Filtering and selection of structural models: combining docking and NMR.

Area of Science:

• Structural biology
• Computational biology
• Biophysics

Background:

• Protein structure is crucial for function and more conserved than sequence.
• Accurate ab initio protein structure prediction remains a challenge, especially for complexes.
• Postgenomic research necessitates robust computational methods for 3D structure determination.

Purpose of the Study:

• To present a novel methodology for predicting protein structures using NMR constraints.
• To improve the accuracy of determining protein-protein complexes and domain-domain orientations.
• To integrate experimental data with computational modeling for structure prediction.

Main Methods:

• Generating structural models via docking of known substructures.
• Utilizing experimental Nuclear Magnetic Resonance (NMR) constraints, specifically residual dipolar couplings and chemical shift mapping.
• Combining NMR data with heuristic biochemical knowledge and contact potentials for model selection and filtering.

Main Results:

• Demonstrated successful determination of a protein-protein complex (EIN/HPr).
• Established domain-domain orientation in a chimeric protein (human-Escherichia coli thioredoxin).
• Validated the methodology's effectiveness in predicting complex structures.

Conclusions:

• The presented methodology offers a robust approach for protein structure determination.
• Integrating NMR data with computational methods enhances prediction accuracy for complexes and domain arrangements.
• This approach addresses limitations in current ab initio prediction algorithms.