Antibody modeling using the prediction of immunoglobulin structure (PIGS) web server [corrected]

Recombinant humanized Fab fragments targeting the CFC domain of human Cripto-1

In the era of immunotherapy, the targeting of disease-specific biomarkers goes hand in hand with the development of highly selective antibody-based reagents having optimal pharmacological/toxicological profiles. One interesting and debated biomaker for several types of cancers is the onco-fetal protein Cripto-1 that is selectively expressed in many solid tumours and has been actively investigated as potential theranostic target. Starting from previously described anti-CFC/Cripto-1 murine monoclonal antibodies, we have moved forward to prepare the humanized recombinant Fabs which have been engineered so as to bear an MTGase site useful for a one-step site-specific labelling. The purified and bioconjugated molecules have been extensively characterized and tested on Cripto-1-positive cancer cells through in vitro binding assays. These recombinant Fab fragments recognize the target antigen in its native form on intact cells suggesting that they can be further developed as reagents for detecting Cripto-1 in theranostic settings.

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

Production in Bacteria and Characterization of Engineered Humanized Fab Fragment against the Nodal Protein

Drug development in recent years is increasingly focused on developing personalized treatments based on blocking molecules selective for therapeutic targets specifically present in individual patients. In this perspective, the specificity of therapeutic targets and blocking agents plays a crucial role. Monoclonal antibodies (mAbs) and their surrogates are increasingly used in this context thanks to their ability to bind therapeutic targets and to inhibit their activity or to transport bioactive molecules into the compartments in which the targets are expressed. Small antibody-like molecules, such as Fabs, are often used in certain clinical settings where small size and better tissue penetration are required. In the wake of this research trend, we developed a murine mAb (3D1) neutralizing the activity of Nodal, an oncofetal protein that is attracting an ever-increasing interest as a selective therapeutic target for several cancer types. Here, we report the preparation of a recombinant Fab of 3D1 that has been humanized through a computational approach starting from the sequence of the murine antibody. The Fab has been expressed in bacterial cells (1 mg/L bacterial culture), biochemically characterized in terms of stability and binding properties by circular dichroism and bio-layer interferometry techniques and tested in vitro on Nodal-positive cancer cells.

Computational Modeling of Antibody and T-Cell Receptor (CDR3 Loops)

Antibodies and T-cell receptors have been a subject of much interest due to their central role in the immune system and their potential applications in several biotechnological and medical applications from cancer therapy to vaccine development. A unique feature of these two lymphocyte receptors is their ability to bind a huge variety of different (pathogen) targets. This ability stems from six short loops in the binding domain that have hypervariable sequence due to genetic recombination mechanism. Particularly one of these loops, the third complementarity determining region (CDR3), has the highest sequence variability and a dominant role in binding the target. However, it has also been proven the most difficult to be modeled structurally, which is vitally important for downstream tasks such as binding prediction. This difficulty stems from its variability in sequence that both reduces the possibility of finding homologues and introduces unique structural features in the loop. We present here a general protocol for modeling such loops in antibodies and T-cell receptors. We also discuss the difficulties in loop modeling and the advantages and limitations of different modeling methods.

Homology Modeling of Antibody Variable Regions: Methods and Applications

Adaptive immunity specifically protects us from antigenic challenges. Antibodies are key effector proteins of adaptive immunity, and they are remarkable in their ability to recognize a virtually limitless number of antigens. Fragment variable (F_V), the antigen-binding region of antibodies, can be split into two main components, namely, framework and complementarity determining regions. The framework (FR) consists of light-chain framework (FR_L) and heavy-chain framework (FR_H). Similarly, the complementarity determining regions (CDRs) comprises of light-chain CDRs 1-3 (CDRs L1-3) and heavy-chain CDRs 1-3 (CDRs H1-3). While FRs are relatively constant in sequence and structure across diverse antibodies, sequence variation in CDRs leading to differential conformations of CDR loops accounts for the distinct antigenic specificities of diverse antibodies. The conserved structural features in FRs and conformity of CDRs to a limited set of standard conformations allow for the accurate prediction of F_V models using homology modeling techniques. Antibody structure prediction from its amino acid sequence has numerous important applications including prediction of antibody-antigen interaction interfaces and redesign of therapeutically and biotechnologically useful antibodies with improved affinity. This chapter summarizes the current practices employed in the successful homology modeling of antibody variable regions and the potential applications of the generated homology models.

Differential performance of RoseTTAFold in antibody modeling

Antibodies are essential to life, and knowing their structures can facilitate the understanding of antibody-antigen recognition mechanisms. Precise antibody structure prediction has been a core challenge for a prolonged period, especially the accuracy of H3 loop prediction. Despite recent progress, existing methods cannot achieve atomic accuracy, especially when the homologous structures required for these methods are not available. Recently, RoseTTAFold, a deep learning-based algorithm, has shown remarkable breakthroughs in predicting the 3D structures of proteins. To assess the antibody modeling ability of RoseTTAFold, we first retrieved the sequences of 30 antibodies as the test set and used RoseTTAFold to model their 3D structures. We then compared the models constructed by RoseTTAFold with those of SWISS-MODEL in a different way, in which we stratified Global Model Quality Estimate (GMQE) into three different ranges. The results indicated that RoseTTAFold could achieve results similar to SWISS-MODEL in modeling most CDR loops, especially the templates with a GMQE score under 0.8. In addition, we also compared the structures modeled by RoseTTAFold, SWISS-MODEL and ABodyBuilder. In brief, RoseTTAFold could accurately predict 3D structures of antibodies, but its accuracy was not as good as the other two methods. However, RoseTTAFold exhibited better accuracy for modeling H3 loop than ABodyBuilder and was comparable to SWISS-MODEL. Finally, we discussed the limitations and potential improvements of the current RoseTTAFold, which may help to further the accuracy of RoseTTAFold's antibody modeling.

VHH Structural Modelling Approaches: A Critical Review

V_HH, 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 V_HH 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 V_HHs 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 V_HH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of V_HHs. 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 V_HH 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 V_HH from its sequence.

Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics

Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.

Quantitative Description of Surface Complementarity of Antibody-Antigen Interfaces

Antibodies have the remarkable ability to recognise their cognate antigens with extraordinary affinity and specificity. Discerning the rules that define antibody-antigen recognition is a fundamental step in the rational design and engineering of functional antibodies with desired properties. In this study we apply the 3D Zernike formalism to the analysis of the surface properties of the antibody complementary determining regions (CDRs). Our results show that shape and electrostatic 3DZD descriptors of the surface of the CDRs are predictive of antigen specificity, with classification accuracy of 81% and area under the receiver operating characteristic curve (AUC) of 0.85. Additionally, while in terms of surface size, solvent accessibility and amino acid composition, antibody epitopes are typically not distinguishable from non-epitope, solvent-exposed regions of the antigen, the 3DZD descriptors detect significantly higher surface complementarity to the paratope, and are able to predict correct paratope-epitope interaction with an AUC = 0.75.

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.

32A9, a novel human antibody for designing an immunotoxin and CAR-T cells against glypican-3 in hepatocellular carcinoma

Background

Treatment of hepatocellular carcinoma (HCC) using antibody-based targeted therapies, such as antibody conjugates and chimeric antigen receptor T (CAR-T) cell therapy, shows potent antitumor efficacy. Glypican-3 (GPC3) is an emerging HCC therapeutic target; therefore, antibodies against GPC3 would be useful tools for developing immunotherapies for HCC.

Methods

We isolated a novel human monoclonal antibody, 32A9, by phage display technology. We determined specificity, affinity, epitope and anti-tumor activity of 32A9, and developed 32A9-based immunotherapy technologies for evaluating the potency of HCC treatment in vitro or in vivo.

Results

32A9 recognized human GPC3 with potent affinity and specificity. The epitope of 32A9 was located in the region of the GPC3 protein core close to the modification sites of the HS chain and outside of the Wnt-binding site of GPC3. The 32A9 antibody significantly inhibited HCC xenograft tumor growth in vivo. We then pursued two 32A9-based immunotherapeutic strategies by constructing an immunotoxin and CAR-T cells. The 32A9 immunotoxin exhibited specific cytotoxicity to GPC3-positive cancer cells, while 32A9 CAR-T cells efficiently eliminated GPC3-positive HCC cells in vitro and caused HCC xenograft tumor regressions in vivo.

Conclusions

Our study provides a rationale for 32A9 as a promising GPC3-specific antibody candidate for HCC immunotherapy.

Computational elucidation and validation of the three-dimensional structure of humanized aldolase catalytic antibody 38C2

Catalytic antibodies are immunoglobulin proteins that are capable of catalyzing multiple reactions with diverse substrates. Aldolase catalytic antibody 38C2 catalyzes aldol and retro-aldol reactions via an enamine mechanism. Therefore, 38C2 has a high potential to be used in prodrug activation, and it is currently developed for selective chemotherapy. For medical applications, its humanization is essential, and therefore, the understanding of the three-dimensional (3D) spatial atomistic structure of 38C2 is mandatory. In this study, it was attempted to construct the 3D atomic structure of humanized abzyme 38C2 using computational methods. A homology modeled structure was simulated for 100 ns with classical molecular dynamics simulations for its dynamics stability. The accuracy of the constructed model was further evaluated with various theoretical methods. The binding of four selected natural substrates to the constructed structure was studied in detail to further validate the model. Finally, to evaluate the reaction readiness of the constructed protein, the first step of the catalytic reaction has been successfully carried out with QST3/IRC calculations using the DFT/B3LYP-6-31G level of theory in the presence of extracted catalytic residues with the preserved coordinates in implicit water. Hence, the reaction readiness of the proposed protein structure, along with all the other validation tests, strongly proves that the modeled structure has high accuracy. This study, therefore, sheds new light on the structure, mechanism of action and applications of the 38C2 abzyme by constructing and validating its full 3D atomistic model. Further, this highly reliable modeled structure will expedite and facilitate future 38C2-based drug discovery.Communicated by Ramaswamy H. Sarma.

Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies

Improved understanding of properties that mediate protein solubility and resistance to aggregation are important for developing biopharmaceuticals, and more generally in biotechnology and synthetic biology. Recent acquisition of large datasets for antibody biophysical properties enables the search for predictive models. In this report, machine learning methods are used to derive models for 12 biophysical properties. A physicochemical perspective is maintained in analysing the models, leading to the observation that models cluster largely according to charge (cross-interaction measurements) and hydrophobicity (self-interaction methods). These two properties also overlap in some cases, for example in a new interpretation of variation in hydrophobic interaction chromatography. Since the models are developed from differences of antibody variable loops, the next stage is to extend models to more diverse protein sets.

AVAILABILITY

The web application for the sequence-based algorithms are available on the protein-sol webserver, at https://protein-sol.manchester.ac.uk/abpred, with models and virtualisation software available at https://protein-sol.manchester.ac.uk/software.

SWISS-MODEL: homology modelling of protein structures and complexes

Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.

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.

High-Throughput Antibody Structure Modeling and Design Using ABodyBuilder

Antibodies are proteins of the adaptive immune system; they can be designed to bind almost any molecule, and are increasingly being used as biotherapeutics. Experimental antibody design is an expensive and time-consuming process, and computational antibody design methods can now be used to help develop new therapeutics and diagnostics. Within the design pipeline, accurate antibody structure modeling is essential, as it provides the basis for antibody-antigen docking, binding affinity prediction, and estimating thermal stability. Ideally, models should be rapidly generated, allowing the exploration of the breadth of antibody space. This allows methods to replicate the natural processes of antibody diversification (e.g., V(D)J recombination and somatic hypermutation), and cope with large volumes of data that are typical of next-generation sequencing datasets. Here we describe ABodyBuilder and PEARS, algorithms that build and mutate antibody model structures. These methods take ~30 s to generate a model antibody structure.

Antibody Specific B-Cell Epitope Predictions: Leveraging Information From Antibody-Antigen Protein Complexes

B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools.

Non-H3 CDR template selection in antibody modeling through machine learning

Antibodies are proteins generated by the adaptive immune system to recognize and counteract a plethora of pathogens through specific binding. This adaptive binding is mediated by structural diversity in the six complementary determining region (CDR) loops (H1, H2, H3, L1, L2 and L3), which also makes accurate structural modeling of CDRs challenging. Both homology and de novo modeling approaches have been used; to date, the former has achieved greater accuracy for the non-H3 loops. The homology modeling of non-H3 CDRs is more accurate because non-H3 CDR loops of the same length and type can be grouped into a few structural clusters. Most antibody-modeling suites utilize homology modeling for the non-H3 CDRs, differing only in the alignment algorithm and how/if they utilize structural clusters. While RosettaAntibody and SAbPred do not explicitly assign query CDR sequences to clusters, two other approaches, PIGS and Kotai Antibody Builder, utilize sequence-based rules to assign CDR sequences to clusters. While the manually curated sequence rules can identify better structural templates, because their curation requires extensive literature search and human effort, they lag behind the deposition of new antibody structures and are infrequently updated. In this study, we propose a machine learning approach (Gradient Boosting Machine [GBM]) to learn the structural clusters of non-H3 CDRs from sequence alone. The GBM method simplifies feature selection and can easily integrate new data, compared to manual sequence rule curation. We compare the classification results using the GBM method to that of RosettaAntibody in a 3-repeat 10-fold cross-validation (CV) scheme on the cluster-annotated antibody database PyIgClassify and we observe an improvement in the classification accuracy of the concerned loops from 84.5% ± 0.24% to 88.16% ± 0.056%. The GBM models reduce the errors in specific cluster membership misclassifications when the involved clusters have relatively abundant data. Based on the factors identified, we suggest methods that can enrich structural classes with sparse data to further improve prediction accuracy in future studies.

Modeling of Protein Tertiary and Quaternary Structures Based on Evolutionary Information

Proteins are subject to evolutionary forces that shape their three-dimensional structure to meet specific functional demands. The knowledge of the structure of a protein is therefore instrumental to gain information about the molecular basis of its function. However, experimental structure determination is inherently time consuming and expensive, making it impossible to follow the explosion of sequence data deriving from genome-scale projects. As a consequence, computational structural modeling techniques have received much attention and established themselves as a valuable complement to experimental structural biology efforts. Among these, comparative modeling remains the method of choice to model the three-dimensional structure of a protein when homology to a protein of known structure can be detected.The general strategy consists of using experimentally determined structures of proteins as templates for the generation of three-dimensional models of related family members (targets) of which the structure is unknown. This chapter provides a description of the individual steps needed to obtain a comparative model using SWISS-MODEL, one of the most widely used automated servers for protein structure homology modeling.

Biotherapeutics: Challenges and Opportunities for Predictive Toxicology of Monoclonal Antibodies

Biotherapeutics are a rapidly growing portion of the total pharmaceutical market accounting for almost one-half of recent new drug approvals. A major portion of these approvals each year are monoclonal antibodies (mAbs). During development, non-clinical pharmacology and toxicology testing of mAbs differs from that done with chemical entities since these biotherapeutics are derived from a biological source and therefore the animal models must share the same epitopes (targets) as humans to elicit a pharmacological response. Mechanisms of toxicity of mAbs are both pharmacological and non-pharmacological in nature; however, standard in silico predictive toxicological methods used in research and development of chemical entities currently do not apply to these biotherapeutics. Challenges and potential opportunities exist for new methodologies to provide a more predictive program to assess and monitor potential adverse drug reactions of mAbs for specific patients before and during clinical trials and after market approval.

Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia

Background

Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing.

Results

Here we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors.

Conclusions

The experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins.

Structure-function analysis and therapeutic efficacy of antibodies to fungal melanin for melanoma radioimmunotherapy

Metastatic melanoma remains difficult to treat despite recent approvals of several new drugs. Recently we reported encouraging results of Phase I clinical trial of radiolabeled with 188Re murine monoclonal IgM 6D2 to melanin in patients with Stage III/IV melanoma. Subsequently we generated a novel murine IgG 8C3 to melanin. IgGs are more amenable to humanization and cGMP (current Good Manufacturing Practice) manufacturing than IgMs. We performed comparative structural analysis of melanin-binding IgM 6D2 and IgG 8C3. The therapeutic efficacy of 213Bi- and 188Re-labeled 8C3 and its comparison with anti-CTLA4 immunotherapy was performed in B16-F10 murine melanoma model. The primary structures of these antibodies revealed significant homology, with the CDRs containing a high percentage of positively charged amino acids. The 8C3 model has a negatively charged binding surface and significant number of aromatic residues in its H3 domain, suggesting that hydrophobic interactions contribute to the antibody-melanin interaction. Radiolabeled IgG 8C3 showed significant therapeutic efficacy in murine melanoma, safety towards healthy melanin-containing tissues and favorable comparison with the anti-CTLA4 antibody. We have demonstrated that antibody binding to melanin relies on both charge and hydrophobic interactions while the in vivo data supports further development of 8C3 IgG as radioimmunotherapy reagent for metastatic melanoma.

Antibody Design and Humanization via In Silico Modeling

Antibody humanization process converts any nonhuman antibody sequence into humanized antibodies. This can be achieved using different methods of antibody design and engineering. This chapter will primarily focus on antibody design using a homology model followed by framework shuffling of murine to human germline sequence for humanization. Historically, mouse antibodies have been humanized using sequence-based approaches, in which all the murine frameworks are replaced with most homologous human germline sequence or related scaffold. Most often this humanized antibody design, when tested, has a significantly reduced binding or no binding to the cognate antigen. This is due to noncompatibility of mouse CDRs being supported by non-native human framework scaffold. This mismatch between mouse, human structural fold, and elimination of key conformational residues often leads to antibody humanization failures. Recently, there has been advent of homology modelor structure-guided antibody humanization. Instead of humanization based on linear sequence, this approach takes into account the tertiary structure and fold of the mouse antibody. A mouse homology model of the fragment variable is created, and based on sequence alignment with human germline, residues that are different in mouse are replaced with humanized sequence in the model. Energy minimization is applied to this humanized model that also delineates residues which might have steric clashes due to change in the overall tertiary conformation of the humanized antibody. Therefore, a homology model-guided with rational mutations, and reintroduction of key conformational residues from mouse antibody not only eliminates steric clashes but might also restore function in relation to binding affinity to its antigen.

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.

Computationally-driven identification of antibody epitopes

Understanding where antibodies recognize antigens can help define mechanisms of action and provide insights into progression of immune responses. We investigate the extent to which information about binding specificity implicitly encoded in amino acid sequence can be leveraged to identify antibody epitopes. In computationally-driven epitope localization, possible antibody–antigen binding modes are modeled, and targeted panels of antigen variants are designed to experimentally test these hypotheses. Prospective application of this approach to two antibodies enabled epitope localization using five or fewer variants per antibody, or alternatively, a six-variant panel for both simultaneously. Retrospective analysis of a variety of antibodies and antigens demonstrated an almost 90% success rate with an average of three antigen variants, further supporting the observation that the combination of computational modeling and protein design can reveal key determinants of antibody–antigen binding and enable efficient studies of collections of antibodies identified from polyclonal samples or engineered libraries.

Rapid and accurate in silico solubility screening of a monoclonal antibody library

Antibodies represent essential tools in research and diagnostics and are rapidly growing in importance as therapeutics. Commonly used methods to obtain novel antibodies typically yield several candidates capable of engaging a given target. The development steps that follow, however, are usually performed with only one or few candidates since they can be resource demanding, thereby increasing the risk of failure of the overall antibody discovery program. In particular, insufficient solubility, which may lead to aggregation under typical storage conditions, often hinders the ability of a candidate antibody to be developed and manufactured. Here we show that the selection of soluble lead antibodies from an initial library screening can be greatly facilitated by a fast computational prediction of solubility that requires only the amino acid sequence as input. We quantitatively validate this approach on a panel of nine distinct monoclonal antibodies targeting nerve growth factor (NGF), for which we compare the predicted and measured solubilities finding a very close match, and we further benchmark our predictions with published experimental data on aggregation hotspots and solubility of mutational variants of one of these antibodies.

Fully automated antibody structure prediction using BIOVIA tools: Validation study

We describe the methodology and results from our validation study of the fully automated antibody structure prediction tool available in the BIOVIA (formerly Accelrys) protein modeling suite. Extending our previous study, we have validated the automated approach using a larger and more diverse data set (157 unique antibody Fv domains versus 11 in the previous study). In the current study, we explore the effect of varying several parameter settings in order to better understand their influence on the resulting model quality. Specifically, we investigated the dependence on different methods of framework model construction, antibody numbering schemes (Chothia, IMGT, Honegger and Kabat), the influence of compatibility of loop templates using canonical type filtering, wider exploration of model solution space, and others. Our results show that our recently introduced Top5 framework modeling method results in a small but significant improvement in model quality whereas the effect of other parameters is not significant. Our analysis provides improved guidelines of best practices for using our protocol to build antibody structures. We also identify some limitations of the current computational model which will enhance proper evaluation of model quality by users and suggests possible future enhancements.

Superposition-free comparison and clustering of antibody binding sites: implications for the prediction of the nature of their antigen

We describe here a superposition free method for comparing the surfaces of antibody binding sites based on the Zernike moments and show that they can be used to quickly compare and cluster sets of antibodies. The clusters provide information about the nature of the bound antigen that, when combined with a method for predicting the number of direct antibody antigen contacts, allows the discrimination between protein and non-protein binding antibodies with an accuracy of 76%. This is of relevance in several aspects of antibody science, for example to select the framework to be used for a combinatorial antibody library.

The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design

The task of epitope discovery and vaccine design is increasingly reliant on bioinformatics analytic tools and access to depositories of curated data relevant to immune reactions and specific pathogens. The Immune Epitope Database and Analysis Resource (IEDB) was indeed created to assist biomedical researchers in the development of new vaccines, diagnostics, and therapeutics. The Analysis Resource is freely available to all researchers and provides access to a variety of epitope analysis and prediction tools. The tools include validated and benchmarked methods to predict MHC class I and class II binding. The predictions from these tools can be combined with tools predicting antigen processing, TCR recognition, and B cell epitope prediction. In addition, the resource contains a variety of secondary analysis tools that allow the researcher to calculate epitope conservation, population coverage, and other relevant analytic variables. The researcher involved in vaccine design and epitope discovery will also be interested in accessing experimental published data, relevant to the specific indication of interest. The database component of the IEDB contains a vast amount of experimentally derived epitope data that can be queried through a flexible user interface. The IEDB is linked to other pathogen-specific and immunological database resources.

Biopharmaceutical Informatics: supporting biologic drug development via molecular modelling and informatics

Objectives

The purpose of this article is to introduce an emerging field called ‘Biopharmaceutical Informatics’. It describes how tools from Information technology and Molecular Biophysics can be adapted, developed and gainfully employed in discovery and development of biologic drugs.

Key Findings

The findings described here are based on literature surveys and the authors’ collective experiences in the field of biologic drug product development. A strategic framework to forecast early the hurdles faced during drug product development is weaved together and elucidated using chemical degradation as an example. Efficiency of translating biologic drug discoveries into drug products can be significantly improved by combining learnings from experimental biophysical and analytical data on the drug candidates with molecular properties computed from their sequences and structures via molecular modeling and simulations.

Summary

Biopharmaceutical Informatics seeks to promote applications of computational tools towards discovery and development of biologic drugs. When fully implemented, industry-wide, it will enable rapid materials-free developability assessments of biologic drug candidates at early stages as well as streamline drug product development activities such as commercial scale production, purification, formulation, analytical characterization, safety and in vivo performance.

Modeling and docking of antibody structures with Rosetta

We describe Rosetta-based computational protocols for predicting the 3D structure of an antibody from sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally determined structures, as well as offering (i) energetic calculations to minimize loops, (ii) docking methodology to refine the V_L-V_H relative orientation and (iii) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody-antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully automated via the ROSIE web server (http://rosie.rosettacommons.org/) or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody-antigen docking. Tasks can be completed in under a day by using public supercomputers.

VH-VL orientation prediction for antibody humanization candidate selection: A case study

Antibody humanization describes the procedure of grafting a non-human antibody's complementarity-determining regions, i.e., the variable loop regions that mediate specific interactions with the antigen, onto a β-sheet framework that is representative of the human variable region germline repertoire, thus reducing the number of potentially antigenic epitopes that might trigger an anti-antibody response. The selection criterion for the so-called acceptor frameworks (one for the heavy and one for the light chain variable region) is traditionally based on sequence similarity. Here, we propose a novel approach that selects acceptor frameworks such that the relative orientation of the 2 variable domains in 3D space, and thereby the geometry of the antigen-binding site, is conserved throughout the process of humanization. The methodology relies on a machine learning-based predictor of antibody variable domain orientation that has recently been shown to improve the quality of antibody homology models. Using data from 3 humanization campaigns, we demonstrate that preselecting humanization variants based on the predicted difference in variable domain orientation with regard to the original antibody leads to subsets of variants with a significant improvement in binding affinity.

De novo protein sequencing, humanization and in vitro effects of an antihuman CD34 mouse monoclonal antibody

QBEND/10 is a mouse immunoglobulin lambda-chain monoclonal antibody with strict specificity against human hematopoietic progenitor cell antigen CD34. Our in vitro study showed that QBEND/10 impairs the tube formation of human umbilical vein endothelial cells (HUVECs), suggesting that the antibody may be of potential benefit in blocking tumor angiogenesis. We provided a de novo protein sequencing method through tandem mass spectrometry to identify the amino acid sequences in the variable heavy and light chains of QBEND/10. To reduce immunogenicity for clinical applications, QBEND/10 was further humanized using the resurfacing approach. We demonstrate that the de novo sequenced and humanized QBEND/10 retains the biological functions of the parental mouse counterpart, including the binding kinetics to CD34 and blockage of the tube formation of the HUVECs.

High-accuracy modeling of antibody structures by a search for minimum-energy recombination of backbone fragments

Current methods for antibody structure prediction rely on sequence homology to known structures. Although this strategy often yields accurate predictions, models can be stereo-chemically strained. Here, we present a fully automated algorithm, called AbPredict, that disregards sequence homology, and instead uses a Monte Carlo search for low-energy conformations built from backbone segments and rigid-body orientations that appear in antibody molecular structures. We find cases where AbPredict selects accurate loop templates with sequence identity as low as 10%, whereas the template of highest sequence identity diverges substantially from the query's conformation. Accordingly, in several cases reported in the recent Antibody Modeling Assessment benchmark, AbPredict models were more accurate than those from any participant, and the models' stereo-chemical quality was consistently high. Furthermore, in two blind cases provided to us by crystallographers prior to structure determination, the method achieved <1.5 Ångstrom overall backbone accuracy. Accurate modeling of unstrained antibody structures will enable design and engineering of improved binders for biomedical research directly from sequence. Proteins 2016; 85:30-38. © 2016 Wiley Periodicals, Inc.

Applicability of predictive toxicology methods for monoclonal antibody therapeutics: status Quo and scope

Biopharmaceuticals, monoclonal antibody (mAb)-based therapeutics in particular, have positively impacted millions of lives. MAbs and related therapeutics are highly desirable from a biopharmaceutical perspective as they are highly target specific and well tolerated within the human system. Nevertheless, several mAbs have been discontinued or withdrawn based either on their inability to demonstrate efficacy and/or due to adverse effects. Approved monoclonal antibodies and derived therapeutics have been associated with adverse effects such as immunogenicity, cytokine release syndrome, progressive multifocal leukoencephalopathy, intravascular haemolysis, cardiac arrhythmias, abnormal liver function, gastrointestinal perforation, bronchospasm, intraocular inflammation, urticaria, nephritis, neuropathy, birth defects, fever and cough to name a few. The advances made in this field are also impeded by a lack of progress in bioprocess development strategies as well as increasing costs owing to attrition, wherein the lack of efficacy and safety accounts for nearly 60 % of all factors contributing to attrition. This reiterates the need for smarter preclinical development using quality by design-based approaches encompassing carefully designed predictive models during early stages of drug development. Different in vitro and in silico methods are extensively used for predicting biological activity as well as toxicity during small molecule drug development; however, their full potential has not been utilized for biological drug development. The scope of in vitro and in silico tools in early developmental stages of monoclonal antibody-based therapeutics production and how it contributes to lower attrition rates leading to faster development of potential drug candidates has been evaluated. The applicability of computational toxicology approaches in this context as well as the pitfalls and promises of extending such techniques to biopharmaceutical development has been highlighted.

Promiscuous antibodies characterised by their physico-chemical properties: From sequence to structure and back

Human B cells produce antibodies, which bind to their cognate antigen based on distinct molecular properties of the antibody CDR loop. We have analysed a set of 10 antibodies showing a clear difference in their binding properties to a panel of antigens, resulting in two subsets of antibodies with a distinct binding phenotype. We call the observed binding multiplicity ‘promiscuous’ and selected physico-chemical CDRH3 characteristics and conformational preferences may characterise these promiscuous antibodies. To classify CDRH3 physico-chemical properties playing a role in their binding properties, we used statistical analyses of the sequences annotated by Kidera factors. To characterise structure-function requirements for antigen binding multiplicity we employed Molecular Modelling and Monte Carlo based coarse-grained simulations. The ability to predict the molecular causes of promiscuous, multi-binding behaviour would greatly improve the efficiency of the therapeutic antibody discovery process.

ABodyBuilder: Automated antibody structure prediction with data-driven accuracy estimation

Computational modeling of antibody structures plays a critical role in therapeutic antibody design. Several antibody modeling pipelines exist, but no freely available methods currently model nanobodies, provide estimates of expected model accuracy, or highlight potential issues with the antibody's experimental development. Here, we describe our automated antibody modeling pipeline, ABodyBuilder, designed to overcome these issues. The algorithm itself follows the standard 4 steps of template selection, orientation prediction, complementarity-determining region (CDR) loop modeling, and side chain prediction. ABodyBuilder then annotates the 'confidence' of the model as a probability that a component of the antibody (e.g., CDRL3 loop) will be modeled within a root-mean square deviation threshold. It also flags structural motifs on the model that are known to cause issues during in vitro development. ABodyBuilder was tested on 4 separate datasets, including the 11 antibodies from the Antibody Modeling Assessment-II competition. ABodyBuilder builds models that are of similar quality to other methodologies, with sub-Angstrom predictions for the 'canonical' CDR loops. Its ability to model nanobodies, and rapidly generate models (∼30 seconds per model) widens its potential usage. ABodyBuilder can also help users in decision-making for the development of novel antibodies because it provides model confidence and potential sequence liabilities. ABodyBuilder is freely available at http://opig.stats.ox.ac.uk/webapps/abodybuilder .

MoFvAb: Modeling the Fv region of antibodies

Knowledge of the 3-dimensional structure of the antigen-binding region of antibodies enables numerous useful applications regarding the design and development of antibody-based drugs. We present a knowledge-based antibody structure prediction methodology that incorporates concepts that have arisen from an applied antibody engineering environment. The protocol exploits the rich and continuously growing supply of experimentally derived antibody structures available to predict CDR loop conformations and the packing of heavy and light chain quickly and without user intervention. The homology models are refined by a novel antibody-specific approach to adapt and rearrange sidechains based on their chemical environment. The method achieves very competitive all-atom root mean square deviation values in the order of 1.5 Å on different evaluation datasets consisting of both known and previously unpublished antibody crystal structures.

Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity

The immune system has developed a number of distinct complex mechanisms to shape and control the antibody repertoire. One of these mechanisms, the affinity maturation process, works in an evolutionary-like fashion: after binding to a foreign molecule, the antibody-producing B-cells exhibit a high-frequency mutation rate in the genome region that codes for the antibody active site. Eventually, cells that produce antibodies with higher affinity for their cognate antigen are selected and clonally expanded. Here, we propose a new statistical approach based on maximum entropy modeling in which a scoring function related to the binding affinity of antibodies against a specific antigen is inferred from a sample of sequences of the immune repertoire of an individual. We use our inference strategy to infer a statistical model on a data set obtained by sequencing a fairly large portion of the immune repertoire of an HIV-1 infected patient. The Pearson correlation coefficient between our scoring function and the IC50 neutralization titer measured on 30 different antibodies of known sequence is as high as 0.77 (p-value 10-6), outperforming other sequence- and structure-based models.

Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique

Immunoglobulins, also called antibodies, are a group of cell surface proteins which are produced by the immune system in response to the presence of a foreign substance (called antigen).

Synthetic Antibodies Inhibit Bcl-2-associated X Protein (BAX) through Blockade of the N-terminal Activation Site

The BCL-2 protein family plays a critical role in regulating cellular commitment to mitochondrial apoptosis. Pro-apoptotic Bcl-2-associated X protein (BAX) is an executioner protein of the BCL-2 family that represents the gateway to mitochondrial apoptosis. Following cellular stresses that induce apoptosis, cytosolic BAX is activated and translocates to the mitochondria, where it inserts into the mitochondrial outer membrane to form a toxic pore. How the BAX activation pathway proceeds and how this may be inhibited is not yet completely understood. Here we describe synthetic antibody fragments (Fabs) as structural and biochemical probes to investigate the potential mechanisms of BAX regulation. These synthetic Fabs bind with high affinity to BAX and inhibit its activation by the BH3-only protein tBID (truncated Bcl2 interacting protein) in assays using liposomal membranes. Inhibition of BAX by a representative Fab, 3G11, prevented mitochondrial translocation of BAX and BAX-mediated cytochrome c release. Using NMR and hydrogen-deuterium exchange mass spectrometry, we showed that 3G11 forms a stoichiometric and stable complex without inducing a significant conformational change on monomeric and inactive BAX. We identified that the Fab-binding site on BAX involves residues of helices α1/α6 and the α1-α2 loop. Therefore, the inhibitory binding surface of 3G11 overlaps with the N-terminal activation site of BAX, suggesting a novel mechanism of BAX inhibition through direct binding to the BAX N-terminal activation site. The synthetic Fabs reported here reveal, as probes, novel mechanistic insights into BAX inhibition and provide a blueprint for developing inhibitors of BAX activation.

LYRA, a webserver for lymphocyte receptor structural modeling

The accurate structural modeling of B- and T-cell receptors is fundamental to gain a detailed insight in the mechanisms underlying immunity and in developing new drugs and therapies. The LYRA (LYmphocyte Receptor Automated modeling) web server (http://www.cbs.dtu.dk/services/LYRA/) implements a complete and automated method for building of B- and T-cell receptor structural models starting from their amino acid sequence alone. The webserver is freely available and easy to use for non-specialists. Upon submission, LYRA automatically generates alignments using ad hoc profiles, predicts the structural class of each hypervariable loop, selects the best templates in an automatic fashion, and provides within minutes a complete 3D model that can be downloaded or inspected online. Experienced users can manually select or exclude template structures according to case specific information. LYRA is based on the canonical structure method, that in the last 30 years has been successfully used to generate antibody models of high accuracy, and in our benchmarks this approach proves to achieve similarly good results on TCR modeling, with a benchmarked average RMSD accuracy of 1.29 and 1.48 Å for B- and T-cell receptors, respectively. To the best of our knowledge, LYRA is the first automated server for the prediction of TCR structure.

Antibody specific epitope prediction-emergence of a new paradigm

The development of accurate tools for predicting B-cell epitopes is important but difficult. Traditional methods have examined which regions in an antigen are likely binding sites of an antibody. However, it is becoming increasingly clear that most antigen surface residues will be able to bind one or more of the myriad of possible antibodies. In recent years, new approaches have emerged for predicting an epitope for a specific antibody, utilizing information encoded in antibody sequence or structure. Applying such antibody-specific predictions to groups of antibodies in combination with easily obtainable experimental data improves the performance of epitope predictions. We expect that further advances of such tools will be possible with the integration of immunoglobulin repertoire sequencing data.

Prediction of Antibody Epitopes

Antibodies recognize their cognate antigens in a precise and effective way. In order to do so, they target regions of the antigenic molecules that have specific features such as large exposed areas, presence of charged or polar atoms, specific secondary structure elements, and lack of similarity to self-proteins. Given the sequence or the structure of a protein of interest, several methods exploit such features to predict the residues that are more likely to be recognized by an immunoglobulin. Here, we present two methods (BepiPred and DiscoTope) to predict linear and discontinuous antibody epitopes from the sequence and/or the three-dimensional structure of a target protein.