FELLS

FELLS predicts multiple structural and physicochemical features from protein sequences to provide integrated analysis of local protein structure and properties.

Key Features:

• Secondary Structure Prediction: Includes the fast estimator of secondary structure (FESS), a trained estimator for rapid local secondary-structure prediction.
• Intrinsic Disorder Prediction: Predicts regions of intrinsic disorder to identify dynamic and context-dependent segments of proteins.
• Aggregation Propensity: Assesses likelihood of protein aggregation, relevant to amyloidosis and other protein misfolding disorders.
• Sequence Complexity Analysis: Evaluates sequence complexity to identify regions prone to functional variability or evolutionary change.
• Amino Acid Propensity Estimation: Estimates local amino-acid propensities, including amphipathicity, to inform membrane interactions and other biomolecular contacts.
• Speed-Optimized Computation: Employs computational optimizations to enable fast processing suitable for large datasets.

Scientific Applications:

• Large-scale proteomic analysis: Enables rapid, comprehensive prediction of multiple structural features across proteomes.
• Disease mechanism studies: Supports investigation of amyloidosis and other protein misfolding-related diseases via aggregation and disorder predictions.
• Protein–protein and protein–membrane interaction studies: Informs analysis of interaction interfaces through secondary structure, amphipathicity, and propensity estimates.
• Evolutionary and sequence-function analysis: Aids identification of regions with variable sequence complexity and potential functional divergence.

Methodology:

FELLS applies a novel fast estimator of secondary structure (FESS) trained for rapid prediction and uses advanced computational algorithms with calculations optimized for speed.