Computing Highly Correlated Positions Using Mutual Information and Graph Theory for G Protein-Coupled Receptors
2009

Identifying Key Positions in G Protein-Coupled Receptors

Sample size: 358 publication 10 minutes Evidence: moderate

Author Information

Author(s): Fatakia Sarosh N., Costanzi Stefano, Chow Carson C.

Primary Institution: National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health

Hypothesis

The study aims to identify a cohort of non-conserved, yet correlated positions in G protein-coupled receptors (GPCRs).

Conclusion

The algorithm successfully identified key positions in GPCRs that are likely involved in ligand binding and receptor activation.

Supporting Evidence

  • The identified key positions are located within the ligand-binding cavity for classes A and C GPCRs.
  • The algorithm provides a ranked list of positions based on their connectivity.
  • Key positions were confirmed to be involved in ligand recognition in various GPCRs.

Takeaway

The researchers found important spots in proteins that help them interact with drugs, which could help in designing better medicines.

Methodology

The study used a novel algorithm based on mutual information and graph theory to analyze multiple sequence alignments of GPCRs.

Potential Biases

Potential biases may arise from the dataset used and the assumptions made in the algorithm.

Limitations

The algorithm may not identify all biologically relevant residues due to the focus on non-conserved positions.

Statistical Information

P-Value

p<0.05

Statistical Significance

p<0.05

Digital Object Identifier (DOI)

10.1371/journal.pone.0004681

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