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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens
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PriorCCI: Interpretable Deep Learning Framework for Identifying Key Ligand-Receptor Interactions Between Specific

Hanbyeol Kim1, Eunyoung Choi1, Yujeong Shim1

  • 1Bioinformatics Branch, National Cancer Center, Goyang 10408, Republic of Korea.

International Journal of Molecular Sciences
|August 14, 2025
PubMed
Summary
This summary is machine-generated.

PriorCCI, a deep learning framework, enhances the analysis of cell-cell interactions (CCI) in single-cell RNA sequencing data. It accurately identifies key ligand-receptor interactions within complex tumor microenvironments.

Keywords:
angiogenesiscell-cell interactionconvolutional neural networkendothelial cellexplainable AIprioritizationsingle-cell RNA-seq datatumor microenvironment

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Measuring Transcellular Interactions through Protein Aggregation in a Heterologous Cell System
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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Understanding cell-cell interactions (CCI) is crucial for studying biological processes like immune responses and cancer.
  • Current methods using single-cell RNA sequencing (scRNA-seq) data struggle with sparsity and heterogeneity, often missing key interactions.
  • Existing statistical and network-based techniques lack the precision to prioritize biologically significant CCI.

Purpose of the Study:

  • To develop a deep learning framework, PriorCCI, for scalable, interpretable, and biologically meaningful identification of CCI from scRNA-seq data.
  • To overcome limitations in current methods for prioritizing ligand-receptor interactions in complex biological systems.
  • To provide a robust method for analyzing gene-gene interactions across cell types, particularly within tumor microenvironments.

Main Methods:

  • Developed PriorCCI, a deep learning framework utilizing a convolutional neural network (CNN).
  • Integrated Grad-CAM++, an explainable artificial intelligence algorithm, for visual interpretation of gene-pair contributions.
  • Applied the framework to single-cell RNA sequencing data from complex environments, such as tumors.

Main Results:

  • PriorCCI effectively prioritizes interactions between cancer cells and other cell types in the tumor microenvironment.
  • The framework accurately identifies biologically significant interactions, including those related to angiogenesis.
  • Visual interpretation of gene-pair contributions enhances the robustness of inferred gene-gene interactions.

Conclusions:

  • PriorCCI offers a powerful and interpretable approach for systematic identification and prioritization of CCI from scRNA-seq data.
  • The framework addresses challenges posed by data sparsity and heterogeneity in complex biological samples.
  • PriorCCI facilitates deeper insights into cellular communication networks within physiological and pathological contexts.