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

Gauge fixing for sequence-function relationships

Quantitative models of sequence-function relationships are ubiquitous in computational biology, e.g., for modeling the DNA binding of transcription factors or the fitness landscapes of proteins. Interpreting these models, however, is complicated by the fact that the values of model parameters can often be changed without affecting model predictions. Before the values of model parameters can be meaningfully interpreted, one must remove these degrees of freedom (called "gauge freedoms" in physics) by imposing additional constraints (a process called "fixing the gauge"). However, strategies for fixing the gauge of sequence-function relationships have received little attention. Here we derive an analytically tractable family of gauges for a large class of sequence-function relationships. These gauges are derived in the context of models with all-order interactions, but an important subset of these gauges can be applied to diverse types of models, including additive models, pairwise-interaction models, and models with higher-order interactions. Many commonly used gauges are special cases of gauges within this family. We demonstrate the utility of this family of gauges by showing how different choices of gauge can be used both to explore complex activity landscapes and to reveal simplified models that are approximately correct within localized regions of sequence space. The results provide practical gauge-fixing strategies and demonstrate the utility of gauge-fixing for model exploration and interpretation.

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space

Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objective for protein engineers. Powerful high-throughput methods like fluorescent activated cell sorting and next-generation sequencing have dramatically improved directed evolution experiments. However, it is unclear how to best leverage these data to characterize protein fitness landscapes more completely and identify lead candidates. In this work, we develop a simple yet powerful framework to improve protein optimization by predicting continuous protein properties from simple directed evolution experiments using interpretable, linear machine learning models. Importantly, we find that these models, which use data from simple but imprecise experimental estimates of protein fitness, have predictive capabilities that approach more precise but expensive data. Evaluated across five diverse protein engineering tasks, continuous properties are consistently predicted from readily available deep sequencing data, demonstrating that protein fitness space can be reasonably well modeled by linear relationships among sequence mutations. To prospectively test the utility of this approach, we generated a library of stapled peptides and applied the framework to predict affinity and specificity from simple cell sorting data. We then coupled integer linear programming, a method to optimize protein fitness from linear weights, with mutation scores from machine learning to identify variants in unseen sequence space that have improved and co-optimal properties. This approach represents a versatile tool for improved analysis and identification of protein variants across many domains of protein engineering.

Recent advances in generative biology for biotherapeutic discovery

Generative biology combines artificial intelligence (AI), advanced life sciences technologies, and automation to revolutionize the process of designing novel biomolecules with prescribed properties, giving drug discoverers the ability to escape the limitations of biology during the design of next-generation protein therapeutics. Significant hurdles remain, namely: (i) the inherently complex nature of drug discovery, (ii) the bewildering number of promising computational and experimental techniques that have emerged in the past several years, and (iii) the limited availability of relevant protein sequence-function data for drug-like molecules. There is a need to focus on computational methods that will be most practically effective for protein drug discovery and on building experimental platforms to generate the data most appropriate for these methods. Here, we discuss recent advances in computational and experimental life sciences that are most crucial for impacting the pace and success of protein drug discovery.

Learning the shape of protein microenvironments with a holographic convolutional neural network

Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from its sequence or structure remains a major challenge. Here, we introduce holographic convolutional neural network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

COV2Var, a function annotation database of SARS-CoV-2 genetic variation

The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has resulted in the loss of millions of lives and severe global economic consequences. Every time SARS-CoV-2 replicates, the viruses acquire new mutations in their genomes. Mutations in SARS-CoV-2 genomes led to increased transmissibility, severe disease outcomes, evasion of the immune response, changes in clinical manifestations and reducing the efficacy of vaccines or treatments. To date, the multiple resources provide lists of detected mutations without key functional annotations. There is a lack of research examining the relationship between mutations and various factors such as disease severity, pathogenicity, patient age, patient gender, cross-species transmission, viral immune escape, immune response level, viral transmission capability, viral evolution, host adaptability, viral protein structure, viral protein function, viral protein stability and concurrent mutations. Deep understanding the relationship between mutation sites and these factors is crucial for advancing our knowledge of SARS-CoV-2 and for developing effective responses. To fill this gap, we built COV2Var, a function annotation database of SARS-CoV-2 genetic variation, available at http://biomedbdc.wchscu.cn/COV2Var/. COV2Var aims to identify common mutations in SARS-CoV-2 variants and assess their effects, providing a valuable resource for intensive functional annotations of common mutations among SARS-CoV-2 variants.

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

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

Stringent and complex sequence constraints of an IGHV1-69 broadly neutralizing antibody to influenza HA stem

IGHV1-69 is frequently utilized by broadly neutralizing influenza antibodies to the hemagglutinin (HA) stem. These IGHV1-69 HA stem antibodies have diverse complementarity-determining region (CDR) H3 sequences. Besides, their light chains have minimal to no contact with the epitope. Consequently, sequence determinants that confer IGHV1-69 antibodies with HA stem specificity remain largely elusive. Using high-throughput experiments, this study reveals the importance of light-chain sequence for the IGHV1-69 HA stem antibody CR9114, which is the broadest influenza antibody known to date. Moreover, we demonstrate that the CDR H3 sequences from many other IGHV1-69 antibodies, including those to the HA stem, are incompatible with CR9114. Along with mutagenesis and structural analysis, our results indicate that light-chain and CDR H3 sequences coordinately determine the HA stem specificity of IGHV1-69 antibodies. Overall, this work provides molecular insights into broadly neutralizing antibody responses to influenza virus, which have important implications for universal influenza vaccine development.

Antibody-directed evolution reveals a mechanism for enhanced neutralization at the HIV-1 fusion peptide site

The HIV-1 fusion peptide (FP) represents a promising vaccine target, but global FP sequence diversity among circulating strains has limited anti-FP antibodies to ~60% neutralization breadth. Here we evolve the FP-targeting antibody VRC34.01 in vitro to enhance FP-neutralization using site saturation mutagenesis and yeast display. Successive rounds of directed evolution by iterative selection of antibodies for binding to resistant HIV-1 strains establish a variant, VRC34.01_mm28, as a best-in-class antibody with 10-fold enhanced potency compared to the template antibody and ~80% breadth on a cross-clade 208-strain neutralization panel. Structural analyses demonstrate that the improved paratope expands the FP binding groove to accommodate diverse FP sequences of different lengths while also recognizing the HIV-1 Env backbone. These data reveal critical antibody features for enhanced neutralization breadth and potency against the FP site of vulnerability and accelerate clinical development of broad HIV-1 FP-targeting vaccines and therapeutics.

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

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

Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles

An important challenge in genetics, evolution and biotechnology is to understand and predict how mutations combine to alter phenotypes, including molecular activities, fitness and disease. In diploids, mutations in a gene can combine on the same chromosome or on different chromosomes as a "heteroallelic combination". However, a direct comparison of the extent, sign, and stability of the genetic interactions between variants within and between alleles is lacking. Here we use thermodynamic models of protein folding and ligand-binding to show that interactions between mutations within and between alleles are expected in even very simple biophysical systems. Protein folding alone generates within-allele interactions and a single molecular interaction is sufficient to cause between-allele interactions and dominance. These interactions change differently, quantitatively and qualitatively as a system becomes more complex. Altering the concentration of a ligand can, for example, switch alleles from dominant to recessive. Our results show that intra-molecular epistasis and dominance should be widely expected in even the simplest biological systems but also reinforce the view that they are plastic system properties and so a formidable challenge to predict. Accurate prediction of both intra-molecular epistasis and dominance will require either detailed mechanistic understanding and experimental parameterization or brute-force measurement and learning.

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

MOTIVATION

Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries.

RESULTS

Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns.

AVAILABILITY AND IMPLEMENTATION

All deep sequencing datasets and code to perform the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper.

Stringent and complex sequence constraints of an IGHV1-69 broadly neutralizing antibody to influenza HA stem

IGHV1-69 is frequently utilized by broadly neutralizing influenza antibodies to the hemagglutinin (HA) stem. These IGHV1-69 HA stem antibodies have diverse complementarity-determining region (CDR) H3 sequences. Besides, their light chains have minimal to no contact with the epitope. Consequently, sequence determinants that confer IGHV1-69 antibodies with HA stem specificity remain largely elusive. Using high-throughput experiments, this study revealed the importance of light chain sequence for the IGHV1-69 HA stem antibody CR9114, which is the broadest influenza antibody known to date. Moreover, we demonstrated that the CDR H3 sequences from many other IGHV1-69 antibodies, including those to HA stem, were incompatible with CR9114. Along with mutagenesis and structural analysis, our results indicate that light chain and CDR H3 sequences coordinately determine the HA stem specificity of IGHV1-69 antibodies. Overall, this work provides molecular insights into broadly neutralizing antibody responses to influenza virus, which have important implications for universal influenza vaccine development.

Large-scale antibody immune response mapping of splenic B cells and bone marrow plasma cells in a transgenic mouse model

Molecular characterization of antibody immunity and human antibody discovery is mainly carried out using peripheral memory B cells, and occasionally plasmablasts, that express B cell receptors (BCRs) on their cell surface. Despite the importance of plasma cells (PCs) as the dominant source of circulating antibodies in serum, PCs are rarely utilized because they do not express surface BCRs and cannot be analyzed using antigen-based fluorescence-activated cell sorting. Here, we studied the antibodies encoded by the entire mature B cell populations, including PCs, and compared the antibody repertoires of bone marrow and spleen compartments elicited by immunization in a human immunoglobulin transgenic mouse strain. To circumvent prior technical limitations for analysis of plasma cells, we applied single-cell antibody heavy and light chain gene capture from the entire mature B cell repertoires followed by yeast display functional analysis using a cytokine as a model immunogen. We performed affinity-based sorting of antibody yeast display libraries and large-scale next-generation sequencing analyses to follow antibody lineage performance, with experimental validation of 76 monoclonal antibodies against the cytokine antigen that identified three antibodies with exquisite double-digit picomolar binding affinity. We observed that spleen B cell populations generated higher affinity antibodies compared to bone marrow PCs and that antigen-specific splenic B cells had higher average levels of somatic hypermutation. A degree of clonal overlap was also observed between bone marrow and spleen antibody repertoires, indicating common origins of certain clones across lymphoid compartments. These data demonstrate a new capacity to functionally analyze antigen-specific B cell populations of different lymphoid organs, including PCs, for high-affinity antibody discovery and detailed fundamental studies of antibody immunity.

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.

Mutational scan inferred binding energetics and structure in intrinsically disordered protein CcdA

Unlike globular proteins, mutational effects on the function of Intrinsically Disordered Proteins (IDPs) are not well-studied. Deep Mutational Scanning of a yeast surface displayed mutant library yields insights into sequence-function relationships in the CcdA IDP. The approach enables facile prediction of interface residues and local structural signatures of the bound conformation. In contrast to previous titration-based approaches which use a number of ligand concentrations, we show that use of a single rationally chosen ligand concentration can provide quantitative estimates of relative binding constants for large numbers of protein variants. This is because the extended interface of IDP ensures that energetic effects of point mutations are spread over a much smaller range than for globular proteins. Our data also provides insights into the much-debated role of helicity and disorder in partner binding of IDPs. Based on this exhaustive mutational sensitivity dataset, a rudimentary model was developed in an attempt to predict mutational effects on binding affinity of IDPs that form alpha-helical structures upon binding.

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).

Toward real-world automated antibody design with combinatorial Bayesian optimization

Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.

Hierarchical sequence-affinity landscapes shape the evolution of breadth in an anti-influenza receptor binding site antibody

Broadly neutralizing antibodies (bnAbs) that neutralize diverse variants of a particular virus are of considerable therapeutic interest. Recent advances have enabled us to isolate and engineer these antibodies as therapeutics, but eliciting them through vaccination remains challenging, in part due to our limited understanding of how antibodies evolve breadth. Here, we analyze the landscape by which an anti-influenza receptor binding site (RBS) bnAb, CH65, evolved broad affinity to diverse H1 influenza strains. We do this by generating an antibody library of all possible evolutionary intermediates between the unmutated common ancestor (UCA) and the affinity-matured CH65 antibody and measure the affinity of each intermediate to three distinct H1 antigens. We find that affinity to each antigen requires a specific set of mutations - distributed across the variable light and heavy chains - that interact non-additively (i.e., epistatically). These sets of mutations form a hierarchical pattern across the antigens, with increasingly divergent antigens requiring additional epistatic mutations beyond those required to bind less divergent antigens. We investigate the underlying biochemical and structural basis for these hierarchical sets of epistatic mutations and find that epistasis between heavy chain mutations and a mutation in the light chain at the V_H-V_L interface is essential for binding a divergent H1. Collectively, this is the first work to comprehensively characterize epistasis between heavy and light chain mutations and shows that such interactions are both strong and widespread. Together with our previous study analyzing a different class of anti-influenza antibodies, our results implicate epistasis as a general feature of antibody sequence-affinity landscapes that can potentiate and constrain the evolution of breadth.

Ter-Seq: A high-throughput method to stabilize transient ternary complexes and measure associated kinetics

Regulation of biological processes by proteins often involves the formation of transient, multimeric complexes whose characterization is mechanistically important but challenging. The bacterial toxin CcdB binds and poisons DNA Gyrase. The corresponding antitoxin CcdA extracts CcdB from its complex with Gyrase through the formation of a transient ternary complex, thus rejuvenating Gyrase. We describe a high throughput methodology called Ter-Seq to stabilize probable ternary complexes and measure associated kinetics using the CcdA-CcdB-GyrA14 ternary complex as a model system. The method involves screening a yeast surface display (YSD) saturation mutagenesis library of one partner (CcdB) for mutants that show enhanced ternary complex formation. We also isolated CcdB mutants that were either resistant or sensitive to rejuvenation, and used surface plasmon resonance (SPR) with purified proteins to validate the kinetics measured using the surface display. Positions, where CcdB mutations lead to slower rejuvenation rates, are largely involved in CcdA-binding, though there were several notable exceptions suggesting allostery. Mutations at these positions reduce the affinity towards CcdA, thereby slowing down the rejuvenation process. Mutations at GyrA14-interacting positions significantly enhanced rejuvenation rates, either due to reduced affinity or complete loss of CcdB binding to GyrA14. We examined the effect of different parameters (CcdA affinity, GyrA14 affinity, surface accessibilities, evolutionary conservation) on the rate of rejuvenation. Finally, we further validated the Ter-Seq results by monitoring the kinetics of ternary complex formation for individual CcdB mutants in solution by fluorescence resonance energy transfer (FRET) studies.

Native holdup (nHU) to measure binding affinities from cell extracts

Characterizing macromolecular interactions is essential for understanding cellular processes, yet most methods currently used to detect protein interactions from cells are qualitative. Here, we introduce the native holdup (nHU) approach to estimate equilibrium binding constants of protein interactions directly from cell extracts. Compared to other pull-down-based assays, nHU requires less sample preparation and can be coupled to any analytical methods as readouts, such as Western blotting or mass spectrometry. We use nHU to explore interactions of SNX27, a cargo adaptor of the retromer complex and find good agreement between in vitro affinities and those measured directly from cell extracts using nHU. We discuss the strengths and limitations of nHU and provide simple protocols that can be implemented in most laboratories.

Compensatory epistasis maintains ACE2 affinity in SARS-CoV-2 Omicron BA.1

The Omicron BA.1 variant emerged in late 2021 and quickly spread across the world. Compared to the earlier SARS-CoV-2 variants, BA.1 has many mutations, some of which are known to enable antibody escape. Many of these antibody-escape mutations individually decrease the spike receptor-binding domain (RBD) affinity for ACE2, but BA.1 still binds ACE2 with high affinity. The fitness and evolution of the BA.1 lineage is therefore driven by the combined effects of numerous mutations. Here, we systematically map the epistatic interactions between the 15 mutations in the RBD of BA.1 relative to the Wuhan Hu-1 strain. Specifically, we measure the ACE2 affinity of all possible combinations of these 15 mutations (2¹⁵ = 32,768 genotypes), spanning all possible evolutionary intermediates from the ancestral Wuhan Hu-1 strain to BA.1. We find that immune escape mutations in BA.1 individually reduce ACE2 affinity but are compensated by epistatic interactions with other affinity-enhancing mutations, including Q498R and N501Y. Thus, the ability of BA.1 to evade immunity while maintaining ACE2 affinity is contingent on acquiring multiple interacting mutations. Our results implicate compensatory epistasis as a key factor driving substantial evolutionary change for SARS-CoV-2 and are consistent with Omicron BA.1 arising from a chronic infection.

Deep mutational scans for ACE2 binding, RBD expression, and antibody escape in the SARS-CoV-2 Omicron BA.1 and BA.2 receptor-binding domains

SARS-CoV-2 continues to acquire mutations in the spike receptor-binding domain (RBD) that impact ACE2 receptor binding, folding stability, and antibody recognition. Deep mutational scanning prospectively characterizes the impacts of mutations on these biochemical properties, enabling rapid assessment of new mutations seen during viral surveillance. However, the effects of mutations can change as the virus evolves, requiring updated deep mutational scans. We determined the impacts of all single amino acid mutations in the Omicron BA.1 and BA.2 RBDs on ACE2-binding affinity, RBD folding, and escape from binding by the LY-CoV1404 (bebtelovimab) monoclonal antibody. The effects of some mutations in Omicron RBDs differ from those measured in the ancestral Wuhan-Hu-1 background. These epistatic shifts largely resemble those previously seen in the Alpha variant due to the convergent epistatically modifying N501Y substitution. However, Omicron variants show additional lineage-specific shifts, including examples of the epistatic phenomenon of entrenchment that causes the Q498R and N501Y substitutions present in Omicron to be more favorable in that background than in earlier viral strains. In contrast, the Omicron substitution Q493R exhibits no sign of entrenchment, with the derived state, R493, being as unfavorable for ACE2 binding in Omicron RBDs as in Wuhan-Hu-1. Likely for this reason, the R493Q reversion has occurred in Omicron sub-variants including BA.4/BA.5 and BA.2.75, where the affinity buffer from R493Q reversion may potentiate concurrent antigenic change. Consistent with prior studies, we find that Omicron RBDs have reduced expression, and identify candidate stabilizing mutations that ameliorate this deficit. Last, our maps highlight a broadening of the sites of escape from LY-CoV1404 antibody binding in BA.1 and BA.2 compared to the ancestral Wuhan-Hu-1 background. These BA.1 and BA.2 deep mutational scanning datasets identify shifts in the RBD mutational landscape and inform ongoing efforts in viral surveillance.

Randomized gates eliminate bias in sort‐seq assays

Sort‐seq assays are a staple of the biological engineering toolkit, allowing researchers to profile many groups of cells based on any characteristic that can be tied to fluorescence. However, current approaches, which segregate cells into bins deterministically based on their measured fluorescence, introduce systematic bias. We describe a surprising result: one can obtain unbiased estimates by incorporating randomness into sorting. We validate this approach in simulation and experimentally, and describe extensions for both estimating group level variances and for using multi‐bin sorters.

Highly protective antimalarial antibodies via precision library generation and yeast display screening

The monoclonal antibody CIS43 targets the Plasmodium falciparum circumsporozoite protein (PfCSP) and prevents malaria infection in humans for up to 9 mo following a single intravenous administration. To enhance the potency and clinical utility of CIS43, we used iterative site-saturation mutagenesis and DNA shuffling to screen precise gene-variant yeast display libraries for improved PfCSP antigen recognition. We identified several mutations that improved recognition, predominately in framework regions, and combined these to produce a panel of antibody variants. The most improved antibody, CIS43_Var10, had three mutations and showed approximately sixfold enhanced protective potency in vivo compared to CIS43. Co-crystal and cryo-electron microscopy structures of CIS43_Var10 with the peptide epitope or with PfCSP, respectively, revealed functional roles for each of these mutations. The unbiased site-directed mutagenesis and screening pipeline described here represent a powerful approach to enhance protective potency and to enable broader clinical use of antimalarial antibodies.

Shifting mutational constraints in the SARS-CoV-2 receptor-binding domain during viral evolution

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved variants with substitutions in the spike receptor-binding domain (RBD) that affect its affinity for angiotensin-converting enzyme 2 (ACE2) receptor and recognition by antibodies. These substitutions could also shape future evolution by modulating the effects of mutations at other sites-a phenomenon called epistasis. To investigate this possibility, we performed deep mutational scans to measure the effects on ACE2 binding of all single-amino acid mutations in the Wuhan-Hu-1, Alpha, Beta, Delta, and Eta variant RBDs. Some substitutions, most prominently Asn501→Tyr (N501Y), cause epistatic shifts in the effects of mutations at other sites. These epistatic shifts shape subsequent evolutionary change-for example, enabling many of the antibody-escape substitutions in the Omicron RBD. These epistatic shifts occur despite high conservation of the overall RBD structure. Our data shed light on RBD sequence-function relationships and facilitate interpretation of ongoing SARS-CoV-2 evolution.

Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space

Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.

Functional and Biochemical Characterization of the MazEF6 Toxin-Antitoxin System of Mycobacterium tuberculosis

The Mycobacterium tuberculosis genome harbors nine toxin-antitoxin (TA) systems that are members of the mazEF family, unlike other prokaryotes, which have only one or two. Although the overall tertiary folds of MazF toxins are predicted to be similar, it is unclear how they recognize structurally different RNAs and antitoxins with divergent sequence specificity. Here, we have expressed and purified the individual components and complex of the MazEF6 TA system from M. tuberculosis. Size exclusion chromatography-multiangle light scattering (SEC-MALS) was performed to determine the oligomerization status of the toxin, antitoxin, and the complex in different stoichiometric ratios. The relative stabilities of the proteins were determined by nano-differential scanning fluorimetry (nano-DSF). Microscale thermophoresis (MST) and yeast surface display (YSD) were performed to measure the relative affinities between the cognate toxin-antitoxin partners. The interaction between MazEF6 complexes and cognate promoter DNA was also studied using MST. Analysis of paired-end RNA sequencing data revealed that the overexpression of MazF6 resulted in differential expression of 323 transcripts in M. tuberculosis. Network analysis was performed to identify the nodes from the top-response network. The analysis of mRNA protection ratios resulted in identification of putative MazF6 cleavage site in its native host, M. tuberculosis. IMPORTANCE M. tuberculosis harbors a large number of type II toxin-antitoxin (TA) systems, the exact roles for most of which are unclear. Prior studies have reported that overexpression of several of these type II toxins inhibits bacterial growth and contributes to the formation of drug-tolerant populations in vitro. To obtain insights into M. tuberculosis MazEF6 type II TA system function, we determined stability, oligomeric states, and binding affinities of cognate partners with each other and with their promoter operator DNA. Using RNA-seq data obtained from M. tuberculosis overexpression strains, we have identified putative MazF6 cleavage sites and targets in its native, cellular context.

Mapping the energetic and allosteric landscapes of protein binding domains

Allosteric communication between distant sites in proteins is central to biological regulation but still poorly characterized, limiting understanding, engineering and drug development^1-6. An important reason for this is the lack of methods to comprehensively quantify allostery in diverse proteins. Here we address this shortcoming and present a method that uses deep mutational scanning to globally map allostery. The approach uses an efficient experimental design to infer en masse the causal biophysical effects of mutations by quantifying multiple molecular phenotypes-here we examine binding and protein abundance-in multiple genetic backgrounds and fitting thermodynamic models using neural networks. We apply the approach to two of the most common protein interaction domains found in humans, an SH3 domain and a PDZ domain, to produce comprehensive atlases of allosteric communication. Allosteric mutations are abundant, with a large mutational target space of network-altering 'edgetic' variants. Mutations are more likely to be allosteric closer to binding interfaces, at glycine residues and at specific residues connecting to an opposite surface within the PDZ domain. This general approach of quantifying mutational effects for multiple molecular phenotypes and in multiple genetic backgrounds should enable the energetic and allosteric landscapes of many proteins to be rapidly and comprehensively mapped.

ACE2 binding is an ancestral and evolvable trait of sarbecoviruses

Two different sarbecoviruses have caused major human outbreaks in the past two decades1,2. Both of these sarbecoviruses, SARS-CoV-1 and SARS-CoV-2, engage ACE2 through the spike receptor-binding domain2-6. However, binding to ACE2 orthologues of humans, bats and other species has been observed only sporadically among the broader diversity of bat sarbecoviruses7-11. Here we use high-throughput assays12 to trace the evolutionary history of ACE2 binding across a diverse range of sarbecoviruses and ACE2 orthologues. We find that ACE2 binding is an ancestral trait of sarbecovirus receptor-binding domains that has subsequently been lost in some clades. Furthermore, we reveal that bat sarbecoviruses from outside Asia can bind to ACE2. Moreover, ACE2 binding is highly evolvable-for many sarbecovirus receptor-binding domains, there are single amino-acid mutations that enable binding to new ACE2 orthologues. However, the effects of individual mutations can differ considerably between viruses, as shown by the N501Y mutation, which enhances the human ACE2-binding affinity of several SARS-CoV-2 variants of concern12 but substantially decreases it for SARS-CoV-1. Our results point to the deep ancestral origin and evolutionary plasticity of ACE2 binding, broadening the range of sarbecoviruses that should be considered to have spillover potential.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for Machine Learning-Assisted Metabolite Biosensor Response Curve Profiling in Yeast

Genetically encoded biosensors are valuable tools used in the precise engineering of metabolism. Although a large number of biosensors have been developed, the fine-tuning of their dose-response curves, which promotes the applications of biosensors in various scenarios, still remains challenging. To address this issue, we leverage a DNA trackable assembly method and fluorescence-activated cell sorting coupled with next-generation sequencing (FACS-seq) technology to set up a novel workflow for construction and comprehensive characterization of thousands of biosensors in a massively parallel manner. An FapR-fapO-based malonyl-CoA biosensor was used as proof of concept to construct a trackable combinatorial library, containing 5184 combinations with 6 levels of transcription factor dosage, 4 different operator positions, and 216 possible upstream enhancer sequence (UAS) designs. By applying the FACS-seq technique, the response curves of 2632 biosensors out of 5184 combinations were successfully characterized to provide large-scale genotype-phenotype association data of the designed biosensors. Finally, machine-learning algorithms were applied to predict the genotype-phenotype relationships of the uncharacterized combinations to generate a panoramic scanning map of the combinatorial space. With the assistance of our novel workflow, a malonyl-CoA biosensor with the largest dynamic response range was successfully obtained. Moreover, feature importance analysis revealed that the recognition sequence insertion scheme and the choice of UAS have a significant impact on the dynamic range. Taken together, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Native proline-rich motifs exploit sequence context to target actin-remodeling Ena/VASP protein ENAH

The human proteome is replete with short linear motifs (SLiMs) of four to six residues that are critical for protein-protein interactions, yet the importance of the sequence surrounding such motifs is underexplored. We devised a proteomic screen to examine the influence of SLiM sequence context on protein-protein interactions. Focusing on the EVH1 domain of human ENAH, an actin regulator that is highly expressed in invasive cancers, we screened 36-residue proteome-derived peptides and discovered new interaction partners of ENAH and diverse mechanisms by which context influences binding. A pocket on the ENAH EVH1 domain that has diverged from other Ena/VASP paralogs recognizes extended SLiMs and favors motif-flanking proline residues. Many high-affinity ENAH binders that contain two proline-rich SLiMs use a noncanonical site on the EVH1 domain for binding and display a thermodynamic signature consistent with the two-motif chain engaging a single domain. We also found that photoreceptor cilium actin regulator (PCARE) uses an extended 23-residue region to obtain a higher affinity than any known ENAH EVH1-binding motif. Our screen provides a way to uncover the effects of proteomic context on motif-mediated binding, revealing diverse mechanisms of control over EVH1 interactions and establishing that SLiMs can’t be fully understood outside of their native context.

Rapid characterization of spike variants via mammalian cell surface display

The SARS-CoV-2 spike protein is a critical component of vaccines and a target for neutralizing monoclonal antibodies (nAbs). Spike is also undergoing immunogenic selection with variants that increase infectivity and partially escape convalescent plasma. Here, we describe Spike Display, a high-throughput platform to rapidly characterize glycosylated spike ectodomains across multiple coronavirus-family proteins. We assayed ∼200 variant SARS-CoV-2 spikes for their expression, ACE2 binding, and recognition by 13 nAbs. An alanine scan of all five N-terminal domain (NTD) loops highlights a public epitope in the N1, N3, and N5 loops recognized by most NTD-binding nAbs. NTD mutations in variants of concern B.1.1.7 (alpha), B.1.351 (beta), B.1.1.28 (gamma), B.1.427/B.1.429 (epsilon), and B.1.617.2 (delta) impact spike expression and escape most NTD-targeting nAbs. Finally, B.1.351 and B.1.1.28 completely escape a potent ACE2 mimic. We anticipate that Spike Display will accelerate antigen design, deep scanning mutagenesis, and antibody epitope mapping for SARS-CoV-2 and other emerging viral threats.

Deep mutational scanning for therapeutic antibody engineering

The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which combines the power of exhaustive protein mutagenesis and functional screening with deep sequencing and bioinformatics. The application of DMS has yielded significant improvements to the affinity, specificity, and stability of several preclinical antibodies alongside novel applications such as introducing multi-specific binding properties. DMS has also been applied directly on target antigens to precisely map antibody-binding epitopes and notably to profile the mutational escape potential of viral targets (e.g., SARS-CoV-2 variants). Finally, DMS combined with machine learning is enabling advances in the computational screening and engineering of therapeutic antibodies.

An overview of methods for the structural and functional mapping of epitopes recognized by anti-SARS-CoV-2 antibodies

This mini-review presents a critical survey of techniques used for epitope mapping on the SARS-CoV-2 Spike protein. The sequence and structures for common neutralizing and non-neutralizing epitopes on the Spike protein are described as determined by X-ray crystallography, electron microscopy and linear peptide epitope mapping, among other methods. An additional focus of this mini-review is an analytical appraisal of different deep mutational scanning workflows for conformational epitope mapping and identification of mutants on the Spike protein which escape antibody neutralization. Such a focus is necessary as a critical review of deep mutational scanning for conformational epitope mapping has not been published. A perspective is presented on the use of different epitope determination methods for development of broadly potent antibody therapies and vaccines against SARS-CoV-2.

Neutralizing Monoclonal Antibodies That Target the Spike Receptor Binding Domain Confer Fc Receptor-Independent Protection against SARS-CoV-2 Infection in Syrian Hamsters

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein is the main target for neutralizing antibodies. These antibodies can be elicited through immunization or passively transferred as therapeutics in the form of convalescent-phase sera or monoclonal antibodies (MAbs). Potently neutralizing antibodies are expected to confer protection; however, it is unclear whether weakly neutralizing antibodies contribute to protection. Also, their mechanism of action in vivo is incompletely understood. Here, we demonstrate that 2B04, an antibody with an ultrapotent neutralizing activity (50% inhibitory concentration [IC₅₀] of 0.04 μg/ml), protects hamsters against SARS-CoV-2 in a prophylactic and therapeutic infection model. Protection is associated with reduced weight loss and viral loads in nasal turbinates and lungs after challenge. MAb 2B04 also blocked aerosol transmission of the virus to naive contacts. We next examined three additional MAbs (2C02, 2C03, and 2E06), recognizing distinct epitopes within the receptor binding domain of spike protein that possess either minimal (2C02 and 2E06, IC₅₀ > 20 μg/ml) or weak (2C03, IC₅₀ of 5 μg/ml) virus neutralization capacity in vitro. Only 2C03 protected Syrian hamsters from weight loss and reduced lung viral load after SARS-CoV-2 infection. Finally, we demonstrated that Fc-Fc receptor interactions were not required for protection when 2B04 and 2C03 were administered prophylactically. These findings inform the mechanism of protection and support the rational development of antibody-mediated protection against SARS-CoV-2 infections.

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.

Mutational fitness landscapes reveal genetic and structural improvement pathways for a vaccine-elicited HIV-1 broadly neutralizing antibody

Vaccine-based elicitation of broadly neutralizing antibodies holds great promise for preventing HIV-1 transmission. However, the key biophysical markers of improved antibody recognition remain uncertain in the diverse landscape of potential antibody mutation pathways, and a more complete understanding of anti-HIV-1 fusion peptide (FP) antibody development will accelerate rational vaccine designs. Here we survey the mutational landscape of the vaccine-elicited anti-FP antibody, vFP16.02, to determine the genetic, structural, and functional features associated with antibody improvement or fitness. Using site-saturation mutagenesis and yeast display functional screening, we found that 1% of possible single mutations improved HIV-1 envelope trimer (Env) affinity, but generally comprised rare somatic hypermutations that may not arise frequently in vivo. We observed that many single mutations in the vFP16.02 Fab could enhance affinity >1,000-fold against soluble FP, although affinity improvements against the HIV-1 trimer were more measured and rare. The most potent variants enhanced affinity to both soluble FP and Env, had mutations concentrated in antibody framework regions, and achieved up to 37% neutralization breadth compared to 28% neutralization of the template antibody. Altered heavy- and light-chain interface angles and conformational dynamics, as well as reduced Fab thermal stability, were associated with improved HIV-1 neutralization breadth and potency. We also observed parallel sets of mutations that enhanced viral neutralization through similar structural mechanisms. These data provide a quantitative understanding of the mutational landscape for vaccine-elicited FP-directed broadly neutralizing antibody and demonstrate that numerous antigen-distal framework mutations can improve antibody function by enhancing affinity simultaneously toward HIV-1 Env and FP.

ORACLE reveals a bright future to fight bacteria

A new way to alter the genome of bacteriophages helps produce large libraries of variants, allowing these bacteria-killing viruses to be designed to target species harmful to human health.

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 Profiling of Antibody Immune Repertoires in Convalescent Zika Virus Disease Patients

The re-emergence of Zika virus (ZIKV) caused widespread infections that were linked to Guillain-Barré syndrome in adults and congenital malformation in fetuses, and epidemiological data suggest that ZIKV infection can induce protective antibody responses. A more detailed understanding of anti-ZIKV antibody responses may lead to enhanced antibody discovery and improved vaccine designs against ZIKV and related flaviviruses. Here, we applied recently-invented library-scale antibody screening technologies to determine comprehensive functional molecular and genetic profiles of naturally elicited human anti-ZIKV antibodies in three convalescent individuals. We leveraged natively paired antibody yeast display and NGS to predict antibody cross-reactivities and coarse-grain antibody affinities, to perform in-depth immune profiling of IgM, IgG, and IgA antibody repertoires in peripheral blood, and to reveal virus maturation state-dependent antibody interactions. Repertoire-scale comparison of ZIKV VLP-specific and non-specific antibodies in the same individuals also showed that mean antibody somatic hypermutation levels were substantially influenced by donor-intrinsic characteristics. These data provide insights into antiviral antibody responses to ZIKV disease and outline systems-level strategies to track human antibody immune responses to emergent viral infections.

Computational design and experimental optimization of protein binders with prospects for biomedical applications

Protein-based binders have become increasingly more attractive candidates for drug and imaging agent development. Such binders could be evolved from a number of different scaffolds, including antibodies, natural protein effectors and unrelated small protein domains of different geometries. While both computational and experimental approaches could be utilized for protein binder engineering, in this review we focus on various computational approaches for protein binder design and demonstrate how experimental selection could be applied to subsequently optimize computationally-designed molecules. Recent studies report a number of designed protein binders with pM affinities and high specificities for their targets. These binders usually characterized with high stability, solubility, and low production cost. Such attractive molecules are bound to become more common in various biotechnological and biomedical applications in the near future.

Using B cell receptor lineage structures to predict affinity

We are frequently faced with a large collection of antibodies, and want to select those with highest affinity for their cognate antigen. When developing a first-line therapeutic for a novel pathogen, for instance, we might look for such antibodies in patients that have recovered. There exist effective experimental methods of accomplishing this, such as cell sorting and baiting; however they are time consuming and expensive. Next generation sequencing of B cell receptor (BCR) repertoires offers an additional source of sequences that could be tapped if we had a reliable method of selecting those coding for the best antibodies. In this paper we introduce a method that uses evolutionary information from the family of related sequences that share a naive ancestor to predict the affinity of each resulting antibody for its antigen. When combined with information on the identity of the antigen, this method should provide a source of effective new antibodies. We also introduce a method for a related task: given an antibody of interest and its inferred ancestral lineage, which branches in the tree are likely to harbor key affinity-increasing mutations? We evaluate the performance of these methods on a wide variety of simulated samples, as well as two real data samples. These methods are implemented as part of continuing development of the partis BCR inference package, available at https://github.com/psathyrella/partis. Comments Please post comments or questions on this paper as new issues at https://git.io/Jvxkn.

Measurements drive progress in directed evolution for precise engineering of biological systems

Precise engineering of biological systems requires quantitative, high-throughput measurements, exemplified by progress in directed evolution. New approaches allow high-throughput measurements of phenotypes and their corresponding genotypes. When integrated into directed evolution, these quantitative approaches enable the precise engineering of biological function. At the same time, the increasingly routine availability of large, high-quality data sets supports the integration of machine learning with directed evolution. Together, these advances herald striking capabilities for engineering biology.

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.

Multiplex Evolution of Antibody Fragments Utilizing a Yeast Surface Display Platform

Advances in high-throughput synthetic biology technologies based on the CRISPR/Cas9 system have enabled a comprehensive assessment of mutations conferring desired phenotypes, as well as a better understanding of genotype-phenotype correlations in protein engineering. Engineering antibodies to enhance properties such as binding affinity and stability plays an essential role in therapeutic applications. Here we report a method, multiplex navigation of antibody structure (MINAS), that combines a CRISPR/Cas9-based trackable editing method and fluorescent-activated cell sorting (FACS) of yeast-displayed libraries. We designed mutations in all of the complementarity-determining and framework regions of a well-characterized scFv antibody and mapped the contribution of these regions to enhanced properties. We identified specific mutants that showed higher binding affinities up to 100-fold compared to the wild-type. This study expands the applicability of CRISPR/Cas9-based trackable protein engineering by combining it with a surface display platform.

How to measure and evaluate binding affinities

Quantitative measurements of biomolecule associations are central to biological understanding and are needed to build and test predictive and mechanistic models. Given the advances in high-throughput technologies and the projected increase in the availability of binding data, we found it especially timely to evaluate the current standards for performing and reporting binding measurements. A review of 100 studies revealed that in most cases essential controls for establishing the appropriate incubation time and concentration regime were not documented, making it impossible to determine measurement reliability. Moreover, several reported affinities could be concluded to be incorrect, thereby impacting biological interpretations. Given these challenges, we provide a framework for a broad range of researchers to evaluate, teach about, perform, and clearly document high-quality equilibrium binding measurements. We apply this framework and explain underlying fundamental concepts through experimental examples with the RNA-binding protein Puf4.

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.

Quantitative mapping of binding specificity landscapes for homologous targets by using a high-throughput method

To facilitate investigations of protein-protein interactions (PPIs), we developed a novel platform for quantitative mapping of protein binding specificity landscapes, which combines the multi-target screening of a mutagenesis library into high- and low-affinity populations with sophisticated next-generation sequencing analysis. Importantly, this method generates accurate models to predict affinity and specificity values for any mutation within a protein complex, and requires only a few experimental binding affinity measurements using purified proteins for calibration. We demonstrated the utility of the approach by mapping quantitative landscapes for interactions between the N-terminal domain of the tissue inhibitor of metalloproteinase 2 (N-TIMP2) and three matrix metalloproteinases (MMPs) having homologous structures but different affinities (MMP-1, MMP-3, and MMP-14). The binding landscapes for N-TIMP2/MMP-1 and N-TIMP2/MMP-3 showed the PPIs to be almost fully optimized, with most single mutations giving a loss of affinity. In contrast, the non-optimized PPI for N-TIMP2/MMP-14 was reflected in a wide range of binding affinities, where single mutations exhibited a far more attenuated effect on the PPI. Our new platform reliably and comprehensively identified not only hot- and cold-spot residues, but also specificity-switch mutations that shape target affinity and specificity. Thus, our approach provides a methodology giving an unprecedentedly rich quantitative analysis of the binding specificity landscape, which will broaden the understanding of the mechanisms and evolutionary origins of specific PPIs and facilitate the rational design of specific inhibitors for structurally similar target proteins.

Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization

Quantifying the effects of various mutations on binding free energy is crucial for understanding the evolution of protein-protein interactions and would greatly facilitate protein engineering studies. Yet, measuring changes in binding free energy (ΔΔG_bind) remains a tedious task that requires expression of each mutant, its purification, and affinity measurements. We developed an attractive approach that allows us to quantify ΔΔG_bind for thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, deep sequencing, and a few experimental ΔΔG_bind data points on purified proteins to generate ΔΔG_bind values for the remaining numerous mutants of the same protein complex. Using this methodology, we comprehensively map the single-mutant binding landscape of one of the highest-affinity interaction between BPTI and Bovine Trypsin (BT). We show that ΔΔG_bind for this interaction could be quantified with high accuracy over the range of 12 kcal mol⁻¹ displayed by various BPTI single mutants.

Quantifying the effect of mutations on binding free energy is important to understand protein-protein interaction (PPI). Here the authors develop a method based on yeast display and next-generation sequencing to generate quantitative binding landscapes for any PPI regardless of their Kd value.