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

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

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

A benchmark for evaluation of structure-based online tools for antibody-antigen binding affinity

The prediction of binding affinity changes caused by missense mutations can elucidate antigen-antibody interactions. A few accessible structure-based online computational tools have been proposed. However, selecting suitable software for particular research is challenging, especially research on the SARS-CoV-2 spike protein with antibodies. Therefore, benchmarking of the mutation-diverse SARS-CoV-2 datasets is critical. Here, we collected the datasets including 1216 variants about the changes in binding affinity of antigens from 22 complexes for SARS-CoV-2 S proteins and 22 monoclonal antibodies as well as applied them to evaluate the performance of seven binding affinity prediction tools. The tested tools' Pearson correlations between predicted and measured changes in binding affinity were between -0.158 and 0.657, while accuracy in classification tasks on predicting increasing or decreasing affinity ranged from 0.444 to 0.834. These tools performed relatively better on predicting single mutations, especially at epitope sites, whereas poor performance on extremely decreasing affinity. The tested tools were relatively insensitive to the experimental techniques used to obtain structures of complexes. In summary, we constructed a list of datasets and evaluated a range of structure-based online prediction tools that will explicate relevant processes of antigen-antibody interactions and enhance the computational design of therapeutic monoclonal antibodies.

Phage display technology and its impact in the discovery of novel protein-based drugs

INTRODUCTION

Phage display technology is a well-established versatile in vitro display technology that has been used for over 35 years to identify peptides and antibodies for use as reagents and therapeutics, as well as exploring the diversity of alternative scaffolds as another option to conventional therapeutic antibody discovery. Such successes have been responsible for spawning a range of biotechnology companies, as well as many complementary technologies devised to expedite the drug discovery process and resolve bottlenecks in the discovery workflow.

AREAS COVERED

In this perspective, the authors summarize the application of phage display for drug discovery and provide examples of protein-based drugs that have either been approved or are being developed in the clinic. The amenability of phage display to generate functional protein molecules to challenging targets and recent developments of strategies and techniques designed to harness the power of sampling diverse repertoires are highlighted.

EXPERT OPINION

Phage display is now routinely combined with cutting-edge technologies to deep-mine antibody-based repertoires, peptide, or alternative scaffold libraries generating a wealth of data that can be leveraged, e.g. via artificial intelligence, to enable the potential for clinical success in the discovery and development of protein-based therapeutics.

An in silico investigation on the binding site preference of PD-1 and PD-L1 for designing antibodies for targeted cancer therapy

Cancer control and treatment remain a significant challenge in cancer therapy and recently immune checkpoints has considered as a novel treatment strategy to develop anti-cancer drugs. Many cancer types use the immune checkpoints and its ligand, PD-1/PD-L1 pathway, to evade detection and destruction by the immune system, which is associated with altered effector function of PD-1 and PD-L1 overexpression on cancer cells to deactivate T cells. In recent years, mAbs have been employed to block immune checkpoints, therefore normalization of the anti-tumor response has enabled the scientists to develop novel biopharmaceuticals. In vivo affinity maturation of antibodies in targeted therapy has sometimes failed, and current experimental methods cannot accommodate the accurate structural details of protein-protein interactions. Therefore, determining favorable binding sites on the protein surface for modulator design of these interactions is a major challenge. In this study, we used the in silico methods to identify favorable binding sites on the PD-1 and PD-L1 and to optimize mAb variants on a large scale. At first, all the binding areas on PD-1 and PD-L1 have been identified. Then, using the RosettaDesign protocol, thousands of antibodies have been generated for 11 different regions on PD-1 and PD-L1 and then the designs with higher stability, affinity, and shape complementarity were selected. Next, molecular dynamics simulations and MM-PBSA analysis were employed to understand the dynamic, structural features of the complexes and measure the binding affinity of the final designs. Our results suggest that binding sites 1, 3 and 6 on PD-1 and binding sites 9 and 11 on PD-L1 can be regarded as the most appropriate sites for the inhibition of PD-1-PD-L1 interaction by the designed antibodies. This study provides comprehensive information regarding the potential binding epitopes on PD-1 which could be considered as hotspots for designing potential biopharmaceuticals. We also showed that mutations in the CDRs regions will rearrange the interaction pattern between the designed antibodies and targets (PD-1 and PD-L1) with improved affinity to effectively inhibit protein-protein interaction and block the immune checkpoint.

Antibody Recognition of Human Epidermal Growth Factor Receptor-2 (HER2) Juxtamembrane Domain Enhances Anti-Tumor Response of Chimeric Antigen Receptor (CAR)-T Cells

Chimeric antigen receptor (CAR) T cell therapy shows promise in treating malignant tumors. However, the use of human epidermal growth factor receptor-2 (HER2) CAR-T cells carries the risk of severe toxicity, including cytokine release syndrome, due to their "on-target off-tumor" recognition of HER2. Enhancing the quality and functionality of HER2 CARs could greatly improve the therapeutic potential of CAR-T cells. In this study, we developed a novel anti-HER2 monoclonal antibody, Ab8, which targets domain III of HER2, distinct from the domain IV recognition of trastuzumab. Although two anti-HER2 mAbs induced similar levels of antibody-dependent cellular cytotoxicity, trastuzumab-based CAR-T cells exhibited potent antitumor activity against HER2-positive cancer cells. In conclusion, our findings provide scientific evidence that antibody recognition of the membrane-proximal domain promotes the anti-tumor response of HER2-specific CAR-T cells.

A dual computational and experimental strategy to enhance TSLP antibody affinity for improved asthma treatment

Thymic stromal lymphopoietin is a key cytokine involved in the pathogenesis of asthma and other allergic diseases. Targeting TSLP and its signaling pathways is increasingly recognized as an effective strategy for asthma treatment. This study focused on enhancing the affinity of the T6 antibody, which specifically targets TSLP, by integrating computational and experimental methods. The initial affinity of the T6 antibody for TSLP was lower than the benchmark antibody AMG157. To improve this, we utilized alanine scanning, molecular docking, and computational tools including mCSM-PPI2 and GEO-PPI to identify critical amino acid residues for site-directed mutagenesis. Subsequent mutations and experimental validations resulted in an antibody with significantly enhanced blocking capacity against TSLP. Our findings demonstrate the potential of computer-assisted techniques in expediting antibody affinity maturation, thereby reducing both the time and cost of experiments. The integration of computational methods with experimental approaches holds great promise for the development of targeted therapeutic antibodies for TSLP-related diseases.

Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction

Recent years have seen an uptick in the use of computational applications in antibody engineering. These tools have enhanced our ability to predict interactions with antigens and immunogenicity, facilitate humanization, and serve other critical functions. However, several studies highlight the concern of potential trade-offs between antibody affinity and stability in antibody engineering. In this study, we analyzed anti-measles virus antibodies as a case study, to examine the relationship between binding affinity and stability, upon identifying the binding hotspots. We leverage in silico tools like Rosetta and FoldX, along with molecular dynamics (MD) simulations, offering a cost-effective alternative to traditional in vitro mutagenesis. We introduced a pattern in identifying key residues in pairs, shedding light on hotspots identification. Experimental physicochemical analysis validated the predicted key residues by confirming significant decrease in binding affinity for the high-affinity antibodies to measles virus hemagglutinin. Through the nature of the identified pairs, which represented the relative hydropathy of amino acid side chain, a connection was proposed between affinity and stability. The findings of the study enhance our understanding of the interactions between antibody and measles virus hemagglutinin. Moreover, the implications of the observed correlation between binding affinity and stability extend beyond the field of anti-measles virus antibodies, thereby opening doors for advancements in antibody research.

Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning

The application of machine learning (ML) models to optimize antibody affinity to an antigen is gaining prominence. Unfortunately, the small and biased nature of the publicly available antibody-antigen interaction datasets makes it challenging to build an ML model that can accurately predict binding affinity changes due to mutations (ΔΔG). Recognizing these inherent limitations, we reformulated the problem to ask whether an ML model capable of classifying deleterious vs non-deleterious mutations can guide antibody affinity maturation in a practical setting. To test this hypothesis, we developed a Random Forest classifier (Antibody Random Forest Classifier or AbRFC) with expert-guided features and integrated it into a computational-experimental workflow. AbRFC effectively predicted non-deleterious mutations on an in-house validation dataset that is free of biases seen in the publicly available training datasets. Furthermore, experimental screening of a limited number of predictions from the model (<10^2 designs) identified affinity-enhancing mutations in two unrelated SARS-CoV-2 antibodies, resulting in constructs with up to 1000-fold increased binding to the SARS-COV-2 RBD. Our findings indicate that accurate prediction and screening of non-deleterious mutations using machine learning offers a powerful approach to improving antibody affinity.

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.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

Rational strategies for enhancing mAb binding to SARS-CoV-2 variants through CDR diversification and antibody-escape prediction

Since the emergence of SARS-CoV-2, dozens of variants of interest and half a dozen variants of concern (VOCs) have been documented by the World Health Organization. The emergence of these VOCs due to the continuous evolution of the virus is a major concern for COVID-19 therapeutic antibodies and vaccines because they are designed to target prototype/previous strains and lose effectiveness against new VOCs. Therefore, there is a need for time- and cost-effective strategies to estimate the immune escape and redirect therapeutic antibodies against newly emerging variants. Here, we computationally predicted the neutralization escape of the SARS-CoV-2 Delta and Omicron variants against the mutational space of RBD-mAbs interfaces. Leveraging knowledge of the existing RBD-mAb interfaces and mutational space, we fine-tuned and redirected CT-p59 (Regdanvimab) and Etesevimab against the escaped variants through complementarity-determining regions (CDRs) diversification. We identified antibodies against the Omicron lineage BA.1 and BA.2 and Delta variants with comparable or better binding affinities to that of prototype Spike. This suggests that CDRs diversification by hotspot grafting, given an existing insight into the Ag-Abs interface, is an exquisite strategy to redirect antibodies against preselected epitopes and combat the neutralization escape of emerging SARS-CoV-2 variants.

The RESP AI model accelerates the identification of tight-binding antibodies

High-affinity antibodies are often identified through directed evolution, which may require many iterations of mutagenesis and selection to find an optimal candidate. Deep learning techniques hold the potential to accelerate this process but the existing methods cannot provide the confidence interval or uncertainty needed to assess the reliability of the predictions. Here we present a pipeline called RESP for efficient identification of high affinity antibodies. We develop a learned representation trained on over 3 million human B-cell receptor sequences to encode antibody sequences. We then develop a variational Bayesian neural network to perform ordinal regression on a set of the directed evolution sequences binned by off-rate and quantify their likelihood to be tight binders against an antigen. Importantly, this model can assess sequences not present in the directed evolution library and thus greatly expand the search space to uncover the best sequences for experimental evaluation. We demonstrate the power of this pipeline by achieving a 17-fold improvement in the K_D of the PD-L1 antibody Atezolizumab and this success illustrates the potential of RESP in facilitating general antibody development.

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

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

The paradigm of immune escape by SARS-CoV-2 variants and strategies for repositioning subverted mAbs against escaped VOCs

The perpetual emergence of SARS-CoV-2 variants is a serious issue that makes it difficult for the therapeutic antibodies and vaccines to end the COVID-19 pandemic. This article discusses the trend of increasing host fitness and immune escape by the virus and how to devise computational strategies for antibodies design and their affinity maturation against emerging SARS-CoV-2 variants.

Prediction of the structural interface between fibroblast growth factor23 and Burosumab using alanine scanning and molecular docking

Burosumab, an FGF23 targeting monoclonal antibody, was approved by the FDA in 2018 for use in children and adults with X-linked hypophosphatemia (or XLH). While several clinical studies have demonstrated the long-term safety and efficacy of Burosumab, the molecular basis of FGF23-Burosumab interaction which underpins its mechanism of action remains unknown. In this study, we employed molecular docking combined with alanine scanning of epitope and paratope to predict a model of FGF23-Burosumab interaction. Then, we used the model to understand the species-species cross-reactivity of Burosumab and to reverse engineer mouse FGF23 with 'back to human' mutations to bind Burosumab. Finally, we redesigned the CDRs with two mutations to engineer an affinity enhanced variant of the antibody. Our study provides insights into the FGF23-Burosumab interaction and demonstrates that alanine-scanning coupled with molecular docking can be used to optimize antibody candidates (e.g., structure-guided affinity maturation) for therapeutic use.

Viral Mimicry of Interleukin-17A by SARS-CoV-2 ORF8

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection triggers cytokine-mediated inflammation, leading to a myriad of clinical presentations in COVID-19. The SARS-CoV-2 open reading frame 8 (ORF8) is a secreted and rapidly evolving glycoprotein. Patients infected with SARS-CoV-2 variants with ORF8 deleted are associated with mild disease outcomes, but the molecular mechanism behind this is unknown. Here, we report that SARS-CoV-2 ORF8 is a viral cytokine that is similar to but distinct from interleukin 17A (IL-17A) as it induces stronger and broader human IL-17 receptor (hIL-17R) signaling than IL-17A. ORF8 primarily targeted blood monocytes and induced the heterodimerization of hIL-17RA and hIL-17RC, triggering a robust inflammatory response. Transcriptome analysis revealed that besides its activation of the hIL-17R pathway, ORF8 upregulated gene expression for fibrosis signaling and coagulation dysregulation. A naturally occurring ORF8 L84S variant that was highly associated with mild COVID-19 showed reduced hIL-17RA binding and attenuated inflammatory responses. This study reveals how SARS-CoV-2 ORF8 by a viral mimicry of the IL-17 cytokine contributes to COVID-19 severe inflammation. IMPORTANCE Patients infected with SARS-CoV-2 variants lacking open reading frame 8 (ORF8) have been associated with milder infection and disease outcome, but the molecular mechanism behind how this viral accessory protein mediates disease pathogenesis is not yet known. In our study, we revealed that secreted ORF8 protein mimics host IL-17 to activate IL-17 receptors A and C (IL-17RA/C) and induces a significantly stronger inflammatory response than host IL-17A, providing molecular insights into the role of ORF8 in COVID-19 pathogenesis and serving as a potential therapeutic target.

Identification of single domain antibodies with insect cytotoxicity using phage-display antibody library screening and Plutella xylostella ATP-binding cassette transporter subfamily C member 2 (ABCC2) -based insect cell expression system

It is extremely imminent to study a new strategy to manage agricultural pest like Plutella xylostella (P. xylostella) which is currently resistant to most of pesticides, including three domain-Cry toxins from Bacillus thuringiensis (Bt). In this study, we reported a phage displayed single domain antibody screening from human domain antibody (DAb) library targeted on Spodoptera frugiperda 9 (Sf9) cells expressed Cry1Ac toxin receptor, ATP-dependent binding cassette transporter C2 in P. xylostella (PxABCC2). After three rounds of panning, three cytotoxic antibodies (1D2, 2B7, 3C4) were obtained from thirty-eight antibodies and displayed high binding ability towards PxABCC2-expressed Sf9 cells. Through homology modeling and molecular docking, the interaction mode indicated that the most cytotoxic 1D2 of the three antibodies presented the lowest binding free energy required and had the most hydrogen bond formed with PxABCC2 in molecular docking analysis. Functional assay of key regions in 1D2 via Alanine replacement indicated that complementarity-determining region (CDR) 3 played a crucial role in antibody exerts binding activity and cytotoxicity. This study provides the first trial for discovering of potential cytotoxic antibodies from the human antibody library via specific receptor-expressed insect cell system biopanning.

Simultaneous affinity maturation and developability enhancement using natural liability-free CDRs

Affinity maturation is often a necessary step for the development of potent therapeutic molecules. Many different diversification strategies have been used for antibody affinity maturation, including error-prone PCR, chain shuffling, and targeted complementary-determining region (CDR) mutation. Although effective, they can negatively impact antibody stability or alter epitope recognition. Moreover, they do not address the presence of sequence liabilities, such as glycosylation, asparagine deamidation, aspartate isomerization, aggregation motifs, and others. Such liabilities, if present or inadvertently introduced, can potentially create the need for new rounds of engineering, or even abolish the value of the antibody as a therapeutic molecule. Here, we demonstrate a sequence agnostic method to improve antibody affinities, while simultaneously eliminating sequence liabilities and retaining the same epitope binding as the parental antibody. This was carried out using a defined collection of natural CDRs as the diversity source, purged of sequence liabilities, and matched to the antibody germline gene family. These CDRs were inserted into the lead molecule in one or two sites at a time (LCDR1-2, LCDR3, HCDR1-2) while retaining the HCDR3 and framework regions unchanged. The final analysis of 92 clones revealed 81 unique variants, with each of 24 tested variants having the same epitope specificity as the parental molecule. Of these, the average affinity improved by over 100 times (to 96 pM), and the best affinity improvement was 231-fold (to 32 pM).

Communication pathways bridge local and global conformations in an IgG4 antibody

The affinity of an antibody for its antigen is primarily determined by the specific sequence and structural arrangement of the complementarity-determining regions (CDRs). Recent evidence, however, points toward a nontrivial relation between the CDR and distal sites: variations in the binding strengths have been observed upon mutating residues separated from the paratope by several nanometers, thus suggesting the existence of a communication network within antibodies, whose extension and relevance might be deeper than insofar expected. In this work, we test this hypothesis by means of molecular dynamics (MD) simulations of the IgG4 monoclonal antibody pembrolizumab, an approved drug that targets the programmed cell death protein 1 (PD-1). The molecule is simulated in both the apo and holo states, totalling 4 μs of MD trajectory. The analysis of these simulations shows that the bound antibody explores a restricted range of conformations with respect to the apo one, and that the global conformation of the molecule correlates with that of the CDR. These results support the hypothesis that pembrolizumab featues a multi-scale hierarchy of intertwined global and local conformational changes. The analysis pipeline developed in this work is general, and it can help shed further light on the mechanistic aspects of antibody function.

Paratope states in solution improve structure prediction and docking

Structure-based antibody design and accurate predictions of antibody-antigen interactions remain major challenges in computational biology. By using molecular dynamics simulations, we show that a single static X-ray structure is not sufficient to identify determinants of antibody-antigen recognition. Here, we investigate antibodies that undergo substantial conformational changes upon antigen binding and have been classified as difficult cases in an extensive benchmark for antibody-antigen docking. We present thermodynamics and transition kinetics of these conformational rearrangements and show that paratope states can be used to improve antibody-antigen docking. By using the unbound antibody X-ray structure as starting structure for molecular dynamics simulations, we retain a binding competent conformation substantially different to the unbound antibody X-ray structure. We also observe that the kinetically dominant antibody paratope conformations are chosen by the bound antigen conformation with the highest probability. Thus, we show that paratope states in solution can improve antibody-antigen docking and structure prediction.

Antibody VH domain sequence analysis by a bioinformatics approach based on electronic amino acid properties may help to predict paratop location

Gene as the basic functional unit of DNA encodes information about the product such as protein. The majority of proteins realize function through protein-protein interactions involving short protein motifs. However, some proteins such as antibodies are established by the rearrangement of several (V-D-J) gene segments with the potential addition of nontemplated nucleotides that may change information encoded by the respective gene segment used. Antibody VH domain sequence analysis by ISM bioinformatics approach that is based on amino acids physicochemical features, enable to distinguish the contribution of the information encoded by VH gene or generated during VDJ gene recombination for antibody-antigen interaction. The data presented in this report revealed the significance of CDRH3 for the interaction of antibody specific for immunogenic molecules while CDRH3 contribution is minor for antibody interaction with nonimmunogenic molecules such as haptens and native mammalian dsDNA. Thus, paratopes might be located in the CDRH3 or VH regions.

Functional antibody characterization via direct structural analysis and information-driven protein-protein docking

Detailed description of the mechanism of action of the therapeutic antibodies is essential for the functional characterization and future optimization of potential clinical agents. We recently developed KD035, a fully human antibody targeting vascular endothelial growth factor receptor 2 (VEGFR2). KD035 blocked VEGF‐A, and VEGF‐C‐mediated VEGFR2 activation, as demonstrated by the in vitro binding and competition assays and functional cellular assays. Here, we report a computational model of the complex between the variable fragment of KD035 (KD035(Fv)) and the domains 2 and 3 of the extracellular portion of VEGFR2 (VEGFR2(D2‐3)). Our modeling was guided by a priori experimental information including the X‐ray structures of KD035 and related antibodies, binding assays, target domain mapping and comparison of KD035 affinity for VEGFR2 from different species. The accuracy of the model was assessed by molecular dynamics simulations, and subsequently validated by mutagenesis and binding analysis. Importantly, the steps followed during the generation of this model can set a precedent for future in silico efforts aimed at the accurate description of the antibody–antigen and more broadly protein–protein complexes.

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI.

Estimating the binding affinities of protein-protein interactions (PPIs) is crucial to understand protein function and design new functional proteins. Since the experimental measurement in wet-labs is labor-intensive and time-consuming, fast and accurate in silico approaches have received much attention. Although considerable efforts have been made in this direction, predicting the effects of mutations on the protein-protein binding affinity is still a challenging research problem. In this work, we introduce GeoPPI, a novel computational approach that uses deep geometric representations of protein complexes to predict the effects of mutations on the binding affinity. The geometric representations are first learned via a self-supervised learning scheme and then integrated with gradient-boosting trees to accomplish the prediction. We find that the learned representations encode meaningful patterns underlying the interactions between atoms in protein structures. Also, extensive tests on major benchmark datasets show that GeoPPI has made an important improvement over the existing methods in predicting the effects of mutations on the binding affinity.

Computational and Rational Design of Single-Chain Antibody against Tick-Borne Encephalitis Virus for Modifying Its Specificity

Tick-borne encephalitis virus (TBEV) causes 5−7 thousand cases of human meningitis and encephalitis annually. The neutralizing and protective antibody ch14D5 is a potential therapeutic agent. This antibody exhibits a high affinity for binding with the D3 domain of the glycoprotein E of the Far Eastern subtype of the virus, but a lower affinity for the D3 domains of the Siberian and European subtypes. In this study, a 2.2-fold increase in the affinity of single-chain antibody sc14D5 to D3 proteins of the Siberian and European subtypes of the virus was achieved using rational design and computational modeling. This improvement can be further enhanced in the case of the bivalent binding of the full-length chimeric antibody containing the identified mutation.

Optimization of therapeutic antibodies

In this review, we have summarized the current landscape of therapeutic antibody optimization for successful development. By engineering antibodies with display technology, computer-aided design and site mutagenesis, various properties of the therapeutic antibody candidates can be improved with the purpose of enhancing their safety, efficacy and developability. These properties include antigen binding affinity and specificity, biological efficacy, pharmacokinetics and pharmacodynamics, immunogenicity and physicochemical developability features. A best-in-class strategy may require the optimization of all these properties to generate a good therapeutic antibody.

Computationally-guided design and affinity improvement of a protein binder targeting a specific site on HER2

A protein binder with a desired epitope and binding affinity is critical to the development of therapeutic agents. Here we present computationally-guided design and affinity improvement of a protein binder recognizing a specific site on domain IV of human epidermal growth factor receptor 2 (HER2). As a model, a protein scaffold composed of Leucine-rich repeat (LRR) modules was used. We designed protein binders which appear to bind a target site on domain IV using a computational method. Top 10 designs were expressed and tested with binding assays, and a lead with a low micro-molar binding affinity was selected. Binding affinity of the selected lead was further increased by two-orders of magnitude through mutual feedback between computational and experimental methods. The utility and potential of our approach was demonstrated by determining the binding interface of the developed protein binder through its crystal structure in complex with the HER2 domain IV.

Progress toward improved understanding of antibody maturation

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

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

Characterization of Variable Region Genes and Discovery of Key Recognition Sites in the Complementarity Determining Regions of the Anti-Thiacloprid Monoclonal Antibody

Sequence-defined recombinant antibodies (rAbs) have emerged as alternatives to hybridoma-secreted monoclonal antibodies (mAbs) for performing immunoassays. However, the polyploidy nature of hybridomas often leads to the coexistence of aberrant or non-specific functional variable region (VR) gene transcripts, which complicates the identification of correct VR sequences. Herein, we introduced the use of LC-MS/MS combined with next-generation sequencing to characterize VR sequences in an anti-thiacloprid mAb, which was produced by a hybridoma with genetic antibody diversity. The certainty of VR sequences was verified by the functional analysis based on the recombinant antibody (rAb) expressed by HEK293 mammalian cells. The performance of the rAb was similar to that of the parental mAb, with IC50 values of 0.73 and 0.46 μg/L as measured by ELISAs. Moreover, molecular docking analysis revealed that Ser52 (H-CDR2), Trp98, and Trp93 (L-CDR3) residues in the complementarity determining regions (CDRs) of the identified VR sequences predominantly contributed to thiacloprid-specific recognition through hydrogen bonds and the CH-π interaction. Through single-site-directed alanine mutagenesis, we found that Trp98 and Trp93 (L-CDR3) showed high affinity to thiacloprid, while Ser52 (H-CDR2) had an auxiliary effect on the specific binding. This study presents an efficient and reliable way to determine the key recognition sites of hapten-specific mAbs, facilitating the improvement of antibody properties.

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

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

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

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

Small design from big alignment: engineering proteins with multiple sequence alignment as the starting point

Multiple sequence alignment (MSA) is a fundamental way to gain information that cannot be obtained from the analysis of any individual sequence included in the alignment. It provides ways to investigate the relationship between sequence and function from a perspective of evolution. Thus, the MSA of proteins can be employed as a reference for protein engineering. In this paper, we reviewed the recent advances to highlight how protein engineering was benefited from the MSA of proteins. These methods include (1) engineering the thermostability or solubility of proteins by making it closer to the consensus sequence of the alignment through introducing site mutations; (2) structure-based engineering proteins with comparative modeling; (3) creating paleoenzymes featured with high thermostability and promiscuity by constructing the ancestral sequences derived from multiple sequence alignment; and (4) incorporating site-mutations targeting the evolutionarily coupled sites identified from multiple sequence alignment.

Seeking high-priority mutations enabling successful antibody-breeding: systematic analysis of a mutant that gained over 100-fold enhanced affinity

"Antibody-breeding" has provided therapeutic/diagnostic antibody mutants with greater performance than native antibodies. Typically, random point mutations are introduced into the VH and VL domains of parent antibodies to generate diverse libraries of single-chain Fv fragments (scFvs), from which evolved mutants are selected. We produced an scFv against estradiol-17β with 11 amino acid substitutions and a >100-fold improved affinity constant (Ka = 1.19 × 1010 M-1) over the parent scFv, enabling immunoassays with >30-fold higher sensitivity. We systematically analyzed contributions of these substitutions to the affinity enhancement. Comparing various partial scFv revertants based on their Kas indicated that a revertant with four substitutions (VH-L100gQ, VL-I29V, -L36M, -S77G) exhibited somewhat higher affinity (Ka = 1.46 × 1010 M-1). Finally, the VH-L100gQ substitution, occurring in VH complementarity-determining region (CDR) 3, was found to be the highest-priority for improving the affinity, and VL-I29V and/or VL-L36M cooperated significantly. These findings encouraged us to reconsider the potential of VH-CDR3-targeting mutagenesis, which has been frequently attempted. The substitution(s) wherein might enable a "high rate of return" in terms of selecting mutants with dramatically enhanced affinities. The "high risk" of generating a tremendous excess of "junk mutants" can be overcome with the efficient selection systems that we developed.

Affinity-matured variants derived from nimotuzumab keep the original fine specificity and exhibit superior biological activity

Nimotuzumab is a humanized monoclonal antibody against the Epidermal Growth Factor Receptor with a long history of therapeutic use, recognizing an epitope different from the ones targeted by other antibodies against the same antigen. It is also distinguished by much less toxicity resulting in a better safety profile, which has been attributed to its lower affinity compared to these other antibodies. Nevertheless, the ideal affinity window for optimizing the balance between anti-tumor activity and toxic effects has not been determined. In the current work, the paratope of the phage-displayed nimotuzumab Fab fragment was evolved in vitro to obtain affinity-matured variants. Soft-randomization of heavy chain variable region CDRs and phage selection resulted in mutated variants with improved binding ability. Two recombinant antibodies were constructed using these variable regions, which kept the original fine epitope specificity and showed moderate affinity increases against the target (3-4-fold). Such differences were translated into a greatly enhanced inhibitory capacity upon ligand-induced receptor phosphorylation on tumor cells. The new antibodies, named K4 and K5, are valuable tools to explore the role of affinity in nimotuzumab biological properties, and could be used for applications requiring a fine-tuning of the balance between binding to tumor cells and healthy tissues.