Intermediate divergence levels maximize the strength of structure–sequence correlations in enzymes and viral proteins

Jackson, Eleisha L.; Shahmoradi, Amir; Spielman, Scott R.; Jack, Benjamin R.; Wilke, Claus O.

doi:10.1002/pro.2920

Cited by 8 publications

(9 citation statements)

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“…Interestingly, this result contrasts somewhat with identified 335 predictors of protein substitution rate, where protein design has had minimal predictive 336 ability compared to WCN or RSA [31,32]. For deletions, although WCN was still a 337 better single predictor than was protein design mean score, including protein design in 338 multi-predictor models led to consistent model improvement (Tables 1 and 2).…”

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confidence: 90%

Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein

Jackson

Spielman

Wilke

2016

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Proteins evolve through two primary mechanisms: substitution, where mutations alter a protein's amino-acid sequence, and insertions and deletions (indels), where amino acids are either added to or removed from the sequence. Protein structure has been shown to influence the rate at which substitutions accumulate across sites in proteins, but whether structure similarly constrains the occurrence of indels has not been rigorously studied. Here, we investigate the extent to which structural properties known to covary with protein evolutionary rates might also predict protein tolerance to indels. Specifically, we analyze a publicly available dataset of single-amino-acid deletion mutations in enhanced green fluorescent protein (eGFP) to assess how well the functional effect of deletions can be predicted from protein structure. We find that weighted contact number (WCN), which measures how densely packed a residue is within the protein's three-dimensional structure, provides the best single predictor for whether eGFP will tolerate a given deletion. We additionally find that using protein design to explicitly model deletions results in improved predictions of functional status when combined with other structural predictors. Our work suggests that structure plays fundamental role in constraining deletions at sites in proteins, and further that similar biophysical constraints influence both substitutions and deletions. This study therefore provides a solid foundation for future work to examine how protein structure influences tolerance of more complex indel events, such as insertions or large deletions.

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confidence: 90%

Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein

Jackson

Spielman

Wilke

2016

Preprint

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“…Interestingly, this result contrasts somewhat with identified predictors of protein substitution rate, where protein design has had minimal predictive ability compared to WCN or RSA [33, 34]. For deletions, although WCN was still a better single predictor than was protein design mean score, including protein design in multi-predictor models led to consistent model improvement (Tables 1 and 2).…”

Section: Discussionmentioning

confidence: 74%

Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein

2017

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Proteins evolve through two primary mechanisms: substitution, where mutations alter a protein’s amino-acid sequence, and insertions and deletions (indels), where amino acids are either added to or removed from the sequence. Protein structure has been shown to influence the rate at which substitutions accumulate across sites in proteins, but whether structure similarly constrains the occurrence of indels has not been rigorously studied. Here, we investigate the extent to which structural properties known to covary with protein evolutionary rates might also predict protein tolerance to indels. Specifically, we analyze a publicly available dataset of single—amino-acid deletion mutations in enhanced green fluorescent protein (eGFP) to assess how well the functional effect of deletions can be predicted from protein structure. We find that weighted contact number (WCN), which measures how densely packed a residue is within the protein’s three-dimensional structure, provides the best single predictor for whether eGFP will tolerate a given deletion. We additionally find that using protein design to explicitly model deletions results in improved predictions of functional status when combined with other structural predictors. Our work suggests that structure plays fundamental role in constraining deletions at sites in proteins, and further that similar biophysical constraints influence both substitutions and deletions. This study therefore provides a solid foundation for future work to examine how protein structure influences tolerance of more complex indel events, such as insertions or large deletions.

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“…Studies which have examined the correlations between site-specific dN/dS, or conversely amino-acid level evolutionary rates, and such structural quantities have recovered correlations with strengths widely ranging from 0.1-0.8 (Shih and Hwang 2012;Huang et al 2014;Yeh et al 2014b,a;Shahmoradi et al 2014;Meyer and Wilke 2015b,c;Jackson et al 2016). Recent work by Jackson et al (2016) has attributed the source of this wide range of correlations directly to the extent of sequence divergence present in a given dataset, such that more diverged datasets display higher correlations and less diverged datasets display lower correlations. Our findings that increased divergence levels contribute strongly to dN/dS inference accuracy are fully consistent with those of Jackson et al (2016).…”

Section: Discussionmentioning

confidence: 99%

“…Recent work by Jackson et al (2016) has attributed the source of this wide range of correlations directly to the extent of sequence divergence present in a given dataset, such that more diverged datasets display higher correlations and less diverged datasets display lower correlations. Our findings that increased divergence levels contribute strongly to dN/dS inference accuracy are fully consistent with those of Jackson et al (2016). Therefore, we suggest that future work examining the relationship between protein evolutionary rate and structure should focus on datasets with intermediate-to-high divergences, which are most likely to provide meaningful information about long-term evolutionary constraints.…”

Section: Discussionmentioning

confidence: 99%

A comparison of one-rate and two-rate inference frameworks for site-specificdN/dSestimation

Spielman

Wan

Wilke

2015

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Two broad paradigms exist for inferring dN=dS; the ratio of nonsynonymous to synonymous substitution rates, from coding sequences: (i) a one-rate approach, where dN=dS is represented with a single parameter, or (ii) a two-rate approach, where d N and d S are estimated separately. The performances of these two approaches have been well studied in the specific context of proper model specification, i.e., when the inference model matches the simulation model. By contrast, the relative performances of one-rate vs. two-rate parameterizations when applied to data generated according to a different mechanism remain unclear. Here, we compare the relative merits of one-rate and two-rate approaches in the specific context of model misspecification by simulating alignments with mutation-selection models rather than with dN=dS-based models. We find that one-rate frameworks generally infer more accurate dN=dS point estimates, even when d S varies among sites. In other words, modeling d S variation may substantially reduce accuracy of dN=dS point estimates. These results appear to depend on the selective constraint operating at a given site. For sites under strong purifying selection (dN=dS ≲ 0:3), one-rate and two-rate models show comparable performances. However, one-rate models significantly outperform two-rate models for sites under moderate-to-weak purifying selection. We attribute this distinction to the fact that, for these more quickly evolving sites, a given substitution is more likely to be nonsynonymous than synonymous. The data will therefore be relatively enriched for nonsynonymous changes, and modeling d S contributes excessive noise to dN=dS estimates. We additionally find that high levels of divergence among sequences, rather than the number of sequences in the alignment, are more critical for obtaining precise point estimates.KEYWORDS dN/dS; mutation-selection models; evolutionary rate; sequence simulation; molecular evolution A variety of computational approaches have been developed to infer selection pressure from protein-coding sequences in a phylogenetically aware context. Among the most commonly used methods are those that compute the evolutionary rate ratio dN=dS; which represents the ratio of nonsynonymous to synonymous substitution rates. Beginning in the mid-1990s, this value has been calculated using maximum-likelihood (ML) approaches (Goldman and Yang 1994;Muse and Gaut 1994), and since then, a wide variety of inference frameworks have been developed to infer dN=dS at individual sites in protein-coding sequences (Nielsen and Yang 1998;Yang et al. 2000;Yang and Nielsen 2002;Yang and Swanson 2002;Lemey et al. 2012;Murrell et al. 2012bMurrell et al. , 2013.Typically, the performance of evolutionary models is examined using simulation-based approaches wherein sequences are simulated according to the model being examined, and inferences are subsequently performed on the simulated data. This approach ensures that simulated and inferred parameters correspond. Although useful, this strategy fundam...

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Intermediate divergence levels maximize the strength of structure–sequence correlations in enzymes and viral proteins

Cited by 8 publications

References 63 publications

Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein

Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein

Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein

A comparison of one-rate and two-rate inference frameworks for site-specificdN/dSestimation

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