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Proteins associated with diseases show enhanced sequence correlation between charged residues.
Ruxandra I Dima1, D Thirumalai
1Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA. dimar@Glue.umd.edu
Bioinformatics (Oxford, England)
|April 10, 2004
Summary
We developed a method to analyze protein sequence correlations, finding that low sequence correlation entropy (SCE) indicates disease association and aggregation tendency. This work advances understanding of protein function and disease mechanisms.
Area of Science:
- Computational Biology
- Protein Bioinformatics
- Structural Biology
Background:
- Protein function often relies on communication between distant residues, crucial for processes like allosteric regulation.
- Identifying sequence properties linked to protein aggregation, particularly in disease-related proteins (e.g., Abeta peptides, prions), is an active research area.
- Existing bioinformatic and structural methods infer correlated residues, but a general method for sequence-based aggregation tendency is needed.
Purpose of the Study:
- To introduce a general method for probing covariations in charged residues along protein sequences.
- To classify protein families based on sequence correlation entropy (SCE).
- To identify sequence characteristics indicative of protein aggregation and disease association.
Main Methods:
- Computation of sequence correlation entropy (SCE) using quenched probability P(sk)(i,j) for residue pairs at varying sequence separations (sk).
- Classification of 839 protein families (approx. 500,000 sequences) using SCE and principal component analysis-based clustering.
- Analysis of residue clustering in 3D structures and identification of charged-hydrophobic (CH) and charged-polar (CP) runs in low-SCE proteins.
Main Results:
- Proteins with low SCE values were predominantly associated with various diseases.
- Low-SCE proteins exhibited significant numbers of mixed charged-hydrophobic (CH) and charged-polar (CP) runs.
- Residues contributing to peaks in P(sk)(i,j) were often clustered in the 3D structure for several families.
Conclusions:
- Low SCE values and the presence of CH/CP runs may indicate disease association or aggregation propensity.
- Protein functions may be linked for proteins with similar SCE values, suggesting a basis for functional classification.
- Overall charge correlations in proteins likely affect amyloid formation kinetics, relevant to neurodegenerative diseases.