XRRpred

XRRpred predicts resolution and R-free values of protein crystal structures directly from amino acid sequences to assess protein crystal structure quality.

Key Features:

• Predictive capability: Predicts both resolution and R-free values from amino acid sequences, enabling assessment of expected crystal structure quality prior to experimental validation.
• Feature integration: Integrates original sequence profiles with hand-crafted features to inform prediction models.
• Modeling approach: Uses empirically selected and parametrized regressors combined with modern resampling techniques to improve prediction accuracy.
• Performance validation: Evaluated on an independent dataset, accurately modeling the relationship between resolution and R-free and reflecting structure quality across different structural classes.
• Comparative advantage: Outperforms indirect approaches based on crystallization propensity or alignment-based methods in predicting structure quality.

Scientific Applications:

• Experimental design optimization: Forecasts likely structure quality to prioritize constructs and experimental strategies.
• Target selection for structural studies: Guides selection of protein targets with a higher likelihood of yielding high-quality crystal structures.
• Structure-based drug design and molecular docking: Informs choice of targets and expected structure quality for rational drug design and molecular docking applications.

Methodology:

Integrates original sequence profiles with hand-crafted features and applies empirically selected and parametrized regressors together with modern resampling techniques.