.

Description

[Technical Field]

[0001] The research leading to this invention was funded by the European Research Council under the European Union's Seventh Framework Programme (FP7 / 2007-2013) / ERC Grant Agreement No. 337969.

[0002] This disclosure relates to a method for quantifying the interactions between components of a biomolecular system that exhibits phase separation into two phases, a first and a second phase, under specific conditions. In one example, the first and second phases are a dilute phase and a concentrated phase, respectively. [Background technology]

[0003] Biomolecular phase separation (e.g., liquid-liquid phase separation (LLPS)) has emerged as a crucial process in cell physiology. Numerous examples have been found where the formation of liquid-like, protein and / or RNA-rich droplets performs physiological functions in cells or is involved in neurodegenerative diseases.

[0004] Liquid-liquid phase separation (LLPS), the spontaneous demixing of high-molecular-weight solutions (e.g., solutions of high-molecular-weight polymers such as proteins, peptides, and nucleic acids) into coexisting concentrated and dilute phases, has become a fascinating subject due to the newly realized importance of this process in regulating biological functions. For example, phase-separated protein (e.g., biomolecular protein) condensates act as microreactors by compartmentalizing and organizing cellular space and localizing biomolecules. Condensates are crucial for a variety of fundamental biochemical processes, including the regulation of transcription and translation and the control of cellular stress responses. They are also significantly involved in protein misfolding diseases, including motor neuron diseases and cancer pathogenesis, making them attractive targets for therapeutic interventions.

[0005] Experimentally, phase-separated biomolecules (e.g., proteins) can typically be tagged with fluorescent markers (e.g., dyes), and their concentrations can be measured based on this. This provides a one-dimensional picture of the phase-separation behavior of the system. However, this is an oversimplification because more solute dimensions (possible examples including pH, ionic strength, and buffer) are ignored, for the perfectly reasonable excuse that it is virtually impossible to determine all of their concentrations inside and outside the condensate. As a result, important mechanistic information may be lost. The multidimensional properties of phase-separated systems make it difficult to construct a faithful representation of the system's phase-separation behavior from experimental data.

[0006] Currently, experimental data obtained by varying only a few dimensions of the phase separation system, e.g., two or three dimensions, are used to estimate how phase separation behavior, such as the position of the phase boundary, is affected by specific changes in system conditions. However, obtaining information about biomolecular interactions that affect phase separation behavior has been exceptionally difficult. [Overview of the Initiative] [Problems that the invention aims to solve]

[0007] The purpose of this disclosure is to address, at least partially, some of the issues described above. [Means for solving the problem]

[0008] According to a first aspect of this disclosure, a computer-based method is provided for quantifying the interactions between components of a biomolecular system exhibiting phase separation into two phases, first and second phases, under specific conditions, the method comprising receiving input data, the input data comprising data describing a biomolecular system under a plurality of different conditions that differ with respect to at least two parameters, the parameters comprising the concentration of at least one constituent biomolecule in the biomolecular system, and for each of the different conditions, the input data comprising data describing at least two parameters and the concentration of at least one component of at least one component in the first phase and / or second phase, and based on the input data, the first component in the first phase The method comprises determining a concentration boundary between a first dataset substantially containing data where the concentration of a component is above a threshold and a second dataset substantially containing data where the concentration of a first component in a first phase is below a threshold; determining a gradient of the concentration boundary with respect to the concentration of the first component with respect to at least one of at least two other parameters, wherein the gradient corresponds to a portion of the concentration boundary corresponding to a subset of the first dataset and the second dataset that substantially contains data for conditions in which both the first and second phases exist; and determining stoichiometric parameters that quantify the interactions between components of a biomolecular system based on the gradient.

[0009] Optionally, input data is obtained by acquiring biomolecular system preparations under multiple conditions, and determining data for each biomolecular system preparation under each of the multiple conditions regarding its respective parameters and the concentration of at least one component among the at least one component in the first and / or second phases.

[0010] A second aspect of this disclosure provides a method for quantifying the interactions between components of a biomolecular system exhibiting phase separation into two phases, first and second phases, under specific conditions, the method comprising obtaining preparations of the biomolecular system under a plurality of different conditions that differ with respect to at least two parameters, the parameters including the concentration of at least one constituent biomolecule in the biomolecular system, The method involves determining data for each of the biomolecular system preparations under multiple conditions, relating to at least two parameters and the concentration of at least one component among at least one component in the first and / or second phases; preparing input data, the input data including data describing the biomolecular system under multiple conditions, and for each of the different conditions, the input data including data describing at least two parameters and the concentration of at least one component among at least one component in the first and / or second phases; and a computer receiving the input data and, based on the input data, determining data for the conditions in which the concentration of the first component in the first phase exceeds a threshold. The method includes the steps of determining a concentration boundary between a first dataset substantially containing data and a second dataset substantially containing data for conditions in which the concentration of a first component in a first phase is below a threshold, determining a gradient of the concentration boundary with respect to the concentration of the first component with respect to at least one of at least two other parameters, wherein the gradient corresponds to a portion of the concentration boundary corresponding to a subset of the first dataset and the second dataset that substantially contains data for conditions in which both the first and second phases are present, and determining stoichiometric parameters for quantifying the interactions between components of a biomolecular system based on the gradient.

[0011] Optionally, at least two parameters of the input data include the concentrations of at least two constituent biomolecules within the biomolecular system.

[0012] Optionally, the method of any of the aspects comprises determining a phase boundary between a third data set substantially including data about conditions in which both the first and second phases are present and a fourth data set substantially including data about conditions in which only the first phase is present, based on input data, the third data set further comprising determining, defined by the determined phase boundary, the gradient corresponding to a portion of a concentration boundary corresponding to a subset on the side of the phase boundary corresponding to the presence of both the first and second phases in the first data set and the second data set.

[0013] Optionally, the phase boundary is determined based on data describing the concentration of at least a first constituent of at least two constituents in the first phase and / or the second phase.

[0014] Optionally, the phase boundary is determined by identifying a plurality of subsets of the input data, each subset having a changing concentration of the first constituent and substantially constant values for the other of at least two parameters, each substantially constant value being different for each subset, identifying, and for each subset, determining a portion of the phase boundary such that the ratio of the concentration of the first constituent in the first phase and / or the second phase to the total concentration of the first constituent corresponds to the total concentration of a first parameter that substantially deviates from a constant value, and determining the phase boundary by combining respective portions of the phase boundary for each subset.

[0015] Optionally, the gradient corresponds to a portion of the concentration boundary close to the common portion with the phase boundary, preferably substantially corresponding to the concentration boundary at the common portion with the phase boundary.

[0016] Optionally, the threshold concentration of the first constituent is selected such that, for the concentration of the first constituent with respect to at least one other parameter of at least two parameters, the tangent to the phase boundary at the threshold is substantially parallel to the line connecting data where at least one of the other constituents is constant.

[0017] Optionally, a plurality of different concentration boundaries corresponding to different thresholds are determined, a plurality of respective gradients are determined, a plurality of respective stoichiometric parameters are determined, and / or stoichiometric parameters are determined based on a combination of a plurality of gradients corresponding to different thresholds.

[0018] Optionally, a plurality of different gradients corresponding to different input data sets are determined, the different input data sets differ with respect to the concentration of at least one additional component that is not one of the at least one component, a plurality of respective stoichiometric parameters are determined, and / or stoichiometric parameters are determined based on a combination of a plurality of gradients corresponding to different input data sets.

[0019] Optionally, a preparation of a biomolecular system having a plurality of conditions is prepared by forming the preparation of the biomolecular system within a microfluidic device configured to systematically vary the concentration of at least two parameters.

[0020] Optionally, the parameters are determined based on imaging of the preparation of the biomolecular system. Optionally, the imaging is fluorescence imaging including fluorescence tagging of at least one component. Optionally, parameters regarding the concentration of at least one component are determined based on the intensity of the fluorescence corresponding to each component.

[0021] Optionally, parameters regarding the concentration of a component in the first phase and / or the second phase are determined by analyzing an image of the preparation of the biomolecular system to identify regions corresponding to the first phase and / or the second phase and determining the intensity of the fluorescence corresponding to each component in each region.

[0022] Optionally, at least one constituent biomolecule consists of two constituent biomolecules.

[0023] Selectively, the first phase is the dilute phase, and the second phase is the concentrated phase.

[0024] A third aspect of this disclosure provides a method for identifying potential drug targets, which includes applying a method of any of the preceding aspects.

[0025] Selectively, a potential drug target is one of at least one components of a biomolecular system.

[0026] When multiple different gradients are determined for different input datasets at random, and the different input datasets differ with respect to the concentration of at least one additional component that is not one of the at least one component, then the potential drug target is at least one additional component that is not one of the at least one component.

[0027] Selectively, potential drug targets are identified based on whether their stoichiometric parameters meet one or more predetermined conditions.

[0028] A fourth aspect of this disclosure provides a method for identifying a potential therapeutic agent that targets a drug target, which includes applying the method of the first or second aspect.

[0029] Selectively, a therapeutic agent is one of at least one components of a biomolecular system.

[0030] Selectively, a drug target is one of at least one components of a biomolecular system.

[0031] When multiple different gradients are determined for different input datasets at random, and the different input datasets differ with respect to the concentration of at least one additional component that is not one of the at least one component, then the potential drug target is at least one additional component that is not one of the at least one component.

[0032] Selectively, potential therapeutic agents are identified based on whether their stoichiometric parameters meet one or more predetermined conditions.

[0033] A fifth aspect of this disclosure provides a computer-operated method for estimating a phase boundary for a biomolecular system exhibiting phase separation into two phases into first and second phases under specific conditions, the method comprising: receiving input data, the input data comprising data describing a biomolecular system under a plurality of different conditions that differ with respect to at least two parameters, the parameters comprising the concentration of at least one constituent biomolecule in the biomolecular system, and for each of the different conditions, the input data comprising data describing at least two parameters and the concentration of at least one constituent component of at least one component in the first and / or second phases; and based on the input data, a third dataset substantially comprising data for conditions in which both the first and second phases exist and conditions in which only the first phase exists Determining a phase boundary between a third dataset and a fourth dataset substantially containing data about the conditions for which the third dataset is defined by the determined phase boundary, and identifying a plurality of subsets of input data, each subset having a varying concentration of a first component, where the other of at least two parameters each has a substantially constant value, and each substantially constant value is different for each subset, determining for each subset a portion of the phase boundary to correspond to the total concentration of a first parameter where the ratio of the concentration of the first component to the total concentration of the first component in the first phase and / or second phase substantially deviates from a constant value, and determining the phase boundary for each subset by combining the respective portions of the phase boundary.

[0034] According to a sixth aspect of this disclosure, a computer program product is provided in which, when the program is executed by a computer, the computer includes instructions that cause the computer to perform one of the first, second, or fifth aspects.

[0035] According to a seventh aspect of this disclosure, a data processing system is provided which includes means for performing any one of the methods of the first, second, or fifth aspects.

[0036] According to the eighth aspect of this disclosure, a computer-readable storage medium is provided that, when executed by a computer, contains instructions causing the computer to perform one of the first, second, or fifth aspects of the disclosure.

[0037] Further features of this disclosure, as non-limiting examples, are described below with reference to the attached drawings. [Brief explanation of the drawing]

[0038] [Figure 1] This figure exemplifies a higher-dimensional phase space structure. [Figure 2] This diagram exemplifies a method for exploring higher-dimensional phase spaces using two-dimensional data. [Figure 3] This figure exemplifies a small region on a higher-dimensional phase boundary. [Figure 4] This figure shows stoichiometric parameters obtained from 2D experimental data acquired from high-dimensional biomolecular systems. [Figure 5] This figure shows the data used to determine the phase boundary. [Figure 6] This figure shows the phase boundary obtained based on the data in Figure 5. [Figure 7] This figure shows the stoichiometric parameters obtained from 2D experimental data acquired from further biomolecular systems. [Figure 8] This figure shows the stoichiometric parameters obtained from 2D experimental data acquired from further biomolecular systems. [Modes for carrying out the invention]

[0039] The present disclosure relates to a method for quantifying the interactions between components of a biomolecular system that exhibits phase separation into a two-phase first and second phase under specific conditions. For example, the method can be for measuring stoichiometric parameters that quantify the interactions between components of a biomolecular system. The first and second phases can be, for example, a dilute phase and a concentrated phase, respectively. The biomolecular system can include a plurality of biomolecular species in solution.

[0040] In a solution of N molecular species, the phase space is characterized by N-dimensional coordinates Φ ≡ [Φ1, Φ2,..., Φ - , - , and for α = 1, 2,..., N, each Φ α represents the concentration of solute α. The free energy density f is described as f = f(Φ), and the chemical potential and osmotic pressure are

Equation

[0041] Phase separation occurs when there exists a pair of different points ψ α (ψ - ) = μ α (ψ + ) and Π(ψ - ) = Π(ψ + ), as shown in FIG. 1. ψ - and ψ + represent the molecular compositions of the dilute and concentrated phases, and the conjugate line corresponding to this configuration is, of course, characterized by the vector k ≡ ψ - - ψ + . Representing the volume fraction of the concentrated phase as v where 0 < v < -1, the point ψ + + νk represents the average molecular composition of the system on this conjugate line.

[0042] <000C0196>​​​​Due to the smooth nature of free energy density, we can find a set of such dilute and concentrated point pairs, and the dimension of this set can be calculated by counting the number of equations and unknowns. Since the chemical potential and osmotic pressure give rise to N+1 equations, and the point pairs introduce 2N degrees of freedom, the total dimension of the solution set is 2N-(N+1)=N-1, i.e., an (N-1)-dimensional manifold in N-dimensional space. To facilitate the following explanation, we use the symbol T to represent the (N-1)-dimensional variable that formally parameterizes this manifold. The assumption of binary phase separation is, for example, a point pair ψ instead of a triple of points. ± It is necessary that only (T) can be found, which ensures that the generated conjugate lines do not intersect each other. This fact can also be explained in terms of dimensions, and instead of using Φ to characterize a point in the phase separation region, we can use the (N-1) dimensional parameter T and volume fraction v. T specifies the conjugate line on which the system rests, and v specifies the distance between the system and the dilute phase on the line. (N-1) dimensional plane ψ ± (T) is an (N-2) dimensional plane corresponding to the intersection between the binodal boundary and the spinodal boundary, where the spinodal region is condition

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[0043] Figure 1 illustrates a higher-dimensional phase space structure. The entire phase space is characterized by an N-dimensional coordinate system Φ and a dilute phase surface ψ - (T), thick phase surface ψ + (T), and conjugate lines (solid black and gray lines) connecting pairs of points on these planes.

[0044] It is not possible to efficiently characterize an N-dimensional space experimentally. However, a portion of this N-dimensional space can be explored by varying two of the molecular species while keeping the others constant, and thus reducing the higher-dimensional space to a two-dimensional plane. This corresponds to varying the concentrations φ1 and φ2 of two solutes while keeping φi constant for i=3,4,...,N. Therefore, we can follow the convention that the index i runs from 3 to N below.

[0045] This can also be generalized to varying parameters that describe conditions other than the concentrations of constituent components, such as temperature. According to the method of this disclosure, data describing a biomolecular system can be obtained under several different conditions, each with at least two different parameters. The parameters may include the concentration of at least one constituent biomolecule in the biomolecular system. In certain examples, the parameters may further include the concentration of at least one additional constituent component (i.e., the parameters may include the concentrations of at least two constituent biomolecules). In other examples, the parameters may include at least one parameter that is not the concentration of a constituent component, such as the temperature of the system. For each of the different conditions, the obtained data includes data describing the values ​​of at least two parameters. Optionally, the obtained data may include data describing the presence or absence of a first phase and / or a second phase.

[0046] Figure 2 illustrates how two-dimensional experimental data coupled to dilute phase concentration measurements explores higher-dimensional space. The upper and middle panels show diagrams of the entire phase space and the corresponding two-dimensional experimental plane. The lower panel shows the experimental data. From left to right: (A) shows the experimental plane intersecting the entire phase boundary to generate a two-dimensional phase boundary (dark red solid line), (B) shows the division of a uniform space according to φ1, (C) shows the division of a phase-separated space according to the dilute phase φ1, and (D) shows multiple divisions of the same space. To better visualize the data, alternating shading is assigned to the bands in (D).

[0047] Pre-set φi The value of π i Expressed as such, this is the plane φ i =π i This corresponds to generating a set of points on the image, and with appropriate selection of experimental conditions, some of the points will fall into phase-separated regions and some into non-phase-separated regions. Binary classification makes it possible to distinguish phase-separated samples from homogeneous samples through image analysis, resulting in a phase boundary in this two-dimensional plane. This procedure corresponds to taking a two-dimensional cross-section of the (N-1)-dimensional phase boundary (as shown in Figure 2A).

[0048] While experimental data provide a precise description of the phase boundary in the selected cross-section, the boundary alone contains very limited information about the interactions between molecular species, as opposing interactions can produce visually similar boundaries. To better characterize the phase space, measurements of a single dilute phase concentration φ1 of the solute are performed to provide further insight into higher-dimensional space.

[0049] According to the method of this disclosure, the acquired data describing a biomolecular system under different conditions may further include the concentration of at least one component among at least one component in a first phase and / or a second phase, for example, a dilute phase.

[0050] The data points on the 2D experimental plane have their dilute phase φ1 concentrations set to a threshold π. i The data can be grouped by comparison, and the boundary formed between them can be examined. According to the method of this disclosure, a concentration boundary can be determined between a first dataset substantially containing data in which the concentration of a first component of a first phase is above a threshold, and a second dataset substantially containing data in which the concentration of a first component of a first phase is below a threshold. The concentration boundary may be determined by known mathematical methods such as regression.

[0051] In a homogeneous region where no concentrated phase is formed, the dilute phase concentration is equivalent to the total concentration. Therefore, the phase space is simply divided by the plane φ1=π1 (as shown in Figure 2B), and the newly introduced concentration boundary is perpendicular to the φ1 axis. At the phase boundary, the φ1=π1 plane intersects the phase boundary to form an (N-2)-dimensional subboundary (yellow solid line in Figure 2), which is generally curved. The subboundary intersects the experimental plane at a single point (0 dimensions), and its φ2 coordinate is denoted as π2. This point will be referred to as π in the following explanation.

[0052] In the phase separation region, the dilute phase concentration is no longer the same as the total concentration, so the division of phase separation data points is not trivial. For each of these data points, the dilute phase φ1 has a point with coordinate φ, and along the conjugate line in which it is located, ψ - This can be calculated by tracing back to the (T) plane, and thus partitioning the phase separation space requires extending the subboundary within this space along the conjugate lines. The dimension of this extruded plane is (N-2)+1=(N-1), where (N-2) is the dimension of the subboundary and the dimension of the set of conjugate lines. This (N-1)-dimensional plane intersects with the 2D experimental plane to produce a 1D line boundary as a continuation of the trivial line boundary in homogeneous space (as shown in Figure 2C). It is worth noting that the band boundaries within the phase separation region are not straight lines due to the curved nature of the entire phase boundary, even though the conjugate lines are parallel to each other. By repeating this procedure for different values ​​of π1, bands can be created in the 2D plane (as shown in Figure 2D).

[0053] Figure 3 illustrates a small region on a higher-dimensional phase boundary. In the small region near π, ψ - (T) is nearly flat with a surface normal ω, and its conjugate lines are parallel to each other with the same direction vector k. ψ near π - If we denote the points on (T) as π + δπ, then they satisfy the following:

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[0054] Thus, using the two equations satisfied by δπ, the (N-2)-dimensional subboundary is generated as described above. To extend the subboundary to the 2-dimensional experimental plane, we can represent the points on it as φ, where the following applies:

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[0055] The above [1+1+(N-2)+N]=2N equations [equations (1) to (4)] are satisfied by (2N+1) unknowns δπ, φ, and v, leaving one degree of freedom in the system. Using equations (2) and (4) for index 1, φ1 = π1 + νk1 Therefore, the result is as follows:

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[0056] Next, combining equations (3) and (4) gives

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[0057] ratio:

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[0058] According to the method of this disclosure, a gradient of concentration boundaries can be determined based on input data for the concentration of a first component with respect to at least one other parameter. Preferably, the at least one other parameter is the concentration of a second component. Specifically, the gradient may correspond to a portion of the concentration boundary corresponding to a subset of the first and second datasets that forms part of a third dataset substantially containing data for conditions in which both the first and second phases exist. Based on the gradient, stoichiometric parameters can be determined to quantify the interactions between components of a biomolecular system. The data processing steps of the method may be performed by a computer or computer system. The stoichiometric parameters may be output by the method. For example, the parameters may be output to be stored on a data storage medium and / or displayed on a computer user interface.

[0059] Furthermore, based on the input data, a phase boundary can be determined between a third dataset substantially containing data for conditions in which both the first and second phases exist, and a fourth dataset substantially containing data for conditions in which only the first phase exists. Specifically, the gradient may correspond to portions of the concentration boundary corresponding to subsets of the first and second datasets that lie on the side of the phase boundary corresponding to the presence of both the first and second phases. Preferably, the determined gradient corresponds to a portion of the concentration boundary close to the intersection with the phase boundary. More preferably, the determined gradient lies substantially in the intersection with the determined phase boundary.

[0060] According to the method of this disclosure, the phase boundary may be determined based on data describing the concentration of at least a first component of at least two components in a first phase and / or a second phase. For example, the phase boundary may be determined by identifying several subsets of input data, each subset containing data in which the concentration of the first component varies, but the other parameter of at least two parameters each has a substantially constant value. The substantially constant value differs for each subset. The substantially constant value may lie within a range centered on a particular value, and the range is less than about 10% of the entire range of input data for a given parameter. For each subset, the portion of the phase boundary corresponding to the substantially constant value of the other parameter of at least two parameters is determined to correspond to the total concentration of the first parameter where the ratio of the concentration of the first component in the first phase and / or the second phase to the total concentration of the first component deviates substantially from a constant value. This may be determined as an inflection point in a linear fit of the data.

[0061] Protein dilute phase concentration measurement c dil Given a dataset with and any other arbitrary axes, the data can be divided into groups where only the total protein concentration changes. For example, in a two-dimensional protein-RNA system (Figure 5a), slices of data are taken around fixed RNA concentrations (Figure 5b - the section of data with RNA ≈ 75 is highlighted), and dilute versus total protein concentrations are plotted around these points (Figure 5c). For each data section, a point-linear fit is used to plot the saturated concentration c sat Furthermore, the homotype response R is extracted. The functional form is as follows.

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[0062] The calculated phase boundary exhibits the same general trend as that obtained from image-based condensate detection (Figure 6). Figure 6 shows a comparison of the two methods for estimating the phase boundary. The solid black line represents the phase boundary estimated from the inflection linear fit, and the gray shaded area shows the standard deviation from 10 iterations of the fitting with 50% dropout. The plotted points in the scatter plot are experimental data, with red and blue indicating the presence or absence of condensates, respectively.

[0063] The phase boundary may be determined by known mathematical methods, such as regression, based on data describing the presence or absence of a first phase and / or a second phase for each of the different conditions.

[0064] According to the method of this disclosure, multiple different concentration boundaries corresponding to different thresholds may be determined, and multiple gradients for each of them may be determined. Multiple stoichiometric parameters for each may be determined, and / or stoichiometric parameters may be determined based on a combination of multiple gradients corresponding to different thresholds. For example, the stoichiometric parameters may be the average of two or more gradients, or a ratio or difference between two gradients.

[0065] Equation (7) explains why the dilute phase band has a gradient that varies across the experimental two-dimensional plane. At different points on the two-dimensional phase boundary, the true N-dimensional boundary has different ωs, so even if k is constant, the band gradient still changes. The region of interest is where the two-dimensional boundary is parallel to the φ1 direction and ω2 takes very large values. In this limit, only the first term of equation (7) survives. In this case, K ≈ k2 / k1, and as a result, the influence from other constituent components can be ignored, and the change in K almost certainly originates from k2 / k1.

[0066] On the other hand, when the boundary is nearly parallel to the φ2 direction, ω2 becomes small, and K ≈ ∞ for almost certain. This result provides clear guidance on which parts of the phase diagram should be examined to gain more precise insights into the interaction between two specific molecular species characterized by φ1 and φ2.

[0067] According to the method of this disclosure, the threshold concentration of the first component may be selected such that, with respect to the concentrations of the first component with respect to other parameters, the tangent to the phase boundary at the threshold is substantially parallel to the line connecting the data where the other parameters are constant.

[0068] At least one component whose concentration changes is preferably a polymer, more preferably comprising one or more proteins and nucleic acids. The biomolecular system may, in some examples, be part of a cell, an intracellular organelle (e.g., nucleus or mitochondria), or a cell lysate. Further components may include one or more of the pH buffer, phase separator, salt solution, cell, cell lysate, and therapeutic / drug candidate. As further described below, some or all of the components (e.g., the target component whose relative concentration is measured) may additionally include optical markers (also called "barcodes") (for the above components). The pH buffer does not have to be optically marked. The phase separator may include crowding agents (polymers of biological and non-biological origin, e.g., PEG and dextran), proteins, nucleic acids, different salts, or small molecules that induce phase separation. The therapeutic / drug candidate may be a small molecule or biologic including, but not limited to, proteins, nucleic acids, lipids, peptides, or antibodies.

[0069] As an example, experimental data from a well-established phase separation system containing the protein G3BP1 and poly(A)RNA were obtained, and the concentrations of G3BP1 and poly(A)RNA represented two changing parameters (see bottom panel of Figure 2). G3BP1 is a 60 kDa RNA-binding protein involved in stress granule formation, and experiments were performed using 80 mM KCl in 20 mM PIPES buffer at pH 7.2.

[0070] Experimentally, since the G3BP1-RNA phase boundary is parallel to the G3BP1 axis in the high-G3BP1 region, the initial gradient of the band boundary should provide a good approximation of the G3BP1:RNA stoichiometry. Indeed, by limiting the analysis to data points close to the vertical boundary, their gradients take on nearly constant values, from which the stoichiometry RNA:G3BP1=(52±5)ng / μL:1μM can be determined.

[0071] The method of this disclosure may yield input data in which further parameters of the biomolecular system are modified, i.e., input data in which at least two of the above parameters are not present. Different sets of input data may be obtained, each set comprising data describing the biomolecular system under several different conditions, each set having at least two different parameters but further parameters having different constant values ​​for each set.

[0072] According to the method of this disclosure, multiple different gradients may be determined for different input datasets. Multiple stoichiometric parameters may be determined for each of these, and / or the stoichiometric parameters may be determined based on a combination of multiple gradients corresponding to different input datasets. For example, the stoichiometric parameters may be the mean of two or more gradients, or a ratio or difference between two gradients.

[0073] Figure 4 shows experimental data from G3BP1-RNA experiments with increasing amounts of DDX3X (from left to right). DDX3X dissolves condensates, as can be seen from the phase boundary shift (dark red solid line), while the dilute phase band boundary (black dashed and solid lines) has the same gradient, close to the vertical boundary, at high [G3BP1] levels. The plotted phase and band boundaries are for illustrative purposes only.

[0074] Experiments were conducted on the G3BP1-RNA system in the presence of two regulators known to dissolve condensates (corresponding to the further parameters mentioned above), namely DDX3X and suramin. G3BP1 protein was produced from insect cell lines and purified according to a previously established protocol [1], and poly(A)RNA with molecular weights of 700–3500 kDa was purchased from Sigma. In the experiment, each droplet was first classified based on the presence of condensates, and the dilute phase concentration of the droplets was extracted. The droplets were then subdivided into bands according to their dilute phase concentrations. The data could be plotted on a phase diagram with [G3BP1] and [RNA] as axes, with phase-separated points colored red and unseparated points colored blue. Alternating shades of blue and red were assigned to each dilute phase band, and the boundaries between these bands corresponded to dilute phase cuts as outlined earlier. In the presence of DDX3X (as shown in Figure 4), the dilute phase concentration simply corresponds to the total concentration in the non-phase-separated droplet, so the uniform blue dots form a band with a boundary perpendicular to the G3BP1 axis. It should first be noted that in the case of phase-separated droplets, the boundary is not a straight line, especially close to the concave angle. This curvature is characteristic of the multidimensionality of the phase space. However, near the G3BP1 axis, the boundary is perpendicular to the RNA axis, thereby creating a local ω RNAThe threshold is very large, and the band boundary has a similar gradient very close to this edge. In this case, the boundary gradient is a good approximation of the G3BP1-RNA conjugation line gradient, and it is reasonable to further assume that the conjugation line gradient is constant in the explored phase space. It is interesting to note that as the DDX3X concentration increases, the phase boundary shifts toward higher G3BP1 and RNA concentrations as the condensate dissolves, but the band boundary has a similar initial gradient at high [G3BP1]. This shows a nearly constant [G3BP1]:[RNA] stoichiometry even at different [DDX3X] (as shown in Figure 5). On the other hand, suramin also dissolves the condensate, and the initial gradient of the band boundary changes with the addition of suramin (as shown in Figure 5), so the data can be interpreted as suramin having a modulating effect on G3BP1-RNA cross-interactions.

[0075] Figures 7 and 8 show further experimental data in the form of a phase diagram, where the stoichiometric parameters calculated in the form of a dashed gradient are shown.

[0076] Figure 7 shows the PEG-induced phase separation of β-catenin regulated by the β-catenin-binding factor (BBF) peptide. From left to right, Figure 7 shows the phase diagram at increasing BBF concentrations. Blue dots represent homogeneity, and red dots represent phase separation. Points with dilute phase [BC] > 2 μM are assigned darker shades. The boundary slope decreases (dashed line), indicating a decrease in the contribution of β-catenin to phase separation with increasing BBF concentration.

[0077] Figure 8 shows the phase diagram of heteromorphic phase separation between β-catenin and the cofactor protein TCF7L2 at a constant PEG concentration (3.25%) and increasing BBF peptide concentrations. The gradient of the dilute phase band boundary (at a dilute phase of 4 μM [BC]) decreases with BBF, indicating the inhibitory effect of BBF on TCF7L2-enhanced β-catenin phase separation.

[0078] Stoichiometric parameters may be used in a method to identify potential drug targets or potential therapeutic agents that target drug targets. For example, potential drug targets or potential therapeutic agents that target drug targets may be validated or invalidated, i.e., screened, based on whether a stoichiometric parameter satisfies one or more predetermined conditions. A potential drug target or potential therapeutic agent may be one of at least one component of a biomolecular system in which the gradient described above is a variable parameter directly related. Alternatively, a potential drug target or potential therapeutic agent that targets drug targets may be at least one further component that defines the different input datasets described above.

[0079] Input data may be obtained by acquiring preparations of the biomolecular system under each of several conditions. Then, for each preparation of the biomolecular system under each of the several conditions, parameters may be determined relating to the total concentration of each of the at least two components in the biomolecular system, the presence or absence of a first phase and / or a second phase, and the concentration of at least the first component of the at least two components in the first phase and / or second phase.

[0080] For example, data may be obtained using the methods and apparatus described in International Publication No. WO2021 / 234410, which is incorporated in its entirety herein by reference.

[0081] Therefore, the method for acquiring input data may include generating a stream of microdroplets, each of which represents a preparation of a biomolecular system. The method includes varying at least two parameters, the parameters including the concentration of at least one constituent biomolecule in the biomolecular system within the microdroplet, and measuring the relative concentrations of the components of the microdroplet and the phases present within the microdroplet.

[0082] The conditions within a microdroplet are altered by selectively changing the relative concentrations of its constituent components. Alternatively, or additionally, the conditions within a microdroplet are altered by changing its temperature. Optionally, the temperature of the microdroplet is altered by controlling the temperature of the channel through which the microdroplet flows.

[0083] Selectively, the flow of microdroplets is a continuous flow. Selectively, the measurement is performed continuously with respect to the flow of microdroplets.

[0084] Selectively, microdroplets are collected, and measurements are performed on the collected microdroplets.

[0085] Optionally, a microdroplet flow is generated by injecting a flow of a first fluid containing its constituent components into a flow of a second fluid, the second fluid being immiscible with the first fluid. Optionally, the flows of each constituent component of the microdroplet merge to form a flow of the first fluid. Optionally, the relative concentrations of the constituent components are altered by changing the relative flow rates of each constituent component of the microdroplet. Optionally, the flow proceeds through channels in a microfluidic system.

[0086] Optionally, the relative concentrations of the components may be measured by a first optical means. Optionally, the first optical means illuminates the microdroplets with illumination light and detects the response. Optionally, the relative concentrations of the components are determined based on the response of each component to the illumination light. Optionally, each component responds differently to the illumination light. Optionally, each component contains a different fluorophore that emits light of a specific wavelength in response to the illumination light.

[0087] Optionally, the phase present in the microdroplet is measured by a second optical means. Optionally, the second optical means acquires an image of the microdroplet, and the phase present in the microdroplet is determined based on the characteristics of the image indicating a specific phase. Alternatively, the second optical means acquires a light scattering profile of the microdroplet, and the polymer phase present in the microdroplet is determined based on the characteristics of the light scattering profile indicating a specific phase.

[0088] It should be understood that the above examples can be modified without departing from the intent or scope of this disclosure.

[0089] [1]:S.Qamar,GZWang,SJRandle,FSRuggeri,JAVarela,JQLin,ECPhillips,A.Miyashita,D.Williams,F.Str ohl,W.Meadows,R.Ferry,VJDardov,GGTartaglia,LAFarrer,GSKaminski Schierle, CF Kaminski, CE Holt, PEFraser, G. Schmitt-Ulms, D. Klenerman, T. Knowles, M. Vendruscolo, P. St George-Hyslop, Cell 173, 720 (2018).

Claims

1. A computer-based method for quantifying the interactions between components of a biomolecular system that exhibits phase separation into a first and second phase under specific conditions, Receiving input data, wherein the input data includes data describing the biomolecular system under a plurality of different conditions that differ with respect to at least two parameters, the parameters including the concentration of at least one constituent biomolecule in the biomolecular system, and for each of the different conditions, the input data includes data describing the at least two parameters and the concentration of at least one constituent component among the at least one constituent component in the first phase and / or the second phase. Based on the input data, a concentration boundary is determined between a first dataset substantially containing data in which the concentration of the first component in the first phase exceeds a threshold, and a second dataset substantially containing data in which the concentration of the first component in the first phase falls below the threshold. Determining the gradient of the concentration boundary with respect to the concentration of the first component for at least one other parameter of the at least two parameters, wherein the gradient corresponds to a portion of the concentration boundary corresponding to a subset of the first dataset and the second dataset that substantially includes data for conditions in which both the first and second phases exist. Based on the aforementioned gradient, a stoichiometric parameter is determined to quantify the interaction between the constituent components of the biomolecular system. Methods that include...

2. The aforementioned input data is To obtain the preparation of the biomolecular system under each of the aforementioned multiple conditions, To determine, for each of the preparations of the biomolecular system under each of the aforementioned multiple conditions, the respective parameters and the data relating to the concentration of at least one component among the at least one component in the first phase and / or the second phase. The method according to claim 1, obtained by...

3. A method for quantifying the interactions between components of a biomolecular system that exhibits phase separation into a first and second phase under specific conditions, Obtaining preparations of the biomolecular system under multiple different conditions that differ with respect to at least two parameters, wherein the parameters include the concentration of at least one constituent biomolecule in the biomolecular system. To determine data for each of the preparations of the biomolecular system under each of the aforementioned multiple conditions, relating to each of the at least two parameters and the concentration of at least one component among the at least one component in the first phase and / or the second phase, The preparation of input data, wherein the input data includes data describing the biomolecular system under the plurality of conditions, and for each of the different conditions, the input data includes data describing each of the at least two parameters and the concentration of at least the first component among the at least one component in the first phase and / or the second phase. The computer receives the input data and proceeds with the following steps: A step of determining a concentration boundary between a first dataset substantially containing data for conditions in which the concentration of the first component in the first phase exceeds a threshold, and a second dataset substantially containing data for conditions in which the concentration of the first component in the first phase falls below a threshold, based on the input data. A step of determining the gradient of the concentration boundary with respect to the concentration of the first component for at least one other parameter of the at least two parameters, wherein the gradient corresponds to a portion of the concentration boundary that corresponds to a subset of the first dataset and the second dataset that substantially includes data for conditions in which both the first and second phases exist, and The step of determining stoichiometric parameters that quantify the interactions between the components of the biomolecular system based on the gradient. To execute and Methods that include...

4. The method according to any one of claims 1 to 3, wherein the at least two parameters of the input data include the concentrations of at least two constituent biomolecules in the biomolecular system.

5. Based on the input data, determine a phase boundary between a third dataset substantially containing data for conditions in which both the first and second phases exist, and a fourth dataset substantially containing data for conditions in which only the first phase exists, wherein the third dataset is defined by the determined phase boundary, further comprising determining The method according to any one of claims 1 to 4, wherein the gradient corresponds to the portion of the concentration boundary corresponding to a subset of the first dataset and the second dataset that lies on the side of the phase boundary corresponding to the presence of both the first and second phases.

6. The method according to claim 5, wherein the phase boundary is determined based on data describing the concentration of at least the first component among the at least two components in the first phase and / or the second phase.

7. The aforementioned phase boundary is Identifying multiple subsets of the input data, wherein each subset has a varying concentration of the first component, and each of the other two parameters has a substantially constant value, and each of these substantially constant values ​​differs for each subset. For each subset, the phase boundary portion is determined to correspond to the total concentration of the first parameter such that the ratio of the concentration of the first component in the first phase and / or the second phase to the total concentration of the first component substantially deviates from a constant value, The phase boundary is determined by combining each part of the phase boundary for each subset. The method according to claim 6, as determined by...

8. The method according to claim 6 or 7, wherein the gradient corresponds to the portion of the concentration boundary near the common portion with the phase boundary, preferably substantially to the concentration boundary in the common portion with the phase boundary.

9. The method according to claim 6, 7, or 8, wherein the threshold concentration of the first component is selected such that the tangent at the threshold to the phase boundary is substantially parallel to a line connecting data where at least one of the other components is constant, with respect to the concentration of the first component with respect to at least one of the other two parameters.

10. Multiple different concentration boundaries corresponding to different thresholds are determined, and multiple gradients are determined for each of them. The method according to any one of claims 1 to 9, wherein a plurality of stoichiometric parameters are determined, and / or the stoichiometric parameters are determined based on a combination of the plurality of gradients corresponding to different thresholds.

11. Multiple different gradients are determined for different input datasets, and the different input datasets differ with respect to the concentration of at least one further component that is not one of the at least one component. The method according to any one of claims 1 to 10, wherein a plurality of stoichiometric parameters are determined, and / or the stoichiometric parameters are determined based on a combination of the plurality of gradients corresponding to different input datasets.

12. The method according to claim 11, wherein the preparation of the biomolecular system having the plurality of conditions is prepared by forming the preparation of the biomolecular system in a microfluidic device configured to systematically change the concentrations of at least two parameters.

13. The method according to claim 11 or 12, wherein the parameters are determined based on imaging of the preparation of the biomolecular system.

14. The method according to claim 13, wherein the imaging is fluorescence imaging including the fluorescent labeling of at least one component.

15. The method according to claim 14, wherein the parameter relating to the concentration of the at least one component is determined based on the fluorescence intensity corresponding to each of the components.

16. The method according to claim 14 or 15, wherein the parameters relating to the concentration of the components in the first phase and / or the second phase are determined by analyzing an image of the biomolecular system preparation to identify regions corresponding to the first phase and / or the second phase, and determining the intensity of fluorescence corresponding to each component in each of the respective regions.

17. The method according to any one of claims 1 to 16, wherein the at least one constituent biomolecule consists of two constituent biomolecules.

18. The method according to any one of claims 1 to 17, wherein the first phase is a dilute phase and the second phase is a concentrated phase.

19. A method for identifying a potential drug target, comprising applying the method according to any one of claims 1 to 18.

20. The method according to claim 19, wherein the potential drug target is one of the at least one component of the biomolecular system.

21. The method according to claim 19 or 20, wherein, by reference to claim 11 or any other claim relating thereto, the potential drug target is the at least one further component that is not one of the at least one component.

22. The method according to any one of claims 19 to 21, wherein the potential drug target is identified based on whether the stoichiometric parameter satisfies one or more predetermined conditions.

23. A method for identifying a potential therapeutic agent that targets a drug target, comprising applying the method according to any one of claims 1 to 18.

24. The method according to claim 22, wherein the therapeutic agent is one of the at least one component of the biomolecular system.

25. The method according to claim 23 or 24, wherein the drug target is one of the at least one components of the biomolecular system.

26. The method according to claim 24, wherein, when referring to claim 11 or any claim relating thereto, the potential drug target is the at least one further component that is not one of the at least one component.

27. The method according to any one of claims 23 to 26, wherein the potential therapeutic agent is identified based on whether the stoichiometric parameter satisfies one or more predetermined conditions.

28. A computer-based method for estimating the phase boundary for a biomolecular system that exhibits phase separation into a first and second phase under specific conditions, Receiving input data, wherein the input data includes data describing the biomolecular system under a plurality of different conditions that differ with respect to at least two parameters, the parameters including the concentration of at least one constituent biomolecule in the biomolecular system, and for each of the different conditions, the input data includes data describing the at least two parameters and the concentration of at least one constituent component among the at least one constituent component in the first phase and / or the second phase. Based on the input data, determine a phase boundary between a third dataset substantially containing data for conditions in which both the first and second phases exist, and a fourth dataset substantially containing data for conditions in which only the first phase exists, wherein the third dataset is defined by the determined phase boundary. Identifying multiple subsets of the input data, wherein each subset has a varying concentration of the first component, and each of the other two parameters has a substantially constant value, and each of these substantially constant values ​​differs for each subset. For each subset, the phase boundary portion is determined to correspond to the total concentration of the first parameter such that the ratio of the concentration of the first component in the first phase and / or the second phase to the total concentration of the first component substantially deviates from a constant value, The phase boundary is determined by combining each part of the phase boundary for each subset. Methods that include...

29. A computer program product that, when executed by a computer, includes instructions causing the computer to perform the method according to any one of claims 1 to 18 or 28.

30. A data processing system comprising means for performing the method according to any one of claims 1 to 18 or 28.

31. A computer-readable storage medium that, when executed by a computer, includes instructions causing the computer to perform the method according to any one of claims 1 to 18 or 28.

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