Deep mutational scanning of hepatitis B virus reveals a mechanism for cis-preferential reverse transcription.

Hepatitis B virus (HBV) is a small double-stranded DNA virus that chronically infects 296 million people. Over half of its compact genome encodes proteins in two overlapping reading frames, and during evolution, multiple selective pressures can act on shared nucleotides. This study combines an RNA-based HBV cell culture system with deep mutational scanning (DMS) to uncouple cis- and trans-acting sequence requirements in the HBV genome. The results support a leaky ribosome scanning model for polymerase translation, provide a fitness map of the HBV polymerase at single-nucleotide resolution, and identify conserved prolines adjacent to the HBV polymerase termination codon that stall ribosomes. Further experiments indicated that stalled ribosomes tether the nascent polymerase to its template RNA, ensuring cis-preferential RNA packaging and reverse transcription of the HBV genome.

Saturation mutagenesis-reinforced functional assays for disease-related genes.

Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods are obstacles for achieving genome-wide resolution of variants in disease-related genes. Our framework, saturation mutagenesis-reinforced functional assays (SMuRF), offers simple and cost-effective saturation mutagenesis paired with streamlined functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genes FKRP and LARGE1, we generated functional scores for all possible coding single-nucleotide variants, which aid in resolving clinically reported variants of uncertain significance. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Overall, our approach enables variant-to-function insights for disease genes in a cost-effective manner that can be broadly implemented by standard research laboratories.

An AsCas12f-based compact genome-editing tool derived by deep mutational scanning and structural analysis.

SpCas9 and AsCas12a are widely utilized as genome-editing tools in human cells. However, their relatively large size poses a limitation for delivery by cargo-size-limited adeno-associated virus (AAV) vectors. The type V-F Cas12f from Acidibacillus sulfuroxidans is exceptionally compact (422 amino acids) and has been harnessed as a compact genome-editing tool. Here, we developed an approach, combining deep mutational scanning and structure-informed design, to successfully generate two AsCas12f activity-enhanced (enAsCas12f) variants. Remarkably, the enAsCas12f variants exhibited genome-editing activities in human cells comparable with those of SpCas9 and AsCas12a. The cryoelectron microscopy (cryo-EM) structures revealed that the mutations stabilize the dimer formation and reinforce interactions with nucleic acids to enhance their DNA cleavage activities. Moreover, enAsCas12f packaged with partner genes in an all-in-one AAV vector exhibited efficient knock-in/knock-out activities and transcriptional activation in mice. Taken together, enAsCas12f variants could offer a minimal genome-editing platform for in vivo gene therapy.

A pseudovirus system enables deep mutational scanning of the full SARS-CoV-2 spike.

A major challenge in understanding SARS-CoV-2 evolution is interpreting the antigenic and functional effects of emerging mutations in the viral spike protein. Here, we describe a deep mutational scanning platform based on non-replicative pseudotyped lentiviruses that directly quantifies how large numbers of spike mutations impact antibody neutralization and pseudovirus infection. We apply this platform to produce libraries of the Omicron BA.1 and Delta spikes. These libraries each contain ∼7,000 distinct amino acid mutations in the context of up to ∼135,000 unique mutation combinations. We use these libraries to map escape mutations from neutralizing antibodies targeting the receptor-binding domain, N-terminal domain, and S2 subunit of spike. Overall, this work establishes a high-throughput and safe approach to measure how ∼105 combinations of mutations affect antibody neutralization and spike-mediated infection. Notably, the platform described here can be extended to the entry proteins of many other viruses.

Deep mutational scanning identifies SARS-CoV-2 Nucleocapsid escape mutations of currently available rapid antigen tests.

The effects of mutations in continuously emerging variants of SARS-CoV-2 are a major concern for the performance of rapid antigen tests. To evaluate the impact of mutations on 17 antibodies used in 11 commercially available antigen tests with emergency use authorization, we measured antibody binding for all possible Nucleocapsid point mutations using a mammalian surface-display platform and deep mutational scanning. The results provide a complete map of the antibodies' epitopes and their susceptibility to mutational escape. Our data predict no vulnerabilities for detection of mutations found in variants of concern. We confirm this using the commercial tests and sequence-confirmed COVID-19 patient samples. The antibody escape mutational profiles generated here serve as a valuable resource for predicting the performance of rapid antigen tests against past, current, as well as any possible future variants of SARS-CoV-2, establishing the direct clinical and public health utility of our system.

High-resolution profiling of pathways of escape for SARS-CoV-2 spike-binding antibodies.

Defining long-term protective immunity to SARS-CoV-2 is one of the most pressing questions of our time and will require a detailed understanding of potential ways this virus can evolve to escape immune protection. Immune protection will most likely be mediated by antibodies that bind to the viral entry protein, spike (S). Here, we used Phage-DMS, an approach that comprehensively interrogates the effect of all possible mutations on binding to a protein of interest, to define the profile of antibody escape to the SARS-CoV-2 S protein using coronavirus disease 2019 (COVID-19) convalescent plasma. Antibody binding was common in two regions, the fusion peptide and the linker region upstream of the heptad repeat region 2. However, escape mutations were variable within these immunodominant regions. There was also individual variation in less commonly targeted epitopes. This study provides a granular view of potential antibody escape pathways and suggests there will be individual variation in antibody-mediated virus evolution.

Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding.

The receptor binding domain (RBD) of the SARS-CoV-2 spike glycoprotein mediates viral attachment to ACE2 receptor and is a major determinant of host range and a dominant target of neutralizing antibodies. Here, we experimentally measure how all amino acid mutations to the RBD affect expression of folded protein and its affinity for ACE2. Most mutations are deleterious for RBD expression and ACE2 binding, and we identify constrained regions on the RBD's surface that may be desirable targets for vaccines and antibody-based therapeutics. But a substantial number of mutations are well tolerated or even enhance ACE2 binding, including at ACE2 interface residues that vary across SARS-related coronaviruses. However, we find no evidence that these ACE2-affinity-enhancing mutations have been selected in current SARS-CoV-2 pandemic isolates. We present an interactive visualization and open analysis pipeline to facilitate use of our dataset for vaccine design and functional annotation of mutations observed during viral surveillance.

Restriction of HIV-1 Escape by a Highly Broad and Potent Neutralizing Antibody.

Broadly neutralizing antibodies (bNAbs) represent a promising approach to prevent and treat HIV-1 infection. However, viral escape through mutation of the HIV-1 envelope glycoprotein (Env) limits clinical applications. Here we describe 1-18, a new VH1-46-encoded CD4 binding site (CD4bs) bNAb with outstanding breadth (97%) and potency (GeoMean IC50 = 0.048 µg/mL). Notably, 1-18 is not susceptible to typical CD4bs escape mutations and effectively overcomes HIV-1 resistance to other CD4bs bNAbs. Moreover, mutational antigenic profiling uncovered restricted pathways of HIV-1 escape. Of most promise for therapeutic use, even 1-18 alone fully suppressed viremia in HIV-1-infected humanized mice without selecting for resistant viral variants. A 2.5-Å cryo-EM structure of a 1-18-BG505SOSIP.664 Env complex revealed that these characteristics are likely facilitated by a heavy-chain insertion and increased inter-protomer contacts. The ability of 1-18 to effectively restrict HIV-1 escape pathways provides a new option to successfully prevent and treat HIV-1 infection.

Deep mutational scanning reveals functional constraints and antibody-escape potential of Lassa virus glycoprotein complex.

Lassa virus is estimated to cause thousands of human deaths per year, primarily due to spillovers from its natural host, Mastomys rodents. Efforts to create vaccines and antibody therapeutics must account for the evolutionary variability of the Lassa virus's glycoprotein complex (GPC), which mediates viral entry into cells and is the target of neutralizing antibodies. To map the evolutionary space accessible to GPC, we used pseudovirus deep mutational scanning to measure how nearly all GPC amino-acid mutations affected cell entry and antibody neutralization. Our experiments defined functional constraints throughout GPC. We quantified how GPC mutations affected neutralization with a panel of monoclonal antibodies. All antibodies tested were escaped by mutations that existed among natural Lassa virus lineages. Overall, our work describes a biosafety-level-2 method to elucidate the mutational space accessible to GPC and shows how prospective characterization of antigenic variation could aid the design of therapeutics and vaccines.

The full spectrum of SLC22 OCT1 mutations illuminates the bridge between drug transporter biophysics and pharmacogenomics.

Mutations in transporters can impact an individual's response to drugs and cause many diseases. Few variants in transporters have been evaluated for their functional impact. Here, we combine saturation mutagenesis and multi-phenotypic screening to dissect the impact of 11,213 missense single-amino-acid deletions, and synonymous variants across the 554 residues of OCT1, a key liver xenobiotic transporter. By quantifying in parallel expression and substrate uptake, we find that most variants exert their primary effect on protein abundance, a phenotype not commonly measured alongside function. Using our mutagenesis results combined with structure prediction and molecular dynamic simulations, we develop accurate structure-function models of the entire transport cycle, providing biophysical characterization of all known and possible human OCT1 polymorphisms. This work provides a complete functional map of OCT1 variants along with a framework for integrating functional genomics, biophysical modeling, and human genetics to predict variant effects on disease and drug efficacy.

Functional Segregation of Overlapping Genes in HIV.

Overlapping genes pose an evolutionary dilemma as one DNA sequence evolves under the selection pressures of multiple proteins. Here, we perform systematic statistical and mutational analyses of the overlapping HIV-1 genes tat and rev and engineer exhaustive libraries of non-overlapped viruses to perform deep mutational scanning of each gene independently. We find a "segregated" organization in which overlapped sites encode functional residues of one gene or the other, but never both. Furthermore, this organization eliminates unfit genotypes, providing a fitness advantage to the population. Our comprehensive analysis reveals the extraordinary manner in which HIV minimizes the constraint of overlapping genes and repurposes that constraint to its own advantage. Thus, overlaps are not just consequences of evolutionary constraints, but rather can provide population fitness advantages.

Base editor screens for in situ mutational scanning at scale.

A fundamental challenge in biology is understanding the molecular details of protein function. How mutations alter protein activity, regulation, and response to drugs is of critical importance to human health. Recent years have seen the emergence of pooled base editor screens for in situ mutational scanning: the interrogation of protein sequence-function relationships by directly perturbing endogenous proteins in live cells. These studies have revealed the effects of disease-associated mutations, discovered novel drug resistance mechanisms, and generated biochemical insights into protein function. Here, we discuss how this "base editor scanning" approach has been applied to diverse biological questions, compare it with alternative techniques, and describe the emerging challenges that must be addressed to maximize its utility. Given its broad applicability toward profiling mutations across the proteome, base editor scanning promises to revolutionize the investigation of proteins in their native contexts.

An Antigenic Atlas of HIV-1 Escape from Broadly Neutralizing Antibodies Distinguishes Functional and Structural Epitopes.

Anti-HIV broadly neutralizing antibodies (bnAbs) have revealed vaccine targets on the virus's envelope (Env) protein and are themselves promising immunotherapies. The efficacy of bnAb-based therapies and vaccines depends in part on how readily the virus can escape neutralization. Although structural studies can define contacts between bnAbs and Env, only functional studies can define mutations that confer escape. Here, we mapped how all possible single amino acid mutations in Env affect neutralization of HIV by nine bnAbs targeting five epitopes. For most bnAbs, mutations at only a small fraction of structurally defined contact sites mediated escape, and most escape occurred at sites near, but not in direct contact with, the antibody. The Env mutations selected by two pooled bnAbs were similar to those expected from the combination of the bnAbs's independent action. Overall, our mutation-level antigenic atlas provides a comprehensive dataset for understanding viral immune escape and refining therapies and vaccines.

Evidence of antigenic drift in the fusion machinery core of SARS-CoV-2 spike.

Antigenic drift of SARS-CoV-2 is typically defined by mutations in the N-terminal domain and receptor binding domain of spike protein. In contrast, whether antigenic drift occurs in the S2 domain remains largely elusive. Here, we perform a deep mutational scanning experiment to identify S2 mutations that affect binding of SARS-CoV-2 spike to three S2 apex public antibodies. Our results indicate that spatially diverse mutations, including D950N and Q954H, which are observed in Delta and Omicron variants, respectively, weaken the binding of spike to these antibodies. Although S2 apex antibodies are known to be nonneutralizing, we show that they confer protection in vivo through Fc-mediated effector functions. Overall, this study indicates that the S2 domain of SARS-CoV-2 spike can undergo antigenic drift, which represents a potential challenge for the development of more universal coronavirus vaccines.

Systematically testing human HMBS missense variants to reveal mechanism and pathogenic variation.

Defects in hydroxymethylbilane synthase (HMBS) can cause acute intermittent porphyria (AIP), an acute neurological disease. Although sequencing-based diagnosis can be definitive, ∼⅓ of clinical HMBS variants are missense variants, and most clinically reported HMBS missense variants are designated as "variants of uncertain significance" (VUSs). Using saturation mutagenesis, en masse selection, and sequencing, we applied a multiplexed validated assay to both the erythroid-specific and ubiquitous isoforms of HMBS, obtaining confident functional impact scores for >84% of all possible amino acid substitutions. The resulting variant effect maps generally agreed with biochemical expectations and provide further evidence that HMBS can function as a monomer. Additionally, the maps implicated specific residues as having roles in active site dynamics, which was further supported by molecular dynamics simulations. Most importantly, these maps can help discriminate pathogenic from benign HMBS variants, proactively providing evidence even for yet-to-be-observed clinical missense variants.

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.

High-throughput screening of mutations affecting SARS-CoV-2 spike functions.

The spike (S) protein of SARS-CoV-2, which is undergoing rapid evolution, plays crucial roles in viral immune escape, infectivity, and transmissibility. To gain clinical insight, Dadonaite et al. developed a novel deep mutational scanning (DMS) platform for mapping the effects of S protein mutations on immune evasion and viral infectivity.

A massively parallel assay accurately discriminates between functionally normal and abnormal variants in a hotspot domain of KCNH2.

Many genes, including KCNH2, contain "hotspot" domains associated with a high density of variants associated with disease. This has led to the suggestion that variant location can be used as evidence supporting classification of clinical variants. However, it is not known what proportion of all potential variants in hotspot domains cause loss of function. Here, we have used a massively parallel trafficking assay to characterize all single-nucleotide variants in exon 2 of KCNH2, a known hotspot for variants that cause long QT syndrome type 2 and an increased risk of sudden cardiac death. Forty-two percent of KCNH2 exon 2 variants caused at least 50% reduction in protein trafficking, and 65% of these trafficking-defective variants exerted a dominant-negative effect when co-expressed with a WT KCNH2 allele as assessed using a calibrated patch-clamp electrophysiology assay. The massively parallel trafficking assay was more accurate (AUC of 0.94) than bioinformatic prediction tools (REVEL and CardioBoost, AUC of 0.81) in discriminating between functionally normal and abnormal variants. Interestingly, over half of variants in exon 2 were found to be functionally normal, suggesting a nuanced interpretation of variants in this "hotspot" domain is necessary. Our massively parallel trafficking assay can provide this information prospectively.

Deep mutational scanning quantifies DNA binding and predicts clinical outcomes of PAX6 variants.

Nonsense and missense mutations in the transcription factor PAX6 cause a wide range of eye development defects, including aniridia, microphthalmia and coloboma. To understand how changes of PAX6:DNA binding cause these phenotypes, we combined saturation mutagenesis of the paired domain of PAX6 with a yeast one-hybrid (Y1H) assay in which expression of a PAX6-GAL4 fusion gene drives antibiotic resistance. We quantified binding of more than 2700 single amino-acid variants to two DNA sequence elements. Mutations in DNA-facing residues of the N-terminal subdomain and linker region were most detrimental, as were mutations to prolines and to negatively charged residues. Many variants caused sequence-specific molecular gain-of-function effects, including variants in position 71 that increased binding to the LE9 enhancer but decreased binding to a SELEX-derived binding site. In the absence of antibiotic selection, variants that retained DNA binding slowed yeast growth, likely because such variants perturbed the yeast transcriptome. Benchmarking against known patient variants and applying ACMG/AMP guidelines to variant classification, we obtained supporting-to-moderate evidence that 977 variants are likely pathogenic and 1306 are likely benign. Our analysis shows that most pathogenic mutations in the paired domain of PAX6 can be explained simply by the effects of these mutations on PAX6:DNA association, and establishes Y1H as a generalisable assay for the interpretation of variant effects in transcription factors.

Unsupervised modeling of mutational landscapes of adeno-associated viruses viability.

Adeno-associated viruses 2 (AAV2) are minute viruses renowned for their capacity to infect human cells and akin organisms. They have recently emerged as prominent candidates in the field of gene therapy, primarily attributed to their inherent non-pathogenic nature in humans and the safety associated with their manipulation. The efficacy of AAV2 as gene therapy vectors hinges on their ability to infiltrate host cells, a phenomenon reliant on their competence to construct a capsid capable of breaching the nucleus of the target cell. To enhance their infection potential, researchers have extensively scrutinized various combinatorial libraries by introducing mutations into the capsid, aiming to boost their effectiveness. The emergence of high-throughput experimental techniques, like deep mutational scanning (DMS), has made it feasible to experimentally assess the fitness of these libraries for their intended purpose. Notably, machine learning is starting to demonstrate its potential in addressing predictions within the mutational landscape from sequence data. In this context, we introduce a biophysically-inspired model designed to predict the viability of genetic variants in DMS experiments. This model is tailored to a specific segment of the CAP region within AAV2's capsid protein. To evaluate its effectiveness, we conduct model training with diverse datasets, each tailored to explore different aspects of the mutational landscape influenced by the selection process. Our assessment of the biophysical model centers on two primary objectives: (i) providing quantitative forecasts for the log-selectivity of variants and (ii) deploying it as a binary classifier to categorize sequences into viable and non-viable classes.