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Related Concept Videos

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Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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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.
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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Related Experiment Video

Updated: Sep 4, 2025

Visualization and Quantification of TGFβ/BMP/SMAD Signaling under Different Fluid Shear Stress Conditions using Proximity-Ligation-Assay
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Deep learning for de-convolution of Smad2 versus Smad3 binding sites.

Jeremy W K Ng1, Esther H Q Ong1, Lisa Tucker-Kellogg2

  • 1Department of Biological Sciences, National University of Singapore, Singapore, Singapore.

BMC Genomics
|July 20, 2022
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Summary

This study introduces a deep learning model to distinguish Smad2 and Smad3 binding sites, crucial for understanding TGF β-1 signaling in cancer. The method accurately classifies these sites, aiding cancer progression research.

Keywords:
Feature engineeringMachine learningTranscription regulation

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Area of Science:

  • Molecular biology
  • Cancer research
  • Bioinformatics

Background:

  • Transforming growth factor beta-1 (TGF β-1) has dual roles in cancer.
  • TGF β-1 signaling outcomes depend on Smad2 and Smad3 proteins.
  • Identifying specific Smad binding sites is challenging due to a lack of antibodies.

Purpose of the Study:

  • To develop a method for identifying Smad2- and Smad3-specific binding sites.
  • To apply deep learning for dissecting R-Smad roles in breast cancer progression.

Main Methods:

  • Utilized localization and affinity purification (LAP) tags to isolate Smad-bound sites.
  • Developed a convolutional neural network with long-short term memory (CNN-LSTM) deep learning model.
  • Employed ChIP-seq data for training and validating the CNN-LSTM model.

Main Results:

  • The CNN-LSTM model accurately classified Smad2- versus Smad3-bound sites.
  • Successfully identified Smad-specific binding sites using LAP-tagged Smad proteins.
  • Dissected the distinct roles of Smad2 and Smad3 in breast cancer progression using the developed model.

Conclusions:

  • Deep learning models can effectively differentiate binding site specificity for related transcription factors.
  • This approach facilitates the study of complex signaling pathways like TGF β-1 in cancer.
  • Enables a deeper understanding of R-Smad functions in carcinogenesis.