ARIA

ARIA automates NOE cross-peak assignment and computes macromolecular three-dimensional structures from Nuclear Magnetic Resonance (NMR) data to derive inter‑proton distance restraints.

Key Features:

• Automated NOE Assignment: Automated assignment of NOE cross-peaks to derive inter-proton distance restraints from NMR spectra.
• Iterative Structure Calculation: Iterative simultaneous NOE assignment and structure calculation that refines assignments and coordinates until convergence.
• Log-Harmonic Distance Restraint Potential: Implements a log-harmonic distance-restraint potential for representing inter-proton distances during structure calculation.
• Bayesian Weighting of Distance Restraints: Applies Bayesian weighting to distance restraints to address ambiguity and noise in NOE-derived distances.
• Integration with CCPN Data Model: Uses the Collaborative Computing Project for NMR (CCPN) object-oriented data model for data storage and exchange.
• Support for Solid-State NMR (MAS): Supports magic-angle spinning (MAS) solid-state NMR datasets for structure calculation.
• Advanced Protocols and Modifications: Includes protocol modifications such as softening of the force field with adapted hydrogen radii and automated adjustment of violation tolerance in restraint selection.

Scientific Applications:

• NMR Structure Determination: Automated derivation of inter-proton distance restraints and three-dimensional structures from solution-state NMR data.
• Solid-State NMR Structure Determination: Structure calculation applied to magic-angle spinning (MAS) solid-state NMR datasets.
• Structural Genomics and Benchmarking (CASD-NMR): Use in structural genomics projects and CASD-NMR blind-test benchmarking of NMR structure determination methods.
• Integrative Structural Biology: Handling of ambiguous and noisy distance data for integrative approaches that combine multiple experimental methods.

Methodology:

Iterative refinement of NOE assignments and structure calculation until convergence; use of a log-harmonic distance-restraint potential and Bayesian statistics to weight distance restraints; incorporation of force-field softening via adapted hydrogen radii and automated violation-tolerance adjustment; integration with the CCPN object-oriented data model and support for MAS solid-state NMR.