StrucTFactor
StrucTFactor leverages 3D protein structures for precise transcription factor prediction, outperforming existing methods.
Overview
StrucTFactor is a novel deep learning-based method for predicting transcription factors by utilizing 3D secondary structural features of proteins. Unlike traditional sequence-based methods, StrucTFactor employs a CNN-based architecture to analyze structural information, significantly improving the accuracy of transcription factor prediction. The model has been evaluated on extensive datasets, outperforming state-of-the-art methods in metrics like Matthews correlation coefficient and AU-PRC. It is an innovative tool for identifying novel transcription factors and offers insights into DNA-binding domains without requiring prior annotations.
Run StrucTFactor on Neurosnap
The StrucTFactor online webserver allows anybody with a Neurosnap account to run and access StrucTFactor, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- Predicts transcription factors using 3D secondary structural features of proteins.
- Outperforms state-of-the-art methods like DeepTFactor and DeepReg in accuracy and robustness.
- Leverages secondary structural annotations (α-helix, β-sheet, coil-turn).
- Applicable for identifying novel transcription factors without requiring prior knowledge of DNA-binding domains.
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
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API Request
Access StrucTFactor using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
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Citations
Please cite the original work when using StrucTFactor in publications or research outputs.
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Neuhaus F, Liebold J, Baumbach J, Newaz K. Transcription factor prediction using protein 3D structures. bioRxiv. 2024:2024-03. |
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Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983; 22:2577-2637. |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |