SamCC-Turbo

SamCC-Turbo performs automated per-residue detection and parametrization of protein coiled-coil structures to quantify properties such as degree and handedness of supercoiling, rotational state, and helical offset for structural analysis and modeling.

Key Features:

• Automatic detection: Automatically detects coiled-coil regions in protein structures.
• Parametric description: Leverages parametric equations to numerically describe degree and handedness of supercoiling, rotational state, and helical offset.
• Per-residue measurements: Produces per-residue measurements of coiled-coil geometry.
• Fragment decomposition: Decomposes coiled-coil structures into fragments based on varying degrees of supercoiling.
• PDB atlas: Surveyed the Protein Data Bank to produce an atlas of approximately 50,000 coiled-coil regions.
• Machine-learning-ready dataset: Generates a machine-learning-ready dataset that includes detailed measurements and decompositions.

Scientific Applications:

• Large-scale structural surveys: Enables systematic analysis of coiled-coil geometry across the Protein Data Bank.
• Structure–function analysis: Supports identification of structural features associated with conformational plasticity.
• Prediction and modeling: Supplies quantitative descriptors for prediction and modeling of coiled-coil structures.
• Machine learning: Provides curated, measurement-rich data suitable for training machine-learning models on coiled-coil properties.

Methodology:

Uses parametric equations to describe coiled-coil properties (degree and handedness of supercoiling, rotational state, helical offset), performs automated per-residue measurements, decomposes structures into fragments by supercoiling degree, and scanned the Protein Data Bank to assemble an atlas of ~50,000 coiled-coil regions and a machine-learning-ready dataset.