Rapid fine conformational epitope mapping using comprehensive mutagenesis and deep sequencing

A review of in vitro stochastic and non-stochastic affinity maturation strategies for phage display derived monoclonal antibodies

Monoclonal antibodies identified using display technologies like phage display occasionally suffers from a lack of affinity making it unsuitable for application. This drawback is circumvented with the application of affinity maturation. Affinity maturation is an essential step in the natural evolution of antibodies in the immune system. The evolution of molecular based methods has seen the development of various mutagenesis approaches. This allows for the natural evolutionary process during somatic hypermutation to be replicated in the laboratories for affinity maturation to fine-tune the affinity and selectivity of antibodies. In this review, we will discuss affinity maturation strategies for mAbs generated through phage display systems. The review will highlight various in vitro stochastic and non-stochastic affinity maturation approaches that includes but are not limited to random mutagenesis, site-directed mutagenesis, and gene synthesis.

Mammalian cell display with automated oligo design and library assembly allows for rapid residue level conformational epitope mapping

Precise epitope determination of therapeutic antibodies is of great value as it allows for further comprehension of mechanism of action, therapeutic responsiveness prediction, avoidance of unwanted cross reactivity, and vaccine design. The golden standard for discontinuous epitope determination is the laborious X-ray crystallography method. Here, we present a combinatorial method for rapid mapping of discontinuous epitopes by mammalian antigen display, eliminating the need for protein expression and purification. The method is facilitated by automated workflows and tailored software for antigen analysis and oligonucleotide design. These oligos are used in automated mutagenesis to generate an antigen receptor library displayed on mammalian cells for direct binding analysis by flow cytometry. Through automated analysis of 33930 primers an optimized single condition cloning reaction was defined allowing for mutation of all surface-exposed residues of the receptor binding domain of SARS-CoV-2. All variants were functionally expressed, and two reference binders validated the method. Furthermore, epitopes of three novel therapeutic antibodies were successfully determined followed by evaluation of binding also towards SARS-CoV-2 Omicron BA.2. We find the method to be highly relevant for rapid construction of antigen libraries and determination of antibody epitopes, especially for the development of therapeutic interventions against novel pathogens.

Epitope binning for multiple antibodies simultaneously using mammalian cell display and DNA sequencing

Epitope binning, an approach for grouping antibodies based on epitope similarities, is a critical step in antibody drug discovery. However, conventional methods are complex, involving individual antibody production. Here, we established Epitope Binning-seq, an epitope binning platform for simultaneously analyzing multiple antibodies. In this system, epitope similarity between the query antibodies (qAbs) displayed on antigen-expressing cells and a fluorescently labeled reference antibody (rAb) targeting a desired epitope is analyzed by flow cytometry. The qAbs with epitope similar to the rAb can be identified by next-generation sequencing analysis of fluorescence-negative cells. Sensitivity and reliability of this system are confirmed using rAbs, pertuzumab and trastuzumab, which target human epidermal growth factor receptor 2. Epitope Binning-seq enables simultaneous epitope evaluation of 14 qAbs at various abundances in libraries, grouping them into respective epitope bins. This versatile platform is applicable to diverse antibodies and antigens, potentially expediting the identification of clinically useful antibodies.

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.

Yeast-based screening platforms to understand and improve human health

Detailed molecular understanding of the human organism is essential to develop effective therapies. Saccharomyces cerevisiae has been used extensively for acquiring insights into important aspects of human health, such as studying genetics and cell-cell communication, elucidating protein-protein interaction (PPI) networks, and investigating human G protein-coupled receptor (hGPCR) signaling. We highlight recent advances and opportunities of yeast-based technologies for cost-efficient chemical library screening on hGPCRs, accelerated deciphering of PPI networks with mating-based screening and selection, and accurate cell-cell communication with human immune cells. Overall, yeast-based technologies constitute an important platform to support basic understanding and innovative applications towards improving human health.

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.

A general platform for targeting MHC-II antigens via a single loop

Class-II major histocompatibility complexes (MHC-IIs) are central to the communications between CD4+ T cells and antigen presenting cells (APCs), but intrinsic structural features associated with MHC-II make it difficult to develop a general targeting system with high affinity and antigen specificity. Here, we introduce a protein platform, Targeted Recognition of Antigen-MHC Complex Reporter for MHC-II (TRACeR-II), to enable the rapid development of peptide-specific MHC-II binders. TRACeR-II has a small helical bundle scaffold and uses an unconventional mechanism to recognize antigens via a single loop. This unique antigen-recognition mechanism renders this platform highly versatile and amenable to direct structural modeling of the interactions with the antigen. We demonstrate that TRACeR-II binders can be rapidly evolved across multiple alleles, while computational protein design can produce specific binding sequences for a SARS-CoV-2 peptide of unknown complex structure. TRACeR-II sheds light on a simple and straightforward approach to address the MHC peptide targeting challenge, without relying on combinatorial selection on complementarity determining region (CDR) loops. It presents a promising basis for further exploration in immune response modulation as well as a broad range of theragnostic applications.

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 ten 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.

Standardizing cassette-based deep mutagenesis by Golden Gate assembly

Protocols for the construction of large, deeply mutagenized protein encoding libraries via Golden Gate assembly of synthetic DNA cassettes employ disparate, system-specific methodology. Here we present a standardized Golden Gate method for building user-defined libraries. We demonstrate that a 25 μL reaction, using 40 fmol of input DNA, can generate a library on the order of 1 × 106 members and that reaction volume or input DNA concentration can be scaled up with no losses in transformation efficiency. Such libraries can be constructed from dsDNA cassettes generated either by degenerate oligonucleotides or oligo pools. We demonstrate its real-world effectiveness by building custom, user-defined libraries on the order of 104 -107 unique protein encoding variants for two orthogonal protein engineering systems. We include a detailed protocol and provide several general-use destination vectors.

Targeting Trop2 in solid tumors: a look into structures and novel epitopes

Trophoblast cell surface antigen 2 (Trop2) exhibits limited expression in normal tissues but is over-expressed across various solid tumors. The effectiveness of anti-Trop2 antibody-drug conjugate (ADC) in managing breast cancer validates Trop2 as a promising therapeutic target for cancer treatment. However, excessive toxicity and a low response rate of ADCs pose ongoing challenges. Safer and more effective strategies should be developed for Trop2-positive cancers. The dynamic structural attributes and the oligomeric assembly of Trop2 present formidable obstacles to the progression of innovative targeted therapeutics. In this review, we summarize recent advancements in understanding Trop2's structure and provide an overview of the epitope characteristics of Trop2-targeted agents. Furthermore, we discuss the correlation between anti-Trop2 agents' epitopes and their respective functions, particularly emphasizing their efficacy and specificity in targeted therapies.

Engineering minimal tissue inhibitors of metalloproteinase targeting MMPs via gene shuffling and yeast surface display

Overexpression of specific matrix metalloproteinases (MMPs) has a key role in development of several diseases, such as cancer, neurological disorders, and cardiovascular diseases due to their critical role in degradation and remodeling of the extracellular matrix (ECM). Tissue inhibitors of metalloproteinases (TIMPs), a family of four in humans, are endogenous inhibitors of MMPs. TIMPs have a high level of sequence and structure homology, with a broad range of binding and inhibition to the family of MMPs. It is important to identify the key motifs of TIMPs responsible for inhibition of MMPs to develop efficient therapeutics targeting specific MMPs. We used DNA shuffling between the human TIMP family to generate a minimal TIMP hybrid library in yeast to identify the dominant minimal MMP inhibitory regions. The minimal TIMP variants screened toward MMP-3 and MMP-9 using fluorescent-activated cell sorting (FACS). Interestingly, several minimal TIMP variants selected after screening toward MMP-3cd or MMP-9cd, with lengths as short as 20 amino acids, maintained or improved binding to MMP-3 and MMP-9. The TIMP-MMP binding dissociation constant (KD ), in the nM range, and MMP inhibition constants (Ki ), in the pM range, of these minimal TIMP variants were similar to the N-terminal domain of TIMP-1 on the yeast surface and in solution indicating the potency of these minimal variants as MMP inhibitors. We further used molecular modeling simulation, and molecular docking of the minimal TIMP variants in complex with MMP-3cd to understand the binding and inhibition mechanism of these variants.

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.

Protein engineering via sequence-performance mapping

Discovery and evolution of new and improved proteins has empowered molecular therapeutics, diagnostics, and industrial biotechnology. Discovery and evolution both require efficient screens and effective libraries, although they differ in their challenges because of the absence or presence, respectively, of an initial protein variant with the desired function. A host of high-throughput technologies-experimental and computational-enable efficient screens to identify performant protein variants. In partnership, an informed search of sequence space is needed to overcome the immensity, sparsity, and complexity of the sequence-performance landscape. Early in the historical trajectory of protein engineering, these elements aligned with distinct approaches to identify the most performant sequence: selection from large, randomized combinatorial libraries versus rational computational design. Substantial advances have now emerged from the synergy of these perspectives. Rational design of combinatorial libraries aids the experimental search of sequence space, and high-throughput, high-integrity experimental data inform computational design. At the core of the collaborative interface, efficient protein characterization (rather than mere selection of optimal variants) maps sequence-performance landscapes. Such quantitative maps elucidate the complex relationships between protein sequence and performance-e.g., binding, catalytic efficiency, biological activity, and developability-thereby advancing fundamental protein science and facilitating protein discovery and evolution.

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

All deep sequencing datasets and code to do the analyses presented within are available via GitHub.

Contact

Peter Tessier, ptessier@umich.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

Deciphering cross-species reactivity of LAMP-1 antibodies using deep mutational epitope mapping and AlphaFold

Delineating the precise regions on an antigen that are targeted by antibodies has become a key step for the development of antibody therapeutics. X-ray crystallography and cryogenic electron microscopy are considered the gold standard for providing precise information about these binding sites at atomic resolution. However, they are labor-intensive and a successful outcome is not guaranteed. We used deep mutational scanning (DMS) of the human LAMP-1 antigen displayed on yeast surface and leveraged next-generation sequencing to observe the effect of individual mutants on the binding of two LAMP-1 antibodies and to determine their functional epitopes on LAMP-1. Fine-tuned epitope mapping by DMS approaches is augmented by knowledge of experimental antigen structure. As human LAMP-1 structure has not yet been solved, we used the AlphaFold predicted structure of the full-length protein to combine with DMS data and ultimately finely map antibody epitopes. The accuracy of this method was confirmed by comparing the results to the co-crystal structure of one of the two antibodies with a LAMP-1 luminal domain. Finally, we used AlphaFold models of non-human LAMP-1 to understand the lack of mAb cross-reactivity. While both epitopes in the murine form exhibit multiple mutations in comparison to human LAMP-1, only one and two mutations in the Macaca form suffice to hinder the recognition by mAb B and A, respectively. Altogether, this study promotes a new application of AlphaFold to speed up precision mapping of antibody-antigen interactions and consequently accelerate antibody engineering for optimization.

Computational epitope mapping of class I fusion proteins using low complexity supervised learning methods

Antibody epitope mapping of viral proteins plays a vital role in understanding immune system mechanisms of protection. In the case of class I viral fusion proteins, recent advances in cryo-electron microscopy and protein stabilization techniques have highlighted the importance of cryptic or ‘alternative’ conformations that expose epitopes targeted by potent neutralizing antibodies. Thorough epitope mapping of such metastable conformations is difficult but is critical for understanding sites of vulnerability in class I fusion proteins that occur as transient conformational states during viral attachment and fusion. We introduce a novel method Accelerated class I fusion protein Epitope Mapping (AxIEM) that accounts for fusion protein flexibility to improve out-of-sample prediction of discontinuous antibody epitopes. Harnessing data from previous experimental epitope mapping efforts of several class I fusion proteins, we demonstrate that accuracy of epitope prediction depends on residue environment and allows for the prediction of conformation-dependent antibody target residues. We also show that AxIEM can identify common epitopes and provide structural insights for the development and rational design of vaccines.

Understanding and Modulating Antibody Fine Specificity: Lessons from Combinatorial Biology

Combinatorial biology methods such as phage and yeast display, suitable for the generation and screening of huge numbers of protein fragments and mutated variants, have been useful when dissecting the molecular details of the interactions between antibodies and their target antigens (mainly those of protein nature). The relevance of these studies goes far beyond the mere description of binding interfaces, as the information obtained has implications for the understanding of the chemistry of antibody–antigen binding reactions and the biological effects of antibodies. Further modification of the interactions through combinatorial methods to manipulate the key properties of antibodies (affinity and fine specificity) can result in the emergence of novel research tools and optimized therapeutics.

Yeast Surface Display: New Opportunities for a Time-Tested Protein Engineering System

Yeast surface display has proven to be a powerful tool for the discovery of antibodies and other novel binding proteins and for engineering the affinity and selectivity of existing proteins for their targets. In the decades since the first demonstrations of the approach, the range of yeast display applications has greatly expanded to include many different protein targets and has grown to encompass methods for rapid protein characterization. Here, we briefly summarize the development of yeast display methodologies and highlight several selected examples of recent applications to timely and challenging protein engineering and characterization problems.

Yeast Surface Display for Protein Engineering: Library Generation, Screening, and Affinity Maturation

Yeast surface display is a powerful directed evolution method for developing and engineering protein molecules to attain desired properties. Here, updated protocols are presented for purposes of identification of lead binders and their affinity maturation. Large libraries are screened by magnetic bead selections followed by flow cytometric selections. Upon identification and characterization of single clones, their affinities are improved by an iterative process of mutagenesis and fluorescence-activated cell sorting.

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.

Identification of a specific surface epitope of OmpC for Escherichia coli O157:H7 with protein topology facilitated affinity mass spectrometry

Abstract

The goal of this work was to identify the target protein and epitope of a previously reported Escherichia coli O157:H7 (ECO157)–specific monoclonal antibody (mAb) 2G12. mAb 2G12 has shown high specificity for the recovery and detection of ECO157. To achieve this goal, the target protein was first separated by two-dimensional gel electrophoresis (2-DE) and located by Western blot (WB). The protein spots were identified to be the outer membrane protein (Omp) C by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF–MS). After that, the target protein was purified by immunoaffinity chromatography (IAC) and subjected to in situ enzymatic cleavage of the vulnerable peptides. Eight eluted peptides of OmpC identified by liquid chromatography–tandem mass spectrometry (LC–MS/MS) were further mapped onto the homologous protein structure of E. coli OmpC (2IXX). The topology of OmpC showed that three peptides had extracellular loops. Epitope mapping with overlapping peptide library and sequence homology analysis revealed that the epitope consisted of a specific peptide, “LGVING,” and an adjacent conservative peptide, “TQTYNATRVGSLG.” Both peptides loop around the overall structure of the epitope. To test the availability of the epitope when ECO157 was grown under different osmolarity, pH, and nutrition levels, the binding efficacy of mAb 2G12 with ECO157 grown in these conditions was evaluated. Results further demonstrated the good stability of this epitope under potential stressful environmental conditions. In summary, this study revealed that mAb 2G12 targeted one specific and one conservative extracellular loop (peptide) of the OmpC present on ECO157, and the epitope was stable and accessible on ECO157 cells grown in different environment.

Key points

• OmpC is the target of a recently identified ECO157-specific mAb 2G12.

• Eight peptides were identified from the OmpC by using LC–MS/MS.

• The specificity of mAb 2G12 is mainly determined by the “LGVING” peptide.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00253-021-11511-8.

Evaluation of Anti-PT Antibody Response after Pertussis Vaccination and Infection: The Importance of Both Quantity and Quality

Pertussis toxin (PT) is considered the main virulence factor causing whooping cough or pertussis. The protein is widely studied and its composition was revealed and sequenced already during the 1980s. The human immune system creates a good response against PT when measured in quantity. However, the serum anti-PT antibodies wane rapidly, and only a small amount of these antibodies are found a few years after vaccination/infection. Therefore, multiple approaches to study the functionality (quality) of these antibodies, e.g., avidity, neutralizing capacity, and epitope specificity, have been investigated. In addition, the long-term B cell memory (Bmem) to PT is crucial for good protection throughout life. In this review, we summarize the findings from functional PT antibody and Bmem studies. These results are discussed in line with the quantity of serum anti-PT antibodies. PT neutralizing antibodies and anti-PT antibodies with proper avidity are crucial for good protection against the disease, and certain epitopes have been identified to have multiple functions in the protection. Although PT-specific Bmem responses are detectable at least five years after vaccination, long-term surveillance is lacking. Variation of the natural boosting of circulating Bordetella pertussis in communities is an important confounding factor in these memory studies.

The Role of TROP2 in BCC and Cutaneous SCC: A Clinical and Immunohistochemical Study

BACKGROUND

Nonmelanoma skin cancer (NMSC) mainly includes basal (BCC) and squamous (SCC) cell carcinoma. Trophoblast cell-surface antigen2 (TROP2), a cell-signal transduction, is one of the tumor-related calcium signal transducer gene family. TROP2 was highly expressed in many cancers, however, its role in BCC and SCC has not yet been studied.

OBJECTIVE

To investigate TROP2 immunohistochemical expression in BCC and SCC (lesional and peri-lesional) skin compared to controls and correlates its expression with the clinicopathologic parameters of the studied cases.

METHODS

This case-control study included 17 BCC and 15 SCC patients as well as 12 age and sex matched controls. History and clinical examination were completed. Histological examination of skin biopsies was done together with TROP2 immune-staining.

RESULTS

In the studied BCC and SCC cases, there was a significant stepwise up-regulation of TROP2 H score from control to peri-lesional, ended by lesional epidermis in one hand (p=0.003 for BCC and p<0.001 for SCC) and tumor island in another hand (p=0.001 for BCC and p=0.003 for SCC). TROP2 expression in both BCC and SCC tumor tissues was not affected by any of the studied clinicopathological parameters of the investigated cases.

CONCLUSION

TROP2 could have an important role in BCC and SCC pathogenesis. TROP2 targeting may have appraising effect in clinical application in BCC and SCC management.

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.

Noncompetitive Allosteric Antagonism of Death Receptor 5 by a Synthetic Affibody Ligand

Fatty acid-induced upregulation of death receptor 5 (DR5) and its cognate ligand, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), promotes hepatocyte lipoapoptosis, which is a key mechanism in the progression of fatty liver disease. Accordingly, inhibition of DR5 signaling represents an attractive strategy for treating fatty liver disease. Ligand competition strategies are prevalent in tumor necrosis factor receptor antagonism, but recent studies have suggested that noncompetitive inhibition through perturbation of the receptor conformation may be a compelling alternative. To this end, we used yeast display and a designed combinatorial library to identify a synthetic 58-amino acid affibody ligand that specifically binds DR5. Biophysical and biochemical studies show that the affibody neither blocks TRAIL binding nor prevents the receptor-receptor interaction. Live-cell fluorescence lifetime measurements indicate that the affibody induces a conformational change in transmembrane dimers of DR5 and favors an inactive state of the receptor. The affibody inhibits apoptosis in TRAIL-treated Huh-7 cells, an in vitro model of fatty liver disease. Thus, this lead affibody serves as a potential drug candidate, with a unique mechanism of action, for fatty liver disease.

Phage-DMS: A Comprehensive Method for Fine Mapping of Antibody Epitopes

Understanding the antibody response is critical to developing vaccine and antibody-based therapies and has inspired the recent development of new methods to isolate antibodies. Methods to define the antibody-antigen interactions that determine specificity or allow escape have not kept pace. We developed Phage-DMS, a method that combines two powerful approaches-immunoprecipitation of phage peptide libraries and deep mutational scanning (DMS)-to enable high-throughput fine mapping of antibody epitopes. As an example, we designed sequences encoding all possible amino acid variants of HIV Envelope to create phage libraries. Using Phage-DMS, we identified sites of escape predicted using other approaches for four well-characterized HIV monoclonal antibodies with known linear epitopes. In some cases, the results of Phage-DMS refined the epitope beyond what was determined in previous studies. This method has the potential to rapidly and comprehensively screen many antibodies in a single experiment to define sites essential for binding interactions.

Tissue mechanics and expression of TROP2 in oral squamous cell carcinoma with varying differentiation

Background

Trophoblast cell surface antigen 2 (TROP2) is overexpressed in many squamous cell carcinomas and promotes tumor development and invasion. The association between TROP2 expression and occurrence and development of oral squamous cell carcinoma (OSCC) remains to be understood.

Methods

We investigated the role of TROP2 in OSCC patients using a combination of biophysical approaches. A total of 108 OSCC patient specimens with varying degrees of differentiation were subjected to hematoxylin and eosin staining, immunohistochemistry, Kaplan-Meier survival curve analysis, and atomic force microscopy to analyze TROP2 expression, morphology, and mechanical properties of OSCC tissues.

Results

TROP2 was overexpressed in 34% of poorly differentiated OSCC samples. High levels of TROP2 were associated with 10.2% survival rate lower than 45.4% and patient age (odds ratio [OR] = 0.437, P = 0.039, 95% confidence interval [CI, 0.198–0.966]), tumor size (OR = 13.148, P = 0.000, 95% CI [5.060–34.168]), and TNM stage (OR = 0.141, P = 0.000, 95% CI [0.082–0.244]). Average surface roughness of low, medium, and highly differentiated OSCC tissues were 448.9 ± 54.8, 792.7 ± 83.6, and 993.0 ± 104.3 nm, respectively. The Pearson coefficient revealed a negative association between tumor stiffness and TROP2 expression (r = − 0.84, P < 0.01).

Conclusion

Overexpression of TROP2 negatively associated with patient survival, degree of tumor differentiation, and tissue mechanics. Taken together, our findings demonstrated that TROP2 may be an indicator of OSCC differentiation leading to the altered mechanical properties of OSCC tissues.

A Method for User-defined Mutagenesis by Integrating Oligo Pool Synthesis Technology with Nicking Mutagenesis

Saturation mutagenesis is a fundamental enabling technology for protein engineering and epitope mapping. Nicking mutagenesis (NM) allows the user to rapidly construct libraries of all possible single mutations in a target protein sequence from plasmid DNA in a one-pot procedure. Briefly, one strand of the plasmid DNA is degraded using a nicking restriction endonuclease and exonuclease treatment. Mutagenic primers encoding the desired mutations are annealed to the resulting circular single-stranded DNA, extended with high-fidelity polymerase, and ligated into covalently closed circular DNA by Taq DNA ligase. The heteroduplex DNA is resolved by selective degradation of the template strand. The complementary strand is synthesized and ligated, resulting in a library of mutated covalently closed circular plasmids. It was later shown that because very little primer is used in the procedure, resuspended oligo pools, which normally require amplification before use, can be used directly in the mutagenesis procedure. Because oligo pools can contain tens of thousands of unique oligos, this enables the construction of libraries of tens of thousands of user-defined mutations in a single-pot mutagenesis reaction, which significantly improves the utility of NM as described below. Use of oligo pools afford an economically advantageous approach to mutagenic experiments. First, oligo pool synthesis is much less expensive per nucleotide synthesized than conventional synthesis. Second, a mixed pool may be generated and used for mutagenesis of multiple different genes. To use the same oligo-pool for mutagenesis of a variety of genes, the user must only quantify the fraction of the oligo-pool specific to her mutagenic experiment and adjust the volume and effective concentration of the oligo-pool for use in nicking mutagenesis.

Improved mutant function prediction via PACT: Protein Analysis and Classifier Toolkit

MOTIVATION

Deep mutational scanning experiments have enabled the measurement of the sequence-function relationship for thousands of mutations in a single experiment. The Protein Analysis and Classifier Toolkit (PACT) is a Python software package that marries the fitness metric of a given mutation within these experiments to sequence and structural features enabling downstream analyses. PACT enables the easy development of user sharable protocols for custom deep mutational scanning experiments as all code is modular and reusable between protocols. Protocols for mutational libraries with single or multiple mutations are included. To exemplify its utility, PACT assessed two deep mutational scanning datasets that measured the tradeoff of enzyme activity and enzyme stability.

RESULTS

PACT efficiently evaluated classifiers that predict protein mutant function tested on deep mutational scanning screens. We found that the classifiers with the lowest false positive and highest true positive rate assesses sequence homology, contact number and if mutation involves proline.

AVAILABILITY AND IMPLEMENTATION

PACT and the processed datasets are distributed freely under the terms of the GPL-3 license. The source code is available at GitHub (https://github.com/JKlesmith/PACT).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Feline Interleukin-31 Shares Overlapping Epitopes with the Oncostatin M Receptor and IL-31RA

Interleukin-31 (IL-31) is a major protein involved in severe inflammatory skin disorders. Its signaling pathway is mediated through two type I cytokine receptors, IL-31RA (also known as the gp130-like receptor) and the oncostatin M receptor (OSMR). Understanding molecular details in these interactions would be helpful for developing antagonist anti-IL-31 monoclonal antibodies (mAbs) as potential therapies. Previous studies suggest that human IL-31 binds to IL-31RA and then recruits OSMR to form a ternary complex. In this model, OSMR cannot interact with IL-31 in the absence of IL-31RA. In this work, we show that feline IL-31 (fIL-31) binds independently with feline OSMR using surface plasmon resonance, an enzyme-linked immunosorbent assay, and yeast surface display. Moreover, competition experiments suggest that OSMR shares a partially overlapping epitope with IL-31RA. We then used deep mutational scanning to map the binding sites of both receptors on fIL-31. In agreement with previous studies of the human homologue, the binding site for IL31-RA contains fIL-31 positions E20 and K82, while the binding site for OSMR comprises the "PADNFERK" motif (P103-K110) and position G38. However, our results also revealed a new overlapping site, composed of positions R69, R72, P73, D76, D81, and E97, between both receptors that we called the "shared site". The conformational epitope of an anti-feline IL-31 mAb that inhibits both OSMR and IL-31RA also mapped to this shared site. Combined, our results show that fIL-31 binds IL-31RA and OSMR independently through a partially shared epitope. These results suggest reexamination of the putative canonical mechanisms for IL-31 signaling in higher animals.

Parallelized identification of on- and off-target protein interactions

Genetic selection combined with next-generation sequencing enables the simultaneous interrogation of the functionality and stability of large numbers of naturally occurring, engineered, or computationally designed protein variants in parallel. We display hundreds of engineered proteins on the surface of yeast cells, select for binding to a set of target molecules by flow cytometry, and sequence the starting pool as well as selected pools to obtain enrichment values for each displayed protein with each target. We show that this high-throughput workflow of multiplex genetic selections followed by large-scale sequencing and comparative analysis allows not only the determination of relative affinities, but also the monitoring of specificity profiles for hundreds to thousands of protein-protein and protein-small molecule interactions in parallel. The approach not only identifies new interactions of designed proteins, but also detects unintended and undesirable off-target interactions. This provides a general framework for screening of engineered protein binders, which often have no negative selection or design step as part of their development pipelines. Hence, this method will be generally useful in the development of protein-based therapeutics.

Fine Epitope Mapping of the CD19 Extracellular Domain Promotes Design

The B-cell surface protein CD19 is present throughout the cell life cycle and is uniformly expressed in leukemias, making it a target for chimeric antigen receptor engineered immune cell therapy. Identifying the sequence dependence of the binding of CD19 to antibodies empowers fundamental study and more tailored development of CD19-targeted therapeutics. To identify the antibody-binding epitopes on CD19, we screened a comprehensive single-site saturation mutation library of the human CD19 extracellular domain to identify mutations detrimental to binding FMC63-the dominant CD19 antibody used in chimeric antigen receptor development-as well as 4G7-2E3 and 3B10, which have been used in various types of CD19 research and development. All three antibodies had partially overlapping, yet distinct, epitopes near the published epitope of antibody B43. The FMC63 conformational epitope spans spatially adjacent, but genetically distant, loops in exons 3 and 4. The 3B10 epitope is a linear peptide sequence that binds CD19 with 440 pM affinity. Along with their primary goal of epitope mapping, the mutational tolerance data also empowered additional CD19 variant design and analysis. A designed CD19 variant with all N-linked glycosylation sites removed successfully bound antibody in the yeast display context, which provides a lead for aglycosylated applications. Screening for thermally stable variants identified mutations to guide further CD19 stabilization for fusion protein applications and revealed evolutionary affinity-stability trade-offs. These fundamental insights into CD19 sequence-function relationships enhance our understanding of antibody-mediated CD19-targeted therapeutics.

MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org ), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

VISTA is an acidic pH-selective ligand for PSGL-1

Co-inhibitory immune receptors can contribute to T cell dysfunction in patients with cancer^1,2. Blocking antibodies against cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed cell death 1 (PD-1) partially reverse this effect and are becoming standard of care in an increasing number of malignancies³. However, many of the other axes by which tumours become inhospitable to T cells are not fully understood. Here we report that V-domain immunoglobulin suppressor of T cell activation (VISTA) engages and suppresses T cells selectively at acidic pH such as that found in tumour microenvironments. Multiple histidine residues along the rim of the VISTA extracellular domain mediate binding to the adhesion and co-inhibitory receptor P-selectin glycoprotein ligand-1 (PSGL-1). Antibodies engineered to selectively bind and block this interaction in acidic environments were sufficient to reverse VISTA-mediated immune suppression in vivo. These findings identify a mechanism by which VISTA may engender resistance to anti-tumour immune responses, as well as an unexpectedly determinative role for pH in immune co-receptor engagement.

Yeast surface display of full-length human microtubule-associated protein tau

Microtubule-associated protein tau is an intrinsically disordered, highly soluble protein found primarily in neurons. Under normal conditions, tau regulates the stability of axonal microtubules and intracellular vesicle transport. However, in patients of neurodegeneration such as Alzheimer's disease (AD), tau forms neurofibrillary deposits, which correlates well with the disease progression. Identifying molecular signatures in tau, such as posttranslational modification, truncation, and conformational change has great potential to detect earliest signs of neurodegeneration and develop therapeutic strategies. Here, we show that full-length human tau, including the longest isoform found in the adult brain, can be robustly displayed on the surface of yeast Saccharomyces cerevisiae. Yeast-displayed tau binds to anti-tau antibodies that cover epitopes ranging from the N-terminus to the 4R repeat region. Unlike tau expressed in the yeast cytosol, surface-displayed tau was not phosphorylated at sites found in AD patients (probed by antibodies AT8, AT270, AT180, and PHF-1). However, yeast-displayed tau showed clear binding to paired helical filament (PHF) tau conformation-specific antibodies Alz-50, MC-1, and Tau-2. Although the tau possessed a conformation found in PHFs, oligomerization or aggregation into larger filaments was undetected. Taken together, yeast-displayed tau enables robust measurement of protein interactions and is of particular interest for characterizing conformational change.

Massively Parallel Assays and Quantitative Sequence-Function Relationships

Over the last decade, a rich variety of massively parallel assays have revolutionized our understanding of how biological sequences encode quantitative molecular phenotypes. These assays include deep mutational scanning, high-throughput SELEX, and massively parallel reporter assays. Here, we review these experimental methods and how the data they produce can be used to quantitatively model sequence-function relationships. In doing so, we touch on a diverse range of topics, including the identification of clinically relevant genomic variants, the modeling of transcription factor binding to DNA, the functional and evolutionary landscapes of proteins, and cis-regulatory mechanisms in both transcription and mRNA splicing. We further describe a unified conceptual framework and a core set of mathematical modeling strategies that studies in these diverse areas can make use of. Finally, we highlight key aspects of experimental design and mathematical modeling that are important for the results of such studies to be interpretable and reproducible.

Cognizance of Molecular Methods for the Generation of Mutagenic Phage Display Antibody Libraries for Affinity Maturation

Antibodies leverage on their unique architecture to bind with an array of antigens. The strength of interaction has a direct relation to the affinity of the antibodies towards the antigen. In vivo affinity maturation is performed through multiple rounds of somatic hypermutation and selection in the germinal centre. This unique process involves intricate sequence rearrangements at the gene level via molecular mechanisms. The emergence of in vitro display technologies, mainly phage display and recombinant DNA technology, has helped revolutionize the way antibody improvements are being carried out in the laboratory. The adaptation of molecular approaches in vitro to replicate the in vivo processes has allowed for improvements in the way recombinant antibodies are designed and tuned. Combinatorial libraries, consisting of a myriad of possible antibodies, are capable of replicating the diversity of the natural human antibody repertoire. The isolation of target-specific antibodies with specific affinity characteristics can also be accomplished through modification of stringent protocols. Despite the ability to screen and select for high-affinity binders, some 'fine tuning' may be required to enhance antibody binding in terms of its affinity. This review will provide a brief account of phage display technology used for antibody generation followed by a summary of different combinatorial library characteristics. The review will focus on available strategies, which include molecular approaches, next generation sequencing, and in silico approaches used for antibody affinity maturation in both therapeutic and diagnostic applications.

Biophysical Characterization Platform Informs Protein Scaffold Evolvability

Evolving specific molecular recognition function of proteins requires strategic navigation of a complex mutational landscape. Protein scaffolds aid evolution via a conserved platform on which a modular paratope can be evolved to alter binding specificity. Although numerous protein scaffolds have been discovered, the underlying properties that permit binding evolution remain unknown. We present an algorithm to predict a protein scaffold's ability to evolve novel binding function based upon computationally calculated biophysical parameters. The ability of 17 small proteins to evolve binding functionality across seven discovery campaigns was determined via magnetic activated cell sorting of 10¹⁰ yeast-displayed protein variants. Twenty topological and biophysical properties were calculated for 787 small protein scaffolds and reduced into independent components. Regularization deduced which extracted features best predicted binding functionality, providing a 4/6 true positive rate, a 9/11 negative predictive value, and a 4/6 positive predictive value. Model analysis suggests a large, disconnected paratope will permit evolved binding function. Previous protein engineering endeavors have suggested that starting with a highly developable (high producibility, stability, solubility) protein will offer greater mutational tolerance. Our results support this connection between developability and evolvability by demonstrating a relationship between protein production in the soluble fraction of Escherichia coli and the ability to evolve binding function upon mutation. We further explain the necessity for initial developability by observing a decrease in proteolytic stability of protein mutants that possess binding functionality over nonfunctional mutants. Future iterations of protein scaffold discovery and evolution will benefit from a combination of computational prediction and knowledge of initial developability properties.

Biophysical Inference of Epistasis and the Effects of Mutations on Protein Stability and Function

Understanding the relationship between protein sequence, function, and stability is a fundamental problem in biology. The essential function of many proteins that fold into a specific structure is their ability to bind to a ligand, which can be assayed for thousands of mutated variants. However, binding assays do not distinguish whether mutations affect the stability of the binding interface or the overall fold. Here, we introduce a statistical method to infer a detailed energy landscape of how a protein folds and binds to a ligand by combining information from many mutated variants. We fit a thermodynamic model describing the bound, unbound, and unfolded states to high quality data of protein G domain B1 binding to IgG-Fc. We infer distinct folding and binding energies for each mutation providing a detailed view of how mutations affect binding and stability across the protein. We accurately infer the folding energy of each variant in physical units, validated by independent data, whereas previous high-throughput methods could only measure indirect changes in stability. While we assume an additive sequence-energy relationship, the binding fraction is epistatic due its nonlinear relation to energy. Despite having no epistasis in energy, our model explains much of the observed epistasis in binding fraction, with the remaining epistasis identifying conformationally dynamic regions.

Humanised monoclonal antibodies neutralise pertussis toxin by receptor blockade and reduced retrograde trafficking

Pertussis toxin (PTx) is a major protective antigen produced by Bordetella pertussis that is included in all current acellular vaccines. Of several well‐characterized monoclonal antibodies binding this toxin, the humanised hu1B7 and hu11E6 antibodies are highly protective in multiple in vitro and in vivo assays. In this study, we determine the molecular mechanisms of protection mediated by these antibodies. Neither antibody directly binds the B. pertussis bacterium nor supports antibody‐dependent complement cytotoxicity. Both antibodies, either individually or as a cocktail, form multivalent complexes with soluble PTx that bind the FcγRIIb receptor more tightly than antibody alone, suggesting that the antibodies may accelerate PTx clearance via immune complex formation. However, a receptor binding assay and cellular imaging indicate that the main mechanism used by hu11E6 is competitive inhibition of PTx binding to its cellular receptor. In contrast, the main hu1B7 neutralising mechanism appears to be inhibition of PTx internalisation and retrograde trafficking. We assessed the effects of hu1B7 on PTx retrograde trafficking in CHO‐K1 cells using quantitative immunofluorescence microscopy. In the absence of hu1B7 or after incubation with an isotype control antibody, PTx colocalizes to organelles in a manner consistent with retrograde transport. However, after preincubation with hu1B7, PTx appears restricted to the membrane surface with colocalization to organelles associated with retrograde transport significantly reduced. Together, these data support a model whereby hu11E6 and hu1B7 interfere with PTx receptor binding and PTx retrograde trafficking, respectively.

High expression of TROP2 is correlated with poor prognosis of oral squamous cell carcinoma

Human trophoblastic cell-surface antigen 2 (TROP2) is a cell surface glycoprotein that exhibits high expression in various carcinomas but low or no expression in normal tissues. High TROP2 expression plays an important role in promoting tumor development and aggressiveness, which is correlated with reduced patient survival. However, there are few studies regarding TROP2 in relation to both oral squamous cell carcinoma (OSCC) and oral potentially malignant lesions. The expression of TROP2 protein and mRNA was investigated in OSCC tissues, oral potentially malignant lesion tissues, and normal oral tissues using immunohistochemistry and quantitative real-time polymerase chain reaction (qRT-PCR). The association between TROP2 expression and clinicopathological characteristics of OSCC was also analyzed, and the prognostic value of TROP2 was evaluated. The expression of TROP2 protein and mRNA were both higher in OSCC tissues than in oral potentially malignant lesion tissues or normal oral tissues. Positive TROP2 expression was related to differentiation, lymph node metastases, TNM stage, perineural infiltration, and vascular invasion. Poor overall survival was associated with high TROP2 expression and other factors associated with poor overall survival including poor differentiation, lymph node metastasis, TNM stage, vascular invasion, and perineural invasion in univariate analyses. TROP2 expression as well as TNM stage and vascular invasion were independent prognostic factors associated with the overall survival of OSCC patients in multivariate analyses. In summary, High TROP2 expression is associated with poor overall survival and serves as an independent prognostic factor in OSCC. The results suggest that TROP2 expression could be an effective prognostic biomarker for OSCC.

Characterization of Individual Human Antibodies That Bind Pertussis Toxin Stimulated by Acellular Immunization

Despite high vaccination rates, the incidence of whooping cough has steadily been increasing in developing countries for several decades. The current acellular pertussis (aP) vaccines all include the major protective antigen pertussis toxin (PTx) and are safer, but they appear to be less protective than infection or older, whole-cell vaccines. To better understand the attributes of individual antibodies stimulated by aP, we isolated plasmablast clones recognizing PTx after booster immunization of two donors. Five unique antibody sequences recognizing native PTx were recovered and expressed as recombinant human IgG1 antibodies. The antibodies all bind different epitopes on the PTx S1 subunit, B oligomer, or S1-B subunit interface, and just one clone neutralized PTx in an in vitro assay. To better understand the epitopes bound by the nonneutralizing S1-subunit antibodies, comprehensive mutagenesis with yeast display provided a detailed map of the epitope recognized by antibodies A8 and E12. Residue R76 is required for antibody A8 binding and is present on the S1 surface but is only partially exposed in the holotoxin, providing a structural explanation for A8's inability to neutralize holotoxin. The B-subunit-specific antibody D8 inhibited PTx binding to a model receptor and neutralized PTx in vitro as well as in an in vivo leukocytosis assay. This is the first study, to our knowledge, to identify individual human antibodies stimulated by the acellular pertussis vaccine and demonstrates the feasibility of using these approaches to address outstanding issues in pertussis vaccinology, including mechanisms of accelerated waning of protective immunity despite repeated aP immunization.

Boosting the efficiency of site-saturation mutagenesis for a difficult-to-randomize gene by a two-step PCR strategy

Site-saturation mutagenesis (SSM) has been used in directed evolution of proteins for a long time. As a special form of saturation mutagenesis, it involves individual randomization at a given residue with formation of all 19 amino acids. To date, the most efficient embodiment of SSM is a one-step PCR-based approach using NNK codon degeneracy. However, in the case of difficult-to-randomize genes, SSM may not deliver all of the expected 19 mutants, which compels the user to invest further efforts by applying site-directed mutagenesis for the construction of the missing mutants. To solve this problem, we developed a two-step PCR-based technique in which a mutagenic primer and a non-mutagenic (silent) primer are used to generate a short DNA fragment, which is recovered and then employed as a megaprimer to amplify the whole plasmid. The present two-step and older one-step (partially overlapped primer approach) procedures were compared by utilizing cytochrome P450-BM3, which is a "difficult-to-randomize" gene. The results document the distinct superiority of the new method by checking the library quality on DNA level based on massive sequence data, but also at amino acid level. Various future applications in biotechnology can be expected, including the utilization when constructing mutability landscapes, which provide semi-rational information for identifying hot spots for protein engineering and directed evolution.

Pro region engineering of nerve growth factor by deep mutational scanning enables a yeast platform for conformational epitope mapping of anti-NGF monoclonal antibodies

Nerve growth factor (NGF) plays a central role in multiple chronic pain conditions. As such, anti-NGF monoclonal antibodies (mAbs) that function by antagonizing NGF downstream signaling are leading drug candidates for non-opioid pain relief. To evaluate anti-canine NGF (cNGF) mAbs we sought a yeast surface display platform of cNGF. Both mature cNGF and pro-cNGF displayed on the yeast surface but bound conformationally sensitive mAbs at most 2.5-fold in mean fluorescence intensity above background, suggesting that cNGF was mostly misfolded. To improve the amount of folded, displayed cNGF, we used comprehensive mutagenesis, FACS, and deep sequencing to identify point mutants in the pro-region of canine NGF that properly enhance the folded protein displayed on the yeast surface. Out of 1,737 tested single point mutants in the pro region, 49 increased the amount of NGF recognized by conformationally sensitive mAbs. These gain-of-function mutations cluster around residues A-61-P-26. Gain-of-function mutants were additive, and a construct containing three mutations increased amount of folded cNGF to 23-fold above background. Using this new cNGF construct, fine conformational epitopes for tanezumab and three anti-cNGF mAbs were evaluated. The epitope revealed by the yeast experiments largely overlapped with the tanezumab epitope previously determined by X-ray crystallography. The other mAbs showed site-specific differences with tanezumab. As the number of binding epitopes of functionally neutralizing anti-NGF mAbs on NGF are limited, subtle differences in the individual interacting residues on NGF that bind each mAb contribute to the understanding of each antibody and variations in its neutralizing activity. These results demonstrate the potential of deep sequencing-guided protein engineering to improve the production of folded surface-displayed protein, and the resulting cNGF construct provides a platform to map conformational epitopes for other anti-neurotrophin mAbs.

Insights into protein structure, stability and function from saturation mutagenesis

Where convenient phenotypic readouts are available, saturation mutagenesis coupled to deep sequencing provides a rapid and facile method to infer sequence determinants of protein structure, stability and function. We provide brief descriptions and currently available options for the various steps involved, and mention limitations of current implementations. We also highlight recent applications such as estimating relative stabilities and affinities of protein variants, mapping epitopes, protein model discrimination and prediction of mutant phenotypes. Most mutational scans have so far been applied to single genes and proteins. Additional methodological improvements are required to expand the scope to study intergenic epistasis and intermolecular interactions in macromolecular complexes.

An Introduction to Epitope Mapping

Antibodies are protein molecules used routinely for therapeutic, diagnostic, and research purposes due to their exquisite ability to selectively recognize and bind a given antigen. The particular area of the antigen recognized by the antibody is called the epitope, and for proteinaceous antigens the epitope can be of complex nature. Information about the binding epitope of an antibody can provide important mechanistic insights and indicate for what applications an antibody might be useful. Therefore, a variety of epitope mapping techniques have been developed to localize such regions. Although the real picture is even more complex, epitopes in protein antigens are broadly grouped into linear or discontinuous epitopes depending on the positioning of the epitope residues in the antigen sequence and the requirement of structure. Specialized methods for mapping of the two different classes of epitopes, using high-throughput or high-resolution methods, have been developed. While different in their detail, all of the experimental methods rely on assessing the binding of the antibody to the antigen or a set of antigen mimics. Early approaches utilizing sets of truncated proteins, small numbers of synthesized peptides, and structural analyses of antibody-antigen complexes have been significantly refined. Current state-of-the-art methods involve combinations of mutational scanning, protein display, and high-throughput screening in conjunction with bioinformatic analyses of large datasets.

Characterizing Protein-Protein Interactions Using Deep Sequencing Coupled to Yeast Surface Display

In this chapter, we discuss a method to determine the affinity and specificity of nearly all single-point mutants for a full-length protein binder. This method combines deep sequencing, comprehensive mutagenesis, yeast surface display, and fluorescence-activated cell sorting. This approach has been used to study sequence-function relationships for protein-protein interactions. The data can be used to determine the fine conformational epitope on the protein binder.