Evolution on the Biophysical Fitness Landscape of an RNA Virus

Biophysical principles predict fitness of SARS-CoV-2 variants

SARS-CoV-2 employs its spike protein's receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, our understanding of how RBD's biophysical properties contribute to SARS-CoV-2's epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the identification of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by dissociation constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto an epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low-frequency variants. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics.

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app .

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.

Mutational biases favor complexity increases in protein interaction networks after gene duplication

Biological systems can gain complexity over time. While some of these transitions are likely driven by natural selection, the extent to which they occur without providing an adaptive benefit is unknown. At the molecular level, one example is heteromeric complexes replacing homomeric ones following gene duplication. Here, we build a biophysical model and simulate the evolution of homodimers and heterodimers following gene duplication using distributions of mutational effects inferred from available protein structures. We keep the specific activity of each dimer identical, so their concentrations drift neutrally without new functions. We show that for more than 60% of tested dimer structures, the relative concentration of the heteromer increases over time due to mutational biases that favor the heterodimer. However, allowing mutational effects on synthesis rates and differences in the specific activity of homo- and heterodimers can limit or reverse the observed bias toward heterodimers. Our results show that the accumulation of more complex protein quaternary structures is likely under neutral evolution, and that natural selection would be needed to reverse this tendency.

Single-cell herpes simplex virus type 1 infection of neurons using drop-based microfluidics reveals heterogeneous replication kinetics

Single-cell analyses of viral infections reveal heterogeneity that is not detected by traditional population-level studies. This study applies drop-based microfluidics to investigate the dynamics of herpes simplex virus type 1 (HSV-1) infection of neurons at the single-cell level. We used micrometer-scale Matrigel beads, termed microgels, to culture individual murine superior cervical ganglia (SCG) neurons or epithelial cells. Microgel-cultured cells are encapsulated in individual media-in-oil droplets with a dual-fluorescent reporter HSV-1, enabling real-time observation of viral gene expression and replication. Infection within drops revealed that the kinetics of initial viral gene expression and replication were dependent on the inoculating dose. Notably, increasing inoculating doses led to earlier onset of viral gene expression and more frequent productive viral replication. These observations provide crucial insights into the complexity of HSV-1 infection in neurons and emphasize the importance of studying single-cell outcomes of viral infection. These techniques for cell culture and infection in drops provide a foundation for future virology and neurobiology investigations.

Biophysical principles predict fitness of SARS-CoV-2 variants

SARS-CoV-2 employs its spike protein's receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, our understanding of how RBD's biophysical properties contribute to SARS-CoV-2's epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the discovery of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by binding constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto a epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low frequency variants. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics.

A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations

Pathogen evolution of drug resistance often occurs in a stepwise manner via the accumulation of multiple mutations that in combination have a non-additive impact on fitness, a phenomenon known as epistasis. The evolution of resistance via the accumulation of point mutations in the DHFR genes of Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) has been studied extensively and multiple studies have shown epistatic interactions between these mutations determine the accessible evolutionary trajectories to highly resistant multiple mutations. Here, we simulated these evolutionary trajectories using a model of molecular evolution, parameterised using Rosetta Flex ddG predictions, where selection acts to reduce the target-drug binding affinity. We observe strong agreement with pathways determined using experimentally measured IC50 values of pyrimethamine binding, which suggests binding affinity is strongly predictive of resistance and epistasis in binding affinity strongly influences the order of fixation of resistance mutations. We also infer pathways directly from the frequency of mutations found in isolate data, and observe remarkable agreement with the most likely pathways predicted by our mechanistic model, as well as those determined experimentally. This suggests mutation frequency data can be used to intuitively infer evolutionary pathways, provided sufficient sampling of the population.

Amino acid substitutions in norovirus VP1 dictate host dissemination via variations in cellular attachment

IMPORTANCE

All viruses initiate infection by utilizing receptors to attach to target host cells. These virus-receptor interactions can therefore dictate viral replication and pathogenesis. Understanding the nature of virus-receptor interactions could also be important for the development of novel therapies. Noroviruses are non-enveloped icosahedral viruses of medical importance. They are a common cause of acute gastroenteritis with no approved vaccine or therapy and are a tractable model for studying fundamental virus biology. In this study, we utilized the murine norovirus model system to show that variation in a single amino acid of the major capsid protein alone can affect viral infectivity through improved attachment to suspension cells. Modulating plasma membrane mobility reduced infectivity, suggesting an importance of membrane mobility for receptor recruitment and/or receptor conformation. Furthermore, different substitutions at this site altered viral tissue distribution in a murine model, illustrating how in-host capsid evolution could influence viral infectivity and/or immune evasion.

Steering and controlling evolution - from bioengineering to fighting pathogens

Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.

Population immunity predicts evolutionary trajectories of SARS-CoV-2

The large-scale evolution of the SARS-CoV-2 virus has been marked by rapid turnover of genetic clades. New variants show intrinsic changes, notably increased transmissibility, and antigenic changes that reduce cross-immunity induced by previous infections or vaccinations. How this functional variation shapes global evolution has remained unclear. Here, we establish a predictive fitness model for SARS-CoV-2 that integrates antigenic and intrinsic selection. The model is informed by tracking of time-resolved sequence data, epidemiological records, and cross-neutralization data of viral variants. Our inference shows that immune pressure, including contributions of vaccinations and previous infections, has become the dominant force driving the recent evolution of SARS-CoV-2. The fitness model can serve continued surveillance in two ways. First, it successfully predicts the short-term evolution of circulating strains and flags emerging variants likely to displace the previously predominant variant. Second, it predicts likely antigenic profiles of successful escape variants prior to their emergence.

Viral Evolution Shaped by Host Proteostasis Networks

Understanding the factors that shape viral evolution is critical for developing effective antiviral strategies, accurately predicting viral evolution, and preventing pandemics. One fundamental determinant of viral evolution is the interplay between viral protein biophysics and the host machineries that regulate protein folding and quality control. Most adaptive mutations in viruses are biophysically deleterious, resulting in a viral protein product with folding defects. In cells, protein folding is assisted by a dynamic system of chaperones and quality control processes known as the proteostasis network. Host proteostasis networks can determine the fates of viral proteins with biophysical defects, either by assisting with folding or by targeting them for degradation. In this review, we discuss and analyze new discoveries revealing that host proteostasis factors can profoundly shape the sequence space accessible to evolving viral proteins. We also discuss the many opportunities for research progress proffered by the proteostasis perspective on viral evolution and adaptation.

Single-cell Herpes Simplex Virus type-1 infection of neurons using drop-based microfluidics reveals heterogeneous replication kinetics

Single-cell analyses of viral infections often reveal heterogeneity that is not detected by traditional population-level studies. This study applies drop-based microfluidics to investigate the dynamics of HSV-1 infection of neurons at the single-cell level. We used micron-scale Matrigel beads, termed microgels, to culture individual murine Superior Cervical ganglia (SCG) neurons or epithelial cells. Microgel-cultured cells are subsequently enclosed in individual media-in-oil droplets with a dual fluorescent-reporter HSV-1, enabling real-time observation of viral gene expression and replication. Infection within drops revealed that the kinetics of initial viral gene expression and replication were dependent on the inoculating dose. Notably, increasing inoculating doses led to earlier onset of viral gene expression and more frequent productive viral replication. These observations provide crucial insights into the complexity of HSV-1 infection in neurons and emphasize the importance of studying single-cell outcomes of viral infection. The innovative techniques presented here for cell culture and infection in drops provide a foundation for future virology and neurobiology investigations.

The influence of immune cells on the existence of virus quasi-species

This article investigate a nonlocal reaction-diffusion system of equations modeling virus distribution with respect to their genotypes in the interaction with the immune response. This study demonstrates the existence of pulse solutions corresponding to virus quasi-species. The proof is based on the Leray-Schauder method, which relies on the topological degree for elliptic operators in unbounded domains and a priori estimates of solutions. Furthermore, linear stability analysis of a spatially homogeneous stationary solution identifies the critical conditions for the emergence of spatial and spatiotemporal structures. Finally, numerical simulations are used to illustrate nonlinear dynamics and pattern formation in the nonlocal model.

Genotype-phenotype landscapes for immune-pathogen coevolution

Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.

The landscape of antibody binding affinity in SARS-CoV-2 Omicron BA.1 evolution

The Omicron BA.1 variant of SARS-CoV-2 escapes convalescent sera and monoclonal antibodies that are effective against earlier strains of the virus. This immune evasion is largely a consequence of mutations in the BA.1 receptor binding domain (RBD), the major antigenic target of SARS-CoV-2. Previous studies have identified several key RBD mutations leading to escape from most antibodies. However, little is known about how these escape mutations interact with each other and with other mutations in the RBD. Here, we systematically map these interactions by measuring the binding affinity of all possible combinations of these 15 RBD mutations (215=32,768 genotypes) to 4 monoclonal antibodies (LY-CoV016, LY-CoV555, REGN10987, and S309) with distinct epitopes. We find that BA.1 can lose affinity to diverse antibodies by acquiring a few large-effect mutations and can reduce affinity to others through several small-effect mutations. However, our results also reveal alternative pathways to antibody escape that does not include every large-effect mutation. Moreover, epistatic interactions are shown to constrain affinity decline in S309 but only modestly shape the affinity landscapes of other antibodies. Together with previous work on the ACE2 affinity landscape, our results suggest that the escape of each antibody is mediated by distinct groups of mutations, whose deleterious effects on ACE2 affinity are compensated by another distinct group of mutations (most notably Q498R and N501Y).

Amino acid substitutions in norovirus VP1 dictate cell tropism via an attachment process dependent on membrane mobility

Viruses interact with receptors on the cell surface to initiate and co-ordinate infection. The distribution of receptors on host cells can be a key determinant of viral tropism and host infection. Unravelling the complex nature of virus-receptor interactions is, therefore, of fundamental importance to understanding viral pathogenesis. Noroviruses are non-enveloped, icosahedral, positive-sense RNA viruses of global importance to human health, with no approved vaccine or antiviral agent available. Here we use murine norovirus as a model for the study of molecular mechanisms of virus-receptor interactions. We show that variation at a single amino acid residue in the major viral capsid protein had a key impact on the interaction between virus and receptor. This variation did not affect virion production or virus growth kinetics, but a specific amino acid was rapidly selected through evolution experiments, and significantly improved cellular attachment when infecting immune cells in suspension. However, reducing plasma membrane mobility counteracted this phenotype, providing insight into for the role of membrane fluidity and receptor recruitment in norovirus cellular attachment. When the infectivity of a panel of recombinant viruses with single amino acid variations was compared in vivo, there were significant differences in the distribution of viruses in a murine model, demonstrating a role in cellular tropism in vivo. Overall, these results highlight the importance of lipid rafts and virus-induced receptor recruitment in viral infection, as well as how capsid evolution can greatly influence cellular tropism, within-host spread and pathogenicity.

Viral Fitness Landscapes Based on Self-organizing Maps

The creation of fitness maps from viral populations especially in the case of RNA viruses, with high mutation rates producing quasispecies, is complex since the mutant spectrum is in a very high-dimensional space. In this work, a new approach is presented using a class of neural networks, Self-Organized Maps (SOM), to represent realistic fitness landscapes in two RNA viruses: Human Immunodeficiency Virus type 1 (HIV-1) and Hepatitis C Virus (HCV). This methodology has proven to be very effective in the classification of viral quasispecies, using as criterium the mutant sequences in the population. With HIV-1, the fitness landscapes are constructed by representing the experimentally determined fitness on the sequence map. This approach permitted the depiction of the evolutionary paths of the variants subjected to processes of fitness loss and gain in cell culture. In the case of HCV, the efficiency was measured as a function of the frequency of each haplotype in the population by ultra-deep sequencing. The fitness landscapes obtained provided information on the efficiency of each variant in the quasispecies environment, that is, in relation to the entire spectrum of mutants. With the SOM maps, it is possible to determine the evolutionary dynamics of the different haplotypes.

Genetic variations affecting ACE2 protein stability in minority populations

While worldwide efforts for improving COVID-19 vaccines are currently considered a top priority, the role of the genetic variants responsible for virus receptor protein stability is less studied. Angiotensin-converting enzyme-2 is the primary target of the SARS-CoV-1/SARS-CoV-2 spike (S) glycoprotein, enabling entry into the human body. Here, we applied computational saturation mutagenesis approaches to determine the folding energy caused by all possible mutations in ACE2 proteins within ACE2 - SARS-CoV-1-S/ACE2 - SARS-CoV-2-S complexes. We observed ACE2 mutations at residue D350 causing the most stabilizing effects on the protein. In addition, we identified ACE2 genetic variations in African Americans (rs73635825, rs766996587, and rs780574871), Latino Americans (rs924799658), and both groups (rs4646116 and rs138390800) affecting stability in the ACE2 - SARS-CoV-2-S complex. The findings in this study may aid in targeting the design of stable neutralizing peptides for treating minority patients.

Single-Cell Infection of Influenza A Virus Using Drop-Based Microfluidics

Drop-based microfluidics has revolutionized single-cell studies and can be applied toward analyzing tens of thousands to millions of single cells and their products contained within picoliter-sized drops. Drop-based microfluidics can shed insight into single-cell virology, enabling higher-resolution analysis of cellular and viral heterogeneity during viral infection. In this work, individual A549, MDCK, and siat7e cells were infected with influenza A virus (IAV) and encapsulated into 100-μm-size drops. Initial studies of uninfected cells encapsulated in drops demonstrated high cell viability and drop stability. Cell viability of uninfected cells in the drops remained above 75%, and the average drop radii changed by less than 3% following cell encapsulation and incubation over 24 h. Infection parameters were analyzed over 24 h from individually infected cells in drops. The number of IAV viral genomes and infectious viruses released from A549 and MDCK cells in drops was not significantly different from bulk infection as measured by reverse transcriptase quantitative PCR (RT-qPCR) and plaque assay. The application of drop-based microfluidics in this work expands the capacity to propagate IAV viruses and perform high-throughput analyses of individually infected cells. IMPORTANCE Drop-based microfluidics is a cutting-edge tool in single-cell research. Here, we used drop-based microfluidics to encapsulate thousands of individual cells infected with influenza A virus within picoliter-sized drops. Drop stability, cell loading, and cell viability were quantified from three different cell lines that support influenza A virus propagation. Similar levels of viral progeny as determined by RT-qPCR and plaque assay were observed from encapsulated cells in drops compared to bulk culture. This approach enables the ability to propagate influenza A virus from encapsulated cells, allowing for future high-throughput analysis of single host cell interactions in isolated microenvironments over the course of the viral life cycle.

Biophysical Fitness Landscape of the SARS-CoV-2 Delta Variant Receptor Binding Domain

Among the five known SARS-CoV-2 variants of concern, Delta is the most virulent leading to severe symptoms and increased mortality among infected people. Our study seeks to examine how the biophysical parameters of the Delta variant correlate to the clinical observations. Receptor binding domain (RBD) is the first point of contact with the human host cells and is the immunodominant form of the spike protein. Delta variant RBD contains two novel mutations L452R and T478K. We examined the effect of single as well as the double mutations on RBD expression in human Expi293 cells, RBD stability using urea and thermal denaturation, and RBD binding to angiotensin converting enzyme 2 (ACE2) receptor and to neutralizing antibodies using isothermal titration calorimetry. Delta variant RBD showed significantly higher expression compared to the wild-type RBD, and the increased expression is due to L452R mutation. Despite their non-conservative nature, none of the mutations significantly affected RBD structure and stability. All mutants showed similar binding affinity to ACE2 and to Class 1 antibodies (CC12.1 and LY-CoV016) as that of the wild-type. Delta double mutant L452R/T478K showed no binding to Class 2 antibodies (P2B-2F6 and LY-CoV555) and a hundred-fold weaker binding to a Class 3 antibody (REGN10987), and the decreased antibody binding is determined by the L452R mutation. These results indicate that the immune escape from neutralizing antibodies, rather than increased receptor binding, is the main biophysical parameter that determined the fitness landscape of the Delta variant RBD.

Droplet Microfluidics for High-Resolution Virology

Microfluidics has enabled a new era of cellular and molecular assays due to the small length scales, parallelization, and the modularity of various analysis and actuation functions. Droplet microfluidics, in particular, has been instrumental in providing new tools for biology with its ability to quickly and reproducibly generate drops that act as individual reactors. A notable beneficiary of this technology has been single-cell RNA sequencing, which has revealed new heterogeneities and interactions for the fundamental unit of life. However, viruses far surpass the diversity of cellular life, affect the dynamics of all ecosystems, and are a chronic source of global health crises. Despite their impact on the world, high-throughput and high-resolution viral profiling has been difficult, with conventional methods being limited to population-level averaging, large sample volumes, and few cultivable hosts. Consequently, most viruses have not been identified and studied. Droplet microfluidics holds the potential to address many of these limitations and offers new levels of sensitivity and throughput for virology. This Feature highlights recent efforts that have applied droplet microfluidics to the detection and study of viruses, including for diagnostics, virus-host interactions, and cell-independent virus assays. In combination with traditional virology methods, droplet microfluidics should prove a potent tool toward achieving a better understanding of the most abundant biological species on Earth.

Convergent Evolution of Multiple Mutations Improves the Viral Fitness of SARS-CoV-2 Variants by Balancing Positive and Negative Selection

Multiple mutations have been seen to undergo convergent evolution in SARS-CoV-2 variants of concern. One such evolution occurs in Beta, Gamma, and Omicron variants at three amino acid positions K417, E484, and N501 in the receptor binding domain of the spike protein. We examined the physical mechanisms underlying the convergent evolution of three mutations K417T/E484K/N501Y by delineating the individual and collective effects of mutations on binding to angiotensin converting enzyme 2 receptor, immune escape from neutralizing antibodies, protein stability, and expression. Our results show that each mutation serves a distinct function that improves virus fitness supporting its positive selection, even though individual mutations have deleterious effects that make them prone to negative selection. Compared to the wild-type, K417T escapes Class 1 antibodies and has increased stability and expression; however, it has decreased receptor binding. E484K escapes Class 2 antibodies; however, it has decreased receptor binding, stability, and expression. N501Y increases receptor binding; however, it has decreased stability and expression. When these mutations come together, the deleterious effects are mitigated due to the presence of compensatory effects. Triple mutant K417T/E484K/N501Y has increased receptor binding, escapes both Class 1 and Class 2 antibodies, and has similar stability and expression as that of the wild-type. These results show that the convergent evolution of multiple mutations enhances viral fitness on different fronts by balancing both positive and negative selection and improves the chances of selection of mutations together.

Receptor binding, immune escape, and protein stability direct the natural selection of SARS-CoV-2 variants

Emergence of new severe acute respiratory syndrome coronavirus 2 variants has raised concerns related to the effectiveness of vaccines and antibody therapeutics developed against the unmutated wildtype virus. Here, we examined the effect of the 12 most commonly occurring mutations in the receptor-binding domain of the spike protein on its expression, stability, activity, and antibody escape potential. Stability was measured using thermal denaturation, and the activity and antibody escape potential were measured using isothermal titration calorimetry in terms of binding to the human angiotensin-converting enzyme 2 and to neutralizing human antibody CC12.1, respectively. Our results show that mutants differ in their expression levels. Of the eight best-expressed mutants, two (N501Y and K417T/E484K/N501Y) showed stronger affinity to angiotensin-converting enzyme 2 compared with the wildtype, whereas four (Y453F, S477N, T478I, and S494P) had similar affinity and two (K417N and E484K) had weaker affinity than the wildtype. Compared with the wildtype, four mutants (K417N, Y453F, N501Y, and K417T/E484K/N501Y) had weaker affinity for the CC12.1 antibody, whereas two (S477N and S494P) had similar affinity, and two (T478I and E484K) had stronger affinity than the wildtype. Mutants also differ in their thermal stability, with the two least stable mutants showing reduced expression. Taken together, these results indicate that multiple factors contribute toward the natural selection of variants, and all these factors need to be considered to understand the evolution of the virus. In addition, since not all variants can escape a given neutralizing antibody, antibodies to treat new variants can be chosen based on the specific mutations in that variant.

Binding affinity landscapes constrain the evolution of broadly neutralizing anti-influenza antibodies

Over the past two decades, several broadly neutralizing antibodies (bnAbs) that confer protection against diverse influenza strains have been isolated. Structural and biochemical characterization of these bnAbs has provided molecular insight into how they bind distinct antigens. However, our understanding of the evolutionary pathways leading to bnAbs, and thus how best to elicit them, remains limited. Here, we measure equilibrium dissociation constants of combinatorially complete mutational libraries for two naturally isolated influenza bnAbs (CR9114, 16 heavy-chain mutations; CR6261, 11 heavy-chain mutations), reconstructing all possible evolutionary intermediates back to the unmutated germline sequences. We find that these two libraries exhibit strikingly different patterns of breadth: while many variants of CR6261 display moderate affinity to diverse antigens, those of CR9114 display appreciable affinity only in specific, nested combinations. By examining the extensive pairwise and higher order epistasis between mutations, we find key sites with strong synergistic interactions that are highly similar across antigens for CR6261 and different for CR9114. Together, these features of the binding affinity landscapes strongly favor sequential acquisition of affinity to diverse antigens for CR9114, while the acquisition of breadth to more similar antigens for CR6261 is less constrained. These results, if generalizable to other bnAbs, may explain the molecular basis for the widespread observation that sequential exposure favors greater breadth, and such mechanistic insight will be essential for predicting and eliciting broadly protective immune responses.

Predicting in vivo escape dynamics of HIV-1 from a broadly neutralizing antibody

Broadly neutralizing antibodies (bnAbs) show promise for antibody-mediated prevention or treatment of HIV-1 infection. Recent clinical trials, however, indicate that the virus can evolve resistance mutations that escape neutralization by bnAbs. Here, we establish a fitness model for the escape dynamics of HIV in humans. We show that this model can be applied universally across different human hosts, providing a proof of principle that the in vivo response of HIV to bnAb therapies is predictable. Our analysis identifies a fitness trade-off in viral evolution that can inform antibody design and therapy protocols.

Broadly neutralizing antibodies are promising candidates for treatment and prevention of HIV-1 infections. Such antibodies can temporarily suppress viral load in infected individuals; however, the virus often rebounds by escape mutants that have evolved resistance. In this paper, we map a fitness model of HIV-1 interacting with broadly neutralizing antibodies using in vivo data from a recent clinical trial. We identify two fitness factors, antibody dosage and viral load, that determine viral reproduction rates reproducibly across different hosts. The model successfully predicts the escape dynamics of HIV-1 in the course of an antibody treatment, including a characteristic frequency turnover between sensitive and resistant strains. This turnover is governed by a dosage-dependent fitness ranking, resulting from an evolutionary trade-off between antibody resistance and its collateral cost in drug-free growth. Our analysis suggests resistance–cost trade-off curves as a measure of antibody performance in the presence of resistance evolution.

Development of antibacterial compounds that constrain evolutionary pathways to resistance

Antibiotic resistance is a worldwide challenge. A potential approach to block resistance is to simultaneously inhibit WT and known escape variants of the target bacterial protein. Here, we applied an integrated computational and experimental approach to discover compounds that inhibit both WT and trimethoprim (TMP) resistant mutants of E. coli dihydrofolate reductase (DHFR). We identified a novel compound (CD15-3) that inhibits WT DHFR and its TMP resistant variants L28R, P21L and A26T with IC₅₀ 50–75 µM against WT and TMP-resistant strains. Resistance to CD15-3 was dramatically delayed compared to TMP in in vitro evolution. Whole genome sequencing of CD15-3-resistant strains showed no mutations in the target folA locus. Rather, gene duplication of several efflux pumps gave rise to weak (about twofold increase in IC₅₀) resistance against CD15-3. Altogether, our results demonstrate the promise of strategy to develop evolution drugs - compounds which constrain evolutionary escape routes in pathogens.

From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.

The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology

Harnessing the process of natural selection to obtain and understand new microbial phenotypes has become increasingly possible due to advances in culturing techniques, DNA sequencing, bioinformatics, and genetic engineering. Accordingly, Adaptive Laboratory Evolution (ALE) experiments represent a powerful approach both to investigate the evolutionary forces influencing strain phenotypes, performance, and stability, and to acquire production strains that contain beneficial mutations. In this review, we summarize and categorize the applications of ALE to various aspects of microbial physiology pertinent to industrial bioproduction by collecting case studies that highlight the multitude of ways in which evolution can facilitate the strain construction process. Further, we discuss principles that inform experimental design, complementary approaches such as computational modeling that help maximize utility, and the future of ALE as an efficient strain design and build tool driven by growing adoption and improvements in automation.

Protein ensembles link genotype to phenotype

Classically, phenotype is what is observed, and genotype is the genetic makeup. Statistical studies aim to project phenotypic likelihoods of genotypic patterns. The traditional genotype-to-phenotype theory embraces the view that the encoded protein shape together with gene expression level largely determines the resulting phenotypic trait. Here, we point out that the molecular biology revolution at the turn of the century explained that the gene encodes not one but ensembles of conformations, which in turn spell all possible gene-associated phenotypes. The significance of a dynamic ensemble view is in understanding the linkage between genetic change and the gained observable physical or biochemical characteristics. Thus, despite the transformative shift in our understanding of the basis of protein structure and function, the literature still commonly relates to the classical genotype-phenotype paradigm. This is important because an ensemble view clarifies how even seemingly small genetic alterations can lead to pleiotropic traits in adaptive evolution and in disease, why cellular pathways can be modified in monogenic and polygenic traits, and how the environment may tweak protein function.

SodaPop: a forward simulation suite for the evolutionary dynamics of asexual populations on protein fitness landscapes

Motivation

Protein evolution is determined by forces at multiple levels of biological organization. Random mutations have an immediate effect on the biophysical properties, structure and function of proteins. These same mutations also affect the fitness of the organism. However, the evolutionary fate of mutations, whether they succeed to fixation or are purged, also depends on population size and dynamics. There is an emerging interest, both theoretically and experimentally, to integrate these two factors in protein evolution. Although there are several tools available for simulating protein evolution, most of them focus on either the biophysical or the population-level determinants, but not both. Hence, there is a need for a publicly available computational tool to explore both the effects of protein biophysics and population dynamics on protein evolution.

Results

To address this need, we developed SodaPop, a computational suite to simulate protein evolution in the context of the population dynamics of asexual populations. SodaPop accepts as input several fitness landscapes based on protein biochemistry or other user-defined fitness functions. The user can also provide as input experimental fitness landscapes derived from deep mutational scanning approaches or theoretical landscapes derived from physical force field estimates. Here, we demonstrate the broad utility of SodaPop with different applications describing the interplay of selection for protein properties and population dynamics. SodaPop is designed such that population geneticists can explore the influence of protein biochemistry on patterns of genetic variation, and that biochemists and biophysicists can explore the role of population size and demography on protein evolution.

Availability and implementation

Source code and binaries are freely available at https://github.com/louisgt/SodaPop under the GNU GPLv3 license. The software is implemented in C++ and supported on Linux, Mac OS/X and Windows.

Supplementary information

Supplementary data are available at Bioinformatics online.

The Role of Evolutionary Selection in the Dynamics of Protein Structure Evolution

Homology modeling is a powerful tool for predicting a protein's structure. This approach is successful because proteins whose sequences are only 30% identical still adopt the same structure, while structure similarity rapidly deteriorates beyond the 30% threshold. By studying the divergence of protein structure as sequence evolves in real proteins and in evolutionary simulations, we show that this nonlinear sequence-structure relationship emerges as a result of selection for protein folding stability in divergent evolution. Fitness constraints prevent the emergence of unstable protein evolutionary intermediates, thereby enforcing evolutionary paths that preserve protein structure despite broad sequence divergence. However, on longer timescales, evolution is punctuated by rare events where the fitness barriers obstructing structure evolution are overcome and discovery of new structures occurs. We outline biophysical and evolutionary rationale for broad variation in protein family sizes, prevalence of compact structures among ancient proteins, and more rapid structure evolution of proteins with lower packing density.