BEST

BEST models X-ray crystallography intensity decay from radiation damage and optimizes protein crystallography data-collection strategies using log-linear models of intensity as functions of resolution and cumulative X-ray dose.

Key Features:

• Radiation Damage Mitigation: Models radiation-induced intensity variation as log-linear functions dependent on resolution and cumulative X-ray dose.
• Predictive Modeling: Predicts characteristics of yet-to-be-collected diffraction data and relates achievable data completeness, resolution, and signal-to-noise statistics.
• Optimization Algorithms: Designs data-collection plans by optimizing experimental parameters to balance data completeness, resolution, and signal-to-noise.

Scientific Applications:

• Protein Crystallography: Planning and optimizing X-ray data collection in structural biology to obtain high-quality diffraction data while minimizing radiation damage.

Methodology:

Models the impact of radiation on intensity variations using log-linear approximation as a function of resolution and cumulative X-ray dose, and employs predictive analytics to forecast experimental outcomes and optimize data-collection strategies based on objectives for data completeness, resolution, and signal-to-noise ratios.