CORNET

CORNET predicts residue contacts within proteins using neural networks and chemo-physical plus evolutionary data to generate contact maps for analysis of protein structure and function.

Key Features:

• Neural network architecture: Employs neural networks trained on 200 non-homologous proteins with well-resolved three-dimensional structures using standard backpropagation to learn associations between covalent protein structure and contact maps.
• Input data: Integrates chemo-physical properties and evolutionary information as inputs for contact prediction.
• Validation dataset: Validated against an independent test set of 408 proteins with known structures that are non-homologous to the training set.
• Performance comparison: Demonstrates improved accuracy relative to previous statistical approaches for predicting protein contact maps.

Scientific Applications:

• Protein structure prediction: Provides contact maps that aid in modeling protein tertiary structure from sequence-derived information.
• Structural biology research: Enables inference of structural features from sequence and derived properties for proteins lacking experimental structures.
• Drug design and development: Supplies residue contact information useful for identifying interaction sites and informing ligand design.

Methodology:

Train neural networks with standard backpropagation using chemo-physical properties and evolutionary information as inputs to learn associations between covalent protein structure and contact maps; training used 200 non-homologous proteins with well-resolved three-dimensional structures and validation used an independent test set of 408 non-homologous proteins.