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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
A generalized analysis of hydrophobic and loop clusters within globular protein sequences
Richard Eudes1, Khanh Le Tuan, Jean Delettré
1Structural Biology Department, IMPMC, CNRS UMR7590, Universités Paris 6 & Paris 7, case 115, 4 place Jussieu, 75252 Paris Cedex 05, France. richard.eudes@impmc.jussieu.fr <richard.eudes@impmc.jussieu.fr>
Hydrophobic Cluster Analysis (HCA) can now be more easily interpreted for protein structures. This study details structural preferences of hydrophobic clusters, aiding in secondary structure prediction and understanding protein folds.
Area of Science:
- Structural bioinformatics
- Computational biology
- Protein structure analysis
Background:
- Hydrophobic Cluster Analysis (HCA) offers insights into protein secondary structures from hydrophobic clusters.
- Current HCA applications are limited by user expertise requirements.
- This study characterizes hydrophobic cluster species in globular protein domains.
Purpose of the Study:
- To elucidate the structural preferences of frequently encountered hydrophobic cluster species.
- To extend HCA analysis to loop-forming clusters using a specialized alphabet.
- To provide a foundation for improved secondary structure prediction tools.
Main Methods:
- Analysis of 294 frequent hydrophobic cluster species within experimental protein structures.
- Investigation of secondary structure associations and propensities.
- Application of a "PGDNS" alphabet for loop cluster analysis.
Main Results:
- Hydrophobic cluster species predominantly associate with alpha-helices and beta-strands (60%).
- Amino acid composition refines secondary structure prediction within clusters.
- Loop clusters are highly associated with coil structures.
- PSI-PRED predictions for alpha-helices and beta-strands were found to be generally over-predicted.
Conclusions:
- A dictionary of hydrophobic clusters aids HCA plot interpretation and understanding protein fold components.
- Loop cluster analysis provides additional information for whole-sequence secondary structure prediction.
- The findings lay groundwork for developing novel secondary structure prediction tools.
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