Regions Of Sequence Space Research Articles

Deep mutational scans of XBB.1.5 and BQ.1.1 reveal ongoing epistatic drift during SARS-CoV-2 evolution.

Substitutions that fix between SARS-CoV-2 variants can transform the mutational landscape of future evolution via epistasis. For example, large epistatic shifts in mutational effects caused by N501Y underlied the original emergence of Omicron, but whether such epistatic saltations continue to define ongoing SARS-CoV-2 evolution remains unclear. We conducted deep mutational scans to measure the impacts of all single amino acid mutations and single-codon deletions in the spike receptor-binding domain (RBD) on ACE2-binding affinity and protein expression in the recent Omicron BQ.1.1 and XBB.1.5 variants, and we compared mutational patterns to earlier viral strains that we have previously profiled. As with previous deep mutational scans, we find many mutations that are tolerated or even enhance binding to ACE2 receptor. The tolerance of sites to single-codon deletion largely conforms with tolerance to amino acid mutation. Though deletions in the RBD have not yet been seen in dominant lineages, we observe tolerated deletions including at positions that exhibit indel variation across broader sarbecovirus evolution and in emerging SARS-CoV-2 variants of interest, most notably the well-tolerated Δ483 deletion in BA.2.86. The substitutions that distinguish recent viral variants have not induced as dramatic of epistatic perturbations as N501Y, but we identify ongoing epistatic drift in SARS-CoV-2 variants, including interaction between R493Q reversions and mutations at positions 453, 455, and 456, including F456L that defines the XBB.1.5-derived EG.5 lineage. Our results highlight ongoing drift in the effects of mutations due to epistasis, which may continue to direct SARS-CoV-2 evolution into new regions of sequence space.

Field-theoretic density estimation for biological sequence space with applications to 5′ splice site diversity and aneuploidy in cancer

Density estimation in sequence space is a fundamental problem in machine learning that is also of great importance in computational biology. Due to the discrete nature and large dimensionality of sequence space, how best to estimate such probability distributions from a sample of observed sequences remains unclear. One common strategy for addressing this problem is to estimate the probability distribution using maximum entropy (i.e., calculating point estimates for some set of correlations based on the observed sequences and predicting the probability distribution that is as uniform as possible while still matching these point estimates). Building on recent advances in Bayesian field-theoretic density estimation, we present a generalization of this maximum entropy approach that provides greater expressivity in regions of sequence space where data are plentiful while still maintaining a conservative maximum entropy character in regions of sequence space where data are sparse or absent. In particular, we define a family of priors for probability distributions over sequence space with a single hyperparameter that controls the expected magnitude of higher-order correlations. This family of priors then results in a corresponding one-dimensional family of maximum a posteriori estimates that interpolate smoothly between the maximum entropy estimate and the observed sample frequencies. To demonstrate the power of this method, we use it to explore the high-dimensional geometry of the distribution of 5' splice sites found in the human genome and to understand patterns of chromosomal abnormalities across human cancers.

Attenuation of RNA viruses by redirecting their evolution in sequence space.

RNA viruses pose serious threats to human health. Their success relies on their capacity to generate genetic variability and, consequently, on their adaptive potential. We describe a strategy to attenuate RNA viruses by altering their evolutionary potential. We rationally altered the genomes of Coxsackie B3 and influenza A viruses to redirect their evolutionary trajectories towards detrimental regions in sequence space. Specifically, viral genomes were engineered to harbour more serine and leucine codons with nonsense mutation targets: codons that could generate Stop mutations after a single nucleotide substitution. Indeed, these viruses generated more Stop mutations both in vitro and in vivo, accompanied by significant losses in viral fitness. In vivo, the viruses were attenuated, generated high levels of neutralizing antibodies and protected against lethal challenge. Our study demonstrates that cornering viruses in ‘risky’ areas of sequence space may be implemented as a broad-spectrum vaccine strategy against RNA viruses.Supplementary informationThe online version of this article (doi:10.1038/nmicrobiol.2017.88) contains supplementary material, which is available to authorized users.

Favipiravir can evoke lethal mutagenesis and extinction of foot-and-mouth disease virus

Antiviral agents are increasingly considered an option for veterinary medicine. An understanding of their mechanism of activity is important to plan their administration either as monotherapy or in combination with other agents. Previous studies have shown that the broad spectrum antiviral agent favipiravir (T-705) and its derivatives T-1105 and T-1106 are efficient inhibitors of foot-and-mouth disease virus (FMDV) replication in cell culture and in vivo. However, no mechanism for their activity against FMDV has been proposed. In the present study we show that favipiravir (T-705) can act as a lethal mutagen for FMDV in cell culture. Evidence includes virus extinction associated with increase in mutation frequency in the mutant spectrum of 860 residues of the 3D (polymerase)-coding region, and a decrease of specific infectivity while the consensus nucleotide sequence of the region analyzed remained invariant. The mutational spectrum evoked by favipiravir differs from that observed with other viruses in that no predominant transition type is observed, indicating that a movement towards A,U- or G,C-rich regions of sequence space is not a prerequisite for virus extinction. We discuss prospects for the use of favipiravir to assist in the control of FMDV, and its possible broader use in veterinary medicine as an extension of its current status as antiviral agent for human influenza virus.

SHAPE MATTERS: EFFECT OF POINT MUTATIONS ON RNA SECONDARY STRUCTURE

A suitable model to dive into the properties of genotype-phenotype landscapes is the relationship between RNA sequences and their corresponding minimum free energy secondary structures. Relevant issues related to molecular evolvability and robustness to mutations have been studied in this framework. Here, we analyze the one-mutant neighborhood of the predicted secondary structure of 46 different RNAs, including tRNAs, viroids, larger molecules such as Hepatitis-δ virus, and several random sequences. The probability distribution of the effect of point mutations in linear structural motifs of the secondary structure is well fit by Pareto or Lognormal probability distributions functions, independent of the origin of the RNA molecule. This extends previous results to the case of natural sequences of diverse origins. We introduce a new indicator of robustness, the average weighted length of linear motifs (AwL) and demonstrate that it correlates with the average effect of point mutations in RNA secondary structures. Structures with a high AwL value display the highest structural robustness and cluster in particular regions of sequence space.

V3 Loop Sequence Space Analysis Suggests Different Evolutionary Patterns of CCR5- and CXCR4-Tropic HIV

The V3 loop of human immunodeficiency virus type 1 (HIV-1) is critical for coreceptor binding and is the main determinant of which of the cellular coreceptors, CCR5 or CXCR4, the virus uses for cell entry. The aim of this study is to provide a large-scale data driven analysis of HIV-1 coreceptor usage with respect to the V3 loop evolution and to characterize CCR5- and CXCR4-tropic viral phenotypes previously studied in small- and medium-scale settings. We use different sequence similarity measures, phylogenetic and clustering methods in order to analyze the distribution in sequence space of roughly 1000 V3 loop sequences and their tropism phenotypes. This analysis affords a means of characterizing those sequences that are misclassified by several sequence-based coreceptor prediction methods, as well as predicting the coreceptor using the location of the sequence in sequence space and of relating this location to the CD4+ T-cell count of the patient. We support previous findings that the usage of CCR5 is correlated with relatively high sequence conservation whereas CXCR4-tropic viruses spread over larger regions in sequence space. The incorrectly predicted sequences are mostly located in regions in which their phenotype represents the minority or in close vicinity of regions dominated by the opposite phenotype. Nevertheless, the location of the sequence in sequence space can be used to improve the accuracy of the prediction of the coreceptor usage. Sequences from patients with high CD4+ T-cell counts are relatively highly conserved as compared to those of immunosuppressed patients. Our study thus supports hypotheses of an association of immune system depletion with an increase in V3 loop sequence variability and with the escape of the viral sequence to distant parts of the sequence space.

Solution structure of a de novo protein from a designed combinatorial library.

Combinatorial libraries of de novo amino acid sequences can provide a rich source of diversity for the discovery of novel proteins. Randomly generated sequences, however, rarely fold into well ordered protein-like structures. To enhance the quality of a library, diversity must be focused into those regions of sequence space most likely to yield well folded structures. We have constructed focused libraries of de novo sequences by designing the binary pattern of polar and nonpolar amino acids to favor structures that contain abundant secondary structure, while simultaneously burying hydrophobic side chains in the protein interior and exposing hydrophilic side chains to solvent. Because binary patterning specifies only the polar/nonpolar periodicity, but not the identities of the side chains, detailed structural features, including packing interactions, cannot be designed a priori. Can binary patterned libraries nonetheless encode well folded proteins? An unambiguous answer to this question requires determination of a 3D structure. We used NMR spectroscopy to determine the structure of S-824, a novel protein from a recently constructed library of 102-residue sequences. This library is "naïve" in that it has not been subjected to high-throughput screens or directed evolution. The experimentally determined structure of S-824 is a four-helix bundle, as specified by the design. As dictated by the binary-code strategy, nonpolar side chains are buried in the protein interior, and polar side chains are exposed to solvent. The polypeptide backbone and buried side chains are well ordered, demonstrating that S-824 is not a molten globule and forms a unique structure. These results show that amino acid sequences that have neither been selected by evolution, nor designed by computer, nor isolated by high-throughput screening, can form native-like structures. These findings validate the binary-code strategy as an effective method for producing vast collections of well folded de novo proteins.

Landscapes and molecular evolution

Evolution of RNA molecules in vitro is visualized as a hill-climbing process on a fitness landscape that can be derived from molecular properties and functions. The optimization process is shaped by a high degree of redundance in sequence-to-structure mappings: there are many more sequences than structures and sequences folding into the same structure are (almost) randomly distributed in sequence space. Two consequences of this redundance are important for evolution: shape space covering by small connected regions in sequence space and the existence of extended neutral networks. Both results together explain how nature can fast and efficiently find solutions to complex optimization problems by trial and error while the number of possible genotypes exceeds all imagination. In the presence of neutral networks populations avoid being caught in evolutionary traps and eventually reach the global optimum through a composite dynamics of adaptive walks and random drift. Results derived from mathematical analysis are confronted with the results of computer simulation and available experimental data.