A system for molecular fishing using bait compounds for semi-targeted search within molecular networks.

Abstract

Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a mass spectrometry instrument support system may include: a first logic for receiving fragmentation data of a reference compound; a second logic for providing the fragmentation data as a virtual bait compound to a molecular network algorithm; a third logic for annotating the reference compound and generating clusters of compounds in a sample surrounding the annotated reference compound; and a fourth logic for providing an overview of the structural relationships between the compound clusters.

Description

Technical Field

[0001] (Related Application) This application claims the benefit of U.S. Provisional Patent Application No. 63 / 486,010, filed Feb. 20, 2024, which is hereby incorporated by reference in its entirety into this application.

Background Art

[0002] Mass spectrometry is a technique for detecting, identifying, and quantifying molecules within a sample based on the mass-to-charge ratio of the ionized molecules. The data generated by the mass spectrometry operation can be used in proteomics and other scientific applications.

Brief Description of the Drawings

[0003] Embodiments will be readily understood by the following detailed description in conjunction with the accompanying drawings. For the sake of ease of explanation, like reference numbers refer to like structural elements. Embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings. [Figure 1] A block diagram of an exemplary mass spectrometry instrument support module according to various embodiments. [Figure 2] A flowchart of an exemplary method for mass spectrometry data analysis according to various embodiments. [Figure 3-1] An exemplary output showing a cluster of compounds surrounding an annotated reference compound within a sample. [Figure 3-2] An exemplary output showing a cluster of compounds surrounding an annotated reference compound within a sample. [Figure 4] An example of a graphical user interface that may be used in implementing some or all of the support methods disclosed herein according to various embodiments. [Figure 5] A block diagram of an exemplary computing device that may implement some or all of the mass spectrometry methods disclosed herein according to various embodiments. [Figure 6]This is a block diagram of an exemplary scientific instrument support system in which some or all of the methods disclosed herein may be implemented in various embodiments. [Modes for carrying out the invention]

[0004] Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a mass spectrometry instrument support system may include: a first logic for receiving fragmentation data of a reference compound; a second logic for providing the fragmentation data as a virtual bait compound to a molecular network algorithm; a third logic for annotating the reference compound and generating clusters of compounds in a sample surrounding the annotated reference compound; and a fourth logic for providing an overview of the structural relationships between the compound clusters.

[0005] The scientific instrument-assisted embodiments disclosed herein can achieve improved performance compared to conventional approaches. As will be discussed in more detail below, in many experimental designs, scientists search for unknown compounds that are structurally related to known parent or reference substances for further analysis. Discovering such candidate compounds in complex samples can be a time-consuming and complex task, even for experienced scientists.

[0006] Embodiments disclosed herein may include systems (e.g., tools) for providing a visual overview of structural relationships between compounds detected by mass spectrometry. By this technique, compounds are clustered together using similarity scoring between their fragmented mass spectra. While these systems provide significant assistance in sample overview, compound identification, and validation, there are still complex scenarios in discovering candidate compounds.

[0007] Therefore, in some embodiments, the described system includes features to facilitate semi-targeted searching of structurally related compound clusters and to improve mass spectrometry of small molecules such as metabolic pathway analysis, degradation product analysis, and forensic analysis. In general, clusters of related compounds can be located even within complex molecular networks if any of their members are identified and annotated. The search can be performed directly by subparts of their names or by observing their structures. However, there are important cases where the sample contains only unknown derivatives of the reference compound, and the corresponding cluster is substantially invisible. For example, in metabolite analysis studies of newly designed drugs, the sample may contain metabolites that cannot be directly identified due to the lack of a reference spectrum.

[0008] To address the aforementioned problems, in some embodiments, the described system receives fragmentation data of a clean reference compound. The substance is then injected directly into the sample and measured together in the same implementation. In such examples, clusters are created around the annotated reference compound, making discovery easier (see Figure 3). However, in some cases, the reference compound cannot be injected into the sample and therefore cannot be measured directly together with other compounds. Fortunately, in such cases, an in-computer technique can be used in which user-defined fragmentation data of the reference compound can be fed into a molecular network algorithm as a virtual "bait compound." This allows the user to perform a semi-targeted search for potential clusters of interest. Furthermore, the source of the reference compound fragmentation data is not limited to the actually obtained tandem mass spectrometry (MS2) spectrum of that compound. Instead, the source may be, for example, an artificially predicted spectrum based on a known structure, or a cumulative spectrum based on diagnostic fragments of a class of similar compounds.

[0009] In some cases, bait compounds are practically indistinguishable from the actual features detected in the sample. Therefore, in some embodiments, the fragmentation data of each bait compound is compared to each detected feature to determine the similarity between them (e.g., in the same way as a normal molecular network). Furthermore, this similarity can be adjusted depending on the type of origin of the bait compound or fragmentation data. For example, computer-generated fragments typically contain many false positive masses, resulting in a low similarity score. For compound classes, precursor masses are not available, and therefore only direct fragment matching can be used, resulting in a lower similarity score.

[0010] In some embodiments, the system incorporates tools to seamlessly integrate bait compounds, making them visible and easier to locate. For example, bait compounds may be provided in “isolation mode,” allowing the user to limit the depth of the displayed graph and remove scoring restrictions without losing control over the displayed graph. This makes it possible to directly analyze only compounds related to the selected bait compound. Furthermore, the described system allows the user to insert custom fragmentation data of a compound or compound class into the molecular network and mark clusters of structurally related compounds for easy access and further analysis. In addition, the semi-targeted search function reduces the effort required to manually locate structurally related compounds of potential interest. By artificially inserting user-defined fragmentation data of individual compounds or compound classes, clusters of structurally related compounds are marked and easily locateable even in highly complex samples. This strategy provides invaluable assistance in discovering compounds unknown to the identification library and compounds derived from novel materials.

[0011] In the following detailed description, references are made to the accompanying drawings which form part of this specification, similar figures indicate similar parts throughout, and possible embodiments are shown as illustrative examples. It should be understood that other embodiments may be utilized and structural or logical modifications may be made without departing from the scope of this disclosure. Therefore, the following detailed description should not be construed as restrictive.

[0012] Various actions may be described sequentially as multiple separate actions or operations in order to best aid in understanding the subject matter disclosed herein. However, the order of description should not be construed as suggesting that these actions necessarily depend on their order. Specifically, these actions may not be performed in the order presented. The actions described may be performed in a different order than in the embodiments described. Various additional actions may be performed, and / or the actions described may be omitted in additional embodiments.

[0013] For the purposes of this disclosure, the phrases "A and / or B" and "A or B" mean (A), (B), or (A and B). For the purposes of this disclosure, the phrases "A, B, and / or C" and "A, B, or C" mean (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C). Some elements may be referred to in the singular form (e.g., "processing device"), but any suitable element may be represented by multiple instances of that element, and vice versa. For example, a set of operations described as being performed by a processing device may be implemented using different operations performed by different processing devices.

[0014] This description uses the phrases “a certain embodiment,” “various embodiments,” and “several embodiments,” each of which may refer to one or more of the same or different embodiments. Furthermore, terms such as “comprising,” “including,” and “having” as used in reference to embodiments of this disclosure are synonymous. When used to describe a range of dimensions, the phrase “X~Y (between X and Y)” refers to a range including X and Y. As used herein, “apparatus” may refer to any individual device, a collection of devices, a part of a device, or a collection of parts of a device. The drawings are not necessarily to scale.

[0015] Figure 1 is a block diagram of a mass spectrometry support module 1000 to provide a visual overview of the structural relationships between compounds detected by mass spectrometry in various embodiments. The support module 1000 may be implemented by circuits (e.g., including electrical and / or optical components) such as programmed computing devices. The logic of the support module 1000 may be contained in a single computing device or distributed across multiple computing devices communicating with each other as needed. Embodiments of computing devices that can implement the support module 1000, either individually or in combination, are considered with reference to computing device 4000 in Figure 5, and embodiments of a system of interconnected computing devices in which the support module 1000 can be implemented across one or more computing devices are considered herein with reference to support system 5000 in Figure 6.

[0016] The support module 1000 may include receiving logic 1002, molecular network algorithm logic 1004, cluster generation logic 1006, and display logic 1008. As used herein, the term “logic” may include devices that perform a set of operations associated with the logic. For example, any of the logic elements included in the support module 1000 may be implemented by one or more computing devices programmed with instructions that cause one or more processing devices of a computing device to perform the set of operations associated with the logic. In certain embodiments, the logic element may include one or more non-temporary computer-readable media having instructions that, when executed by one or more processing devices of a computing device, cause one or more computing devices to perform the set of operations associated with the logic. As used herein, the term “module” may refer to a set of one or more logic elements that together perform one or more functions associated with the module. Different logic elements within a module may take the same form or different forms. For example, some logic within a module may be implemented by programmed general-purpose processing devices, while other logic within a module may be implemented by application-specific integrated circuits (ASICs). In another example, different logic elements within a module may be associated with different sets of instructions executed by one or more processing devices. A module may not necessarily contain all of the logic elements depicted in its associated diagram; for example, a module may contain a subset of the logic elements depicted in its associated diagram when the module performs a subset of the operations discussed herein by reference to that module.

[0017] The receiving logic 1002 may be configured to receive fragmentation data of a reference compound. In some embodiments, the source of the reference compound fragmentation data is an artificially predicted spectrum based on a known structure, a cumulative spectrum based on fragments of a diagnostic class of similar compounds, etc. In some embodiments, the reference compound fragmentation data is user-defined.

[0018] The molecular network algorithm logic 1004 can be configured to provide fragmented data to the molecular network algorithm as a virtual bait compound.

[0019] The cluster generation logic 1006 may be configured to annotate a reference compound and generate clusters of compounds in the sample that surround the annotated reference compound. In some embodiments, compounds are clustered by determining the similarity between the fragmentation data of the reference compound and the actual features detected in the sample. In some embodiments, the determination of similarity between the fragmentation data and the actual features is adjusted depending on the type of virtual bait compound or the origin of the fragmentation data.

[0020] The display logic 1008 can provide the user with an overview of the structural relationships between clusters of compounds made available by the cluster generation logic 1006, via a GUI (such as GUI 3000 in Figure 4). In some embodiments, the overview allows the user to perform a semi-targeted search for potential target clusters. In some embodiments, the overview incorporates virtual bait compounds into an isolation mode, which allows the user to limit the depth of the displayed graph depth and remove scoring restrictions without losing control over the graph.

[0021] Figure 2 is a flowchart of a method 2000 for performing assistance operations according to various embodiments. The operations of method 2000 may be illustrated with reference to specific embodiments disclosed herein (e.g., the scientific instrument assistance module 1000 discussed herein with reference to FIG. 1, the GUI 3000 discussed herein with reference to FIG. 4, the computing device 4000 discussed herein with reference to FIG. 5, and / or the scientific instrument assistance system 5000 discussed herein with reference to FIG. 6), but method 2000 may be used in any suitable configuration to perform any suitable assistance operation. The operations are illustrated in a specific order, each once in FIG. 2, but the operations may be appropriately rearranged and / or repeated as desired (e.g., different operations being performed in parallel as appropriate).

[0022] In 2002, a first operation may be performed. For example, the receiving logic 1002 of the assistance module 1000 may perform the operation of 2002. The first operation may include receiving fragmentation data of a reference compound.

[0023] In 2004, a second operation may be performed. For example, the molecular network algorithm logic 1004 of the assistance module 1000 may perform the operation of 2004. The second operation may include providing the fragmentation data as a virtual bait compound to a molecular network algorithm.

[0024] In 2006, a third operation may be performed. For example, the cluster generation logic 1006 of the assistance module 1000 may perform the operation of 2006. The third operation may include annotating the reference compound and generating a cluster of compounds surrounding the annotated reference compound in the sample.

[0025] In 2008, a fourth operation may be performed. For example, the display logic 1008 of the assistance module 1000 may perform the operation of 2008. The fourth operation may include displaying an overview of the structural relationships between clusters of compounds.

[0026] Figure 3 shows an exemplary output illustrating the cluster of compounds surrounding the annotated reference compound within the sample.

[0027] The scientific instrument-assisted methods disclosed herein may include interactions with a human user (for example, via a user-local computing device 5020, discussed herein with reference to Figure 6). These interactions may include providing the user with information (e.g., information about the operation of a scientific instrument such as the scientific instrument 5010 in Figure 6, information about a sample being analyzed or other tests or measurements performed by the scientific instrument, information retrieved from a local or remote database, or other information), or providing the user with options to input commands (e.g., to control the operation of a scientific instrument such as the scientific instrument 5010 in Figure 6, or to control the analysis of data generated by the scientific instrument), queries (e.g., to a local or remote database), or other information. In some embodiments, these interactions may be carried out through a graphical user interface (GUI), which includes a visual display on a display device (e.g., display device 4010 discussed herein with reference to Figure 4) that provides output to the user and / or prompts the user to provide input (e.g., via one or more input devices, such as a keyboard, mouse, trackpad, or touchscreen, included in other I / O devices 4012 discussed herein with reference to Figure 4). Scientific instrument assistance systems disclosed herein may include any preferred GUI for user interaction.

[0028] Figure 4 depicts an exemplary GUI 3000 that may be used when implementing some or all of the support methods disclosed herein in various embodiments. As described above, the GUI 3000 may be provided on a display device (e.g., display device 4010 as discussed herein with reference to Figure 5) of a computing device (e.g., computing device 4000 as discussed herein with reference to Figure 5) of a scientific instrument support system (e.g., scientific instrument support system 5000 as discussed herein with reference to Figure 6), and a user may interact with the GUI 3000 using any suitable input device (e.g., any of the input devices included in other I / O devices 4012 as discussed herein with reference to Figure 5), and input techniques (e.g., cursor movement, motion capture, face recognition, gesture detection, speech recognition, button activation, etc.).

[0029] GUI3000 may include a data display area 3002, a data analysis area 3004, a scientific instrument control area 3006, and a settings area 3008. The specific number and arrangement of areas depicted in Figure 4 are merely illustrative, and GUI3000 may include any number and arrangement of areas containing any desired features.

[0030] The data display area 3002 may display data generated by a scientific instrument (e.g., the scientific instrument 5010 discussed herein with reference to Figure 6). For example, the data display area 3002 may provide the user with options to provide fragmentation data of a reference compound and an overview of the structural relationships between compound clusters.

[0031] The data analysis area 3004 may display the results of the data analysis. For example, the data analysis area 3004 may display an overview of the structural relationships between compound clusters. In some embodiments, the data display area 3002 and the data analysis area 3004 may be combined in the GUI 3000 (for example, to include data output from scientific instruments and some analysis of the data in a common graph or area).

[0032] The scientific instrument control area 3006 may include options that enable the user to control a scientific instrument (e.g., scientific instrument 5010 discussed herein with reference to Figure 6). The configuration area 3008 may include options that enable the user to control the features and functions of GUI 3000 (and / or other GUIs) and / or perform common computing operations with respect to the data display area 3002 and the data analysis area 3004 (e.g., storing data on a storage device such as storage device 4004 discussed herein with reference to Figure 5, transmitting data to another user, labeling data, etc.).

[0033] As described above, the scientific instrument support module 1000 can be implemented by one or more computing devices. Figure 5 is a block diagram of a computing device 4000 that can implement some or all of the scientific instrument support methods disclosed herein in various embodiments. In some embodiments, the scientific instrument support module 1000 can be implemented by a single computing device 4000 or by multiple computing devices 4000. Furthermore, as discussed below, the computing device 4000 (or multiple computing devices 4000) implementing the scientific instrument support module 1000 may be part of one or more of the scientific instrument 5010, user-local computing device 5020, service-local computing device 5030, or remote computing device 5040 in Figure 6.

[0034] Although the computing device 4000 in Figure 5 is shown having many components, any one or more of these components may be omitted or duplicated as suitable for the application and configuration. In some embodiments, some or all of the components included in the computing device 4000 may be mounted on one or more motherboards and enclosed in a housing (e.g., including plastic, metal, and / or other materials). In some embodiments, some of these components may be fabricated on a single system-on-a-chip (SoC) (for example, the SoC may include one or more processing devices 4002 and one or more storage devices 4004). Additionally, in various embodiments, the computing device 4000 may not include one or more of the components illustrated in Figure 5, but may include an interface circuit configuration (not shown) for coupling to one or more components using any suitable interface (e.g., a Universal Serial Bus (USB) interface, a High-Definition Multimedia Interface (HDMI®) interface, a Controller Area Network (CAN) interface, a Serial Peripheral Interface (SPI) interface, an Ethernet interface, a wireless interface, or any other suitable interface). For example, the computing device 4000 may not include the display device 4010, but may include a display device interface circuit configuration (e.g., a connector and driver circuit configuration) to which the display device 4010 can be coupled.

[0035] The computing device 4000 may include processing devices 4002 (e.g., one or more processing devices). As used herein, the term “processing device” may mean any device or part of a device that processes electronic data from registers and / or memory and converts that electronic data into other electronic data that can be stored in registers and / or memory. The processing devices 4002 may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), cryptographic processors (dedicated processors that execute cryptographic algorithms in hardware), server processors, or any other suitable processing devices.

[0036] The computing device 4000 may include a storage device 4004 (e.g., one or more storage devices). The storage device 4004 may include one or more memory devices such as random access memory (RAM) (e.g., static RAM (SRAM) devices, magnetic RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive RAM (RRAM) devices, or conductive-bridging RAM (CBRAM) devices), hard drive memory devices, solid-state memory devices, networked drives, cloud drives, or any combination of memory devices. In some embodiments, the storage device 4004 may include memory that shares a die with the processing device 4002. In such embodiments, the memory may be used as cache memory and may include, for example, embedded dynamic random access memory (eDRAM) or spin transfer torque magnetic random access memory (STT-MRAM). In some embodiments, the storage device 4004 may include a non-temporary computer-readable medium having instructions that cause the computing device 4000 to perform any suitable method or part thereof of the methods disclosed herein when executed by one or more processing devices (e.g., processing device 4002).

[0037] The computing device 4000 may include an interface device 4006 (for example, one or more interface devices 4006). The interface device 4006 may include one or more communication chips, connectors, and / or other hardware and software for managing communication between the computing device 4000 and other computing devices. For example, the interface device 4006 may include a circuit configuration for managing wireless communication for data transfer to and from the computing device 4000. The term “wireless” and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc., that can communicate data through the use of modulated electromagnetic radiation over a non-solid medium. This term does not mean that the devices in question do not include any wiring, although in some embodiments this may be the case. The circuit configurations included in the interface device 4006 for managing wireless communications may implement any of many wireless standards or protocols, including, but not limited to, Institute for Electrical and Electronic Engineers (IEEE) standards, including Wi-Fi (IEEE 802.11 family), IEEE 802.16 standards (e.g., IEEE 802.16-2005 amendment), and Long-Term Evolution (LTE) projects with any modifications, updates, and / or revisions (e.g., the Advanced LTE project, the Ultra Mobile Broadband (UMB) project (also known as "3GPP®2")).In some embodiments, the circuit configuration included in the interface device 4006 for managing wireless communication includes Global System for Mobile Communication (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA), and Evolved HSPA. It may operate according to HSPA, E-HSPA, or LTE networks. In some embodiments, the circuit configuration included in the interface device 4006 for managing wireless communication may operate according to Enhanced Data for GSM Evolution (EDGE), GSM EDGE Radio Access Network (GERAN), Universal Terrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN). In some embodiments, the circuit configuration included in the interface device 4006 for managing wireless communication may operate according to Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications (DECT), Evolution-Data Optimized (EV-DO), and their derivatives, as well as any other radio protocols designated as 3G, 4G, 5G, and beyond. In some embodiments, the interface device 4006 may include one or more antennas (e.g., one or more antenna arrays) for receiving and / or transmitting wireless communication.

[0038] In some embodiments, the interface device 4006 may include a circuit configuration for managing wired communications, such as electrical, optical, or any other preferred communication protocol. For example, the interface device 4006 may include a circuit configuration to support communications according to Ethernet technology. In some embodiments, the interface device 4006 may support both wireless and wired communications and / or support multiple wired communication protocols and / or multiple wireless communication protocols. For example, a first set of circuit configurations for the interface device 4006 may be dedicated to short-range wireless communications such as Wi-Fi or Bluetooth, and a second set of circuit configurations for the interface device 4006 may be dedicated to long-range wireless communications such as Global Positioning System (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, or others. In some embodiments, a first set of circuit configurations for the interface device 4006 may be dedicated to wireless communications, and a second set of circuit configurations for the interface device 4006 may be dedicated to wired communications.

[0039] The computing device 4000 may include a battery / power circuit configuration 4008. The battery / power circuit configuration 4008 may include one or more energy storage devices (e.g., batteries or capacitors) and / or a circuit configuration for coupling components of the computing device 4000 to an energy source separate from the computing device 4000 (e.g., AC line power).

[0040] The computing device 4000 may include a display device 4010 (for example, multiple display devices). The display device 4010 may include any visual indicator such as a head-up display, computer monitor, projector, touchscreen display, liquid crystal display (LCD), light-emitting diode display, or flat panel display.

[0041] The computing device 4000 may include other input / output (I / O) devices 4012. These other I / O devices 4012 may include, for example, one or more audio output devices (e.g., speakers, headsets, earphones, alarms, etc.), one or more audio input devices (e.g., microphones or microphone arrays), location devices (e.g., GPS devices that communicate with satellite-based systems to receive the location of the computing device 4000, as known in the art), audio codecs, video codecs, printers, sensors (e.g., thermocouples or other temperature sensors, humidity sensors, pressure sensors, vibration sensors, accelerometers, gyroscopes, etc.), image capture devices such as cameras, keyboards, cursor control devices (e.g., mice, styluses, trackballs, or touchpads, etc.), barcode readers, Quick Response (QR) code readers, or radio frequency identification (radio It may include a frequency identification (RFID) reader.

[0042] The computing device 4000 may have any form factor suitable for its application and configuration, such as a handheld or mobile computing device (e.g., a mobile phone, smartphone, mobile internet device, tablet computer, laptop computer, netbook computer, ultrabook computer, personal digital assistant (PDA), ultramobile personal computer, etc.), a desktop computing device, or a server computing device or other networked computing component.

[0043] One or more computing devices that implement any of the scientific instrument support modules or methods disclosed herein may be part of a scientific instrument support system. Figure 6 is a block diagram of an exemplary scientific instrument support system 5000 in which some or all of the scientific instrument support methods disclosed herein may be implemented according to various embodiments. The scientific instrument support modules and methods disclosed herein (e.g., scientific instrument support module 1000 in Figure 1 and method 2000 in Figure 2) may be implemented by one or more of the scientific instrument 5010, user local computing device 5020, service local computing device 5030, or remote computing device 5040 of the scientific instrument support system 5000.

[0044] Any of the scientific instrument 5010, user-local computing device 5020, service-local computing device 5030, or remote computing device 5040 may include any embodiment of the computing device 4000 discussed herein with reference to Figure 5, and any of the scientific instrument 5010, user-local computing device 5020, service-local computing device 5030, or remote computing device 5040 may take the form of any suitable embodiment of the computing device 4000 discussed herein with reference to Figure 5.

[0045] A scientific instrument 5010, a user-local computing device 5020, a service-local computing device 5030, or a remote computing device 5040 may each include a processing device 5002, a storage device 5004, and an interface device 5006. The processing device 5002 may take any preferred form, including any form of the processing device 4002 discussed herein with reference to Figure 4, and the processing device 5002 included in different scientific instruments 5010, user-local computing devices 5020, service-local computing devices 5030, or remote computing devices 5040 may take the same or different forms. The storage device 5004 may take any preferred form, including any form of the storage device 5004 discussed herein with reference to Figure 4, and the storage device 5004 included in different scientific instruments 5010, user-local computing devices 5020, service-local computing devices 5030, or remote computing devices 5040 may take the same or different forms. Interface device 5006 can take any preferred form, including any form of interface device 4006 discussed herein with reference to Figure 4, and interface device 5006 included in different of scientific instruments 5010, user local computing devices 5020, service local computing devices 5030, or remote computing devices 5040 can take the same or different forms.

[0046] The scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, and the remote computing device 5040 may communicate with other elements of the scientific instrument support system 5000 via a communication path 5008. The communication path 5008 may communicatively connect interface devices 5006 of different elements of the scientific instrument support system 5000, as illustrated, and may be a wired or wireless communication path (for example, following any of the communication techniques discussed herein with reference to interface device 4006 of computing device 4000 in Figure 5). The particular scientific instrument support system 5000 depicted in Figure 6 includes communication paths between each pair of scientific instrument 5010, user local computing device 5020, service local computing device 5030, and remote computing device 5040, but this “fully connected” implementation is merely illustrative, and various forms may not have different communication paths 5008. For example, in some embodiments, the service local computing device 5030 may not have a direct communication path 5008 between its interface device 5006 and the interface device 5006 of the scientific instrument 5010. Instead, it may communicate with the scientific instrument 5010 via the communication path 5008 between the service local computing device 5030 and the user local computing device 5020, and the communication path 5008 between the user local computing device 5020 and the scientific instrument 5010.

[0047] The user-local computing device 5020 may be a computing device that is local to the user of the scientific instrument 5010 (for example, according to any embodiment of the computing device 4000 discussed herein). In some embodiments, the user-local computing device 5020 may also be, but not required to be, local to the scientific instrument 5010; for example, the user-local computing device 5020 located in the user's home or office may be remote from the scientific instrument 5010, but capable of communicating with it, so that the user can use the user-local computing device 5020 to control and / or access data from the scientific instrument 5010. In some embodiments, the user-local computing device 5020 may be a laptop, smartphone, or tablet device. In some embodiments, the user-local computing device 5020 may be a portable computing device.

[0048] The service local computing device 5030 may be a computing device that is local to an entity that provides services to the scientific instrument 5010 (for example, according to any embodiment of the computing device 4000 discussed herein). For example, the service local computing device 5030 may be local to the manufacturer of the scientific instrument 5010 or to a third-party service company. In some embodiments, the service local computing device 5030 may communicate with the scientific instrument 5010, the user local computing device 5020, and / or the remote computing device 5040 (for example, via a direct communication path 5008 or via a plurality of “indirect” communication paths 5008, as discussed above) to receive data relating to the operation of the scientific instrument 5010, the user local computing device 5020, and / or the remote computing device 5040 (for example, the results of a self-test of the scientific instrument 5010, calibration coefficients used by the scientific instrument 5010, measurements of sensors associated with the scientific instrument 5010, etc.). In some embodiments, the service local computing device 5030 may communicate with the scientific instrument 5010, the user local computing device 5020, and / or the remote computing device 5040 (for example, via a direct communication path 5008 or via a plurality of “indirect” communication paths 5008, as discussed above) to transmit data to the scientific instrument 5010, the user local computing device 5020, and / or the remote computing device 5040 (for example, to update programmed instructions such as firmware in the scientific instrument 5010, to initiate the execution of a test or calibration sequence in the scientific instrument 5010, to update programmed instructions such as software in the user local computing device 5020 or the remote computing device 5040).A user of the scientific instrument 5010 may use the scientific instrument 5010 or the user local computing device 5020 to communicate with the service local computing device 5030 for purposes such as reporting a problem with the scientific instrument 5010 or the user local computing device 5020, requesting a visit from a technician to improve the operation of the scientific instrument 5010, ordering consumables or replacement parts associated with the scientific instrument 5010, or for other purposes.

[0049] The remote computing device 5040 may be a computing device (for example, according to any embodiment of the computing device 4000 discussed herein) that is remote from the scientific instrument 5010 and / or the user local computing device 5020. In some embodiments, the remote computing device 5040 may be located in a data center or other large-scale server environment. In some embodiments, the remote computing device 5040 may include network-attached storage (for example, as part of storage device 5004). The remote computing device 5040 may store data generated by the scientific instrument 5010, perform analysis of the data generated by the scientific instrument 5010 (for example, according to programmed instructions), facilitate communication between the user local computing device 5020 and the scientific instrument 5010, and / or facilitate communication between the service local computing device 5030 and the scientific instrument 5010.

[0050] In some embodiments, one or more of the elements of the scientific instrument support system 5000 illustrated in Figure 6 may be absent. Furthermore, in some embodiments, multiple of the various elements of the scientific instrument support system 5000 in Figure 6 may be present. For example, the scientific instrument support system 5000 may include multiple user local computing devices 5020 (e.g., different user local computing devices 5020 associated with different users or located in different places). In another example, the scientific instrument support system 5000 may include multiple scientific instruments 5010, all of which communicate with a service local computing device 5030 and / or a remote computing device 5040, in which case the service local computing device 5030 may monitor these multiple scientific instruments 5010, or the service local computing device 5030 may trigger updates or other information may be "broadcast" to the multiple scientific instruments 5010 simultaneously. Different scientific instruments 5010 within the scientific instrument support system 5000 may be located close to each other (e.g., in the same room) or far apart from each other (e.g., on different floors of a building, in different buildings, in different cities, etc.). In some embodiments, the scientific instruments 5010 may be connected to an Internet of Things (IoT) stack that enables command and control of the scientific instruments 5010 through web-based applications, virtual or augmented reality applications, mobile applications, and / or desktop applications. Any of these applications may be accessed by a user operating a user-local computing device 5020 that communicates with the scientific instruments 5010 via an intervening remote computing device 5040. In some embodiments, the scientific instruments 5010 may be sold by the manufacturer as part of a local scientific instrument computing unit 5012, together with one or more associated user-local computing devices 5020. [Examples]

[0051] The following paragraphs provide various examples of the embodiments disclosed herein.

[0052] Example 1 is a mass spectrometry support system comprising: a first logic for receiving fragmentation data of a reference compound; a second logic for providing the fragmentation data as a virtual bait compound to a molecular network algorithm; a third logic for annotating the reference compound and generating clusters of compounds in the sample surrounding the annotated reference compound; and a fourth logic for displaying an overview of the structural relationships between the compound clusters.

[0053] Example 2 includes the subject matter of Example 1 and further describes how compounds are clustered by determining the similarity between fragmentation data of a reference compound and the actual features detected in the sample.

[0054] Example 3 includes the subject matter of either Example 1 or Example 2, and further states that the determination of similarity between fragmentation data and actual features is adjusted depending on the type of virtual bait compound or the origin of the fragmentation data.

[0055] Example 4 includes the subject matter of any of Examples 1-3, and further states that the source of the fragmentation data for the reference compound includes a spectrum artificially predicted based on a known structure, or a cumulative spectrum based on diagnostic fragments of a class of similar compounds.

[0056] Example 5 incorporates the subject matter of any of Examples 1-4, and further, the summary describes enabling the user to perform a semi-targeted search for potential target clusters.

[0057] Example 6 incorporates the subject matter of any of Examples 1-5, and further describes incorporating a virtual bait compound within an isolation mode, which allows the user to limit the depth of the displayed graph and remove scoring restrictions without losing control over the graph.

[0058] Example 7 includes the subject matter of any of Examples 1 to 6, and further describes that the virtual bait compound includes computer data fed into a molecular network algorithm.

[0059] Example 8 includes the subject matter of any of Examples 1 to 7, and further states that the fragmentation data of the reference compound is user-defined.

[0060] Example 9 comprises the subject matter of any of Examples 1 to 8, and further comprises a mass spectrometer, which generates data representing clusters of compounds in the sample.

[0061] Example 10 is a method for generating a candidate compound set based on mass spectrometry data, comprising: receiving data representing the fragmentation of a reference compound using a computing device; providing the data to a molecular network algorithm as virtual bait compounds using the computing device; generating clusters of compounds surrounding the reference compound in a sample using the computing device; and outputting a candidate compound set based on the compound clusters using the computing device.

[0062] Example 11 includes the subject matter of Example 10 and further describes how compounds are clustered by determining the similarity between fragmentation data of a reference compound and the actual features detected in the sample.

[0063] Example 12 includes the subject matter of Example 11 and further describes that the determination of similarity between fragmentation data and actual features is adjusted at least in part on the type of virtual bait compound or the origin of the fragmentation data.

[0064] Example 13 includes the subject matter of any of Examples 10-12 and further states that the source of the fragmentation data for the reference compound includes a spectrum artificially predicted based on a known structure.

[0065] Example 14 includes the subject matter of any of Examples 10-12 and further states that the source of fragmentation data for the reference compound includes a cumulative spectrum based on diagnostic fragments of a class of similar compounds.

[0066] Example 15 includes the subject matter of any of Examples 10 to 14. This further includes enabling users to perform semi-targeted searches within a set of candidate compounds.

[0067] Example 16 includes the subject matter of any of Examples 10-15, and further describes a display including a set of candidate compounds that incorporates virtual bait compounds within an isolation mode, which allows the user to limit the depth of the displayed graph and remove scoring restrictions without losing control over the graph.

[0068] Example 17 includes the subject matter of any of Examples 10-16 and further describes that the virtual bait compound includes computer data fed into a molecular network algorithm.

[0069] Example 18 includes the subject matter of any of Examples 10-17, and further states that the fragmentation data of the reference compound is user-defined.

[0070] Example 19 is a mass spectrometry system comprising a mass spectrometer and a mass spectrometry support device, wherein the mass spectrometry support device is communicatively coupled to the mass spectrometer and is configured to provide fragmentation data of a reference compound as a virtual bait compound to a molecular network algorithm, to annotate the reference compound, to generate clusters of compounds in the sample surrounding the annotated reference compound based on data about the sample generated by the mass spectrometer, and to display an overview of the compound clusters.

[0071] Example 20 includes the subject matter of Example 19 and further states that compounds are clustered by determining the similarity between the fragmentation data of a reference compound and the actual features detected in the sample.

Claims

1. A mass spectrometry support device, A first logic that receives fragmentation data of a reference compound, A second logic provides the aforementioned fragmented data as a virtual bait compound to a molecular network algorithm, A third logic involves annotating the aforementioned reference compound and generating a cluster of compounds in the sample surrounding the annotated reference compound. A mass spectrometry support apparatus comprising a fourth logic that provides an overview of the structural relationships between clusters of the aforementioned compounds.

2. The mass spectrometry support apparatus according to claim 1, wherein the compounds are clustered by determining the similarity between the fragmentation data of the reference compound and the actual features detected in the sample.

3. The mass spectrometry support apparatus according to claim 2, wherein the determination of the similarity between the fragmentation data and the actual features is adjusted according to the type of virtual bait compound or the origin of the fragmentation data.

4. The mass spectrometry support apparatus according to claim 1, wherein the source of the fragmentation data of the reference compound includes a spectrum artificially predicted based on a known structure, or a cumulative spectrum based on diagnostic fragments of a class of similar compounds.

5. The above summary relates to the mass spectrometry support apparatus according to claim 1, which enables the user to perform further semi-targeted searches of clusters.

6. The above summary relates to the mass spectrometry support apparatus according to claim 1, which incorporates the virtual bait compound in an isolation mode, allowing the user to limit the depth of the displayed graph depth and remove scoring restrictions without losing control over the graph.

7. The mass spectrometry support apparatus according to claim 1, wherein the virtual bait compound includes computer data fed into the molecular network algorithm.

8. The fragmentation data of the reference compound is a user-defined mass spectrometry support apparatus according to claim 1.

9. The aforementioned mass spectrometry support device, The mass spectrometry support apparatus according to claim 1, further comprising a mass spectrometer, wherein the mass spectrometer generates data representing the clusters of compounds in the sample.

10. A method for generating a set of candidate compounds based on mass spectrometry data, wherein the method is The computing device receives fragmentation data representing the fragmentation of a reference compound, The computing device provides the fragmented data to the molecular network algorithm as a virtual bait compound, Using the aforementioned computing device, a cluster of compounds surrounding the reference compound in the sample is generated, A method comprising the computing device outputting a set of candidate compounds based on the cluster of compounds.

11. The method according to claim 10, wherein the compounds are clustered by determining the similarity between the fragmentation data of the reference compound and the actual features detected in the sample.

12. The method according to claim 11, wherein determining the similarity between the fragmentation data and the actual features is adjusted at least in part on the type of the virtual bait compound or the origin of the fragmentation data.

13. The method according to claim 10, wherein the source of the fragmentation data of the reference compound includes a spectrum artificially predicted based on a known structure.

14. The method according to claim 10, wherein the source of the fragmentation data of the reference compound includes a cumulative spectrum based on a diagnostic fragment of a class of similar compounds.

15. The method according to claim 10, further comprising enabling the user to perform a semi-targeted search within the set of candidate compounds.

16. The method according to claim 10, wherein the display including the candidate compound set incorporates the virtual bait compound into an isolation mode that allows the user to limit the depth of the displayed graph and remove scoring restrictions without losing control over the graph.

17. The method according to claim 10, wherein the virtual bait compound includes computer data fed into the molecular network algorithm.

18. The method according to claim 10, wherein the fragmentation data of the reference compound is user-defined.

19. A mass spectrometry system, A mass spectrometer and, The system comprises a mass spectrometry support device, the mass spectrometry support device being communicatively coupled to the mass spectrometer, This involves providing fragmentation data of a reference compound as a virtual bait compound to a molecular network algorithm, Annotation of the aforementioned reference compound, Based on the data relating to the sample generated by the mass spectrometer, a cluster of compounds surrounding the annotated reference compound in the sample is generated, A mass spectrometry system configured to display an overview of the aforementioned clusters of compounds.

20. The mass spectrometry system according to claim 19, wherein the compounds are clustered by determining the similarity between the fragmentation data of the reference compound and the actual features detected in the sample.

Images