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Utilizing Time-Resolved Protein-Induced Fluorescence Enhancement to Identify Stable Local Conformations One α-Synuclein Monomer at a Time
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FELLS: fast estimator of latent local structure
Damiano Piovesan1, Ian Walsh1,2, Giovanni Minervini1
1Department of Biomedical Sciences, University of Padua, Padova, Italy.
Bioinformatics (Oxford, England)
|February 11, 2017
Summary
FELLS predicts multiple protein features like disorder and amphipathicity from sequence. This tool offers fast, holistic protein structure analysis for large datasets and complex cases.
Area of Science:
- Protein bioinformatics
- Computational structural biology
Background:
- Protein sequence dictates behavior, including secondary structure, disorder, and amphipathicity.
- Existing tools often focus on single features, limiting holistic protein structure understanding.
- Context-dependent protein behavior requires integrated feature analysis.
Purpose of the Study:
- To introduce Fast Estimator of Latent Local Structure (FELLS) for visualizing protein structural features from sequence.
- To provide a comprehensive tool for predicting disorder, aggregation, low complexity, and amphipathicity.
- To develop a fast secondary structure estimator (FESS) for rapid analysis.
Main Methods:
- Development of the FELLS web server and RESTful API.
- Implementation of a novel fast estimator of secondary structure (FESS).
- Utilizing sequence-based prediction of multiple structural and functional features.
Main Results:
- FELLS provides predictions for disorder, aggregation, low complexity, and amphipathicity.
- The integrated FESS tool offers rapid secondary structure estimation.
- FELLS enables fast, large-scale analysis and detailed examination of challenging protein structures.
Conclusions:
- FELLS offers a holistic approach to protein structure visualization and analysis.
- The tool's speed and comprehensive predictions are valuable for large-scale bioinformatics.
- FELLS aids in understanding context-dependent protein behavior through integrated feature analysis.
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