  Function of proteins or a network of interacting proteins often involves
communication between residues that are well separated in sequence. The classic
example is the participation of distant residues in allosteric regulation.
Bioinformatic and structural analysis methods have been introduced to infer
residues that are correlated. Recently, increasing attention has been paid to
obtain the sequence properties that determine the tendency of disease related
proteins (Abeta peptides, prion proteins, transthyretin etc.) to aggregate and
form fibrils. Motivated in part by the need to identify sequence
characteristics that indicate a tendency to aggregate, we introduce a general
method that probes covariations in charged residues along the sequence in a
given protein family. The method, which involves computing the Sequence
Correlation Entropy (SCE) using the quenched probability Psk(i,j) of finding a
residue pair at a given sequence separation sk, allows us to classify protein
families in terms of their SCE. Our general approach may be a useful way in
obtaining evolutionary covariations of amino acid residues on a genome wide
level.
