Measuring evolutionary rates of proteins in a structural context

A quantitative comparison of the deleteriousness of missense and nonsense mutations using the structurally resolved human protein interactome

The complex genotype-to-phenotype relationships in Mendelian diseases can be elucidated by mutation-induced disturbances to the networks of molecular interactions (interactomes) in human cells. Missense and nonsense mutations cause distinct perturbations within the human protein interactome, leading to functional and phenotypic effects with varying degrees of severity. Here, we structurally resolve the human protein interactome at atomic-level resolutions and perform structural and thermodynamic calculations to assess the biophysical implications of these mutations. We focus on a specific type of missense mutation, known as "quasi-null" mutations, which destabilize proteins and cause similar functional consequences (node removal) to nonsense mutations. We propose a "fold difference" quantification of deleteriousness, which measures the ratio between the fractions of node-removal mutations in datasets of Mendelian disease-causing and non-pathogenic mutations. We estimate the fold differences of node-removal mutations to range from 3 (for quasi-null mutations with folding ΔΔG ≥2 kcal/mol) to 20 (for nonsense mutations). We observe a strong positive correlation between biophysical destabilization and phenotypic deleteriousness, demonstrating that the deleteriousness of quasi-null mutations spans a continuous spectrum, with nonsense mutations at the extreme (highly deleterious) end. Our findings substantiate the disparity in phenotypic severity between missense and nonsense mutations and suggest that mutation-induced protein destabilization is indicative of the phenotypic outcomes of missense mutations. Our analyses of node-removal mutations allow for the potential identification of proteins whose removal or destabilization lead to harmful phenotypes, enabling the development of targeted therapeutic approaches, and enhancing comprehension of the intricate mechanisms governing genotype-to-phenotype relationships in clinically relevant diseases.

Evolutionary and structural constraints influencing apolipoprotein A-I amyloid behavior

Apolipoprotein A-I (apoA-I) has a key function in the reverse cholesterol transport. However, aggregation of apoA-I single point mutants can lead to hereditary amyloid pathology. Although several studies have tackled the biophysical and structural consequences introduced by these mutations, there is little information addressing the relationship between the evolutionary and structural features that contribute to the amyloid behavior of apoA-I. We combined evolutionary studies, in silico mutagenesis and molecular dynamics (MD) simulations to provide a comprehensive analysis of the conservation and pathogenic role of the aggregation-prone regions (APRs) present in apoA-I. Sequence analysis demonstrated that among the four amyloidogenic regions described for human apoA-I, only two (APR1 and APR4) are evolutionary conserved across different species of Sarcopterygii. Moreover, stability analysis carried out with the FoldX engine showed that APR1 contributes to the marginal stability of apoA-I. Structural properties of full-length apoA-I models suggest that aggregation is avoided by placing APRs into highly packed and rigid portions of its native fold. Compared to silent variants extracted from the gnomAD database, the thermodynamic and pathogenic impact of amyloid mutations showed evidence of a higher destabilizing effect. MD simulations of the amyloid variant G26R evidenced the partial unfolding of the alpha-helix bundle with the concomitant exposure of APR1 to the solvent, suggesting an insight into the early steps involved in its aggregation. Our findings highlight APR1 as a relevant component for apoA-I structural integrity and emphasize a destabilizing effect of amyloid variants that leads to the exposure of this region.

SARS-CoV-2 mutations in Brazil: from genomics to putative clinical conditions

Due to the high rate of transmissibility, Brazil became the new COVID-19 outbreak epicenter and, since then, is being monitored to understand how SARS-CoV-2 mutates and spreads. We combined genomic and structural analysis to evaluate genomes isolated from different regions of Brazil and show that the most prevalent mutations were located in the S, N, ORF3a and ORF6 genes, which are involved in different stages of viral life cycle and its interaction with the host cells. Structural analysis brought to light the positions of these mutations on protein structures, contributing towards studies of selective structure-based drug discovery and vaccine development.

Consequences of Stability-Induced Epistasis for Substitution Rates

Do interactions between residues in a protein (i.e., epistasis) significantly alter evolutionary dynamics? If so, what consequences might they have on inference from traditional codon substitution models which assume site-independence for the sake of computational tractability? To investigate the effects of epistasis on substitution rates, we employed a mechanistic mutation-selection model in conjunction with a fitness framework derived from protein stability. We refer to this as the stability-informed site-dependent (S-SD) model and developed a new stability-informed site-independent (S-SI) model that captures the average effect of stability constraints on individual sites of a protein. Comparison of S-SI and S-SD offers a novel and direct method for investigating the consequences of stability-induced epistasis on protein evolution. We developed S-SI and S-SD models for three natural proteins and showed that they generate sequences consistent with real alignments. Our analyses revealed that epistasis tends to increase substitution rates compared with the rates under site-independent evolution. We then assessed the epistatic sensitivity of individual site and discovered a counterintuitive effect: Highly connected sites were less influenced by epistasis relative to exposed sites. Lastly, we show that, despite the unrealistic assumptions, traditional models perform comparably well in the presence and absence of epistasis and provide reasonable summaries of average selection intensities. We conclude that epistatic models are critical to understanding protein evolutionary dynamics, but epistasis might not be required for reasonable inference of selection pressure when averaging over time and sites.

Comparative structural and evolutionary analyses predict functional sites in the artemisinin resistance malaria protein K13

Numerous mutations in the Plasmodium falciparum Kelch13 (K13) protein confer resistance to artemisinin derivatives, the current front-line antimalarial drugs. K13 is an essential protein that contains BTB and Kelch-repeat propeller (KREP) domains usually found in E3 ubiquitin ligase complexes that target substrate protein(s) for ubiquitin-dependent degradation. K13 is thought to bind substrate proteins, but its functional/interaction sites and the structural alterations associated with artemisinin resistance mutations remain unknown. Here, we screened for the most evolutionarily conserved sites in the protein structure of K13 as indicators of structural and/or functional constraints. We inferred structure-dependent substitution rates at each amino acid site of the highly conserved K13 protein during the evolution of Apicomplexa parasites. We found two solvent-exposed patches of extraordinarily conserved sites likely involved in protein-protein interactions, one in BTB and the other one in KREP. The conserved patch in K13 KREP overlaps with a shallow pocket that displays a differential electrostatic surface potential, relative to neighboring sites, and that is rich in serine and arginine residues. Comparative structural and evolutionary analyses revealed that these properties were also found in the functionally-validated shallow pocket of other KREPs including that of the cancer-related KEAP1 protein. Finally, molecular dynamics simulations carried out on PfK13 R539T and C580Y artemisinin resistance mutant structures revealed some local structural destabilization of KREP but not in its shallow pocket. These findings open new avenues of research on one of the most enigmatic malaria proteins with the utmost clinical importance.

Phylogenomic analyses and distribution of terpene synthases among Streptomyces

Terpene synthases are widely distributed among microorganisms and have been mainly studied in members of the genus Streptomyces. However, little is known about the distribution and evolution of the genes for terpene synthases. Here, we performed whole-genome based phylogenetic analysis of Streptomyces species, and compared the distribution of terpene synthase genes among them. Overall, our study revealed that ten major types of terpene synthases are present within the genus Streptomyces, namely those for geosmin, 2-methylisoborneol, epi-isozizaene, 7-epi-α-eudesmol, epi-cubenol, caryolan-1-ol, cyclooctat-9-en-7-ol, isoafricanol, pentalenene and α-amorphene. The Streptomyces species divide in three phylogenetic groups based on their whole genomes for which the distribution of the ten terpene synthases was analysed. Geosmin synthases were the most widely distributed and were found to be evolutionary positively selected. Other terpene synthases were found to be specific for one of the three clades or a subclade within the genus Streptomyces. A phylogenetic analysis of the most widely distributed classes of Streptomyces terpene synthases in comparison to the phylogenomic analysis of this genus is discussed.

Relative Evolutionary Rates in Proteins Are Largely Insensitive to the Substitution Model

The relative evolutionary rates at individual sites in proteins are informative measures of conservation or adaptation. Often used as evolutionarily aware conservation scores, relative rates reveal key functional or strongly selected residues. Estimating rates in a phylogenetic context requires specifying a protein substitution model, which is typically a phenomenological model trained on a large empirical data set. A strong emphasis has traditionally been placed on selecting the "best-fit" model, with the implicit understanding that suboptimal or otherwise ill-fitting models might bias inferences. However, the pervasiveness and degree of such bias has not been systematically examined. We investigated how model choice impacts site-wise relative rates in a large set of empirical protein alignments. We compared models designed for use on any general protein, models designed for specific domains of life, and the simple equal-rates Jukes Cantor-style model (JC). As expected, information theoretic measures showed overwhelming evidence that some models fit the data decidedly better than others. By contrast, estimates of site-specific evolutionary rates were impressively insensitive to the substitution model used, revealing an unexpected degree of robustness to potential model misspecification. A deeper examination of the fewer than 5% of sites for which model inferences differed in a meaningful way showed that the JC model could uniquely identify rapidly evolving sites that models with empirically derived exchangeabilities failed to detect. We conclude that relative protein rates appear robust to the applied substitution model, and any sensible model of protein evolution, regardless of its fit to the data, should produce broadly consistent evolutionary rates.

Relative evolutionary rate inference in HyPhy with LEISR

We introduce LEISR (Likehood Estimation of Individual Site Rates, pronounced “laser”), a tool to infer relative evolutionary rates from protein and nucleotide data, implemented in HyPhy. LEISR is based on the popular Rate4Site (Pupko et al., 2002) approach for inferring relative site-wise evolutionary rates, primarily from protein data. We extend the original method for more general use in several key ways: (i) we increase the support for nucleotide data with additional models, (ii) we allow for datasets of arbitrary size, (iii) we support analysis of site-partitioned datasets to correct for the presence of recombination breakpoints, (iv) we produce rate estimates at all sites rather than at just a subset of sites, and (v) we implemented LEISR as MPI-enabled to support rapid, high-throughput analysis. LEISR is available in HyPhy starting with version 2.3.8, and it is accessible as an option in the HyPhy analysis menu (“Relative evolutionary rate inference”), which calls the HyPhy batchfile LEISR.bf.