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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
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Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics.

Shengkun Ni1, Xiangtai Kong1, Yingying Zhang2

  • 1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China.

Cell Genomics
|September 20, 2024
PubMed
Summary
This summary is machine-generated.

PertKGE enhances drug discovery by accurately deconvoluting compound-protein interactions from perturbation transcriptomics. This knowledge graph embedding method improves target identification for new compounds and virtual screening for new targets.

Keywords:
compound-protein interactiondrug discoveryknowledge graph embeddingmachine learningperturbation transcriptomicstarget inferencevirtual screening

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Area of Science:

  • Computational Biology
  • Pharmacology
  • Genomics

Background:

  • Perturbation transcriptomics offers novel avenues for drug discovery.
  • Current analysis methods exhibit limitations in performance and applicability.

Purpose of the Study:

  • To introduce PertKGE, a knowledge graph embedding method for deconvoluting compound-protein interactions.
  • To enhance the accuracy of target inference and virtual screening in drug discovery.

Main Methods:

  • Utilizing knowledge graph embedding to model multi-level regulatory events.
  • Applying PertKGE to perturbation transcriptomics data.
  • Addressing
  • cold-start
  • challenges in drug target identification.

Main Results:

  • PertKGE significantly improves deconvoluting accuracy in inferring targets for new compounds and virtual screening for new targets.
  • Identified ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target for tankyrase inhibitor K-756.
  • Discovered five novel hits targeting aldehyde dehydrogenase 1B1 with a 10.2% hit rate.

Conclusions:

  • PertKGE effectively deconvolutes compound-protein interactions from perturbation transcriptomics.
  • Incorporating multi-level regulatory events alleviates representational biases.
  • PertKGE shows significant potential to accelerate drug discovery and therapeutic target identification.