The challenge of protein structure determination--lessons from structural genomics

Area of Science:

• Structural biology
• Protein crystallography
• Biochemistry

Background:

• Experimental protein structure determination faces high failure rates.
• Large-scale data from structural genomics enables learning from past failures.
• Identifying predictive features can optimize experimental success.

Purpose of the Study:

• To identify protein features correlated with successful production and crystallization.
• To develop a quantitative score for 'crystallization feasibility'.
• To improve target selection for efficient protein structure determination.

Main Methods:

• Analysis of large datasets from high-throughput structure determination.
• Identification and combination of features strongly correlating with crystallization success.
• Validation of the derived 'crystallization feasibility' score using jackknife procedures and independent benchmark sets.

Main Results:

• Several protein features were identified that strongly correlate with successful protein production and crystallization.
• A novel 'crystallization feasibility' score was developed by combining these features.
• The score demonstrated effectiveness in validation tests and is actively used for target selection.

Conclusions:

• The 'crystallization feasibility' score effectively predicts the likelihood of protein crystallization success.
• Application of this score increases success rates, reduces costs, and shortens timelines in structural genomics.
• The score is relevant for both high-throughput and conventional structural biology, molecular, and biochemistry laboratories.