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Divergent Evolution of a Protein-Protein Interaction Revealed through Ancestral Sequence Reconstruction and Resurrection.

ABSTRACT: The postsynaptic density extends across the postsynaptic dendritic spine with discs large (DLG) as the most abundant scaffolding protein. DLG dynamically alters the structure of the postsynaptic density, thus controlling the function and distribution of specific receptors at the synapse. DLG contains three PDZ domains and one important interaction governing postsynaptic architecture is that between the PDZ3 domain from DLG and a protein called cysteine-rich interactor of PDZ3 (CRIPT). However, little is known regarding functional evolution of the PDZ3:CRIPT interaction. Here, we subjected PDZ3 and CRIPT to ancestral sequence reconstruction, resurrection, and biophysical experiments. We show that the PDZ3:CRIPT interaction is an ancient interaction, which was likely present in the last common ancestor of Eukaryotes, and that high affinity is maintained in most extant animal phyla. However, affinity is low in nematodes and insects, raising questions about the physiological function of the interaction in species from these animal groups. Our findings demonstrate how an apparently established protein-protein interaction involved in cellular scaffolding in bilaterians can suddenly be subject to dynamic evolution including possible loss of function.

SUBMITTER: Laursen L

PROVIDER: S-EPMC7782867 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Ancestral reconstruction of protein interaction networks.

Project description:The molecular and cellular basis of novelty is an active area of research in evolutionary biology. Until very recently, the vast majority of cellular phenomena were so difficult to sample that cross-species studies of biochemistry were rare and comparative analysis at the level of biochemical systems was almost impossible. Recent advances in systems biology are changing what is possible, however, and comparative phylogenetic methods that can handle this new data are wanted. Here, we introduce the term "phylogenetic latent variable models" (PLVMs, pronounced "plums") for a class of models that has recently been used to infer the evolution of cellular states from systems-level molecular data, and develop a new parameterization and fitting strategy that is useful for comparative inference of biochemical networks. We deploy this new framework to infer the ancestral states and evolutionary dynamics of protein-interaction networks by analyzing >16,000 predominantly metazoan co-fractionation and affinity-purification mass spectrometry experiments. Based on these data, we estimate ancestral interactions across unikonts, broadly recovering protein complexes involved in translation, transcription, proteostasis, transport, and membrane trafficking. Using these results, we predict an ancient core of the Commander complex made up of CCDC22, CCDC93, C16orf62, and DSCR3, with more recent additions of COMMD-containing proteins in tetrapods. We also use simulations to develop model fitting strategies and discuss future model developments.

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AncestralClust: clustering of divergent nucleotide sequences by ancestral sequence reconstruction using phylogenetic trees.

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Project description:The selection of the best-fitting substitution model of molecular evolution is a traditional step for phylogenetic inferences, including ancestral sequence reconstruction (ASR). However, a few recent studies suggested that applying this procedure does not affect the accuracy of phylogenetic tree reconstruction. Here, we revisited this debate topic by analyzing the influence of selection among substitution models of protein evolution, with focus on exchangeability matrices, on the accuracy of ASR using simulated and real data. We found that the selected best-fitting substitution model produces the most accurate ancestral sequences, especially if the data present large genetic diversity. Indeed, ancestral sequences reconstructed under substitution models with similar exchangeability matrices were similar, suggesting that if the selected best-fitting model cannot be used for the reconstruction, applying a model similar to the selected one is preferred. We conclude that selecting among substitution models of protein evolution is recommended for reconstructing accurate ancestral sequences.

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The Mutational Road not Taken: Using Ancestral Sequence Resurrection to Evaluate the Evolution of Plant Enzyme Substrate Preferences.

Project description:We investigated the flowering plant salicylic acid methyl transferase (SAMT) enzyme lineage to understand the evolution of substrate preference change. Previous studies indicated that a single amino acid replacement to the SAMT active site (H150M) was sufficient to change ancestral enzyme substrate preference from benzoic acid to the structurally similar substrate, salicylic acid (SA). Yet, subsequent studies have shown that the H150M function-changing replacement did not likely occur during the historical episode of enzymatic divergence studied. Therefore, we reinvestigated the origin of SA methylation preference here and additionally assessed the extent to which epistasis may act to limit mutational paths. We found that the SAMT lineage of enzymes acquired preference to methylate SA from an ancestor that preferred to methylate benzoic acid as previously reported. In contrast, we found that a different amino acid replacement, Y267Q, was sufficient to change substrate preference with others providing small positive-magnitude epistatic improvements. We show that the kinetic basis for the ancestral enzymatic change in substate preference by Y267Q appears to be due to both a reduced specificity constant, kcat/KM, for benzoic acid and an improvement in KM for SA. Therefore, this lineage of enzymes appears to have had multiple mutational paths available to achieve the same evolutionary divergence. While the reasons remain unclear for why one path was taken, and the other was not, the mutational distance between ancestral and descendant codons may be a factor.

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Project description:As whole-genome protein-protein interaction datasets become available for a wide range of species, evolutionary biologists have the opportunity to address some of the unanswered questions surrounding the evolution of these complex systems. Protein interaction networks from divergent organisms may be compared to investigate how gene duplication, deletion, and rewiring processes have shaped the evolution of their contemporary structures. However, current approaches for comparing observed networks from multiple species lack the phylogenetic context necessary to reconstruct the evolutionary history of a network. Here we show how probabilistic modeling can provide a platform for the quantitative analysis of multiple protein interaction networks. We apply this technique to the reconstruction of ancestral networks for the bZIP family of transcription factors and find that excellent agreement is obtained with an alternative sequence-based method for the prediction of leucine zipper interactions. Further analysis shows our probabilistic method to be significantly more robust to the presence of noise in the observed network data than a simple parsimony-based approach. In addition, the integration of evidence over multiple species means that the same method may be used to improve the quality of noisy interaction data for extant species. The ancestral states of a protein interaction network have been reconstructed here by using an explicit probabilistic model of network evolution. We anticipate that this model will form the basis of more general methods for probing the evolutionary history of biochemical networks.

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Alignment Modulates Ancestral Sequence Reconstruction Accuracy.

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