Simon C. Lovell

The SARS-CoV-2 pandemic resulted in an unprecedented scientific response. The scale of global genome sequencing, protein structural determination, and targeted studies of variant dynamics has resulted in a unique dataset, providing a valuable resource for studying viral evolutionary dynamics. Previous analysis of SARS-CoV-2 evolution has revealed apparently saltatory dynamics, with viral variants arising following evolutionary jumps without genetic intermediates represented in the sequence database. We utilize rich SARS-CoV-2 datasets to interrogate the role of protein structural constraint in SARS-CoV-2 evolution and whether saltatory dynamics result from the spike protein accessing previously nonviable sequence space. We apply multiple computational predictors of structural constraint across different structural backgrounds and assess how constraint has changed during SARS-CoV-2 variant evolution. These predictions are validated using substitution data from the SARS-CoV-2 global sequence database. We find that the structural constraint experienced by specific sites has undergone limited change, despite significant phenotypic evolution of the SARS-CoV-2 S protein. The structural constraints acting on signature mutations of variants of concern remain constant regardless of which viral variant structure is used to make predictions. We also develop a machine learning model to assess substitution viability, combining predictors of evolutionary constraint with information about local structural context. This confirms our conclusions, with model performance largely unaffected by the use of different viral variant structures. These results suggest that despite its rapid rate of mutation, the SARS-CoV-2 S protein is subject to strict structural constraints and exhibited limited genomic plasticity following zoonotic transmission into the human population.

The SARS-CoV-2 pandemic resulted in an unprecedented scientific response. The enormous scale of global genome sequencing, protein structural determination and targeted studies of protein and variant dynamics has resulted in a unique dataset which provides a valuable resource for understanding of viral evolutionary dynamics. Previous analysis of SARS-CoV-2 evolution has revealed apparently saltatory dynamics, with viral variants arising following large evolutionary jumps without genetic intermediates represented in the sequence database. We utilise rich SARS-CoV-2 datasets to interrogate the role of protein structural constraint in SARS-CoV-2 evolution and whether these evolutionary jumps may result from the viral spike protein accessing new regions of viable sequence space. We apply multiple computational predictors of structural constraint across different structural backgrounds and assess how constraint has changed during SARS-CoV-2 variant evolution. These predictions are validated using substitution data from the SARS-CoV-2 global sequence database. We find that all predictive methods suggest that the structural constraint experienced by specific sites has undergone very limited change, despite significant phenotypic evolution of the SARS-CoV-2 S protein. Signature mutations for variants of concern are not found to be under structural constraint by any computational predictor regardless of which viral variant structure is used to calculate predictions. We also develop a machine learning model to assess substitution viability, combining predictors of evolutionary constraint with information about local structural context. This confirms our conclusions, with model performance largely unaffected by the use of different viral variant structures. We also find no reduction in the shared proportion of accessible substitutions over evolutionary time, as would be expected if the S protein had entered and explored novel sequence space during variant evolution. These results suggest that despite its rapid rate of mutation, the SARS-CoV-2 S protein is subject to strict structural constraints and shows that viral genomes exhibit limited plasticity following infection of a new host.

Despite occurring in an essential molecule, mutations in RNA polymerase readily emerge and elicit complex pleiotropic effects across different levels of biological organization, which are all modulated by environment. We investigated the impact of temperature on the effects of six mutations on sequence, structure, transcriptome, and organismal traits. We found temperature altered the transcriptomic response and key organismal traits such as growth rate and biofilm formation in a genotype-specific manner. Critically, mechanistic insights into the possible drivers of mutational effects emerged only when examining the relationships between different levels of organization: location of mutations in the tertiary structure and distance to key interacting molecules partly explained the observed transcriptomic differences, which in turn drove the impact of mutations on organismal traits. While falling short of capturing the full complexity of the system, our findings underscore the benefits of integrating insights across multiple biological levels to understand the relationship between environment and mutational effects in molecules with extensive pleiotropic effects.

The rate of spontaneous mutation is a key factor in determining the capacity of a population to adapt to a novel environment, for example, a bacterial population exposed to antibiotics. Genetic and environmental factors controlling the mutation rate commonly also cause shifts in the relative rates of different mutational classes, i.e. the mutational spectrum. When the mutational spectrum is altered, the relatively enriched and depleted mutations may differ in their fitness effects. Here, we explore how a reduced mutation rate and altered mutational spectrum can contribute to adaptation in Escherichia coli. We measure mutation rates across a set of Nudix hydrolase deletants, finding multiple strains with an antimutator phenotype. We focus on the antimutator ΔnudJ, which can cause a 6-fold mutation rate reduction relative to the wildtype, with an altered mutational spectrum biased towards A > C transversions. Its reduced mutation rate, most pronounced at low population densities, appears to occur via NudJ's role in nucleotide and/or prenyl metabolism, with a reduced internal ATP pool. Its effects may be reversed by mutations to genes, including waaZ, affecting the outer membrane. Not only does nudJ deletion reduce the probability of antibiotic resistance arising at all but through enhancing an existing hotspot for low fitness A > C rifampicin resistance mutations reduces the expected fitness of strains when resistance does arise. Thus, our findings with ΔnudJ suggest future anti-evolution drug strategies could suppress spontaneous resistance evolution not only through minimizing resistance mutations but also by specifically limiting access to the fittest mutations.

Inherited eye disorders (IED) are a heterogeneous group of Mendelian conditions that are associated with visual impairment. Although these disorders often exhibit incomplete penetrance and variable expressivity, the scale and mechanisms of these phenomena remain largely unknown. Here, we utilize publicly-available genomic and transcriptomic datasets to gain insights into variable penetrance in IED. Variants in a curated set of 340 IED-implicated genes were extracted from the Human Gene Mutation Database (HGMD) 2019.1 and cross-checked with the Genome Aggregation Database (gnomAD) 2.1 control-only dataset. Genes for which >1 variants were encountered in both HGMD and gnomAD were considered to be associated with variable penetrance (n = 56). Variability in gene expression levels was then estimated for the subset of these genes that was found to be adequately expressed in two relevant resources: the Genotype-Tissue Expression (GTEx) and Eye Genotype Expression (EyeGEx) datasets. We found that genes suspected to be associated with variable penetrance tended to have significantly more variability in gene expression levels in the general population (p = 0.0000015); this finding was consistent across tissue types. The results of this study point to the possible influence of cis and/or trans-acting elements on the expressivity of variants causing Mendelian disorders. They also highlight the potential utility of quantifying gene expression as part of the investigation of families showing evidence of variable penetrance.

Our previous work with fragment-assembly methods has demonstrated specific deficiencies in conformational sampling behaviour that, when addressed through improved sampling algorithms, can lead to more reliable prediction of tertiary protein structure when good fragments are available, and when score values can be relied upon to guide the search to the native basin. In this paper, we present preliminary investigations into two important questions arising from more difficult prediction problems. First, we investigated the extent to which native-like conformational states are generated during multiple runs of our search protocols. We determined that, in cases of difficult prediction, native-like decoys are rarely or never generated. Second, we developed a scheme for decoy retention that balances the objectives of retaining low-scoring structures and retaining conformationally diverse structures sampled during the course of the search. Our method succeeds at retaining more diverse sets of structures, and, for a few targets, more native-like solutions are retained as compared to our original, energy-based retention scheme. However, in general, we found that the rate at which native-like structural states are generated has a much stronger effect on eventual distributions of predictive accuracy in the decoy sets, as compared to the specific decoy retention strategy used. We found that our protocols show differences in their ability to access native-like states for some targets, and this may explain some of the differences in predictive performance seen between these methods. There appears to be an interaction between fragment sets and move operators, which influences the accessibility of native-like structures for given targets. Our results point to clear directions for further improvements in fragment-based methods, which are likely to enable higher accuracy predictions.

Duplication of genes or genomes provides the raw material for evolutionary innovation. After duplication a gene may be lost, recombine with another gene, have its function modified or be retained in an unaltered state. The fate of duplication is usually studied by comparing extant genomes and reconstructing the most likely ancestral states. Valuable as this approach is, it may miss the most rapid evolutionary events. Here, we engineered strains ofSaccharomyces cerevisiaecarrying tandem and non-tandem duplications of the singleton geneIFA38to monitor (i) the fate of the duplicates in different conditions, including time scale and asymmetry of gene loss, and (ii) the changes in fitness and transcriptome of the strains immediately after duplication and after experimental evolution. We found that the duplication brings widespread transcriptional changes, but a fitness advantage is only present in fermentable media. In respiratory conditions, the yeast strains consistently lose the non-tandemIFA38gene copy in a surprisingly short time, within only a few generations. This gene loss appears to be asymmetric and dependent on genome location, since the originalIFA38copy and the tandem duplicate are retained. Overall, this work shows for the first time that gene loss can be extremely rapid and context dependent.

Despite the use of combination antiretroviral drugs for the treatment of HIV-1 infection, the emergence of drug resistance remains a problem. Resistance may be conferred either by a single mutation or a concerted set of mutations. The involvement of multiple mutations can arise due to interactions between sites in the amino acid sequence as a consequence of the need to maintain protein structure. To better understand the nature of such epistatic interactions, we reconstructed the ancestral sequences of HIV-1’s Pol protein, and traced the evolutionary trajectories leading to mutations associated with drug resistance. Using contemporary and ancestral sequences we modelled the effects of mutations (i.e. amino acid replacements) on protein structure to understand the functional effects of residue changes. Although the majority of resistance-associated sequences tend to destabilise the protein structure, we find there is a general tendency for protein stability to decrease across HIV-1’s evolutionary history. That a similar pattern is observed in the non-drug resistance lineages indicates that non-resistant mutations, for example, associated with escape from the immune response, also impacts on protein stability. Maintenance of optimal protein structure therefore represents a major constraining factor to the evolution of HIV-1.

Computational approaches to de novo protein tertiary structure prediction, including those based on the preeminent “fragment-assembly” technique, have failed to scale up fully to larger proteins (on the order of 100 residues and above). A number of limiting factors are thought to contribute to the scaling problem over and above the simple combinatorial explosion, but the key ones relate to the lack of exploration of properly diverse protein folds, and to an acute form of “deception” in the energy function, whereby low-energy conformations do not reliably equate with native structures. In this article, solutions to both of these problems are investigated through a multistage memetic algorithm incorporating the successful Rosetta method as a local search routine. We found that specialised genetic operators significantly add to structural diversity and that this translates well to reaching low energies. The use of a generalised stochastic ranking procedure for selection enables the memetic algorithm to handle and traverse deep energy wells that can be considered deceptive, which further adds to the ability of the algorithm to obtain a much-improved diversity of folds. The results should translate to a tangible improvement in the performance of protein structure prediction algorithms in blind experiments such as CASP, and potentially to a further step towards the more challenging problem of predicting the three-dimensional shape of large proteins.