CABS-fold

CABS-fold predicts three-dimensional protein structures from amino acid sequences using de novo and consensus-based modeling to generate coarse-grained trajectories and all-atom models.

Key Features:

• De novo modeling: Predicts protein structures directly from amino acid sequences without relying on existing structural templates.
• Consensus modeling: Incorporates alternative templates to produce consensus-based structural models.
• Fragmentary distance restraints: Integrates fragmentary distance restraints as spatial constraints during modeling.
• CASP-recognized methodologies: Implements procedures based on methods recognized in previous Critical Assessment of Techniques for Protein Structure Prediction (CASP) assessments.
• Coarse-grained trajectories: Outputs coarse-grained conformational trajectories of generated models.
• All-atom reconstruction: Produces predicted models in all-atom resolution derived from coarse-grained conformations.
• Jmol representation: Provides Jmol representations of predicted structures.
• Analysis tools: Includes computational analysis tools for examination and validation of predicted structures.

Scientific Applications:

• Protein structure prediction: Determining three-dimensional structures from amino acid sequences for individual proteins.
• Structural biology: Studying protein folding, conformational ensembles, and structural hypotheses.
• Bioinformatics: Generating models for computational analyses and comparative structural studies.
• Functional annotation: Inferring protein function and structural features from predicted models.
• Drug discovery: Supporting structure-based drug design and target analysis using predicted all-atom models.

Methodology:

Performs de novo modeling and consensus modeling using alternative templates, integrates fragmentary distance restraints, generates coarse-grained conformational trajectories, and reconstructs all-atom models using methodologies rooted in approaches recognized in CASP.