Application of Assisted Design of Antibody and Protein Therapeutics (ADAPT) improves efficacy of a Clostridium difficile toxin A single-domain antibody

Immunization Strategies Against Clostridioides difficile

Clostridioides difficile (C. difficile) infection (CDI) is an important healthcare but also a community-associated disease. CDI is considered a public health threat and an economic burden. A major problem is the high rate of recurrences. Besides classical antibiotic treatments, new therapeutic strategies are needed to prevent infection, to treat patients, and to prevent recurrences. If fecal transplantation has been recommended to treat recurrences, another key approach is to elicit immunity against C. difficile and its virulence factors. Here, after a summary concerning the virulence factors, the host immune response against C. difficile, and its role in the outcome of disease, we review the different approaches of passive immunotherapies and vaccines developed against CDI. Passive immunization strategies are designed in function of the target antigen, the antibody-based product, and its administration route. Similarly, for active immunization strategies, vaccine antigens can target toxins or surface proteins, and immunization can be performed by parenteral or mucosal routes. For passive immunization and vaccination as well, we first present immunization assays performed in animal models and second in humans and associated clinical trials. The different studies are presented according to the mode of administration either parenteral or mucosal and the target antigens and either toxins or colonization factors.

In Vitro Affinity Maturation of Nanobodies against Mpox Virus A29 Protein Based on Computer-Aided Design

Mpox virus (MPXV), the most pathogenic zoonotic orthopoxvirus, caused worldwide concern during the SARS-CoV-2 epidemic. Growing evidence suggests that the MPXV surface protein A29 could be a specific diagnostic marker for immunological detection. In this study, a fully synthetic phage display library was screened, revealing two nanobodies (A1 and H8) that specifically recognize A29. Subsequently, an in vitro affinity maturation strategy based on computer-aided design was proposed by building and docking the A29 and A1 three-dimensional structures. Ligand-receptor binding and molecular dynamics simulations were performed to predict binding modes and key residues. Three mutant antibodies were predicted using the platform, increasing the affinity by approximately 10-fold compared with the parental form. These results will facilitate the application of computers in antibody optimization and reduce the cost of antibody development; moreover, the predicted antibodies provide a reference for establishing an immunological response against MPXV.

Antibody designing against IIIabc junction (JIIIabc) of HCV IRES through affinity maturation; RNA-Antibody docking and interaction analysis

Hepatitis C virus is a single-stranded RNA based virus which can cause chronic HCV and hepatocellular carcinoma. HCV genotype 3a has relatively higher rate of fibrosis progression, prevalence of steatosis and incidence of HCC. Despite HCVs variation in genomic sequence, the 5' untranslated region containing internal ribosome entry site (IRES) is highly conserved among all genotypes. It is responsible for translation and initiation of the viral protein. In present study, IRES was targeted by designing variants of reported antigen binding fragment (Fab) through affinity maturation approach. Affinity maturation strategy allowed the rational antibody designing with better biophysical properties and antibody-antigen binding interactions. Complementarity determining regions of reported Fab (wild type) were assessed and docked with IRES. Best generated model of Fab was selected and subjected to alanine scanning Three sets of insilico mutations for variants (V) designing were selected; single (1-71), double (a-j) and triple (I-X). Redocking of IRES-Fab variants consequently enabled the discovery of three variants exhibiting better docking score as compared to the wild type Fab. V1, V39 and V4 exhibited docking scores of -446.51, -446.52 and-446.29 kcal/mol respectively which is better as compared to the wild type Fab that exhibited the docking score of -351.23 kcal/mol. Variants exhibiting better docking score were screened for aggregation propensity by assessing the aggregation prone regions in Fab structure. Total A3D scores of wild type Fab, V1, V4 and V39 were predicted as -315.325, -312.727, -316.967 and -317.545 respectively. It is manifested that solubility of V4 and V39 is comparable to wild type Fab. In future, development and invitro assessment of these promising Fab HCV3 variants is aimed.

Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization

Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.

Nanobodies: the Potential Application in Bacterial Treatment and Diagnosis

An infection caused by bacteria is one of the main factors that poses a threat to human health. A recent report from the World Health Organization (WHO) has highlighted that bacteria that cause blood infections have become increasingly drug-resistant. Therefore, it is crucial to research and develop new techniques for detecting and treating these infections. Since their discovery, nanobodies have exhibited numerous outstanding biological properties. They are easy to express, modify, and have high stability, robust permeability and low immunogenicity, all of which indicate their potential as a substitute. Nanobodies have been utilized in a variety of studies on viruses and cancer. This article primarily focuses on nanobodies and introduces their characteristics and application in the diagnosis and treatment of bacterial infections.

Solvated interaction energy: from small-molecule to antibody drug design

Scoring functions are ubiquitous in structure-based drug design as an aid to predicting binding modes and estimating binding affinities. Ideally, a scoring function should be broadly applicable, obviating the need to recalibrate and refit its parameters for every new target and class of ligands. Traditionally, drugs have been small molecules, but in recent years biologics, particularly antibodies, have become an increasingly important if not dominant class of therapeutics. This makes the goal of having a transferable scoring function, i.e., one that spans the range of small-molecule to protein ligands, even more challenging. One such broadly applicable scoring function is the Solvated Interaction Energy (SIE), which has been developed and applied in our lab for the last 15 years, leading to several important applications. This physics-based method arose from efforts to understand the physics governing binding events, with particular care given to the role played by solvation. SIE has been used by us and many independent labs worldwide for virtual screening and discovery of novel small-molecule binders or optimization of known drugs. Moreover, without any retraining, it is found to be transferrable to predictions of antibody-antigen relative binding affinities and as accurate as functions trained on protein-protein binding affinities. SIE has been incorporated in conjunction with other scoring functions into ADAPT (Assisted Design of Antibody and Protein Therapeutics), our platform for affinity modulation of antibodies. Application of ADAPT resulted in the optimization of several antibodies with 10-to-100-fold improvements in binding affinity. Further applications included broadening the specificity of a single-domain antibody to be cross-reactive with virus variants of both SARS-CoV-1 and SARS-CoV-2, and the design of safer antibodies by engineering of a pH switch to make them more selective towards acidic tumors while sparing normal tissues at physiological pH.

Optimizing Antibody-Antigen Binding Affinities with the ADAPT Platform

The ADAPT (Assisted Design of Antibody and Protein Therapeutics) platform guides the selection of mutants that improve/modulate the affinity of antibodies and other biologics. Predicted affinities are based on a consensus z-score from three scoring functions. Computational predictions are interleaved with experimental validation, significantly enhancing the robustness of the design and selection of mutants. A key step is an initial exhaustive virtual single-mutant scan that identifies hot spots and the mutations predicted to improve affinity. A small number of proposed single mutants are then produced and assayed. Only the validated single mutants (i.e., having improved affinity) are used to design double and higher-order mutants in subsequent rounds of design, avoiding the combinatorial explosion that arises from random mutagenesis. Typically, with a total of about 30-50 designed single, double, and triple mutants, affinity improvements of 10- to 100-fold are obtained.

Design of nanobody-based bispecific constructs by in silico affinity maturation and umbrella sampling simulations

Random mutagenesis is the natural opportunity for proteins to evolve and biotechnologically it has been exploited to create diversity and identify variants with improved characteristics in the mutant pools. Rational mutagenesis based on biophysical assumptions and supported by computational power has been proposed as a faster and more predictable strategy to reach the same aim. In this work we confirm that substantial improvements in terms of both affinity and stability of nanobodies can be obtained by using combinations of algorithms, even for binders with already high affinity and elevated thermal stability. Furthermore, in silico approaches allowed the development of an optimized bispecific construct able to bind simultaneously the two clinically relevant antigens TNF-α and IL-23 and, by means of its enhanced avidity, to inhibit effectively the apoptosis of TNF-α-sensitive L929 cells. The results revealed that salt bridges, hydrogen bonds, aromatic-aromatic and cation-pi interactions had a critical role in increasing affinity. We provided a platform for the construction of high-affinity bispecific constructs based on nanobodies that can have relevant applications for the control of all those biological mechanisms in which more than a single antigen must be targeted to increase the treatment effectiveness and avoid resistance mechanisms.

Application of recombinant antibodies for treatment of Clostridioides difficile infection: Current status and future perspective

Clostridioides difficile (C. difficile), known as the major cause of antibiotic-associated diarrhea, is regarded as one of the most common healthcare-associated bacterial infections worldwide. Due to the emergence of hypervirulent strains, development of new therapeutic methods for C. difficile infection (CDI) has become crucially important. In this context, antibodies have been introduced as valuable tools in the research and clinical environments, as far as the effectiveness of antibody therapy for CDI was reported in several clinical investigations. Hence, production of high-performance antibodies for treatment of CDI would be precious. Traditional approaches of antibody generation are based on hybridoma technology. Today, application of in vitro technologies for generating recombinant antibodies, like phage display, is considered as an appropriate alternative to hybridoma technology. These techniques can circumvent the limitations of the immune system and they can be exploited for production of antibodies against different types of biomolecules in particular active toxins. Additionally, DNA encoding antibodies is directly accessible in in vitro technologies, which enables the application of antibody engineering in order to increase their sensitivity and specificity. Here, we review the application of antibodies for CDI treatment with an emphasis on recombinant fragment antibodies. Also, this review highlights the current and future prospects of the aforementioned approaches for antibody-mediated therapy of CDI.

Structure-based dual affinity optimization of a SARS-CoV-1/2 cross-reactive single-domain antibody

The SARS coronavirus 2 (SARS-CoV-2) spike (S) protein binding to the human ACE2 receptor is the molecular event that initiates viral entry into host cells and leads to infection and virus replication. There is a need for agents blocking viral entry into host cells that are cross-reactive with emerging virus variants. VHH-72 is an anti-SARS-CoV-1 single-domain antibody that also exhibits cross-specificity with SARS-CoV-2 but with decreased binding affinity. Here we applied a structure-based approach to affinity-mature VHH-72 for the SARS-CoV-2 spike protein while retaining the original affinity for SARS-CoV-1. This was achieved by employing the computational platform ADAPT in a constrained dual-affinity optimization mode as a means of broadening specificity. Select mutants designed by ADAPT were formatted as fusions with a human IgG1-Fc fragment. These mutants demonstrated improved binding to the SARS-CoV-2 spike protein due to decreased dissociation rates. Functional testing for virus neutralization revealed improvements relative to the parental VHH72-Fc up to 10-fold using a SARS-CoV-2 pseudotyped lentivirus and 20-fold against the SARS-CoV-2 authentic live virus (Wuhan variant). Binding and neutralization improvements were maintained for some other SARS-CoV-2 variants currently in circulation. These improved VHH-72 mutants are predicted to establish novel interactions with the S antigen. They will be useful, alone or as fusions with other functional modules, in the global quest for treatments of COVID-19 infections.

Research Progress and Applications of Multivalent, Multispecific and Modified Nanobodies for Disease Treatment

Recombinant antibodies such as nanobodies are progressively demonstrating to be a valid alternative to conventional monoclonal antibodies also for clinical applications. Furthermore, they do not solely represent a substitute for monoclonal antibodies but their unique features allow expanding the applications of biotherapeutics and changes the pattern of disease treatment. Nanobodies possess the double advantage of being small and simple to engineer. This combination has promoted extremely diversified approaches to design nanobody-based constructs suitable for particular applications. Both the format geometry possibilities and the functionalization strategies have been widely explored to provide macromolecules with better efficacy with respect to single nanobodies or their combination. Nanobody multimers and nanobody-derived reagents were developed to image and contrast several cancer diseases and have shown their effectiveness in animal models. Their capacity to block more independent signaling pathways simultaneously is considered a critical advantage to avoid tumor resistance, whereas the mass of these multimeric compounds still remains significantly smaller than that of an IgG, enabling deeper penetration in solid tumors. When applied to CAR-T cell therapy, nanobodies can effectively improve the specificity by targeting multiple epitopes and consequently reduce the side effects. This represents a great potential in treating malignant lymphomas, acute myeloid leukemia, acute lymphoblastic leukemia, multiple myeloma and solid tumors. Apart from cancer treatment, multispecific drugs and imaging reagents built with nanobody blocks have demonstrated their value also for detecting and tackling neurodegenerative, autoimmune, metabolic, and infectious diseases and as antidotes for toxins. In particular, multi-paratopic nanobody-based constructs have been developed recently as drugs for passive immunization against SARS-CoV-2 with the goal of impairing variant survival due to resistance to antibodies targeting single epitopes. Given the enormous research activity in the field, it can be expected that more and more multimeric nanobody molecules will undergo late clinical trials in the next future. Systematic Review Registration.

Hydrazide-assisted directional antibody conjugation of gold nanoparticles to enhance immunochromatographic assay

The analytical performance of immunochromatographic assay (ICA) is usually determined by the biological activity of antibody and gold nanoparticle conjugates (AuNP probes). However, conventional probes are constructed using the nondirectional coupling method that can cause the improper orientation of antibodies with the poor accessibility of antigen-binding sites. To address these issues, we report a site-specific directional coupling strategy to enhance the bioactivity of AuNP probes through the specific covalent binding of the aldehyde group in the Fc domain of antibodies with the hydrazide group modified on the surface of AuNPs. Through this design, the antibodies can be erected on the AuNP surface to fully expose the Fab domain and achieve the maximized functional availability. Leveraging these AuNP probes as ICA labels, we demonstrate an improved detection of the hepatitis B surface antigen with less used amount of labeled antibody (0.2 mg/pmol AuNPs), shorter reaction time (10 min), better antibody bioactivity, and higher detection sensitivity (2 ng/mL) compared with the carbodiimide method. Overall, this work provides great promise for the design and the construction of high-performance probes to enhance the detection performance of ICA sensors.

Application of an integrated computational antibody engineering platform to design SARS-CoV-2 neutralizers

As the COVID-19 pandemic continues to spread, hundreds of new initiatives including studies on existing medicines are running to fight the disease. To deliver a potentially immediate and lasting treatment to current and emerging SARS-CoV-2 variants, new collaborations and ways of sharing are required to create as many paths forward as possible. Here, we leverage our expertise in computational antibody engineering to rationally design/engineer three previously reported SARS-CoV neutralizing antibodies and share our proposal towards anti-SARS-CoV-2 biologics therapeutics. SARS-CoV neutralizing antibodies, m396, 80R and CR-3022 were chosen as templates due to their diversified epitopes and confirmed neutralization potency against SARS-CoV (but not SARS-CoV-2 except for CR3022). Structures of variable fragment (Fv) in complex with receptor binding domain (RBD) from SARS-CoV or SARS-CoV-2 were subjected to our established in silico antibody engineering platform to improve their binding affinity to SARS-CoV-2 and developability profiles. The selected top mutations were ensembled into a focused library for each antibody for further screening. In addition, we convert the selected binders with different epitopes into the trispecific format, aiming to increase potency and to prevent mutational escape. Lastly, to avoid antibody-induced virus activation or enhancement, we suggest application of NNAS and DQ mutations to the Fc region to eliminate effector functions and extend half-life.

Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations

Understanding how a mutation might affect protein stability is of significant importance to protein engineering and for understanding protein evolution genetic diseases. While a number of computational tools have been developed to predict the effect of missense mutations on protein stability protein stability upon mutations, they are known to exhibit large biases imparted in part by the data used to train and evaluate them. Here, we provide a comprehensive overview of predictive tools, which has provided an evolving insight into the importance and relevance of features that can discern the effects of mutations on protein stability. A diverse selection of these freely available tools was benchmarked using a large mutation-level blind dataset of 1342 experimentally characterised mutations across 130 proteins from ThermoMutDB, a second test dataset encompassing 630 experimentally characterised mutations across 39 proteins from iStable2.0 and a third blind test dataset consisting of 268 mutations in 27 proteins from the newly published ProThermDB. The performance of the methods was further evaluated with respect to the site of mutation, type of mutant residue and by ranging the pH and temperature. Additionally, the classification performance was also evaluated by classifying the mutations as stabilizing (∆∆G ≥ 0) or destabilizing (∆∆G < 0). The results reveal that the performance of the predictors is affected by the site of mutation and the type of mutant residue. Further, the results show very low performance for pH values 6-8 and temperature higher than 65 for all predictors except iStable2.0 on the S630 dataset. To illustrate how stability and structure change upon single point mutation, we considered four stabilizing, two destabilizing and two stabilizing mutations from two proteins, namely the toxin protein and bovine liver cytochrome. Overall, the results on S268, S630 and S1342 datasets show that the performance of the integrated predictors is better than the mechanistic or individual machine learning predictors. We expect that this paper will provide useful guidance for the design and development of next-generation bioinformatic tools for predicting protein stability changes upon mutations.

Progress toward improved understanding of antibody maturation

Upon encountering an antigen, antibodies mature through various rounds of somatic mutations, resulting in higher affinities and specificities to the particular antigen. We review recent progress in four areas of antibody maturation studies. (1) Next-generation and single-cell sequencing have revolutionized the analysis of antibody repertoires by dramatically increasing the sequences available to study the state and evolution of the immune system. Computational methods, including machine learning tools, have been developed for reconstituting antibody clonal lineages and for general repertoire analysis. (2) The availability of X-ray structures, thermodynamic and kinetic data, and molecular dynamics simulations provide information on the biophysical mechanisms responsible for improved affinity. (3) In addition to improved binding to a specific antigen, providing affinity-independent diversity and self/nonself discrimination are fundamental functions of the immune system. Recent studies, including X-ray structures, yield improved understanding of both mechanisms. (4) Results from in vivo maturation help to develop methods of in vitro maturation to improve antibody properties for therapeutic applications, frequently combining computational and experimental approaches.

An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants

Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications for biotherapeutics, immunity, and vaccines. Through a systematic search for high-resolution structures of antibody-antigen complexes and unbound antibody and antigen structures, in conjunction with identification of experimentally determined binding affinities, we have assembled a non-redundant set of test cases for antibody-antigen docking and affinity prediction. This benchmark more than doubles the number of antibody-antigen complexes and corresponding affinities available in our previous benchmarks, providing an unprecedented view of the determinants of antibody recognition and insights into molecular flexibility. Initial assessments of docking and affinity prediction tools highlight the challenges posed by this diverse set of cases, which includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins. This dataset will enable development of advanced predictive modeling and design methods for this therapeutically relevant class of protein-protein interactions.

Structure-based engineering of pH-dependent antibody binding for selective targeting of solid-tumor microenvironment

Recent development of monoclonal antibodies as mainstream anticancer agents demands further optimization of their safety for use in humans. Potent targeting and/or effector activities on normal tissues is an obvious toxicity concern. Optimization of specific tumor targeting could be achieved by taking advantage of the extracellular acidity of solid tumors relative to normal tissues. Here, we applied a structure-based computational approach to engineer anti-human epidermal growth factor receptor 2 (Her2) antibodies with selective binding in the acidic tumor microenvironment. We used an affinity maturation platform in which dual-pH histidine-scanning mutagenesis was implemented for pH selectivity optimization. Testing of a small set of designs for binding to the recombinant Her2 ectodomain led to the identification of antigen-binding fragment (Fab) variants with the desired pH-dependent binding behavior. Binding selectivity toward acidic pH was improved by as much as 25-fold relative to the parental bH1-Fab. In vitro experiments on cells expressing intact Her2 confirmed that designed variants formatted as IgG1/k full-size antibodies have high affinity and inhibit the growth of tumor spheroids at a level comparable to that of the benchmark anti-Her2 antibody trastuzumab (Herceptin®) at acidic pH, whereas these effects were significantly reduced at physiological pH. In contrast, both Herceptin and the parental bH1 antibody exhibited strong cell binding and growth inhibition irrespective of pH. This work demonstrates the feasibility of computational optimization of antibodies for selective targeting of the acidic environment such as that found in many solid tumors.

In vitro affinity maturation to improve the efficacy of a hypoxia-inducible factor 1α single-domain intrabody

Affinity is an important property of therapeutic antibodies, so improving affinity is critical to the biological activity and clinical efficacy. An anti-HIF-1α nanobody, VHH212, was screened via a native ribosome display library with a 26.6 nM of K_D value was used as the parent. In this paper, a Venn-intersection of multi-algorithms screening (VIMAS) strategy for computer-aided binding affinity prediction was designed. Homology modeling and protein docking methods were used to substitute the need for a crystal structure. Finally, a mutant with a 17.5-fold enhancement in binding affinity (1.52 nM) was obtained by using the VIMAS strategy. Furthermore, the biological activity of mutants was verified at the cellular level. Targeting HIF-1α can sensitize PDAC (pancreatic ductal adenocarcinoma) tumors to gemcitabine, which is a potential co-treatment method for pancreatic cancer patients. Our results showed that the cytotoxicity of gemcitabine on pancreatic cancer cell lines increased with the enhanced-affinity of an intrabody under combined treatment.

mmCSM-AB: guiding rational antibody engineering through multiple point mutations

While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. While there have been many efforts to develop computational tools to guide rational antibody engineering, most approaches are of limited accuracy when applied to antibody design, and have largely been limited to analysing a single point mutation at a time. To overcome this gap, we have curated a dataset of 242 experimentally determined changes in binding affinity upon multiple point mutations in antibody-target complexes (89 increasing and 153 decreasing binding affinity). Here, we have shown that by using our graph-based signatures and atomic interaction information, we can accurately analyse the consequence of multi-point mutations on antigen binding affinity. Our approach outperformed other available tools across cross-validation and two independent blind tests, achieving Pearson's correlations of up to 0.95. We have implemented our new approach, mmCSM-AB, as a web-server that can help guide the process of affinity maturation in antibody design. mmCSM-AB is freely available at http://biosig.unimelb.edu.au/mmcsm_ab/.

Recombinant expression of nanobodies and nanobody-derived immunoreagents

Antibody fragments for which the sequence is available are suitable for straightforward engineering and expression in both eukaryotic and prokaryotic systems. When produced as fusions with convenient tags, they become reagents which pair their selective binding capacity to an orthogonal function. Several kinds of immunoreagents composed by nanobodies and either large proteins or short sequences have been designed for providing inexpensive ready-to-use biological tools. The possibility to choose among alternative expression strategies is critical because the fusion moieties might require specific conditions for correct folding or post-translational modifications. In the case of nanobody production, the trend is towards simpler but reliable (bacterial) methods that can substitute for more cumbersome processes requiring the use of eukaryotic systems. The use of these will not disappear, but will be restricted to those cases in which the final immunoconstructs must have features that cannot be obtained in prokaryotic cells. At the same time, bacterial expression has evolved from the conventional procedure which considered exclusively the nanobody and nanobody-fusion accumulation in the periplasm. Several reports show the advantage of cytoplasmic expression, surface-display and secretion for at least some applications. Finally, there is an increasing interest to use as a model the short nanobody sequence for the development of in silico methodologies aimed at optimizing the yields, stability and affinity of recombinant antibodies.

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There is an increasing request for immunoreagents based on nanobodies.

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The multiplicity of their applications requires constructs with different structural complexity.

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Alternative expression methods are necessary to achieve such structural requirements.

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In silico optimization of nanobody biophysical characteristics becomes more and more reliable.

A Novel Single Domain Antibody Targeting FliC Flagellin of Salmonella enterica for Effective Inhibition of Host Cell Invasion

The enteric pathogen, Salmonella enterica is a major cause of human gastroenteritis globally and with increasing bacterial resistance to antibiotics, alternative solutions are urgently needed. Single domain antibodies (sdAbs), the smallest antibody fragments that retain antigen binding specificity and affinity, are derived from variable heavy-chain only fragments (VHH) of camelid heavy-chain-only immunoglobulins. SdAbs typically contain a single disulfide bond simplifying recombinant protein production in microbial systems. These factors make sdAbs ideally suited for the development of effective anti-bacterial therapeutics. To this end, we generated an anti-Salmonella VHH library from which we screened for high affinity sdAbs. We present a novel sdAb (Abi-Se07) that targets the Salmonella virulence factor, FliC, required for bacterial motility and invasion of host cells. We demonstrate that Abi-Se07 bound FliC with a K_D of 16.2 ± 0.1 nM. In addition, Abi-Se07 exhibited cross-serovar binding to whole cells of S. enterica serovar Typhimurium, Heidelberg, and Hadar. Abi-Se07 significantly inhibited bacterial motility and significantly reduced S. enterica colonization in a more native environment of chicken jejunum epithelium. Taken together, we have identified a novel anti-Salmonella sdAb and discuss future efforts toward therapeutic development.

Homology Modeling-Based in Silico Affinity Maturation Improves the Affinity of a Nanobody

Affinity maturation and rational design have a raised importance in the application of nanobody (VHH), and its unique structure guaranteed these processes quickly done in vitro. An anti-CD47 nanobody, Nb02, was screened via a synthetic phage display library with 278 nM of K_D value. In this study, a new strategy based on homology modeling and Rational Mutation Hotspots Design Protocol (RMHDP) was presented for building a fast and efficient platform for nanobody affinity maturation. A three-dimensional analytical structural model of Nb02 was constructed and then docked with the antigen, the CD47 extracellular domain (CD47^ext). Mutants with high binding affinity are predicted by the scoring of nanobody-antigen complexes based on molecular dynamics trajectories and simulation. Ultimately, an improved mutant with an 87.4-fold affinity (3.2 nM) and 7.36 °C higher thermal stability was obtained. These findings might contribute to computational affinity maturation of nanobodies via homology modeling using the recent advancements in computational power. The add-in of aromatic residues which formed aromatic-aromatic interaction plays a pivotal role in affinity and thermostability improvement. In a word, the methods used in this study might provide a reference for rapid and efficient in vitro affinity maturation of nanobodies.

Binding symmetry and surface flexibility mediate antibody self-association

Solution stability is an important factor in the optimization of engineered biotherapeutic candidates such as monoclonal antibodies because of its possible effects on manufacturability, pharmacology, efficacy and safety. A detailed atomic understanding of the mechanisms governing self-association of natively folded protein monomers is required to devise predictive tools to guide screening and re-engineering along the drug development pipeline. We investigated pairs of affinity-matured full-size antibodies and observed drastically different propensities to aggregate from variants differing by a single amino-acid. Biophysical testing showed that antigen-binding fragments (Fabs) from the aggregating antibodies also reversibly associated with equilibrium dissociation constants in the low-micromolar range. Crystal structures (PDB accession codes 6MXR, 6MXS, 6MY4, 6MY5) and bottom-up hydrogen-exchange mass spectrometry revealed that Fab self-association occurs in a symmetric mode that involves the antigen complementarity-determining regions. Subtle local conformational changes incurred upon point mutation of monomeric variants foster formation of complementary polar interactions and hydrophobic contacts to generate a dimeric Fab interface. Testing of popular in silico tools generally indicated low reliabilities for predicting the aggregation propensities observed. A structure-aggregation data set is provided here in order to stimulate further improvements of in silico tools for prediction of native aggregation. Incorporation of intermolecular docking, conformational flexibility, and short-range packing interactions may all be necessary features of the ideal algorithm.

Recent advances in the treatment of C. difficile using biotherapeutic agents

Clostridium difficile (C. difficile) is rapidly becoming one of the most prevalent health care–associated bacterial infections in the developed world. The emergence of new, more virulent strains has led to greater morbidity and resistance to standard therapies. The bacterium is readily transmitted between people where it can asymptomatically colonize the gut environment, and clinical manifestations ranging from frequent watery diarrhea to toxic megacolon can arise depending on the age of the individual or their state of gut dysbiosis. Several inexpensive approaches are shown to be effective against virulent C. difficile in research settings such as probiotics, fecal microbiota transfer and immunotherapies. This review aims to highlight the current advantages and limitations of the aforementioned approaches with an emphasis on recent studies.

Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design

Antibodies are an important class of therapeutics that have significant clinical impact for the treatment of severe diseases. Computational tools to support antibody drug discovery have been developing at an increasing rate over the last decade and typically rely upon a predetermined co-crystal structure of the antibody bound to the antigen for structural predictions. Here, we show an example of successful in silico affinity maturation of a hybridoma derived antibody, AB1, using just a homology model of the antibody fragment variable region and a protein-protein docking model of the AB1 antibody bound to the antigen, murine CCL20 (muCCL20). In silico affinity maturation, together with alanine scanning, has allowed us to fine-tune the protein-protein docking model to subsequently enable the identification of two single-point mutations that increase the affinity of AB1 for muCCL20. To our knowledge, this is one of the first examples of the use of homology modelling and protein docking for affinity maturation and represents an approach that can be widely deployed.

Neutralization of Clostridium difficile toxin B with VHH-Fc fusions targeting the delivery and CROPs domains

An increasing number of antibody-based therapies are being considered for controlling bacterial infections, including Clostridium difficile by targeting toxins A and B. In an effort to develop novel C. difficile immunotherapeutics, we previously isolated several single-domain antibodies (V_HHs) capable of toxin A neutralization through recognition of the extreme C-terminal combined repetitive oligopeptides (CROPs) domain, but failed at identifying neutralizing V_HHs that bound a similar region on toxin B. Here we report the isolation of a panel of 29 V_HHs targeting at least seven unique epitopes on a toxin B immunogen composed of a portion of the central delivery domain and the entire CROPs domain. Despite monovalent affinities as high as K_D = 70 pM, none of the V_HHs tested were capable of toxin B neutralization; however, modest toxin B inhibition was observed with V_HH-V_HH dimers and to a much greater extent with V_HH-Fc fusions, reaching the neutralizing potency of the recently approved anti-toxin B monoclonal antibody bezlotoxumab in in vitro assays. Epitope binning revealed that several V_HH-Fcs bound toxin B at sites distinct from the region recognized by bezlotoxumab, while other V_HH-Fcs partially competed with bezlotoxumab for toxin binding. Therefore, the V_HHs described here are effective at toxin B neutralization when formatted as bivalent V_HH-Fc fusions by targeting toxin B at regions both similar and distinct from the bezlotoxumab binding site.

Structural basis of VHH-mediated neutralization of the food-borne pathogen Listeria monocytogenes

Listeria monocytogenes causes listeriosis, a potentially fatal food-borne disease. The condition is especially harmful to pregnant women. Listeria outbreaks can originate from diverse foods, highlighting the need for novel strategies to improve food safety. The first step in Listeria invasion is internalization of the bacteria, which is mediated by the interaction of the internalin family of virulence factors with host cell receptors. A crucial interaction for Listeria invasion of the placenta, and thus a target for therapeutic intervention, is between internalin B (InlB) and the receptor c-Met. Single-domain antibodies (V_HH, also called nanobodies, or sdAbs) from camel heavy-chain antibodies are a novel solution for preventing Listeria infections. The V_HH R303, R330, and R326 all bind InlB with high affinity; however, the molecular mechanism behind their mode of action was unknown. We demonstrate that despite a high degree of sequence and structural diversity, the V_HH bind a single epitope on InlB. A combination of gentamicin protection assays and florescent microscopy establish that InlB-specific V_HH inhibit Listeria invasion of HeLa cells. A high-resolution X-ray structure of V_HH R303 in complex with InlB showed that the V_HH binds at the c-Met interaction site on InlB, thereby acting as a competitive inhibitor preventing bacterial invasion. These results point to the potential of V_HH as a novel class of therapeutics for the prevention of listeriosis.

Application of Antibody-Mediated Therapy for Treatment and Prevention of Clostridium difficile Infection

Clostridium difficile causes antibiotic- and healthcare-associated diarrhea, which is characterized by a high mortality rate (5–15%) and high recurrence rate of 20% or more. Therapeutic alternatives to antibiotics are urgently needed to improve the overall cure rate. Among these, therapeutic antibodies have shown promising results in clinical studies. Herein, the authors review current monoclonal and polyclonal anti- C. difficile antibodies that have entered the clinical development stage, either for systemic administration or by the oral route. The antibodies can be applied as monotherapy or in combination with standard-of-care to treat an infection with C. difficile or to protect from a recurrence. Bezlotoxumab is the first antibody for secondary prevention of recurrence of C. difficile infection approved by the regulatory agencies in US and Europe. The human monoclonal antibody is administered systemically to patients receiving oral standard-of–care antibiotics. Other antibodies are currently in the clinical pipeline, and some are intended for oral use. They show a good safety profile, high efficacy and low production costs, and can be considered promising therapies of the future. The most promising orally administered drug candidate is a bovine antibody from hyperimmune colostral milk, which is in an advanced clinical development stage. Which antibody will enter the market is dependent on its bioavailability at the site of infection as well as its activity against C. difficile toxins, protection against colonization and possible action on spore formation. The antibody must demonstrate a clear benefit in comparison with other available treatment options to be considered for use by clinicians.