  Algorithms that detect covariance between pairs of columns in multiple
sequence alignments are commonly employed to predict functionally important
residues and structural contacts. However, the assumption that co-variance only
occurs between individual residues in the protein is more driven by
computational convenience rather than fundamental protein architecture. Here we
develop a novel algorithm that defines a covariance score across two groups of
columns where each group represents a stretch of contiguous columns in the
alignment. We define a test set that consists of secondary structure elements
({\alpha}-helixes and {\beta}-strands) across more than 1,100 PFAM families.
Using these alignments to predict segments that are physically close in
structure, we show that our method substantially out-performs approaches that
aggregate the results of algorithms that operate on individual column pairs.
Our approach demonstrates that considering units of proteins beyond pairs of
columns can improve the power and utility of covariance algorithms.
