  Amino-acid substitutions are implicated in a wide range of human diseases,
many of which are lethal. Distinguishing such mutations from polymorphisms
without significant effect on human health is a necessary step in understanding
the etiology of such diseases. Computational methods can be used to select
interesting mutations within a larger set, to corroborate experimental findings
and to elucidate the cause of the deleterious effect. In this work, we show
that taking into account the sequence context in which the mutation appears
allows to improve the predictive and explanatory power of such methods. We
present an unsupervised approach based on the direct-coupling analysis of
homologous proteins. We show its capability to quantify mutations where methods
without context dependence fail. We highlight cases where the context
dependence is interpretable as functional or structural constraints and show
that our simple and unsupervised method has an accuracy similar to
state-of-the-art methods, including supervised ones.
