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Title: Structure and Age Jointly Influence Rates of Protein Evolution}
Article:
Abstract:
What factors determine a protein's rate of evolution are actively debated. Especially unclear is the relative role of intrinsic factors of present-day proteins versus historical factors such as protein age. Here we study the interplay of structural properties and evolutionary age, as determinants of protein evolutionary rate. We use a large set of one-to-one orthologs between human and mouse proteins, with mapped PDB structures. We report that previously observed structural correlations also hold within each age group – including relationships between solvent accessibility, designabililty, and evolutionary rates. However, age also plays a crucial role: age modulates the relationship between solvent accessibility and rate. Additionally, younger proteins, despite being less designable, tend to evolve faster than older proteins. We show that previously reported relationships between age and rate cannot be explained by structural biases among age groups. Finally, we introduce a knowledge-based potential function to study the stability of proteins through large-scale computation. We find that older proteins are more stable for their native structure, and more robust to mutations, than younger ones. Our results underscore that several determinants, both intrinsic and historical, can interact to determine rates of protein evolution.
Introduction:
It is well known that protein evolutionary rates are not homogeneous, with as much variation within an organism as between organisms. In fact, evolutionary rates vary as much as 1, 000-fold among the proteins in the yeast S. cerevisiae[1]. Therefore, there has been longstanding interest in deciphering the causes of this variation, with a large literature of theoretical and empirical studies alike. Numerous possible determinants for protein evolutionary rate have been proposed, such as protein dispensability[2], number of mRNA molecules per cell[3],[4], number of protein molecules per cell[5], codon adaptation index[4],[6], number of protein-protein interactions[7], sequence length[8],[9], role in the interaction network[10], and structural properties such as solvent accessibility and folding robustness[11]–[13]. Some of the proposed determinants are correlated with one another, which makes the identification of causal factors difficult. For this reason Drummond and colleagues[5] tried to disentangle these factors by performing a principal component regression( PCR) analysis. They found that a single component, which included codon adaptation index, protein abundance and gene expression level, accounted for nearly half of the observed variability in protein's evolution. Nonetheless, those expression-related factors have been measured with less noise than other possible factors. This further complicates even the principal component regression[14]. In related work, Drummond and Wilke[15] observed covariation between sequence evolution, codon usage and mRNA level among a broad range of species. They suggested there may be selection for robustness against mistranslation, since mistranslation-induced misfolding would be more deleterious for highly expressed proteins. A protein's three-dimensional structure may also be a key factor in determining its evolutionary rate. The core of a protein is mostly formed by buried residues, which often play a crucial role in the stability of the folded structure[16]. Most mutations in the core of a protein tend to destabilize the protein[17]. Exposed residues are in contact with solvent and they are known to evolve faster than buried ones[11],[12],[18]–[21]. In fact, the more general relationship between solvent exposure and evolutionary rate is linear and very strong[12]. Given these results, we might expect those proteins with a higher fraction of exposed residues to evolve faster. But, surprisingly, Bloom and others found the contrary pattern[11],[12]. Bloom et al explained this incongruence using protein designability, defined roughly as the number of sequences than can fold into a structure. Since a higher number of sequences can fold into highly designable structures, designable structures are more tolerant to mutations and hence, evolve faster. As designability has been related to contact density[22] and contact density is highly correlated with the fraction of buried residues, the authors hypothesize that highly designable proteins have a higher fraction of buried residues. Consequently, highly designable proteins have stable core, allowing the exposed residues to freely mutate without compromising stability. In fact, Franzosa and Xia[12] have demonstrated how large-core proteins( which are the ones having an overall low solvent exposure value) have low solvent exposure values but high dN/dS, specially observing that highly exposed residues in large-core proteins are evolving faster than in small-core proteins. Also, proteins with a higher contact density tend to evolve more rapidly – in fly, yeast, E. coli and human[23]. Moreover, highly designable proteins have been shown to evolve more functional innovations[24]. Bloom and colleagues[11] have carried out a PCR analysis showing that the component measuring expression level could explain around 34% of the rate variation, whereas structural characteristics explained approximately the 10% of the rate variation. There are other structural properties correlated with evolutionary rates, such as the number of intra-protein residue interactions, which tend to reduce rates of evolution[25]. Structure itself could be a determinant of protein evolution, or indeed, could be acting through other mechanisms. For example, it could play a crucial role in the selection for structural robustness against mistranslation in highly expressed proteins, which has already been shown to be a key determinant of protein evolution[11]. Quite aside from the factors discussed above, which are intrinsic to the properties of a protein in an organism today, studies have also shown that the age of a protein, which depends on its evolutionary history, is also correlated with evolutionary rates[26]–[28]. In particular, an inverse relationship between age and evolutionary rate has been widely observed[26],[27],[29], suggesting that a protein's evolution could be shaped in part by its evolutionary origin. This relationship has been reported in a broad range of organisms: primates[30], mammals[26], Drosophila[27],[29], Plasmodium[31], fungi[32] and bacteria[33]. Despite all these findings, what factors determine a protein's evolutionary rate are still under debate – and the relative role of intrinsic factors of present-day proteins, versus historical factors such as protein age, remains poorly characterized. Here we explored the interplay between two very different factors: a protein's age and its structural properties. Our objective is to determine whether structural biases among age groups could explain the reported differences in evolutionary rates with age[26],[27]. To do so we used a dataset of human proteins with homologues in mouse for which we were able to map a PDB structure. Age was assigned to each PDB structure and then structural properties( solvent exposure, designability, stability and secondary structure) were calculated among the PDB structures classified in the age groups. We found that differences in evolutionary rates previously observed among age groups could not be explained due to differences in the structural properties among age groups. Similarly, differences in rates correlated with structural differences cannot be entirely explained by the age of the PDB structure, although a marginal influence of age is observed. Our results therefore reinforce the idea that there is not a single determinant of evolutionary rate, and that both intrinsic present-day properties as well as evolutionary age independently contribute to differential rates of protein evolution.
Discussion:
Interactions among various determinants of protein evolution are not well understood despite several decades of investigation. In this work, we have studied two types of proposed determinants: structural properties intrinsic to present-day proteins, and protein age. We found that several well-known relationships between structural properties and evolutionary rate that had previously been reported, regardless of age, also hold within each age class: residues with high solvent accessibility evolve more quickly[11],[12],[18]–[21], while proteins with a larger fraction of exposed residues evolve more slowly[11],[12]. At the same time, the age of a protein can modulate the correlation between structural properties and evolutionary rates – e. g. the strength of relationship between solvent accessibility and evolutionary rate depends on the age of the protein in which the residue is found. We also studied secondary structures of proteins, and we confirmed that the typical inverse relationship between protein age and evolutionary rate holds within each structural class of residues. This implies that differences in the frequency of structural categories by age class cannot explain the previously reported inverse relationship between age and rate. Finally, we introduced a knowledge-based potential to study the relationships between protein age and stability. We found that older proteins are more stable, on average, than younger proteins, and that older structures are also more robust to mutation than younger structures. Our results provide a more nuanced view on the determinants of protein evolutionary rates. Whereas some determinants of rates hold within each age class, age can nonetheless modulate these effects. And other relationships that hold regardless of age( such as, proteins with a greater fraction of exposed residues evolve more slowly) cannot explain differences in rates among age classes. Our analyses certainly suffer from several drawbacks. Most important, we were able to map a structure to only 14% of the one-to-one orthologs proteins between human and mouse. This fraction would be even smaller if we had chosen other species. Despite the increase in solved structures over the past few years, the number of mapped structures is still a small fraction of known proteins. Additionally, we have to bear in mind that there are biases in the type of proteins that enjoy solved structures. For example disordered regions are poorly represented in PDB, as they are difficult to crystallize. Younger proteins are enriched in low-complexity regions[44],[45], many of which are expected to be disordered[45]. How this adds to the differences in evolutionary rates among age classes is an aspect that remains to be studied. Choi and Kim[46] have reported that old proteins are longer and have more complex tertiary structures( α/β) than younger proteins, hypothesizing that proteins tend to become more complex in their structure along their evolutionary history. Our results also give insights on the evolution of protein structural characteristics, as we have found that older structures are more designable, stable and robust to mutations than younger ones. These findings suggest that structures may acquire stability and robustness to mutations with time. However, these findings also raise new questions. Since stability increases a protein's tolerance to mutations[42] we might expect that younger structures would be evolving slowly due to the destabilizing effect of mutations. But we find them to evolve fast. One possible explanation is that previous studies have assumed proteins are generally under the same degree of selection, regardless of age. But some of our results might be due to differential strengths of selection in old versus young proteins. One possibility is that younger sequences mapped to PDB may be experiencing strong positive selection for stabilizing mutations, which explains their higher rates of evolution; whereas older protein are already stable and robust, and thus lack this type of positive selection. However, using single nucleotide polymorphism( SNP) data Cai and Petrov have found limited evidence for increased positive selection in primate-specific genes, and strong indications that relaxed negative selection is likely to be more important in young genes than in older genes[47]. Therefore, it may be that selection for high stability is reduced in younger proteins. In conclusion, our results reinforce the idea that protein evolution is not explained by a single determinant, but rather by the interplay of many determinants, including even factors that are not intrinsic to the present-day protein but depend on evolutionary age.
Rates of protein evolution vary dramatically within and between organisms . But the factors that determine a protein's evolutionary rate are still under debate , despite extensive studies over the past decade . Several determinants have been proposed , for example gene expression , the importance of the gene for the organism , the number of physical or genetic interactions it has , its structural characteristics , or when it originated . Here we study how age and structural characteristics interact with one another to influence evolutionary rates . We use a set of one-to-one orthologs of human and mouse proteins , with known crystal structures . We find that these two determinants interact: for example , the age of protein modulates how its structure correlates with evolutionary rate . Nonetheless , the influence of age on evolutionary rate cannot be explained by its interplay with structure .