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Abstract
<p class="first" id="d935421e142">The protein kinase catalytic domain is one of the
most abundant domains across all
branches of life. Although kinases share a common core function of phosphoryl-transfer,
they also have wide functional diversity and play varied roles in cell signaling networks,
and for this reason are implicated in a number of human diseases. This functional
diversity is primarily achieved through sequence variation, and uncovering the sequence-function
relationships for the kinase family is a major challenge. In this study we use a statistical
inference technique inspired by statistical physics, which builds a coevolutionary
“Potts” Hamiltonian model of sequence variation in a protein family. We show how this
model has sufficient power to predict the probability of specific subsequences in
the highly diverged kinase family, which we verify by comparing the model’s predictions
with experimental observations in the Uniprot database. We show that the pairwise
(residue-residue) interaction terms of the statistical model are necessary and sufficient
to capture higher-than-pairwise mutation patterns of natural kinase sequences. We
observe that previously identified functional sets of residues have much stronger
correlated interaction scores than are typical.
</p>