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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
Published on: July 16, 2017
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DANCE: Deep Learning-Assisted Analysis of ProteiN Sequences Using Chaos Enhanced Kaleidoscopic Images
Taslim Murad1, Prakash Chourasia1, Sarwan Ali2
1Department of Computer Science, Georgia State University, Atlanta, Georgia, USA.
Summary
This study introduces DANCE, a novel method that converts T cell receptor (TCR) protein sequences into kaleidoscopic images for cancer classification. This approach enhances the analysis of complex biomolecules, aiding in the development of TCR-based immunotherapies.
Area of Science:
- Immunoinformatics
- Computational Biology
- Bioinformatics
Background:
- T cell receptors (TCRs) are vital for immune response against cancer, necessitating accurate classification for effective immunotherapies.
- Analyzing TCR protein sequences presents challenges due to their length, potentially leading to information loss with traditional methods.
- Image-based representations offer a promising alternative for preserving crucial structural and functional details in biomolecule analysis.
Purpose of the Study:
- To develop a novel image-based representation for TCR protein sequences to improve analysis and classification.
- To introduce the Deep Learning-Assisted Analysis of ProteiN Sequences Using Chaos Enhanced Kaleidoscopic Images (DANCE) method.
- To investigate the effectiveness of DANCE in classifying TCRs based on their target cancer cells.
Main Methods:
- Generating protein sequence images using Chaos Game Representation (CGR) and a kaleidoscopic image approach.
- Applying the DANCE technique to convert TCR sequences into visual representations.
- Utilizing deep learning vision models to classify the generated kaleidoscopic images of TCRs.
Main Results:
- The DANCE technique successfully visualizes TCR protein sequences as unique kaleidoscopic images.
- Deep learning models effectively classified TCRs based on their target cancer cells using the generated images.
- The study demonstrates a correlation between visual patterns in the images and underlying protein properties.
Conclusions:
- The DANCE method provides an effective image-based representation for TCR protein sequences, overcoming limitations of traditional methods.
- Combining CGR image generation with deep learning classification offers a powerful approach for analyzing TCRs and advancing cancer immunotherapies.
- This study opens new avenues for protein sequence analysis and the development of targeted cancer treatments.
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