Differential retention of Pfam domains creates long-term evolutionary trends

Abstract

Protein domains that emerged more recently in evolution have higher structural disorder and greater clustering of hydrophobic residues along the primary sequence. It is hard to explain how selection acting via descent with modification could act so slowly as not to saturate over the extraordinarily long timescales over which these trends persist. Here we hypothesize that the trends were created by a higher level of selection which differentially affects the retention probabilities of protein domains with different properties. This hypothesis predicts that loss rates should depend on disorder and clustering trait values. To test this, we inferred loss rates via maximum likelihood for animal Pfam domains, after first performing a set of stringent quality control methods to reduce annotation errors. Intermediate trait values, matching those of ancient domains, are associated with the lowest loss rates, making our results difficult to explain with reference to previously described homology detection biases. Our results support the hypothesis that differential domain loss slowly weeds out those protein domains that have non-optimal levels of disorder and clustering. The same preferences also shape differential diversification of Pfam domains, further impacting proteome composition.

The effectiveness of selection in a species affects the direction of amino acid frequency evolution

“…Our metrics focus on how proteins evolve through descent with modification, because all comparisons are predicated on the presence of homologs in each species. This is different from methods such as James et al ( 2021) and (Basile et al, 2019), in which presence/absence affects the metric, and so results are shaped by de novo birth, duplication, and deletion events (James et al, 2023). CAIS provides a powerful way to get at the subtle effects of selection, by comparing selection in high Ne species to selection in low Ne species.…”

The protein domains of vertebrate species in which selection is more effective have greater intrinsic structural disorder

“…However, compositional differences among proteomes might be driven by the recent birth of ISD-rich proteins in animals (James et al 2021), and/or by differences among sequences in their subsequent tendency to proliferate into many different genes (James et al 2022), rather than by differences in how a given protein sequence evolves as a function of the effectiveness of selection. To focus only on the latter, we use a linear mixed model, with each species having a fixed effect on ISD, while controlling for Pfam domain identity as a random effect.…”

“…However, compositional differences among proteomes might not be driven by differences in how a given protein sequence evolves as a function of the effectiveness of selection. Instead, they might be driven by the recent birth of ISD-rich proteins in animals (James et al 2021), and/or by differences among sequences in their subsequent tendency to proliferate into many different genes (James et al 2023). To focus only on the effects of descent with modification, we use a linear mixed model, with each species having a fixed effect on ISD, while controlling for Pfam domain identity as a random effect.…”

“…We calculated total %GC content (intergenic and genic) during a scan of all six reading frames across genic and intergenic sequences available from NCBI with access dates between May and July 2019 (described in James et al (2022)). Of the 170 vertebrates meeting the quality criteria of James et al (2021), 118 had annotated intergenic sequences within NCBI, so we restricted the dataset further to keep only the 118 species for which total GC content was available.…”

Adaptive Evolution Signatures in Prochlorococcus: ORFeome Resources and Insights from Comparative Genomics

“…Instead, they are more likely to be independent acquisitions from the ubiquitous clouds of cyanophages surrounding cyanobacteria in their native environment [67]. For this reason, focusing on Pfams, instead of segments of genomic sequences can bring new insights that would not be detected by paralog analyses [68]. To identify the genomic determinants of cyanobacterial adaptation to low-and high-light (LL and HL), we used Prochlorococcus marinus strains as models and explored their 40 sequenced and published genomes.…”