Correlated mutation analyses on very large sequence families

Area of Science:

• Bioinformatics and Computational Biology
• Molecular Biology and Biochemistry
• Structural Biology

Background:

• The 'omics era' generates massive biological sequence data requiring advanced interpretation techniques.
• Existing analytical methods struggle to effectively process the large volumes of sequence information.
• Understanding protein function relies on identifying key residues and their interactions.

Purpose of the Study:

• To develop and apply novel analytical techniques for interpreting large-scale sequence data.
• To utilize sequence entropy, variability, and correlated mutation analysis to uncover protein functional roles.
• To investigate G-protein-coupled receptors (GPCRs) by identifying functionally critical residues.

Main Methods:

• Sequence entropy calculation to measure information content in alignments.
• Sequence variability assessment to determine mutational flexibility at residue positions.
• Correlated mutation analysis to detect co-evolving residue positions.
• Application of these methods to well-characterized protein families (globins, ras-like proteins, serine proteases) and subsequently to GPCRs.

Main Results:

• Entropy-variability plots effectively distinguish residues involved in active sites, modulator binding, and signal transduction.
• Correlated mutation analysis identifies groups of residues that function and mutate collectively.
• The combined approach successfully mapped key residues in GPCRs responsible for G-protein coupling, agonist binding, and signal transduction.

Conclusions:

• The integrated analysis of sequence entropy, variability, and correlated mutations is a powerful tool for converting sequence data into functional insights.
• This methodology can accurately identify functionally important residues across diverse protein families, including GPCRs.
• The findings support a two-step evolutionary model for the development of functional proteins.