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Published on: July 14, 2015
Aromatic claw: A new fold with high aromatic content that evades structural prediction
Joseph R Sachleben1, Aashish N Adhikari2, Grzegorz Gawlak3
1Biomolecular NMR Core Facility, University of Chicago, Chicago, Illinois.
Researchers determined the NMR structure of a novel protein, Aq1974, revealing a unique "Aromatic Claw" fold. This discovery highlights limitations in predicting protein structures from sequence alone.
Area of Science:
- Structural biology
- Protein science
- Biophysics
Background:
- The protein Aq1974 from Aquifex aeolicus possesses a highly aromatic sequence with unusually high tryptophan content.
- Its sequence exhibits no detectable homology to known protein structures, indicating a potentially novel fold.
Purpose of the Study:
- To determine the three-dimensional solution structure of the aromatic protein Aq1974 using Nuclear Magnetic Resonance (NMR) spectroscopy.
- To characterize the novel protein fold and assess the accuracy of computational protein structure prediction methods for unique sequences.
Main Methods:
- Nuclear Magnetic Resonance (NMR) spectroscopy was employed for resonance assignment and solution structure determination.
- The determined structure was compared against predictions from the Critical Assessment of protein Structure Prediction (CASP10) competition.
Main Results:
- The NMR structure revealed a primarily alpha-helical protein with a small, two-stranded beta-sheet, forming a novel fold termed the 'Aromatic Claw'.
- Despite initial spectral indications of high beta-sheet content, the final structure was predominantly alpha-helical.
- Computational predictions in CASP10 failed to accurately predict the experimental structure, with limited correlation between prediction scores and accuracy.
Conclusions:
- The experimental determination of the novel 'Aromatic Claw' fold in Aq1974 underscores the challenges in predicting protein structure from primary sequence.
- This finding emphasizes the ongoing need for experimental structural biology to guide and improve computational protein structure prediction algorithms.
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