RosettaAntibody: antibody variable region homology modeling server

Discovery of a pan anti-SARS-CoV-2 monoclonal antibody with highly efficient infected cell killing capacity for novel immunotherapeutic approaches

Unlocking the potential of broadly reactive coronavirus monoclonal antibodies (mAbs) and their derivatives offers a transformative therapeutic avenue against severe COVID-19, especially crucial for safeguarding high-risk populations. Novel mAb-based immunotherapies may help address the reduced efficacy of current vaccines and neutralizing mAbs caused by the emergence of variants of concern (VOCs). Using phage display technology, we discovered a pan-SARS-CoV-2 mAb (C10) that targets a conserved region within the receptor-binding domain (RBD) of the virus. Noteworthy, C10 demonstrates exceptional efficacy in recognizing all assessed VOCs, including recent Omicron variants. While C10 lacks direct neutralization capacity, it efficiently binds to infected lung epithelial cells and induces their lysis via natural killer (NK) cell-mediated antibody-dependent cellular cytotoxicity (ADCC). Building upon this pan-SARS-CoV-2 mAb, we engineered C10-based, Chimeric Antigen Receptor (CAR)-T cells endowed with efficient killing capacity against SARS-CoV-2-infected lung epithelial cells. Notably, NK and CAR-T-cell mediated killing of lung infected cells effectively reduces viral titers. These findings highlight the potential of non-neutralizing mAbs in providing immune protection against emerging infectious diseases. Our work reveals a pan-SARS-CoV-2 mAb effective in targeting infected cells and demonstrates the proof-of-concept for the potential application of CAR-T cell therapy in combating SARS-CoV-2 infections. Furthermore, it holds promise for the development of innovative antibody-based and cell-based therapeutic strategies against severe COVID-19 by expanding the array of therapeutic options available for high-risk populations.Trial registration: ClinicalTrials.gov identifier: NCT04093596.

AI-augmented physics-based docking for antibody-antigen complex prediction

MOTIVATION

Predicting the structure of antibody-antigen complexes is a challenging task with significant implications for the design of better antibody therapeutics. However, the levels of success have remained dauntingly low, particularly when high standards for model quality are required, a necessity for efficient antibody design. Artificial intelligence (AI) has significantly impacted the landscape of structure prediction for antibodies, both alone and in complex with their antigens.

METHODS

We utilized AI-guided antibody modeling tools to generate ensembles displaying diversity in the complementarity-determining region (CDR) and integrated those into our previously published AlphaFold2-rescored docking pipeline, a strategy called AI-augmented physics-based docking. In this study, we also compare docking performance with AlphaFold and Boltz-1, the new state-of-the-art. We distinguish between two types of success tailored to specific downstream applications: (i) criteria sufficient for epitope mapping, where gross quality is adequate and can complement experimental techniques, and (ii) criteria for producing higher-quality models suitable for engineering purposes.

RESULTS

We highlight that the quality of the ensemble is crucial for docking performance, that including too many models can be detrimental, and that prioritization of models is essential for achieving good performance. In a scenario analogous to docking using a crystallized antigen, our results robustly demonstrate the advantages of AI-augmented docking over AlphaFold2, further accentuated when higher standards in quality are imposed. Docking also shows improvements over Boltz-1, but those are less pronounced. Docking performance is still noticeably lower than AlphaFold3 in both epitope mapping and antibody design use cases. We observe a strong dependence on CDR-H3 loop length for physics-based tools on their ability to successfully predict. This helps define an applicability range where physics-based docking can be competitive to the newer generation of AI tools.

AVAILABILITY AND IMPLEMENTATION

The AF2 rescoring scripts are available at github.com/gaudreaultfnrc/AF2-Rescoring.

An outlook on structural biology after AlphaFold: tools, limits and perspectives

AlphaFold and similar groundbreaking, AI-based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab-initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI-based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI-driven advancements. Indeed, user-driven intervention is still as pivotal in the structure prediction process as in complementing state-of-the-art algorithms with functional and biological knowledge.

AlphaFold and Docking Approaches for Antibody-Antigen and Other Targets: Insights From CAPRI Rounds 47-55

Accurate modeling of the structures of protein-protein complexes and other biomolecular interactions represents a longstanding and important challenge for computational biology. The Critical Assessment of PRedicted Interactions (CAPRI) experiment has served for over two decades as a key means to assess and compare current approaches and methods through blind predictive scenarios, highlighting useful strategies, and new developments. Here we describe the performance of our laboratory's team in recent CAPRI rounds, which included submissions for 10 modeling targets. Our team utilized a range of docking and modeling approaches, including ZDOCK, Rosetta, and ZRANK, to model, refine, and score protein-protein and protein-DNA complexes. For recent targets we utilized adaptations of AlphaFold to generate models, leading to near-native models for an antibody-peptide target, and a highly accurate (but low ranked) model for an antibody-MHC complex. These results underscore the utility of AlphaFold-based protocols for predictive protein complex modeling, including for immune recognition, and highlight considerations regarding the use of AlphaFold confidence metrics in model selection.

Molecular Modeling Methods in the Development of Affine and Specific Protein-Binding Agents

High-affinity and specific agents are widely applied in various areas, including diagnostics, scientific research, and disease therapy (as drugs and drug delivery systems). It takes significant time to develop them. For this reason, development of high-affinity agents extensively utilizes computer methods at various stages for the analysis and modeling of these molecules. The review describes the main affinity and specific agents, such as monoclonal antibodies and their fragments, antibody mimetics, aptamers, and molecularly imprinted polymers. The methods of their obtaining as well as their main advantages and disadvantages are briefly described, with special attention focused on the molecular modeling methods used for their analysis and development.

Human antibodies in Mexico and Brazil neutralizing tick-borne flaviviruses

Flaviviruses such as dengue virus (DENV), Zika virus (ZIKV), and yellow fever virus (YFV) are spread by mosquitoes and cause human disease and mortality in tropical areas. In contrast, Powassan virus (POWV), which causes severe neurologic illness, is a flavivirus transmitted by ticks in temperate regions of the Northern hemisphere. We find serologic neutralizing activity against POWV in individuals living in Mexico and Brazil. Monoclonal antibodies P002 and P003, which were derived from a resident of Mexico (where POWV is not reported), neutralize POWV lineage I by recognizing an epitope on the virus envelope domain III (EDIII) that is shared with a broad range of tick- and mosquito-borne flaviviruses. Our findings raise the possibility that POWV, or a flavivirus closely related to it, infects humans in the tropics.

Revisiting Protein-Copolymer Binding Mechanisms: Insights beyond the "Lock-and-Key" Model

The "lock-and-key" model that emphasizes the concept of chemical-structural complementary is the key mechanism for explaining the selectivity between small ligands and a larger adsorbent molecule. In this work, concerning the copolymer chain using only the combination of N-isopropylacrylamide (NIPAm) and hydrophobic N-tert-butylacrylamide (TBAm) monomers and by large-scale atomistic molecular dynamics simulations, our results show that the flexible copolymer chain may exhibit strong binding affinity for the biomarker protein epithelial cell adhesion molecule, in the absence of hydrophobic matching and strong structural complementarity. This surprising binding behavior, which cannot be anticipated by the "lock-and-key" model, can be attributed to the preferential interactions established by the copolymer with the protein's hydrophilic exterior. We observe that increasing the fraction of incorporated TBAm monomers leads to a prevalence of interactions with asparagine and glutamine amino acids due to the emerging hydrogen bonding with both NIPAm and TBAm monomers. Our findings suggest the appearance of highly specific and high-affinity binding sites on the protein created by engineering the copolymer composition, which motivates the applications of copolymers as protein affinity reagents.

Computational optimization of antibody humanness and stability by systematic energy-based ranking

Conventional methods for humanizing animal-derived antibodies involve grafting their complementarity-determining regions onto homologous human framework regions. However, this process can substantially lower antibody stability and antigen-binding affinity, and requires iterative mutational fine-tuning to recover the original antibody properties. Here we report a computational method for the systematic grafting of animal complementarity-determining regions onto thousands of human frameworks. The method, which we named CUMAb (for computational human antibody design; available at http://CUMAb.weizmann.ac.il ), starts from an experimental or model antibody structure and uses Rosetta atomistic simulations to select designs by energy and structural integrity. CUMAb-designed humanized versions of five antibodies exhibited similar affinities to those of the parental animal antibodies, with some designs showing marked improvement in stability. We also show that (1) non-homologous frameworks are often preferred to highest-homology frameworks, and (2) several CUMAb designs that differ by dozens of mutations and that use different human frameworks are functionally equivalent.

Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects

Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.

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.

Human-murine chimeric autoantibodies with high affinity and specificity for systemic sclerosis

Scleroderma 70 (Scl-70) is commonly used in the clinic for aiding systemic sclerosis (SSc) diagnosis due to its recognition as autoantibodies in the serum of SSc patients. However, obtaining sera positive for anti-Scl-70 antibody can be challenging; therefore, there is an urgent need to develop a specific, sensitive, and easily available reference for SSc diagnosis. In this study, murine-sourced scFv library were screened by phage display technology against human Scl-70, and the scFvs with high affinity were constructed into humanized antibodies for clinical application. Finally, ten high-affinity scFv fragments were obtained. Three fragments (2A, 2AB, and 2HD) were select for humanization. The physicochemical properties of the amino acid sequence, three-dimensional structural basis, and electrostatic potential distribution of the protein surface of different scFv fragments revealed differences in the electrostatic potential of their CDR regions determined their affinity for Scl-70 and expression. Notably, the specificity test showed the half-maximal effective concentration values of the three humanized antibodies were lower than that of positive patient serum. Moreover, these humanized antibodies showed high specificity for Scl-70 in diagnostic immunoassays for ANA. Among these three antibodies, 2A exhibited most positive electrostatic potential on the surface of the CDRs and highest affinity and specificity for Scl-70 but with least expression level; thus, it may provide new foundations for developing enhanced diagnostic strategies for SSc.

Computational and artificial intelligence-based methods for antibody development

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.

Binding affinity prediction for antibody-protein antigen complexes: A machine learning analysis based on interface and surface areas

Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibody‒protein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for protein‒protein binding affinity usually fail to predict the binding affinity of an antibody‒protein antigen complex with a comparable level of accuracy. Here, new models specific for antibody‒antigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibody‒antigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibody‒protein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibody‒protein antigen binding affinity prediction are superior to the previously-used general models for predicting the protein‒protein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibody‒protein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibody‒protein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics.

NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning

Antibodies are a rapidly growing class of therapeutics. Recently, single domain camelid VHH antibodies, and their recognition nanobody domain (Nb) appeared as a cost-effective highly stable alternative to full-length antibodies. There is a growing need for high-throughput epitope mapping based on accurate structural modeling of the variable domains that share a common fold and differ in the Complementarity Determining Regions (CDRs). We develop a deep learning end-to-end model, NanoNet, that given a sequence directly produces the 3D coordinates of the backbone and Cβ atoms of the entire VH domain. For the Nb test set, NanoNet achieves 3.16Å average RMSD for the most variable CDR3 loops and 2.65Å, 1.73Å for the CDR1, CDR2 loops, respectively. The accuracy for antibody VH domains is even higher: 2.38Å RMSD for CDR3 and 0.89Å, 0.96Å for the CDR1, CDR2 loops, respectively. NanoNet run times allow generation of ∼1M nanobody structures in less than 4 hours on a standard CPU computer enabling high-throughput structure modeling. NanoNet is available at GitHub: https://github.com/dina-lab3D/NanoNet.

Recent Progress in the Discovery and Development of Monoclonal Antibodies against Viral Infections

Monoclonal antibodies (mAbs), the new revolutionary class of medications, are fast becoming tools against various diseases thanks to a unique structure and function that allow them to bind highly specific targets or receptors. These specialized proteins can be produced in large quantities via the hybridoma technique introduced in 1975 or by means of modern technologies. Additional methods have been developed to generate mAbs with new biological properties such as humanized, chimeric, or murine. The inclusion of mAbs in therapeutic regimens is a major medical advance and will hopefully lead to significant improvements in infectious disease management. Since the first therapeutic mAb, muromonab-CD3, was approved by the U.S. Food and Drug Administration (FDA) in 1986, the list of approved mAbs and their clinical indications and applications have been proliferating. New technologies have been developed to modify the structure of mAbs, thereby increasing efficacy and improving delivery routes. Gene delivery technologies, such as non-viral synthetic plasmid DNA and messenger RNA vectors (DMabs or mRNA-encoded mAbs), built to express tailored mAb genes, might help overcome some of the challenges of mAb therapy, including production restrictions, cold-chain storage, transportation requirements, and expensive manufacturing and distribution processes. This paper reviews some of the recent developments in mAb discovery against viral infections and illustrates how mAbs can help to combat viral diseases and outbreaks.

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery

Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody-antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.

Key aspects of the past 30 years of protein design

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

VHH Structural Modelling Approaches: A Critical Review

VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.

Design of peptides with high affinity binding to a monoclonal antibody as a basis for immunotherapy

About half of the US population is sensitized to one or more allergens, as found by a National Health and Nutrition Examination Survey (NHANES). The most common treatment for seasonal allergic responses is the daily use of oral antihistamines, which can control some of the symptoms, but are not effective for nasal congestion, and can be debilitating in many patients. Peptide immunotherapy is a promising new approach to treat allergic airway diseases. The small size of the immunogens cannot lead to an unwanted allergic reaction in sensitized patients, and the production of peptides with sufficient amounts for immunotherapy is time- and cost-effective. However, it is not known what peptides are the most effective for an immunotherapy of allergens. We previously produced a unique monoclonal antibody (mAb) E58, which can inhibit the binding of multiple groups of mAbs and human IgEs from patients affected by the major group 1 allergens of ragweed (Amb a 1) and conifer pollens (Jun a 1, Cup s 1, and Cry j 1). Here, we demonstrated that a combined approach, starting from two linear E58 epitopes of the tree pollen allergen Jun a 1 and the ragweed pollen allergen Amb a 1, and residue modifications suggested by molecular docking calculations and peptide design could identify a large number of high affinity binding peptides. We propose that this combined experimental and computational approach by structural analysis of linear IgE epitopes and peptide design, can lead to potential new candidates for peptide immunotherapy.

Molecular computations of preferential interactions of proline, arginine.HCl, and NaCl with IgG1 antibodies and their impact on aggregation and viscosity

Preferential interactions of excipients with the antibody surface govern their effect on the stability of antibodies in solution. We probed the preferential interactions of proline, arginine.HCl (Arg.HCl), and NaCl with three therapeutically relevant IgG1 antibodies via experiment and simulation. With simulations, we examined how excipients interacted with different types of surface patches in the variable region (Fv). For example, proline interacted most strongly with aromatic surfaces, Arg.HCl was included near negative residues, and NaCl was excluded from negative residues and certain hydrophobic regions. The differences in interaction of different excipients with the same surface patch on an antibody may be responsible for variations in the antibody's aggregation, viscosity, and self-association behaviors in each excipient. Proline reduced self-association for all three antibodies and reduced aggregation for the antibody with an association-limited aggregation mechanism. The effects of Arg.HCl and NaCl on aggregation and viscosity were highly dependent on the surface charge distribution and the extent of exclusion from highly hydrophobic patches. At pH 5.5, both tended to increase the aggregation of an antibody with a strongly positive charge on the Fv, while only NaCl reduced the aggregation of the antibody with a large negative charge patch on the Fv. Arg.HCl reduced the viscosities of antibodies with either a hydrophobicity-driven mechanism or a charge-driven mechanism. Analysis of this data presents a framework for understanding how amino acid and ionic excipients interact with different protein surfaces, and how these interactions translate to the observed stability behavior.

Human-likeness of antibody biologics determined by back-translation and comparison with large antibody variable gene repertoires

The antibody (Ab) germline gene rearrangement of variable (V), diversity (D), and joining (J) gene segments, as well as somatic hypermutation, give rise to the human Ab variable gene sequence repertoire. It is common to characterize single nucleotide frequencies of the variable region by alignment to species-specific wildtype germline genes. The increasing application of next-generation sequencing to immune repertoire studies has led to the compilation of increasing large adaptive immunome receptor repertoire datasets. We have developed a method that maps the sequence of a target Ab onto an immunome dataset of 326 million human Ab sequences. For this purpose, we created a position- and gene-specific scoring matrix (PGSSM) and its corresponding antibody similarity score. We characterized our PGSSM score and found that it strongly correlated with the phylogenetic distance of 181,355 Ab sequences from GenBank across 20 species. The most likely human nucleotide back-translation was obtained given only PGSSMs and the amino acid sequence of an Ab achieving a nucleotide sequence recovery of 95.9% and 97.2% for human heavy and light chains, respectively. In conclusion, the scoring of our back-translation is a valuable estimate for the similarity of an Ab sequence to the natural human repertoire. As expected, Ab therapeutic molecules developed from a human source showed a higher similarity to the repertoire than engineered Abs. Thus, the PGSSM metric introduced here can be used to engineer human-like Ab therapeutics.

Functional and in silico Characterization of Neutralizing Interactions Between Antibodies and the Foot-and-Mouth Disease Virus Immunodominant Antigenic Site

Molecular knowledge of virus–antibody interactions is essential for the development of better vaccines and for a timely assessment of the spread and severity of epidemics. For foot-and-mouth disease virus (FMDV) research, in particular, computational methods for antigen–antibody (Ag–Ab) interaction, and cross-antigenicity characterization and prediction are critical to design engineered vaccines with robust, long-lasting, and wider response against different strains. We integrated existing structural modeling and prediction algorithms to study the surface properties of FMDV Ags and Abs and their interaction. First, we explored four modeling and two Ag–Ab docking methods and implemented a computational pipeline based on a reference Ag–Ab structure for FMDV of serotype C, to be used as a source protocol for the study of unknown interaction pairs of Ag–Ab. Next, we obtained the variable region sequence of two monoclonal IgM and IgG antibodies that recognize and neutralize antigenic site A (AgSA) epitopes from South America serotype A FMDV and developed two peptide ELISAs for their fine epitope mapping. Then, we applied the previous Ag–Ab molecular structure modeling and docking protocol further scored by functional peptide ELISA data. This work highlights a possible different behavior in the immune response of IgG and IgM Ab isotypes. The present method yielded reliable Ab models with differential paratopes and Ag interaction topologies in concordance with their isotype classes. Moreover, it demonstrates the applicability of computational prediction techniques to the interaction phenomena between the FMDV immunodominant AgSA and Abs, and points out their potential utility as a metric for virus-related, massive Ab repertoire analysis or as a starting point for recombinant vaccine design.

Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.

DFC, Medina E, Sinche F, Santiago Vispo N, Dahoumane SA, Alexis F. Biomedical Science to Tackle the COVID-19 Pandemic: Current Status and Future Perspectives

The coronavirus infectious disease (COVID-19) pandemic emerged at the end of 2019, and was caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which has resulted in an unprecedented health and economic crisis worldwide. One key aspect, compared to other recent pandemics, is the level of urgency, which has started a race for finding adequate answers. Solutions for efficient prevention approaches, rapid, reliable, and high throughput diagnostics, monitoring, and safe therapies are needed. Research across the world has been directed to fight against COVID-19. Biomedical science has been presented as a possible area for combating the SARS-CoV-2 virus due to the unique challenges raised by the pandemic, as reported by epidemiologists, immunologists, and medical doctors, including COVID-19's survival, symptoms, protein surface composition, and infection mechanisms. While the current knowledge about the SARS-CoV-2 virus is still limited, various (old and new) biomedical approaches have been developed and tested. Here, we review the current status and future perspectives of biomedical science in the context of COVID-19, including nanotechnology, prevention through vaccine engineering, diagnostic, monitoring, and therapy. This review is aimed at discussing the current impact of biomedical science in healthcare for the management of COVID-19, as well as some challenges to be addressed.

Solubility, Stability, and Avidity of Recombinant Antibody Fragments Expressed in Microorganisms

Solubility of recombinant proteins (i.e., the extent of soluble versus insoluble expression in heterogeneous hosts) is the first checkpoint criterion for determining recombinant protein quality. However, even soluble proteins often fail to represent functional activity because of the involvement of non-functional, misfolded, soluble aggregates, which compromise recombinant protein quality. Therefore, screening of solubility and folding competence is crucial for improving the quality of recombinant proteins, especially for therapeutic applications. The issue is often highlighted especially in bacterial recombinant hosts, since bacterial cytoplasm does not provide an optimal environment for the folding of target proteins of mammalian origin. Antibody fragments, such as single-chain variable fragment (scFv), single-chain antibody (scAb), and fragment antigen binding (Fab), have been utilized for numerous applications such as diagnostics, research reagents, or therapeutics. Antibody fragments can be efficiently expressed in microorganisms so that they offer several advantages for diagnostic applications such as low cost and high yield. However, scFv and scAb fragments have generally lower stability to thermal stress than full-length antibodies, necessitating a judicious combination of designer antibodies, and bacterial hosts harnessed with robust chaperone function. In this review, we discuss efforts on not only the production of antibodies or antibody fragments in microorganisms but also scFv stabilization via (i) directed evolution of variants with increased stability using display systems, (ii) stabilization of the interface between variable regions of heavy (V_H) and light (V_L) chains through the introduction of a non-native covalent bond between the two chains, (iii) rational engineering of V_H-V_L pair, based on the structure, and (iv) computational approaches. We also review recent advances in stability design, increase in avidity by multimerization, and maintaining the functional competence of chimeric proteins prompted by various types of chaperones.

Methods for sequence and structural analysis of B and T cell receptor repertoires

B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.

Learning context-aware structural representations to predict antigen and antibody binding interfaces

MOTIVATION

Understanding how antibodies specifically interact with their antigens can enable better drug and vaccine design, as well as provide insights into natural immunity. Experimental structural characterization can detail the 'ground truth' of antibody-antigen interactions, but computational methods are required to efficiently scale to large-scale studies. To increase prediction accuracy as well as to provide a means to gain new biological insights into these interactions, we have developed a unified deep learning-based framework to predict binding interfaces on both antibodies and antigens.

RESULTS

Our framework leverages three key aspects of antibody-antigen interactions to learn predictive structural representations: (i) since interfaces are formed from multiple residues in spatial proximity, we employ graph convolutions to aggregate properties across local regions in a protein; (ii) since interactions are specific between antibody-antigen pairs, we employ an attention layer to explicitly encode the context of the partner; (iii) since more data are available for general protein-protein interactions, we employ transfer learning to leverage this data as a prior for the specific case of antibody-antigen interactions. We show that this single framework achieves state-of-the-art performance at predicting binding interfaces on both antibodies and antigens, and that each of its three aspects drives additional improvement in the performance. We further show that the attention layer not only improves performance, but also provides a biologically interpretable perspective into the mode of interaction.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available on github at https://github.com/vamships/PECAN.git.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

DaReUS-Loop: a web server to model multiple loops in homology models

Loop regions in protein structures often have crucial roles, and they are much more variable in sequence and structure than other regions. In homology modeling, this leads to larger deviations from the homologous templates, and loop modeling of homology models remains an open problem. To address this issue, we have previously developed the DaReUS-Loop protocol, leading to significant improvement over existing methods. Here, a DaReUS-Loop web server is presented, providing an automated platform for modeling or remodeling loops in the context of homology models. This is the first web server accepting a protein with up to 20 loop regions, and modeling them all in parallel. It also provides a prediction confidence level that corresponds to the expected accuracy of the loops. DaReUS-Loop facilitates the analysis of the results through its interactive graphical interface and is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/DaReUS-Loop/.

AbPredict 2: a server for accurate and unstrained structure prediction of antibody variable domains

SUMMARY

Methods for antibody structure prediction rely on sequence homology to experimentally determined structures. Resulting models may be accurate but are often stereochemically strained, limiting their usefulness in modeling and design workflows. We present the AbPredict 2 web-server, which instead of using sequence homology, conducts a Monte Carlo-based search for low-energy combinations of backbone conformations to yield accurate and unstrained antibody structures.

AVAILABILITY AND IMPLEMENTATION

We introduce several important improvements over the previous AbPredict implementation: (i) backbones and sidechains are now modeled using ideal bond lengths and angles, substantially reducing stereochemical strain, (ii) sampling of the rigid-body orientation at the light-heavy chain interface is improved, increasing model accuracy and (iii) runtime is reduced 20-fold without compromising accuracy, enabling the implementation of AbPredict 2 as a fully automated web-server (http://abpredict.weizmann.ac.il). Accurate and unstrained antibody model structures may in some cases obviate the need for experimental structures in antibody optimization workflows.

ASAP-SML: An antibody sequence analysis pipeline using statistical testing and machine learning

Antibodies are capable of potently and specifically binding individual antigens and, in some cases, disrupting their functions. The key challenge in generating antibody-based inhibitors is the lack of fundamental information relating sequences of antibodies to their unique properties as inhibitors. We develop a pipeline, Antibody Sequence Analysis Pipeline using Statistical testing and Machine Learning (ASAP-SML), to identify features that distinguish one set of antibody sequences from antibody sequences in a reference set. The pipeline extracts feature fingerprints from sequences. The fingerprints represent germline, CDR canonical structure, isoelectric point and frequent positional motifs. Machine learning and statistical significance testing techniques are applied to antibody sequences and extracted feature fingerprints to identify distinguishing feature values and combinations thereof. To demonstrate how it works, we applied the pipeline on sets of antibody sequences known to bind or inhibit the activities of matrix metalloproteinases (MMPs), a family of zinc-dependent enzymes that promote cancer progression and undesired inflammation under pathological conditions, against reference datasets that do not bind or inhibit MMPs. ASAP-SML identifies features and combinations of feature values found in the MMP-targeting sets that are distinct from those in the reference sets.

Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction

Antigen binding by antibodies requires precise orientation of the complementarity- determining region (CDR) loops in the variable domain to establish the correct contact surface. Members of the family Camelidae have a modified form of immunoglobulin gamma (IgG) with only heavy chains, called Heavy Chain only Antibodies (HCAb). Antigen binding in HCAbs is mediated by only three CDR loops from the single variable domain (V_HH) at the N-terminus of each heavy chain. This feature of the V_HH, along with their other important features, e.g., easy expression, small size, thermo-stability and hydrophilicity, made them promising candidates for therapeutics and diagnostics. Thus, to design better V_HH domains, it is important to thoroughly understand their sequence and structure characteristics and relationship. In this study, sequence characteristics of V_HH domains have been analysed in depth, along with their structural features using innovative approaches, namely a structural alphabet. An elaborate summary of various studies proposing structural models of V_HH domains showed diversity in the algorithms used. Finally, a case study to elucidate the differences in structural models from single and multiple templates is presented. In this case study, along with the above-mentioned aspects of V_HH, an exciting view of various factors in structure prediction of V_HH, like template framework selection, is also discussed.

Computer-based Engineering of Thermostabilized Antibody Fragments

We used the molecular modeling program Rosetta to identify clusters of amino acid substitutions in antibody fragments (scFvs and scAbs) that improve global protein stability and resistance to thermal deactivation. Using this methodology, we increased the melting temperature (T_m) and resistance to heat treatment of an antibody fragment that binds to the Clostridium botulinum hemagglutinin protein (anti-HA33). Two designed antibody fragment variants with two amino acid replacement clusters, designed to stabilize local regions, were shown to have both higher T_m compared to the parental scFv and importantly, to retain full antigen binding activity after 2 hours of incubation at 70 °C. The crystal structure of one thermostabilized scFv variants was solved at 1.6 Å and shown to be in close agreement with the RosettaAntibody model prediction.

Development of the clinical candidate PBD-C06, a humanized pGlu3-Aβ-specific antibody against Alzheimer's disease with reduced complement activation

In clinical trials with early Alzheimer's patients, administration of anti-amyloid antibodies reduced amyloid deposits, suggesting that immunotherapies may be promising disease-modifying interventions against Alzheimer's disease (AD). Specific forms of amyloid beta (Aβ) peptides, for example post-translationally modified Aβ peptides with a pyroglutamate at the N-terminus (pGlu3, pE3), are attractive antibody targets, due to pGlu3-Aβ's neo-epitope character and its propensity to form neurotoxic oligomeric aggregates. We have generated a novel anti-pGlu3-Aβ antibody, PBD-C06, which is based on a murine precursor antibody that binds with high specificity to pGlu3-Aβ monomers, oligomers and fibrils, including mixed aggregates of unmodified Aβ and pGlu3-Aβ peptides. PBD-C06 was generated by first grafting the murine antigen binding sequences onto suitable human variable light and heavy chains. Subsequently, the humanized antibody was de-immunized and site-specific mutations were introduced to restore original target binding, to eliminate complement activation and to improve protein stability. PBD-C06 binds with the same specificity and avidity as its murine precursor antibody and elimination of C1q binding did not compromise Fcγ-receptor binding or in vitro phagocytosis. Thus, PBD-C06 was specifically designed to target neurotoxic aggregates and to avoid complement-mediated inflammatory responses, in order to lower the risk for vasogenic edemas in the clinic.

Antibody Structure and Function: The Basis for Engineering Therapeutics

Antibodies and antibody-derived macromolecules have established themselves as the mainstay in protein-based therapeutic molecules (biologics). Our knowledge of the structure–function relationships of antibodies provides a platform for protein engineering that has been exploited to generate a wide range of biologics for a host of therapeutic indications. In this review, our basic understanding of the antibody structure is described along with how that knowledge has leveraged the engineering of antibody and antibody-related therapeutics having the appropriate antigen affinity, effector function, and biophysical properties. The platforms examined include the development of antibodies, antibody fragments, bispecific antibody, and antibody fusion products, whose efficacy and manufacturability can be improved via humanization, affinity modulation, and stability enhancement. We also review the design and selection of binding arms, and avidity modulation. Different strategies of preparing bispecific and multispecific molecules for an array of therapeutic applications are included.

Molecular Computations of Preferential Interaction Coefficients of IgG1 Monoclonal Antibodies with Sorbitol, Sucrose, and Trehalose and the Impact of These Excipients on Aggregation and Viscosity

Preferential interactions of formulation excipients govern their overall interactions with protein molecules, and molecular dynamics simulations allow for the examination of the interactions at the molecular level. We used molecular dynamics simulations to examine the interactions of sorbitol, sucrose, and trehalose with three different IgG1 antibodies to gain insight into how these excipients impact aggregation and viscosity. We found that sucrose and trehalose reduce aggregation more than sorbitol because of their larger size and their stronger interactions with high-spatial aggregation propensity residues compared to sorbitol. Two of the antibodies had high viscosity in sodium acetate buffer, and for these, we found that sucrose and trehalose tended to have opposite effects on viscosity. The data presented here provide further insight into the mechanisms of interactions of these three carbohydrate excipients with the antibody surface and thus their impact on excipient stabilization of antibody formulations.

PIGSPro: prediction of immunoGlobulin structures v2

PIGSpro is a significant upgrade of the popular PIGS server for the prediction of the structure of immunoglobulins. The software has been completely rewritten in python following a similar pipeline as in the original method, but including, at various steps, relevant modifications found to improve its prediction accuracy, as demonstrated here. The steps of the pipeline include the selection of the appropriate framework for predicting the conserved regions of the molecule by homology; the target template alignment for this portion of the molecule; the selection of the main chain conformation of the hypervariable loops according to the canonical structure model, the prediction of the third loop of the heavy chain (H3) for which complete canonical structures are not available and the packing of the light and heavy chain if derived from different templates. Each of these steps has been improved including updated methods developed along the years. Last but not least, the user interface has been completely redesigned and an automatic monthly update of the underlying database has been implemented. The method is available as a web server at http://biocomputing.it/pigspro.

AbRSA: A robust tool for antibody numbering

The remarkable progress in cancer immunotherapy in recent years has led to the heat of great development for therapeutic antibodies. Antibody numbering, which standardizes a residue index at each position of an antibody variable domain, is an important step in immunoinformatic analysis. It provides an equivalent index for the comparison of sequences or structures, which is particularly valuable for antibody modeling and engineering. However, due to the extremely high diversity of antibody sequences, antibody-numbering tools cannot work in all cases. This article introduces a new antibody-numbering tool named AbRSA, which integrates heuristic knowledge of region-specific features into sequence mapping to enhance the robustness. The benchmarks demonstrate that, AbRSA exhibits robust performance in numbering sequences with diverse lengths and patterns compared with the state-of-the-art tools. AbRSA offers a user-friendly interface for antibody numbering, complementarity-determining region delimitation, and 3D structure rendering. It is freely available at http://cao.labshare.cn/AbRSA.

Humanization and Simultaneous Optimization of Monoclonal Antibody

Antibody humanization is an essential technology for reducing the potential risk of immunogenicity associated with animal-derived antibodies and has been applied to a majority of the therapeutic antibodies on the market. For developing an antibody molecule as a pharmaceutical at the current biotechnology level, however, other properties also have to be considered in parallel with humanization in antibody generation and optimization. This section describes the critical properties of therapeutic antibodies that should be sufficiently qualified, including immunogenicity, binding affinity, physicochemical stability, expression in host cells and pharmacokinetics, and the basic methodologies of antibody engineering involved. By simultaneously optimizing the antibody molecule in light of these properties, it should prove possible to shorten the research and development period necessary to identify a highly qualified clinical candidate and consequently accelerate the start of the clinical trial.

Plant-derived chimeric antibodies inhibit the invasion of human fibroblasts by Toxoplasma gondii

The parasite Toxoplasma gondii causes an opportunistic infection, that is, particularly severe in immunocompromised patients, infants, and neonates. Current antiparasitic drugs are teratogenic and cause hypersensitivity-based toxic side effects especially during prolonged treatment. Furthermore, the recent emergence of drug-resistant toxoplasmosis has reduced the therapeutic impact of such drugs. In an effort to develop recombinant antibodies as a therapeutic alternative, a panel of affinity-matured, T. gondii tachyzoite-specific single-chain variable fragment (scFv) antibodies was selected by phage display and bioinformatic analysis. Further affinity optimization was attempted by introducing point mutations at hotspots within light chain complementarity-determining region 2. This strategy yielded four mutated scFv sequences and a parental scFv that were used to produce five mouse-human chimeric IgGs in Nicotiana benthamiana plants, with yields of 33-72 mg/kg of plant tissue. Immunological analysis confirmed the specific binding of these plant-derived antibodies to T. gondii tachyzoites, and in vitro efficacy was demonstrated by their ability to inhibit the invasion of human fibroblasts and impair parasite infectivity. These novel recombinant antibodies could therefore be suitable for the development of plant-derived immunotherapeutic interventions against toxoplasmosis.

A single epitope of Epstein-Barr Virus stimulate IgG production in mice

Background

Epstein-Barr virus (EBV) is closely associated with the high incidence of nasopharyngeal carcinoma in worldwide. Vaccination is one strategy with the potential to prevent the occurrence of EBV-associated cancers, but a suitable vaccine is yet to be licensed. Much vaccine development research focuses on the GP350/220 protein of EBV as it contains an immunogenic epitope at residues 147–165, which efficiently stimulates IgG production in vitro. We examined the ability of this epitope (EBVepitope) to induce IgG production in mice.

Methods

The antibody binding pattern of the epitope was analyzed using bioinformatics tools. The IgG production in mice were examined by FACS Calibur™ Flow cytometer.

Results

The epitope bound the 72A1 monoclonal antibody at the same site as GP350/220 protein, indicating that the epitope should stimulate B cells to produce antibody. Moreover, in vivo administration of EBVepitope successfully induced IgG expression from B cells, compared with controls. Further investigation indicated that the relative number of B cells expressing IgE in EBVepitope-treated mice was lower than controls.

Conclusions

Our data suggest that this EBV GP350 epitope is able to induce IgG expression in vivo without causing allergic reactions, and represents a potential EBV vaccine candidate.

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Single EBV epitope adequate stimulate production of IgG in Mice.

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EBVepitope has similarity structure and binding pattern on Antibody compare with GP350/220 protein.

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EBVepitope does not stimulate IgE production in Mice.

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EBVepitope is warrant for further investigation to develop safe EBV vaccine.

In Silico Methods in Antibody Design

Antibody therapies with high efficiency and low toxicity are becoming one of the major approaches in antibody therapeutics. Based on high-throughput sequencing and increasing experimental structures of antibodies/antibody-antigen complexes, computational approaches can predict antibody/antigen structures, engineering the function of antibodies and design antibody-antigen complexes with improved properties. This review summarizes recent progress in the field of in silico design of antibodies, including antibody structure modeling, antibody-antigen complex prediction, antibody stability evaluation, and allosteric effects in antibodies and functions. We listed the cases in which these methods have helped experimental studies to improve the affinities and physicochemical properties of antibodies. We emphasized how the molecular dynamics unveiled the allosteric effects during antibody-antigen recognition and antibody-effector recognition.

RosettaAntibodyDesign (RAbD): A general framework for computational antibody design

A structural-bioinformatics-based computational methodology and framework have been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from a widely accepted set of the canonical clusters of CDRs (North et al., J. Mol. Biol., 406:228-256, 2011). It then performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. Starting from an existing experimental or computationally modeled antigen-antibody structure, RAbD can be used to redesign a single CDR or multiple CDRs with loops of different length, conformation, and sequence. We rigorously benchmarked RAbD on a set of 60 diverse antibody-antigen complexes, using two design strategies-optimizing total Rosetta energy and optimizing interface energy alone. We utilized two novel metrics for measuring success in computational protein design. The design risk ratio (DRR) is equal to the frequency of recovery of native CDR lengths and clusters divided by the frequency of sampling of those features during the Monte Carlo design procedure. Ratios greater than 1.0 indicate that the design process is picking out the native more frequently than expected from their sampled rate. We achieved DRRs for the non-H3 CDRs of between 2.4 and 4.0. The antigen risk ratio (ARR) is the ratio of frequencies of the native amino acid types, CDR lengths, and clusters in the output decoys for simulations performed in the presence and absence of the antigen. For CDRs, we achieved cluster ARRs as high as 2.5 for L1 and 1.5 for H2. For sequence design simulations without CDR grafting, the overall recovery for the native amino acid types for residues that contact the antigen in the native structures was 72% in simulations performed in the presence of the antigen and 48% in simulations performed without the antigen, for an ARR of 1.5. For the non-contacting residues, the ARR was 1.08. This shows that the sequence profiles are able to maintain the amino acid types of these conserved, buried sites, while recovery of the exposed, contacting residues requires the presence of the antigen-antibody interface. We tested RAbD experimentally on both a lambda and kappa antibody-antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.

Designing and overproducing a tandem epitope of gp350/220 that shows a potential to become an EBV vaccine

Background

Epstein-Barr virus (EBV) can cause cancer in people from around the world. There is no EBV vaccine available for use on a global scale. However, emerging evidence suggests that the epitope on the gp350/220 capsid protein may be developed into an EBV vaccine. Nevertheless, the production of small, single epitope is challenging of stability issues and possible alteration of peptide structure. In this study, a tandem epitope was developed consisting of three single epitopes, aimed to improve stability, antigenicity and preserve epitope structure.

Materials and methods

A tandem epitope was designed using bioinformatics based on the epitope structure of the gp350/220 protein. The tandem epitope structure was analyzed using a protein folding method with Abalone software, which was further refined via YASARA force field and molecular repairing using a FoldX method. Immunogenicity was examined with Epitopia software, whereas allergen properties were tested using AlgPred. The pattern of the tandem epitope binding with anti-gp350/220 antibodies was performed using Z-dock and snugDock. The tandem epitope was then overproduced in E. coli strain BL21 as a host cell.

Result

Our model demonstrated a successfully designed and overproduced tandem epitope. The tandem epitope demonstrated a similar structure compared with the epitope of whole protein gp350/220. Our epitope also demonstrated non-allergen and antigenicity properties, and possessed antibody binding patterns consistent with whole protein gp350/220.

Conclusion and recommendation

These data suggest a novel tandem epitope composed of three similar epitopes demonstrates antigenicity, structure, and binding properties consistent with whole protein gp350/220. We also demonstrate successful production of the tandem epitope using E. coli strain BL21 as a host. Future in vivo experimental animal research is necessary to test the ability of this tandem epitope to stimulate antibody production.

Functionally Convergent B Cell Receptor Sequences in Transgenic Rats Expressing a Human B Cell Repertoire in Response to Tetanus Toxoid and Measles Antigens

The identification and tracking of antigen-specific immunoglobulin (Ig) sequences within total Ig repertoires is central to high-throughput sequencing (HTS) studies of infections or vaccinations. In this context, public Ig sequences shared by different individuals exposed to the same antigen could be valuable markers for tracing back infections, measuring vaccine immunogenicity, and perhaps ultimately allow the reconstruction of the immunological history of an individual. Here, we immunized groups of transgenic rats expressing human Ig against tetanus toxoid (TT), Modified Vaccinia virus Ankara (MVA), measles virus hemagglutinin and fusion proteins expressed on MVA, and the environmental carcinogen benzo[a]pyrene, coupled to TT. We showed that these antigens impose a selective pressure causing the Ig heavy chain (IgH) repertoires of the rats to converge toward the expression of antibodies with highly similar IgH CDR3 amino acid sequences. We present a computational approach, similar to differential gene expression analysis, that selects for clusters of CDR3s with 80% similarity, significantly overrepresented within the different groups of immunized rats. These IgH clusters represent antigen-induced IgH signatures exhibiting stereotypic amino acid patterns including previously described TT- and measles-specific IgH sequences. Our data suggest that with the presented methodology, transgenic Ig rats can be utilized as a model to identify antigen-induced, human IgH signatures to a variety of different antigens.

How B-Cell Receptor Repertoire Sequencing Can Be Enriched with Structural Antibody Data

Next-generation sequencing of immunoglobulin gene repertoires (Ig-seq) allows the investigation of large-scale antibody dynamics at a sequence level. However, structural information, a crucial descriptor of antibody binding capability, is not collected in Ig-seq protocols. Developing systematic relationships between the antibody sequence information gathered from Ig-seq and low-throughput techniques such as X-ray crystallography could radically improve our understanding of antibodies. The mapping of Ig-seq datasets to known antibody structures can indicate structurally, and perhaps functionally, uncharted areas. Furthermore, contrasting naïve and antigenically challenged datasets using structural antibody descriptors should provide insights into antibody maturation. As the number of antibody structures steadily increases and more and more Ig-seq datasets become available, the opportunities that arise from combining the two types of information increase as well. Here, we review how these data types enrich one another and show potential for advancing our knowledge of the immune system and improving antibody engineering.

Role of Non-local Interactions between CDR Loops in Binding Affinity of MR78 Antibody to Marburg Virus Glycoprotein

An atomic-detail model of the Marburg virus glycoprotein in complex with a neutralizing human monoclonal antibody designated MR78 was constructed using Phenix.Rosetta starting from a 3.6Å crystallographic density map. The Asp at T6 in the HCDR3's bulged torso cannot form the canonical salt bridge as position T2 lacks an Arg or Lys residue. It instead engages in a hydrogen bond interaction with a Tyr contributed by the HCDR1 loop. This inter-CDR loop interaction stabilizes the bulged conformation needed for binding to the viral glycoprotein: a Tyr to Phe mutant displays a binding affinity reduced by a factor of at least 10. We found that 5% of a database of 465 million human antibody sequences has the same residues at T2 and T6 positions in HCDR3 and Tyr in HCDR1 that could potentially form this Asp-Tyr interaction, and that this interaction might contribute to a non-canonical bulged torso conformation.

In silico methods for design of biological therapeutics

It has been twenty years since the first rationally designed small molecule drug was introduced into the market. Since then, we have progressed from designing small molecules to designing biotherapeutics. This class of therapeutics includes designed proteins, peptides and nucleic acids that could more effectively combat drug resistance and even act in cases where the disease is caused because of a molecular deficiency. Computational methods are crucial in this design exercise and this review discusses the various elements of designing biotherapeutic proteins and peptides. Many of the techniques discussed here, such as the deterministic and stochastic design methods, are generally used in protein design. We have devoted special attention to the design of antibodies and vaccines. In addition to the methods for designing these molecules, we have included a comprehensive list of all biotherapeutics approved for clinical use. Also included is an overview of methods that predict the binding affinity, cell penetration ability, half-life, solubility, immunogenicity and toxicity of the designed therapeutics. Biotherapeutics are only going to grow in clinical importance and are set to herald a new generation of disease management and cure.