Predicting Evolutionary Site Variability from Structure in Viral Proteins: Buriedness, Packing, Flexibility, and Design

2016

Preprint

Self Cite

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.

contrasting

confidence: 46%

mentioning

confidence: 99%

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

Jackson

Spielman

2016

Preprint

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“…Both of these quantities provide statistically significant predictions of evolutionary rate in other viral systems [11,18] and in enzymes [12]. Generally, WCN calculated on the amino acid side chain is a stronger predictor of evolutionary rate than RSA calculated in either the monomeric or functional multimeric state [14].…”

Section: Discussionmentioning

confidence: 99%

“…Several such constraints have been described previously by many groups. For example, the relative solvent accessibility (RSA), which measures the extent to which amino acid side chains are exposed on the surface of the protein or buried within the protein structure, has been shown to predict site-wise evolution in eukaryotes [8,9], bacteria [10] and some viral proteins [11]. Similar structural constraints like the weighted contact number (WCN), the linear packing density and the mechanistic stress of a site have been described for a large enzyme dataset [12 -15].…”

Section: Introductionmentioning

confidence: 99%

The utility of protein structure as a predictor of site-wise dN/dSvaries widely among HIV-1 proteins

Meyer

2015

J. R. Soc. Interface.

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Protein structure acts as a general constraint on the evolution of viral proteins. One widely recognized structural constraint explaining evolutionary variation among sites is the relative solvent accessibility (RSA) of residues in the folded protein. In influenza virus, the distance from functional sites has been found to explain an additional portion of the evolutionary variation in the external antigenic proteins. However, to what extent RSA and distance from a reference site in the protein can be used more generally to explain protein adaptation in other viruses and in the different proteins of any given virus remains an open question. To address this question, we have carried out an analysis of the distribution and structural predictors of site-wise dN/dS in HIV-1. Our results indicate that the distribution of dN/dS in HIV follows a smooth gamma distribution, with no special enrichment or depletion of sites with dN/dS at or above one. The variation in dN/dS can be partially explained by RSA and distance from a reference site in the protein, but these structural constraints do not act uniformly among the different HIV-1 proteins. Structural constraints are highly predictive in just one of the three enzymes and one of three structural proteins in HIV-1. For these two proteins, the protease enzyme and the gp120 structural protein, structure explains between 30 and 40% of the variation in dN/dS. Finally, for the gp120 protein of the receptor-binding complex, we also find that glycosylation sites explain just 2% of the variation in dN/dS and do not explain gp120 evolution independently of either RSA or distance from the apical surface.

“…For instance, active sites are generally very conserved [4,5]. The protein core tends to be more conserved than the surface, presumably because mutations in the core are more likely to disturb the protein structure [6,7]. Because the evolutionary rates correspond to structurally and functionally important sites, having a reliable and accurate method for inferring site-wise rate of evolution is essential.…”

Section: Expanded Summarymentioning

confidence: 99%

Theory of measurement for site-specific evolutionary rates in amino-acid sequences

Sydykova

2017

Preprint

Many applications require the calculation of site-specific evolutionary rates from alignments of amino-acid sequences. For example, catalytic residues in enzymes and interface regions in protein complexes can be inferred from observed relative rates. While numerous approaches exist to calculate amino-acid rates, however, it is not entirely clear what physical quantities the inferred rates represent and how these rates relate to the underlying fitness landscape of the evolving protein. Further, amino-acid rates can be calculated in the context of different amino-acid exchangeability matrices, such as JTT, LG, or WAG, and again it is not known how the choice of the matrix influences the physical interpretation of the inferred rates. Here, we develop a theory of measurement for site-specific evolutionary rates, but analytically solving the maximum-likelihood equations for rate inference performed on sequences evolved under a mutation-selection model. We demonstrate that the measurement process can only recover the true expected rates of the mutation-selection model if rates are measured relative to a naïve exchangeability matrix, in which all exchangeabilities are equal to one. Rate measurements using other matrices are quantitatively close but not mathematically correct. Our results demonstrate that insights obtained from phylogenetic-tree inference do not necessarily apply to rate inference, and best practices for the former may be deleterious for the latter. Expanded summaryDifferent sites in a protein evolve at different rates [1,2]. The heterogeneity in rates within a protein sequences is caused by the interplay of functional and structural constraints [3]. For instance, active sites are generally very conserved [4,5]. The protein core tends to be more conserved than the surface, presumably because mutations in the core are more likely to disturb the protein structure [6,7]. Because the evolutionary rates correspond to structurally and functionally important sites, having a reliable and accurate method for inferring site-wise rate of evolution is essential. Specifically, in most viral populations, proteins evolve very rapidly. In influenza, mutations in one site of a surface protein hemagglutinin allow the virus to escape host antibodies and propagate. Thus, detecting sitewise rates of evolution can be crucial to the efforts of viral surveillance and control.Many methods to infer site-wise rate have been developed over the years. These methods employ a substitution matrix, which captures exchangeabilities between all pairs of amino acids. The substitution matrices are made by analyzing large protein data sets and even protein sequences specific to an organism or an organelle. However, it remains an open question which substitution matrix is the most suitable for site-wise rate inference. When we measure site-wise rate, rate is defined as a scalar in front of the substitution matrix. The substitution matrix serves as a ruler by which we measure the rate of evolution at a site. Depending on what ruler or...