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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Insights into Protein Sequence and Structure-Derived Features Mediating 3D Domain Swapping Mechanism using Support
Khader Shameer1, Ganesan Pugalenthi, Krishna Kumar Kandaswamy
1National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore, 560065, India.
Bioinformatics and Biology Insights
|July 17, 2010
Summary
We developed a machine learning model to predict protein 3-dimensional domain swapping, a process implicated in neurodegenerative diseases. This computational tool aids in identifying proteins prone to this structural change.
Area of Science:
- Structural biology
- Computational biology
- Biochemistry
Background:
- 3-dimensional domain swapping involves protein subunits exchanging identical or similar parts to form oligomers.
- This phenomenon is increasingly linked to prions and neurodegenerative diseases, affecting protein function, aggregation, and misfolding.
- Identifying common sequence or structural patterns in swapped proteins is challenging despite structural analysis capabilities.
Purpose of the Study:
- To develop a computational method for predicting 3-dimensional domain swapping events.
- To utilize sequence and structural data for building a predictive classifier.
- To facilitate the identification of novel proteins involved in domain swapping and related deposition diseases.
Main Methods:
- A Support Vector Machine (SVM)-based classifier was developed.
- The classifier was trained using features derived from sequence and structural data of 150 known 3D domain-swapping proteins and 150 non-swapping proteins.
- Performance was evaluated using a separate test set of 63 swapping and 63 non-swapping proteins.
Main Results:
- The SVM classifier achieved 76.33% accuracy during training.
- The model demonstrated 73.81% accuracy on the independent testing dataset.
- Feature selection identified key sequence and structure-derived features relevant to the domain swapping mechanism.
Conclusions:
- The developed SVM classifier provides a valuable tool for predicting 3D domain swapping events.
- This algorithm represents an initial step towards identifying more proteins potentially involved in swapping and deposition diseases.
- Further analysis of identified features can enhance understanding of the 3D domain swapping mechanism.
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Overview