  Local and global inference methods have been developed to infer structural
contacts from multiple sequence alignments of homologous proteins. They rely on
correlations in amino-acid usage at contacting sites. Because homologous
proteins share a common ancestry, their sequences also feature phylogenetic
correlations, which can impair contact inference. We investigate this effect by
generating controlled synthetic data from a minimal model where the importance
of contacts and of phylogeny can be tuned. We demonstrate that global inference
methods, specifically Potts models, are more resilient to phylogenetic
correlations than local methods, based on covariance or mutual information.
This holds whether or not phylogenetic corrections are used, and may explain
the success of global methods. We analyse the roles of selection strength and
of phylogenetic relatedness. We show that sites that mutate early in the
phylogeny yield false positive contacts. We consider natural data and realistic
synthetic data, and our findings generalise to these cases. Our results
highlight the impact of phylogeny on contact prediction from protein sequences
and illustrate the interplay between the rich structure of biological data and
inference.
