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    BRET-BASED CORONAVIRUS MPRO PROTEASE SENSOR AND USES THEREOF
    11.
    发明公开
    BRET-BASED CORONAVIRUS MPRO PROTEASE SENSOR AND USES THEREOF 审中-公开

    公开(公告)号：US20240240228A1

    公开(公告)日：2024-07-18

    申请号：US18562772

    申请日：2022-05-20

    Applicant: Qatar Foundation for Education, Science and Community Development

    Inventor： Anupriya M. Geethakumari ,  Kabir Hassan Biswas

    IPC: C12Q1/37 ,  G01N21/64

    CPC classification number: C12Q1/37 ,  G01N21/6428 ,  G01N21/6458

    Abstract: The SARS-CoV-2 main protease, MPRO, is critical for its replication and is an appealing target for designing anti-SARS-CoV-2 agents. In this regard, a number of assays have been developed based on its cleavage sequence preferences to monitor its activity. These include the usage of Fluorescence Resonance Energy Transfer (FRET)-based substrates in vitro and a FlipGFP reporter, one which fluoresces after MPRO-mediated cleavage, in live cells. Here, a pair of genetically encoded, Bioluminescence Resonance Energy Transfer (BRET)-based sensors have been engineered for detecting SARS-CoV-2 MPRO proteolytic activity in living host cells. The sensors were generated by sandwiching MPRO N-terminal autocleavage sites, either AVLQSGFR (short) or KTSAVLQSGFRKME (long), in between the mNeonGreen and nanoLuc proteins. Co-expression of the sensor with the MPRO in live cells resulted in its cleavage in a dose-dependent manner while mutation of the critical C145 residue (C145A) in MPRO completely abrogated the sensor cleavage. A temporal activity of MPRO in live cells and its inhibition was shown using the well-characterized pharmacological agent GC376. The sensor developed here finds direct utility in studies related to drug discovery targeting the SARS-CoV-2 MPRO and functional genomics application to determine the effect of sequence variation in MPRO Importantly, the BRET-based sensors displayed increased sensitivities and specificities as compared to the recently developed FlipGFP-based MPRO sensor. Additionally, the sensors recapitulated the inhibition of MPRO by the well-characterized pharmacological agent GC376. Further, in vitro assays with the BRET-based MPRO sensors revealed a molecular crowding-mediated increase in the rate of MPRO activity and a decrease in the inhibitory potential of GC376. The sensor developed here finds direct utility in studies related to drug discovery targeting the SARS-CoV-2 MPRO and functional genomics application to determine the effect of sequence variation in MPRO.

    SCALABLE DARKWEB ANALYTICS
    12.
    发明公开
    SCALABLE DARKWEB ANALYTICS 审中-公开

    公开(公告)号：US20240171605A1

    公开(公告)日：2024-05-23

    申请号：US18386486

    申请日：2023-11-02

    Applicant: Qatar Foundation for Education, Science and Community Development

    Inventor： Yazan Boshmaf ,  Isuranga Don

    IPC: H04L9/40 ,  G06F16/53 ,  G06F16/951

    CPC classification number: H04L63/1441 ,  G06F16/53 ,  G06F16/951

    Abstract: Scalable darkweb analytics are provided by crawling content offered by a plurality of onion services; sanitizing the content from each onion service of the plurality of onion services into sanitized content that represents potentially malicious content in a non-malicious form; storing, in a database, the sanitized content in association with a unique identity for each onion service of the plurality of onion services; receiving a request for information related to a given onion service of the plurality of onion services; and providing, in response to the request, the information based on the sanitized content

    DEEP REINFORCEMENT LEARNING AGENT FOR DEMAND RESPONSE IN HOME ENERGY MANAGEMENT SYSTEMS
    16.
    发明公开
    DEEP REINFORCEMENT LEARNING AGENT FOR DEMAND RESPONSE IN HOME ENERGY MANAGEMENT SYSTEMS 审中-公开

    公开(公告)号：US20240079875A1

    公开(公告)日：2024-03-07

    申请号：US18242909

    申请日：2023-09-06

    Applicant: QATAR FOUNDATION FOR EDUCATION, SCIENCE AND COMMUNITY DEVELOPMENT ,  QATAR UNIVERSITY

    Inventor： Aya Amer ,  Khaled Shaban ,  Ahmed Massoud

    IPC: H02J3/14 ,  H02J3/00

    CPC classification number: H02J3/144 ,  H02J3/003

    Abstract: Deep reinforcement learning agents for demand response in home energy management systems are provided via training an agent via power availability data, electricity use data for an electrical load in a household, and an effect on a length of service of a power supply device to optimize a reward function that rewards: reduced electricity usage at peak demand times for the power grid according to the power availability data, increased user satisfaction with activation of the electrical load, and increased length of service for electrical devices used to delivery electricity to the electrical load; deploying the agent to the household; and activating electrical devices that are part of the electrical load for the household according to a schedule generated to optimize the reward function for delivery of power from at least one of the power grid and the power supply device to the household.

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