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On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins.

ABSTRACT: Coevolution-based contact prediction, either directly by coevolutionary couplings resulting from global statistical sequence models or using structural supervision and deep learning, has found widespread application in protein-structure prediction from sequence. However, one of the basic assumptions in global statistical modeling is that sequences form an at least approximately independent sample of an unknown probability distribution, which is to be learned from data. In the case of protein families, this assumption is obviously violated by phylogenetic relations between protein sequences. It has turned out to be notoriously difficult to take phylogenetic correlations into account in coevolutionary model learning. Here, we propose a complementary approach: we develop strategies to randomize or resample sequence data, such that conservation patterns and phylogenetic relations are preserved, while intrinsic (i.e. structure- or function-based) coevolutionary couplings are removed. A comparison between the results of Direct Coupling Analysis applied to real and to resampled data shows that the largest coevolutionary couplings, i.e. those used for contact prediction, are only weakly influenced by phylogeny. However, the phylogeny-induced spurious couplings in the resampled data are compatible in size with the first false-positive contact predictions from real data. Dissecting functional from phylogeny-induced couplings might therefore extend accurate contact predictions to the range of intermediate-size couplings.

SUBMITTER: Rodriguez Horta E

PROVIDER: S-EPMC8177639 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The unpalatable and warning-patterned butterflies Heliconius erato and Heliconius melpomene provide the best studied example of mutualistic Müllerian mimicry, thought-but rarely demonstrated-to promote coevolution. Some of the strongest available evidence for coevolution comes from phylogenetic codivergence, the parallel divergence of ecologically associated lineages. Early evolutionary reconstructions suggested codivergence between mimetic populations of H. erato and H. melpomene, and this was initially hailed as one of the most striking known cases of coevolution. However, subsequent molecular phylogenetic analyses found discrepancies in phylogenetic branching patterns and timing (topological and temporal incongruence) that argued against codivergence. We present the first explicit cophylogenetic test of codivergence between mimetic populations of H. erato and H. melpomene, and re-examine the timing of these radiations. We find statistically significant topological congruence between multilocus coalescent population phylogenies of H. erato and H. melpomene. Cophylogenetic historical reconstructions support repeated codivergence of mimetic populations, from the base of the sampled radiations. Pairwise distance correlation tests, based on our coalescent analyses plus recently published AFLP and wing colour pattern gene data, also suggest that the phylogenies of H. erato and H. melpomene show significant topological congruence. Divergence time estimates, based on a Bayesian coalescent model, suggest that the evolutionary radiations of H. erato and H. melpomene occurred over the same time period, and are compatible with a series of temporally congruent codivergence events. Our results suggest that differences in within-species genetic divergence are the result of a greater overall effective population size for H. erato relative to H. melpomene and do not imply incongruence in the timing of their phylogenetic radiations. Repeated codivergence between Müllerian co-mimics, predicted to exert mutual selection pressures, strongly suggests coevolution. Our results therefore support a history of reciprocal coevolution between Müllerian co-mimics characterised by phylogenetic codivergence and parallel phenotypic change.

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On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins.

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