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PreprintCited by 6Reproductive Biology and FertilityReproductive biology and impacts on aquatic speciesEvolution and Genetic Dynamics

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Domain expansion and functional diversification in vertebrate reproductive proteins

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Rivera, Alberto M., Wilburn, Damien B., Swanson, Willie J.·2022·Molecular Biology and Evolution
DOI 10.1101/2022.01.31.478407PMC PMC9154058

Preprint — published as 10.1093/molbev/msac105 · Paper page

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Abstract

The rapid evolution of fertilization proteins can result in remarkable diversity in their structure and function. Many of the proteins in vertebrate egg coats contain copies (1-6) of the ZP-N domain. These ZP-N domains can facilitate multiple reproduction functions, including species-specific sperm recognition. We integrated phylogenetics and machine learning to investigate how ZP-N domains diversified in structure and function. The most C-terminal ZP-N domain of each paralog is associated with another domain type (ZP-C) which together form a “ZP module.” All modular ZP-N domains were phylogenetically distinct from non-modular or free ZP-N domains. Machine learning-based classification identified 8 residues that form a stabilizing network in modular ZP-N domains that is absent in free domains. Positive selection was identified in some free ZP-N domains. Our findings suggest that purifying selection has conserved an essential structural core in modular ZP-N domains, while free N-terminal domains have been able to experience functionally diversify.

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Figures

Fig. 1. Phylogenetic analysis of ZP-N domain duplication history. (A) A structural alignment of mouse ZP2-N1 and ZP3-N highlights the broad structural conservation of these two classes of ZP-N domains (RMSD = ∼4.7 Å) despite only ∼18% amino acid sequence identity. The protein schematics summarize the ZP proteins included in this analysis. (B) Phylogenetic analysis (Kozlov et al. 2019) of ZP-N sequences (shown as a maximum likelihood tree) supports an ancestral separation between free and modular ZP-N domains (∼78% support). (C) A summary of ZP-N domain evolution based on the gene tree in (B). The ancestral protein contained a ZP module with a C-terminal ZP-N and ZP-C domains, and duplication of the ZP-N produced the most N-terminal domain found in ZP1, ZP4, ZP2, and ZPAX. Later duplication events within ZP2 and ZPAX gave rise to multiple additional ZP-N domains between ZP-N1 and the ZP module.
Fig. 1. Phylogenetic analysis of ZP-N domain duplication history. (A) A structural alignment of mouse ZP2-N1 and ZP3-N highlights the broad structural conservation of these two classes of ZP-N domains (RMSD = ∼4.7 Å) despite only ∼18% amino acid sequence identity. The protein schematics summarize the ZP proteins included in this analysis. (B) Phylogenetic analysis (Kozlov et al. 2019) of ZP-N sequences (shown as a maximum likelihood tree) supports an ancestral separation between free and modular ZP-N domains (∼78% support). (C) A summary of ZP-N domain evolution based on the gene tree in (B). The ancestral protein contained a ZP module with a C-terminal ZP-N and ZP-C domains, and duplication of the ZP-N produced the most N-terminal domain found in ZP1, ZP4, ZP2, and ZPAX. Later duplication events within ZP2 and ZPAX gave rise to multiple additional ZP-N domains between ZP-N1 and the ZP module.CC BY · PubMed Central
Fig. 2. Machine learning–based inference of sequence features that distinguish modular and free ZP-N domains. A logistic regression model with elastic net regularization was trained on the ZP-N multiple sequence alignment generated as part of the phylogenetic analysis, with the data partitioned for training and testing (75% and 25%, respectively), with five-way cross-validation of the training data employed to estimate the error distribution of the score function. We defined our optimal model as the most parsimonious model (i.e., the fewest parameters) within the estimated 95% confidence interval of the unregularized model. (A) The space of regularization hyperparameters was explored during model optimization, plotted as a 3D surface (left). The score is the negative mean-squared error, and the dots correspond to the 2D cross-section shown on the right, with the blue line denoting the intersection between the lower confidence limit of the unregularized model to its intersection with the score as a function of regularization strength. (B) Comparison of the unregularized and optimal logistic regression models as LOGO plots with the height of each amino acid at each position corresponding to its parameter weight, with colored amino acids denoting parameters retained in the regularized model (orange for modular; green for free). Each parameter weight approximating the logs odd ratio for a modular domain prediction, when a residue is present at that position. (C) Sequence LOGOs were constructed for individual clades within the phylogeny. They emphasize the conservation of residues within the modular ZP-N clade. There is also greater conservation of a characteristic ZP-N disulfide bond in the most N-terminal ZP-Ns compared with other free domains. (D) Mapping highly predictive sites onto ZP-N protein models suggest differences in structural properties between free and modular domains. The available crystal structure ZP3-N (3d4c) was used and modeled as a dimer for spatial context. Modular-associated sites are generally buried along the outer edge of the homodimer.
Fig. 2. Machine learning–based inference of sequence features that distinguish modular and free ZP-N domains. A logistic regression model with elastic net regularization was trained on the ZP-N multiple sequence alignment generated as part of the phylogenetic analysis, with the data partitioned for training and testing (75% and 25%, respectively), with five-way cross-validation of the training data employed to estimate the error distribution of the score function. We defined our optimal model as the most parsimonious model (i.e., the fewest parameters) within the estimated 95% confidence interval of the unregularized model. (A) The space of regularization hyperparameters was explored during model optimization, plotted as a 3D surface (left). The score is the negative mean-squared error, and the dots correspond to the 2D cross-section shown on the right, with the blue line denoting the intersection between the lower confidence limit of the unregularized model to its intersection with the score as a function of regularization strength. (B) Comparison of the unregularized and optimal logistic regression models as LOGO plots with the height of each amino acid at each position corresponding to its parameter weight, with colored amino acids denoting parameters retained in the regularized model (orange for modular; green for free). Each parameter weight approximating the logs odd ratio for a modular domain prediction, when a residue is present at that position. (C) Sequence LOGOs were constructed for individual clades within the phylogeny. They emphasize the conservation of residues within the modular ZP-N clade. There is also greater conservation of a characteristic ZP-N disulfide bond in the most N-terminal ZP-Ns compared with other free domains. (D) Mapping highly predictive sites onto ZP-N protein models suggest differences in structural properties between free and modular domains. The available crystal structure ZP3-N (3d4c) was used and modeled as a dimer for spatial context. Modular-associated sites are generally buried along the outer edge of the homodimer.CC BY · PubMed Central
Fig. 3. Amino acid diversity and tests of positive selection in modular and free ZP-N domains. (A) A heatmap showing the within-group and between-group mean phylogenetic distances for the orthologous groups of ZP-N domains (Kumar et al. 2018). (B) Positively selected sites in mammalian ZP2-N1 and ZP2-N2 were identified through maximum likelihood analysis and mapped onto protein models (4wrn for ZP2-N1 and an AlphaFold prediction for ZP2-N2) (Yang 2007).
Fig. 3. Amino acid diversity and tests of positive selection in modular and free ZP-N domains. (A) A heatmap showing the within-group and between-group mean phylogenetic distances for the orthologous groups of ZP-N domains (Kumar et al. 2018). (B) Positively selected sites in mammalian ZP2-N1 and ZP2-N2 were identified through maximum likelihood analysis and mapped onto protein models (4wrn for ZP2-N1 and an AlphaFold prediction for ZP2-N2) (Yang 2007).CC BY · PubMed Central

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Citations

Cites

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Metadata

Authors
Rivera, A. M., Wilburn, D. B., & Swanson, W. J.
Affiliations
University of Washington (US)
Corresponding author
Rivera, Alberto M.
ORCID
Venue
Molecular Biology and Evolution
Publisher
openRxiv
Published
2022-01-31
Volume
39
Issue
5
ISSN
0737-4038 (ISSN-L)
DOI
10.1101/2022.01.31.478407
PMC
PMC9154058
OpenAlex
W4280640654
Cited by
6
References
1
Citations by year
2026: 2 · 2025: 3 · 2023: 1
FWCI
0.88
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Molecular Biology and Evolution (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (R21HD105025, K99HD090201)
Topics
  • Health Sciences › Medicine › Reproductive Medicine › Reproductive Biology and Fertility
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Physiology › Reproductive biology and impacts on aquatic species
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
Keywords
zona pellucida, domain evolution, functional diversification, positive selection, purifying selection, vertebrate reproduction, protein structure evolution
MeSH terms
Zona Pellucida Glycoproteins, Amino Acid Sequence, Animals, Egg Proteins, Vertebrates, Zona Pellucida

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