Evolutionary Landscapes of Host-Virus Arms Races.

Vertebrate immune systems suppress viral infection using both innate restriction factors and adaptive immunity. Viruses mutate to escape these defenses, driving hosts to counterevolve to regain fitness. This cycle recurs repeatedly, resulting in an evolutionary arms race whose outcome depends on the pace and likelihood of adaptation by host and viral genes. Although viruses evolve faster than their vertebrate hosts, their proteins are subject to numerous functional constraints that impact the probability of adaptation. These constraints are globally defined by evolutionary landscapes, which describe the fitness and adaptive potential of all possible mutations. We review deep mutational scanning experiments mapping the evolutionary landscapes of both host and viral proteins engaged in arms races. For restriction factors and some broadly neutralizing antibodies, landscapes favor the host, which may help to level the evolutionary playing field against rapidly evolving viruses. We discuss the biophysical underpinnings of these landscapes and their therapeutic implications.

High-Throughput Discovery and Characterization of Human Transcriptional Effectors.

Thousands of proteins localize to the nucleus; however, it remains unclear which contain transcriptional effectors. Here, we develop HT-recruit, a pooled assay where protein libraries are recruited to a reporter, and their transcriptional effects are measured by sequencing. Using this approach, we measure gene silencing and activation for thousands of domains. We find a relationship between repressor function and evolutionary age for the KRAB domains, discover that Homeodomain repressor strength is collinear with Hox genetic organization, and identify activities for several domains of unknown function. Deep mutational scanning of the CRISPRi KRAB maps the co-repressor binding surface and identifies substitutions that improve stability/silencing. By tiling 238 proteins, we find repressors as short as ten amino acids. Finally, we report new activator domains, including a divergent KRAB. These results provide a resource of 600 human proteins containing effectors and demonstrate a scalable strategy for assigning functions to protein domains.

Combinatorial Genetics Reveals a Scaling Law for the Effects of Mutations on Splicing.

Despite a wealth of molecular knowledge, quantitative laws for accurate prediction of biological phenomena remain rare. Alternative pre-mRNA splicing is an important regulated step in gene expression frequently perturbed in human disease. To understand the combined effects of mutations during evolution, we quantified the effects of all possible combinations of exonic mutations accumulated during the emergence of an alternatively spliced human exon. This revealed that mutation effects scale non-monotonically with the inclusion level of an exon, with each mutation having maximum effect at a predictable intermediate inclusion level. This scaling is observed genome-wide for cis and trans perturbations of splicing, including for natural and disease-associated variants. Mathematical modeling suggests that competition between alternative splice sites is sufficient to cause this non-linearity in the genotype-phenotype map. Combining the global scaling law with specific pairwise interactions between neighboring mutations allows accurate prediction of the effects of complex genotype changes involving >10 mutations.

Measuring Pharmacogene Variant Function at Scale Using Multiplexed Assays.

As costs of next-generation sequencing decrease, identification of genetic variants has far outpaced our ability to understand their functional consequences. This lack of understanding is a central challenge to a key promise of pharmacogenomics: using genetic information to guide drug selection and dosing. Recently developed multiplexed assays of variant effect enable experimental measurement of the function of thousands of variants simultaneously. Here, we describe multiplexed assays that have been performed on nearly 25,000 variants in eight key pharmacogenes (ADRB2, CYP2C9, CYP2C19, NUDT15, SLCO1B1, TMPT, VKORC1, and the LDLR promoter), discuss advances in experimental design, and explore key challenges that must be overcome to maximize the utility of multiplexed functional data.

Multiple mechanisms of self-association of chemokine receptors CXCR4 and CCR5 demonstrated by deep mutagenesis.

Chemokine receptors are members of the rhodopsin-like class A GPCRs whose signaling through G proteins drives the directional movement of cells in response to a chemokine gradient. Chemokine receptors CXCR4 and CCR5 have been extensively studied due to their roles in leukocyte development and inflammation and their status as coreceptors for HIV-1 infection, among other roles. Both receptors form dimers or oligomers of unclear function. While CXCR4 has been crystallized in a dimeric arrangement, available atomic resolution structures of CCR5 are monomeric. To investigate their dimerization interfaces, we used a bimolecular fluorescence complementation (BiFC)-based screen and deep mutational scanning to find mutations that change how the receptors self-associate, either via specific oligomer assembly or alternative mechanisms of clustering in close proximity. Many disruptive mutations promoted self-associations nonspecifically, suggesting they aggregated in the membrane. A mutationally intolerant region was found on CXCR4 that matched the crystallographic dimer interface, supporting this dimeric arrangement in living cells. A mutationally intolerant region was also observed on the surface of CCR5 by transmembrane helices 3 and 4. Mutations predicted from the scan to reduce BiFC were validated and were localized in the transmembrane domains as well as the C-terminal cytoplasmic tails where they reduced lipid microdomain localization. A mutation in the dimer interface of CXCR4 had increased binding to the ligand CXCL12 and yet diminished calcium signaling. There was no change in syncytia formation with cells expressing HIV-1 Env. The data highlight that multiple mechanisms are involved in self-association of chemokine receptor chains.

Sampling Strategies for Experimentally Mapping Molecular Fitness Landscapes Using High-Throughput Methods.

Empirical studies of genotype-phenotype-fitness maps of proteins are fundamental to understanding the evolutionary process, in elucidating the space of possible genotypes accessible through mutations in a landscape of phenotypes and fitness effects. Yet, comprehensively mapping molecular fitness landscapes remains challenging since all possible combinations of amino acid substitutions for even a few protein sites are encoded by an enormous genotype space. High-throughput mapping of genotype space can be achieved using large-scale screening experiments known as multiplexed assays of variant effect (MAVEs). However, to accommodate such multi-mutational studies, the size of MAVEs has grown to the point where a priori determination of sampling requirements is needed. To address this problem, we propose calculations and simulation methods to approximate minimum sampling requirements for multi-mutational MAVEs, which we combine with a new library construction protocol to experimentally validate our approximation approaches. Analysis of our simulated data reveals how sampling trajectories differ between simulations of nucleotide versus amino acid variants and among mutagenesis schemes. For this, we show quantitatively that marginal gains in sampling efficiency demand increasingly greater sampling effort when sampling for nucleotide sequences over their encoded amino acid equivalents. We present a new library construction protocol that efficiently maximizes sequence variation, and demonstrate using ultradeep sequencing that the library encodes virtually all possible combinations of mutations within the experimental design. Insights learned from our analyses together with the methodological advances reported herein are immediately applicable toward pooled experimental screens of arbitrary design, enabling further assay upscaling and expanded testing of genotype space.

Sequence basis for selectivity of ephrin-B2 ligand for Eph receptors and pathogenic henipavirus G glycoproteins.

IMPORTANCE: Ephrin-B2 (EFNB2) is a ligand for six Eph receptors in humans and regulates multiple cell developmental and signaling processes. It also functions as the cell entry receptor for Nipah virus and Hendra virus, zoonotic viruses that can cause respiratory and/or neurological symptoms in humans with high mortality. Here, we investigate the sequence basis of EFNB2 specificity for binding the Nipah virus attachment G glycoprotein over Eph receptors. We then use this information to engineer EFNB2 as a soluble decoy receptor that specifically binds the attachment glycoproteins of the Nipah virus and other related henipaviruses to neutralize infection. These findings further mechanistic understanding of protein selectivity and may facilitate the development of diagnostics or therapeutics against henipavirus infection.

Structural architecture of a dimeric class C GPCR based on co-trafficking of sweet taste receptor subunits.

Class C G protein-coupled receptors (GPCRs) are obligatory dimers that are particularly important for neuronal responses to endogenous and environmental stimuli. Ligand recognition through large extracellular domains leads to the reorganization of transmembrane regions to activate G protein signaling. Although structures of individual domains are known, the complete architecture of a class C GPCR and the mechanism of interdomain coupling during receptor activation are unclear. By screening a mutagenesis library of the human class C sweet taste receptor subunit T1R2, we enhanced surface expression and identified a dibasic intracellular retention motif that modulates surface expression and co-trafficking with its heterodimeric partner T1R3. Using a highly expressed T1R2 variant, dimerization sites along the entire subunit within all the structural domains were identified by a comprehensive mutational scan for co-trafficking with T1R3 in human cells. The data further reveal that the C terminus of the extracellular cysteine-rich domain needs to be properly folded for T1R3 dimerization and co-trafficking, but not for surface expression of T1R2 alone. These results guided the modeling of the T1R2-T1R3 dimer in living cells, which predicts a twisted arrangement of domains around the central axis, and a continuous folded structure between transmembrane domain loops and the cysteine-rich domains. These insights have implications for how conformational changes between domains are coupled within class C GPCRs.

Deep Mutational Scan of the Highly Conserved Influenza A Virus M1 Matrix Protein Reveals Substantial Intrinsic Mutational Tolerance.

Influenza A virus matrix protein M1 is involved in multiple stages of the viral infectious cycle. Despite its functional importance, our present understanding of this essential viral protein is limited. The roles of a small subset of specific amino acids have been reported, but a more comprehensive understanding of the relationship between M1 sequence, structure, and virus fitness remains elusive. In this study, we used deep mutational scanning to measure the effect of every amino acid substitution in M1 on viral replication in cell culture. The map of amino acid mutational tolerance we have generated allows us to identify sites that are functionally constrained in cell culture as well as sites that are less constrained. Several sites that exhibit low tolerance to mutation have been found to be critical for M1 function and production of viable virions. Surprisingly, significant portions of the M1 sequence, especially in the C-terminal domain, whose structure is undetermined, were found to be highly tolerant of amino acid variation, despite having extremely low levels of sequence diversity among natural influenza virus strains. This unexpected discrepancy indicates that not all sites in M1 that exhibit high sequence conservation in nature are under strong constraint during selection for viral replication in cell culture.IMPORTANCE The M1 matrix protein is critical for many stages of the influenza virus infection cycle. Currently, we have an incomplete understanding of this highly conserved protein's function and structure. Key regions of M1, particularly in the C terminus of the protein, remain poorly characterized. In this study, we used deep mutational scanning to determine the extent of M1's tolerance to mutation. Surprisingly, nearly two-thirds of the M1 sequence exhibits a high tolerance for substitutions, contrary to the extremely low sequence diversity observed across naturally occurring M1 isolates. Sites with low mutational tolerance were also identified, suggesting that they likely play critical functional roles and are under selective pressure. These results reveal the intrinsic mutational tolerance throughout M1 and shape future inquiries probing the functions of this essential influenza A virus protein.

Conformational Engineering of HIV-1 Env Based on Mutational Tolerance in the CD4 and PG16 Bound States.

HIV-1 infection is initiated by viral Env engaging the host receptor CD4, triggering Env to transition from a "closed" to "open" conformation during the early events of virus-cell membrane fusion. To understand how Env sequence accommodates this conformational change, mutational landscapes decoupled from virus replication were determined for Env from BaL (clade B) and DU422 (clade C) isolates interacting with CD4 or antibody PG16 that preferentially recognizes closed trimers. Sequence features uniquely important to each bound state were identified, including glycosylation and binding sites. Notably, the Env apical domain and trimerization interface are under selective pressure for PG16 binding. Based on this key observation, mutations were found that increase presentation of quaternary epitopes associated with properly conformed trimers when Env is expressed at the plasma membrane. Many mutations reduce electrostatic repulsion at the Env apex and increase PG16 recognition of Env sequences from clades A and B. Other mutations increase hydrophobic packing at the gp120 inner-outer domain interface and were broadly applicable for engineering Env from diverse strains spanning tiers 1, 2, and 3 across clades A, B, C, and BC recombinants. Core mutations predicted to introduce steric strain in the open state show markedly reduced CD4 interactions. Finally, we demonstrate how our methodology can be adapted to interrogate interactions between membrane-associated Env and the matrix domain of Gag. These findings and methods may assist vaccine design.IMPORTANCE HIV-1 Env is dynamic and undergoes large conformational changes that drive fusion of virus and host cell membranes. Three Env proteins in a trimer contact each other at their apical tips to form a closed conformation that presents epitopes recognized by broadly neutralizing antibodies. The apical tips separate, among other changes, to form an open conformation that binds tightly to host receptors. Understanding how Env sequence facilitates these structural changes can inform the biophysical mechanism and aid immunogen design. Using deep mutational scans decoupled from virus replication, we report mutational landscapes for Env from two strains interacting with conformation-dependent binding proteins. Residues in the Env trimer interface and apical domains are preferentially conserved in the closed conformation, and conformational diversity is facilitated by electrostatic repulsion and an underpacked core between domains. Specific mutations are described that enhance presentation of the trimeric closed conformation across diverse HIV-1 strains.

Deep mutagenesis in the study of COVID-19: a technical overview for the proteomics community.

INTRODUCTION: The spike (S) of SARS coronavirus 2 (SARS-CoV-2) engages angiotensin-converting enzyme 2 (ACE2) on a host cell to trigger viral-cell membrane fusion and infection. The extracellular region of ACE2 can be administered as a soluble decoy to compete for binding sites on the receptor-binding domain (RBD) of S, but it has only moderate affinity and efficacy. The RBD, which is targeted by neutralizing antibodies, may also change and adapt through mutation as SARS-CoV-2 becomes endemic, posing challenges for therapeutic and vaccine development. AREAS COVERED: Deep mutagenesis is a Big Data approach to characterizing sequence variants. A deep mutational scan of ACE2 expressed on human cells identified mutations that increase S affinity and guided the engineering of a potent and broad soluble receptor decoy. A deep mutational scan of the RBD displayed on the surface of yeast has revealed residues tolerant of mutational changes that may act as a source for drug resistance and antigenic drift. EXPERT OPINION: Deep mutagenesis requires a selection of diverse sequence variants; an in vitro evolution experiment that is tracked with next-generation sequencing. The choice of expression system, diversity of the variant library and selection strategy have important consequences for data quality and interpretation.

Mutational scanning of CRX classifies clinical variants and reveals biochemical properties of the transcriptional effector domain.

Cone-Rod Homeobox, encoded by CRX, is a transcription factor (TF) essential for the terminal differentiation and maintenance of mammalian photoreceptors. Structurally, CRX comprises an ordered DNA-binding homeodomain and an intrinsically disordered transcriptional effector domain. Although a handful of human variants in CRX have been shown to cause several different degenerative retinopathies with varying cone and rod predominance, as with most human disease genes the vast majority of observed CRX genetic variants are uncharacterized variants of uncertain significance (VUS). We performed a deep mutational scan (DMS) of nearly all possible single amino acid substitution variants in CRX, using an engineered cell-based transcriptional reporter assay. We measured the ability of each CRX missense variant to transactivate a synthetic fluorescent reporter construct in a pooled fluorescence-activated cell sorting assay and compared the activation strength of each variant to that of wild-type CRX to compute an activity score, identifying thousands of variants with altered transcriptional activity. We calculated a statistical confidence for each activity score derived from multiple independent measurements of each variant marked by unique sequence barcodes, curating a high-confidence list of nearly 2,000 variants with significantly altered transcriptional activity compared to wild-type CRX. We evaluated the performance of the DMS assay as a clinical variant classification tool using gold-standard classified human variants from ClinVar, and determined that activity scores could be used to identify pathogenic variants with high specificity. That this performance could be achieved using a synthetic reporter assay in a foreign cell type, even for a highly cell type-specific TF like CRX, suggests that this approach shows promise for DMS of other TFs that function in cell types that are not easily accessible. Per-position average activity scores closely aligned to a predicted structure of the ordered homeodomain and demonstrated position-specific residue requirements. The intrinsically disordered transcriptional effector domain, by contrast, displayed a qualitatively different pattern of substitution effects, following compositional constraints without specific residue position requirements in the peptide chain. The observed compositional constraints of the effector domain were consistent with the acidic exposure model of transcriptional activation. Together, the results of the CRX DMS identify molecular features of the CRX effector domain and demonstrate clinical utility for variant classification.

Alignment-based Protein Mutational Landscape Prediction: Doing More with Less.

The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have overcome this bottleneck by leveraging the fast homology search of MMseqs2. Here, we show the usefulness of this strategy for mutational outcome prediction through a large-scale assessment of 1.5M missense variants across 72 protein families. Our study demonstrates the feasibility of producing alignment-based mutational landscape predictions that are both high-quality and compute-efficient for entire proteomes. We provide the community with the whole human proteome mutational landscape and simplified access to our predictive pipeline.

The Genetic Landscape of a Metabolic Interaction.

Enzyme abundance, catalytic activity, and ultimately sequence are all shaped by the need of growing cells to maintain metabolic flux while minimizing accumulation of deleterious intermediates. While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To quantify how variation in the activity of one enzyme constrains the biochemical parameters and sequence of another, we focused on dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), a pair of enzymes catalyzing consecutive reactions in folate metabolism. We used deep mutational scanning to quantify the growth rate effect of 2,696 DHFR single mutations in 3 TYMS backgrounds under conditions selected to emphasize biochemical epistasis. Our data are well-described by a relatively simple enzyme velocity to growth rate model that quantifies how metabolic context tunes enzyme mutational tolerance. Together our results reveal the structural distribution of epistasis in a metabolic enzyme and establish a foundation for the design of multi-enzyme systems.

Human gain-of-function variants in HNF1A confer protection from diabetes but independently increase hepatic secretion of atherogenic lipoproteins.

Loss-of-function mutations in hepatocyte nuclear factor 1A (HNF1A) are known to cause rare forms of diabetes and alter hepatic physiology through unclear mechanisms. In the general population, 1:100 individuals carry a rare, protein-coding HNF1A variant, most of unknown functional consequence. To characterize the full allelic series, we performed deep mutational scanning of 11,970 protein-coding HNF1A variants in human hepatocytes and clinical correlation with 553,246 exome-sequenced individuals. Surprisingly, we found that ∼1:5 rare protein-coding HNF1A variants in the general population cause molecular gain of function (GOF), increasing the transcriptional activity of HNF1A by up to 50% and conferring protection from type 2 diabetes (odds ratio [OR] = 0.77, p = 0.007). Increased hepatic expression of HNF1A promoted a pro-atherogenic serum profile mediated in part by enhanced transcription of risk genes including ANGPTL3 and PCSK9. In summary, ∼1:300 individuals carry a GOF variant in HNF1A that protects carriers from diabetes but enhances hepatic secretion of atherogenic lipoproteins.

Comprehensive interrogation of the ADAR2 deaminase domain for engineering enhanced RNA editing activity and specificity.

Adenosine deaminases acting on RNA (ADARs) can be repurposed to enable programmable RNA editing, however their exogenous delivery leads to transcriptome-wide off-targeting, and additionally, enzymatic activity on certain RNA motifs, especially those flanked by a 5' guanosine is very low thus limiting their utility as a transcriptome engineering toolset. Towards addressing these issues, we first performed a novel deep mutational scan of the ADAR2 deaminase domain, directly measuring the impact of every amino acid substitution across 261 residues, on RNA editing. This enabled us to create a domain-wide mutagenesis map while also revealing a novel hyperactive variant with improved enzymatic activity at 5'-GAN-3' motifs. As overexpression of ADAR enzymes, especially hyperactive variants, can lead to significant transcriptome-wide off-targeting, we next engineered a split-ADAR2 deaminase which resulted in >100-fold more specific RNA editing as compared to full-length deaminase overexpression. Taken together, we anticipate this systematic engineering of the ADAR2 deaminase domain will enable broader utility of the ADAR toolset for RNA biotechnology applications.

Interpreting protein variant effects with computational predictors and deep mutational scanning.

Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and scalable method to assess the likely impacts of novel variants. However, it can be difficult to know to what extent we can trust their results. To benchmark their performance, predictors are often tested against large datasets of known pathogenic and benign variants. These benchmarking data may overlap with the data used to train some supervised predictors, which leads to data re-use or circularity, resulting in inflated performance estimates for those predictors. Furthermore, new predictors are usually found by their authors to be superior to all previous predictors, which suggests some degree of computational bias in their benchmarking. Large-scale functional assays known as deep mutational scans provide one possible solution to this problem, providing independent datasets of variant effect measurements. In this Review, we discuss some of the key advances in predictor methodology, current benchmarking strategies and how data derived from deep mutational scans can be used to overcome the issue of data circularity. We also discuss the ability of such functional assays to directly predict clinical impacts of mutations and how this might affect the future need for variant effect predictors.

Deep Mutational Scanning of Viral Glycoproteins and Their Host Receptors.

Deep mutational scanning or deep mutagenesis is a powerful tool for understanding the sequence diversity available to viruses for adaptation in a laboratory setting. It generally involves tracking an in vitro selection of protein sequence variants with deep sequencing to map mutational effects based on changes in sequence abundance. Coupled with any of a number of selection strategies, deep mutagenesis can explore the mutational diversity available to viral glycoproteins, which mediate critical roles in cell entry and are exposed to the humoral arm of the host immune response. Mutational landscapes of viral glycoproteins for host cell attachment and membrane fusion reveal extensive epistasis and potential escape mutations to neutralizing antibodies or other therapeutics, as well as aiding in the design of optimized immunogens for eliciting broadly protective immunity. While less explored, deep mutational scans of host receptors further assist in understanding virus-host protein interactions. Critical residues on the host receptors for engaging with viral spikes are readily identified and may help with structural modeling. Furthermore, mutations may be found for engineering soluble decoy receptors as neutralizing agents that specifically bind viral targets with tight affinity and limited potential for viral escape. By untangling the complexities of how sequence contributes to viral glycoprotein and host receptor interactions, deep mutational scanning is impacting ideas and strategies at multiple levels for combatting circulating and emergent virus strains.

Functional characterization of the p53 "mutome".

Phenotypic characterization of mutations in the tumor protein p53 (TP53) gene has so far focused on a handful of relatively frequent "hotspot" mutations, accounting for only ~ 30% of cases. We expanded the scope and quantitatively measured the impact of thousands of distinct TP53 mutations in vitro and in vivo, providing insights into the connections between structure, function, evolutionary conservation and clinical impact.

Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves.

Despite the central role that antibodies play in the adaptive immune system and in biotechnology, much remains unknown about the quantitative relationship between an antibody's amino acid sequence and its antigen binding affinity. Here we describe a new experimental approach, called Tite-Seq, that is capable of measuring binding titration curves and corresponding affinities for thousands of variant antibodies in parallel. The measurement of titration curves eliminates the confounding effects of antibody expression and stability that arise in standard deep mutational scanning assays. We demonstrate Tite-Seq on the CDR1H and CDR3H regions of a well-studied scFv antibody. Our data shed light on the structural basis for antigen binding affinity and suggests a role for secondary CDR loops in establishing antibody stability. Tite-Seq fills a large gap in the ability to measure critical aspects of the adaptive immune system, and can be readily used for studying sequence-affinity landscapes in other protein systems.