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.
Background:
The transforming growth factor beta-1 (TGF β-1) cytokine exerts both pro-tumor and anti-tumor effects in carcinogenesis. An increasing body of literature suggests that TGF β-1 signaling outcome is partially dependent on the regulatory targets of downstream receptor-regulated Smad (R-Smad) proteins Smad2 and Smad3. However, the lack of Smad-specific antibodies for ChIP-seq hinders convenient identification of Smad-specific binding sites.
Results:
In this study, we use localization and affinity purification (LAP) tags to identify Smad-specific binding sites in a cancer cell line. Using ChIP-seq data obtained from LAP-tagged Smad proteins, we develop a convolutional neural network with long-short term memory (CNN-LSTM) as a deep learning approach to classify a pool of Smad-bound sites as being Smad2- or Smad3-bound. Our data showed that this approach is able to accurately classify Smad2- versus Smad3-bound sites. We use our model to dissect the role of each R-Smad in the progression of breast cancer using a previously published dataset.
Conclusions:
Our results suggests that deep learning approaches can be used to dissect binding site specificity of closely related transcription factors.
Insights
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.
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.
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