SPROUTS

SPROUTS models early-stage protein folding and predicts mutation-induced structural changes by Monte Carlo simulation in a discrete-space framework, calculating the number of non-covalent neighbors per residue.

Key Features:

• Query mode: Provides existing mutation data for a specified protein identified by its Protein Data Bank (PDB) ID.
• Submit mode: Generates new mutation data from a provided PDB ID or user-defined input parameters.
• Monte Carlo simulation: Uses a Monte Carlo simulation-based algorithm to model the initial steps of protein folding within a discrete space framework.
• Non-covalent neighbor calculation: Computes the number of non-covalent neighbors for each residue during early folding stages.
• Intrachain contact analysis: Assesses intrachain contacts driven primarily by hydrophobic interactions to evaluate formation and stabilization of native structure.
• Most Interacting Residues (MIR): Applies the MIR method to classify amino acids as most or least interacting based on involvement in early folding events.
• MIR residue properties: Identifies MIR positions that predominantly correspond to hydrophobic residues with minimal accessible surface area and to residues whose mutation can induce conformational changes.
• Smoothed MIR (SMIR): Applies SMIR postprocessing that incorporates residue hydrophobicity knowledge to refine predictions of structural alterations and secondary structure formation.

Scientific Applications:

• Protein folding analysis: Investigates early stages of protein folding and the formation of intrachain contacts.
• Mutation effect prediction: Predicts mutations likely to induce conformational changes or alter stability based on MIR/SMIR analyses.
• Residue criticality identification: Identifies residues critical to folding using MIR classification and hydrophobicity metrics.
• Secondary structure inference: Refines predictions of secondary structure formation via SMIR-informed hydrophobicity smoothing.
• Structural biology and protein engineering support: Provides mutation datasets and generated mutation hypotheses for structure-function studies and protein engineering investigations.

Methodology:

Monte Carlo simulation-based algorithm in a discrete space framework; computation of the number of non-covalent neighbors per residue during early folding stages; classification of residues using the Most Interacting Residues (MIR) method; Smoothed MIR (SMIR) postprocessing incorporating residue hydrophobicity to refine predictions of structural alterations and secondary structure formation.