Prediction of immunogenicity for humanized and full human therapeutic antibodies

Development of a potent recombinant scFv antibody against the SARS-CoV-2 by in-depth bioinformatics study: Paving the way for vaccine/diagnostics development

BACKGROUND

The SARS-CoV-2 has led to a worldwide disaster. Thus, developing prophylactics/therapeutics is required to overcome this public health issue. Among these, producing the anti-SARS-CoV-2 single-chain variable fragment (scFv) antibodies has attracted a significant attention. Accordingly, this study aims to address this question: Is it possible to bioinformatics-based design of a potent anti-SARS-CoV-2 scFv as an alternative to current production approaches?

METHOD

Using the complexed SARS-CoV-2 spike-antibodies, two sets analyses were performed: (1) B-cell epitopes (BCEs) prediction in the spike receptor-binding domain (RBD) region as a parameter for antibody screening; (2) the computational analysis of antibodies variable domains (VH/VL). Based on these primary screenings, and docking/binding affinity rating, one antibody was selected. The protein-protein interactions (PPIs) among the selected antibody-epitope complex were predicted and its epitope conservancy was also evaluated. Thereafter, some elements were added to the final scFv: (1) the PelB signal peptide; (2) a GSGGGGS linker to connect the VH-VL. Finally, this scFv was analyzed/optimized using various web servers.

RESULTS

Among the antibody library, only one met the various criteria for being an efficient scFv candidate. Moreover, no interaction was predicted between its paratope and RBD hot-spot residues of SARS-CoV-2 variants-of-Concern (VOCs).

CONCLUSIONS

Herein, a step-by-step bioinformatics platform has been introduced to bypass some barriers of traditional antibody production approaches. Based on existing literature, the current study is one of the pioneer works in the field of bioinformatics-based scFv production. This scFv may be a good candidate for diagnostics/therapeutics design against the SARS-CoV-2 as an emerging aggressive pathogen.

AbImmPred: An immunogenicity prediction method for therapeutic antibodies using AntiBERTy-based sequence features

Due to the unnecessary immune responses induced by therapeutic antibodies in clinical applications, immunogenicity is an important factor to be considered in the development of antibody therapeutics. To a certain extent, there is a lag in using wet-lab experiments to test the immunogenicity in the development process of antibody therapeutics. Developing a computational method to predict the immunogenicity at once the antibody sequence is designed, is of great significance for the screening in the early stage and reducing the risk of antibody therapeutics development. In this study, a computational immunogenicity prediction method was proposed on the basis of AntiBERTy-based features of amino sequences in the antibody variable region. The AntiBERTy-based sequence features were first calculated using the AntiBERTy pre-trained model. Principal component analysis (PCA) was then applied to reduce the extracted feature to two dimensions to obtain the final features. AutoGluon was then used to train multiple machine learning models and the best one, the weighted ensemble model, was obtained through 5-fold cross-validation on the collected data. The data contains 199 commercial therapeutic antibodies, of which 177 samples were used for model training and 5-fold cross-validation, and the remaining 22 samples were used as an independent test dataset to evaluate the performance of the constructed model and compare it with other prediction methods. Test results show that the proposed method outperforms the comparison method with 0.7273 accuracy on the independent test dataset, which is 9.09% higher than the comparison method. The corresponding web server is available through the official website of GenScript Co., Ltd., https://www.genscript.com/tools/antibody-immunogenicity.

Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics

Adequate stability, manufacturability, and safety are crucial to bringing an antibody-based biotherapeutic to the market. Following the concept of holistic in silico developability, we introduce a physicochemical description of 91 market-stage antibody-based biotherapeutics based on orthogonal molecular properties of variable regions (Fvs) embedded in different simulation environments, mimicking conditions experienced by antibodies during manufacturing, formulation, and in vivo. In this work, the evaluation of molecular properties includes conformational flexibility of the Fvs using molecular dynamics (MD) simulations. The comparison between static homology models and simulations shows that MD significantly affects certain molecular descriptors like surface molecular patches. Moreover, the structural stability of a subset of Fv regions is linked to changes in their specific molecular interactions with ions in different experimental conditions. This is supported by the observation of differences in protein melting temperatures upon addition of NaCl. A DEvelopability Navigator In Silico (DENIS) is proposed to compare mAb candidates for their similarity with market-stage biotherapeutics in terms of physicochemical properties and conformational stability. Expanding on our previous developability guidelines (Ahmed et al. Proc. Natl. Acad. Sci. 2021, 118 (37), e2020577118), the hydrodynamic radius and the protein strand ratio are introduced as two additional descriptors that enable a more comprehensive in silico characterization of biotherapeutic drug candidates. Test cases show how this approach can facilitate identification and optimization of intrinsically developable lead candidates. DENIS represents an advanced computational tool to progress biotherapeutic drug candidates from discovery into early development by predicting drug properties in different aqueous environments.

Development and characterization of a camelid derived antibody targeting a linear epitope in the hinge domain of human PCSK9 protein

PCSK9 is an effective target for lowering LDL-c. Previously, a camelid-human chimeric heavy chain antibody VHH-B11-Fc targeting human PCSK9 was designed. It had a potent hypolipidemic effect. However, the nanobody VHH-B11 interacts with PCSK9 at low affinity, while camelid VHH exhibits some immunogenicity. Moreover, the interacting epitope is yet to be identified, although VHH-B11 was shown to have distinct hPCSK9-binding epitopes for Evolocumab. This might impede the molecule’s progress from bench to bedside. In the present study, we designed various configurations to improve the affinity of VHH-B11 with hPCSK9 (< 10 nM) that in turn enhanced the druggability of VHH-B11-Fc. Then, 17 amino acids were specifically mutated to increase the degree of humanization of the nanobody VHH-B11. Using phage display and sequencing technology, the linear epitope “STHGAGW” (amino acids 447–452) was identified in the hinge region of PCSK9 as the interacting site between VHH-B11-Fc and hPCSK9. Unlike the interaction epitope of Evolocumab, located in the catalytic region of PCSK9, the binding epitope of VHH-B11 is located in the hinge region of PCSK9, which is rarely reported. These findings indicated that a specific mechanism underlying this interaction needs to be explored.

Genome-wide pharmacogenetics of anti-drug antibody response to bococizumab highlights key residues in HLA DRB1 and DQB1

In this largest to-date genetic analysis of anti-drug antibody (ADA) response to a therapeutic monoclonal antibody (MAb), genome-wide association was performed for five measures of ADA status among 8844 individuals randomized to bococizumab, which targets PCSK9 for LDL-C lowering and cardiovascular protection. Index associations prioritized specific amino acid substitutions at the DRB1 and DQB1 MHC class II genes rather than canonical haplotypes. Two clusters of missense variants at DRB1 were associated with general ADA measures (residues 9, 11, 13; and 96, 112, 120, 180) and a third cluster of missense variants in DQB1 was associated with ADA measures including neutralizing antibody (NAb) titers (residues 66, 67, 71, 74, 75). The structural disposition of the missense substitutions implicates peptide antigen binding and CD4 effector function, mechanisms that are potentially generalizable to other therapeutic mAbs.

Clinicaltrials.gov: NCT01968954, NCT01968967, NCT01968980, NCT01975376, NCT01975389, NCT02100514.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

A structural perspective on the design of decoy immune modulators

Therapeutic mAbs have dominated the class of immunotherapeutics in general and immune checkpoint inhibitors in particular. The high specificity of mAbs to the target molecule as well as their extended half-life and (or) the effector functions raised by the Fc part are some of the important aspects that contribute to the success of this class of therapeutics. Equally potential candidates are decoys and their fusions that can address some of the inherent limitations of mAbs, like immunogenicity, resistance development, low bio-availability and so on, besides maintaining the advantages of mAbs. The decoys are molecules that trap the ligands and prevent them from interacting with the signaling receptors. Although a few FDA-approved decoy immune modulators are very successful, the potential of this class of drugs is yet to be fully realized. Here, we review various strategies employed in fusion protein therapeutics with a focus on the design of decoy immunomodulators from the structural perspective and discuss how the information on protein structure and function can strategically guide the development of next-generation immune modulators.