Correlated mutation analysis is an increasingly powerful approach used to help predict protein and protein-complex structures. The premise behind such methods is that mutations that occur at a given position in a protein are compensated by other mutations of residues close in space; such coevolving residues can be identified by multiple sequence alignments. However, the approach is subject to false positives stemming from indirect interactions or common ancestry. Jacob et al. report a clever way to help reduce such false positives by also considering codon-level multiple sequence alignme…

Source: Bioinformatics: Improving correlated mutation analysis : Nature Methods : Nature Publishing Group