Computational docking of antibody-antigen complexes, opportunities and pitfalls illustrated by influenza hemagglutinin

Human antibodies in Mexico and Brazil neutralizing tick-borne flaviviruses

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

Comparative assessment of different anti-CD147/Basigin 2 antibodies as a potential therapeutic anticancer target by molecular modeling and dynamic simulation

Cluster of differentiation 147 (CD147) is an attractive target for anticancer therapy since it is pivotal in developing and progressing several of malignant tumors in the context of its high expression levels. Although anti-CD147 antibodies by different laboratories are designed for the Ig-like domains of CD147, there is a demand to provide priority among these anti-CD147 antibodies for developing of therapeutic anti-CD147 antibody before experimental validations. This study uses molecular docking and dynamic simulation techniques to compare the binding modes and affinities of nine antibody models against the Ig-like domains of CD147. After obtaining the model antibodies by homology modeling via Robetta, we predicted the CDRs of nine antibodies and the epitopes of CD147 to reach more accurate results for antigen affinity in molecular docking. Next, from HADDOCK 2.4., we meticulously handpicked the most superior model clusters (Z-Score: - 2.5 to - 1.2) and identified that meplazumab had higher affinities according to the success rate as the percentage of a scoring scale. We achieved stable simulations of CD147-antibody interaction. Our outcomes hold hypothetical importance for further experimental cancer research on the design and development of the relevant model antibodies.

Development of a [89Zr]Zr-labeled Human Antibody using a Novel Phage-displayed Human scFv Library

PURPOSE

Tax-interacting protein 1 (TIP1) is a cancer-specific radiation-inducible cell surface antigen that plays a role in cancer progression and resistance to therapy. This study aimed to develop a novel anti-TIP1 human antibody for noninvasive PET imaging in patients with cancer.

EXPERIMENTAL DESIGN

A phage-displayed single-chain variable fragment (scFv) library was created from healthy donors' blood. High-affinity anti-TIP1 scFvs were selected from the library and engineered to human IgG1. Purified Abs were characterized by size exclusion chromatography high-performance liquid chromatography (SEC-HPLC), native mass spectrometry (native MS), ELISA, BIAcore, and flow cytometry. The labeling of positron emitter [89Zr]Zr to the lead Ab, L111, was optimized using deferoxamine (DFO) chelator. The stability of [89Zr]Zr-DFO-L111 was assessed in human serum. Small animal PET studies were performed in lung cancer tumor models (A549 and H460).

RESULTS

We obtained 95% pure L111 by SEC-HPLC. Native MS confirmed the intact mass and glycosylation pattern of L111. Conjugation of three molar equivalents of DFO led to the optimal DFO-to-L111 ratio of 1.05. Radiochemical purity of 99.9% and specific activity of 0.37 MBq/μg was obtained for [89Zr]Zr-DFO-L111. [89Zr]Zr-DFO-L111 was stable in human serum over 7 days. The immunoreactive fraction in cell surface binding studies was 96%. In PET, preinjection with 4 mg/kg cold L111 before [89Zr]Zr-DFO-L111 (7.4 MBq; 20 μg) significantly (P < 0.01) enhanced the tumor-to-muscle standard uptake values (SUVmax) ratios on day 5 compared with day 2 postinjection.

CONCLUSIONS

L111 Ab targets lung cancer cells in vitro and in vivo. [89Zr]Zr-DFO-L111 is a human antibody that will be evaluated in the first in-human study of safety and PET imaging.

Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.

Bioinformatics analysis of multi-epitope peptide vaccines against Hepatitis C virus: a molecular docking study

BACKGROUND

Hepatitis C Virus (HCV) infection is one of the causal agents of liver disease burden. Six multiple antigenic peptides were synthesized including (P315, P412, and P517) plus (P1771, P2121, and P2941) to induce humoral and cellular responses, respectively against HCV infection.

AIM

This paper aimed to employ computational tools to evaluate the efficacy of each peptide individually and to determine the most effective one for better vaccine development and/or immunotherapy.

METHODS

VaxiJen web and AllerTOP servers were used for antigenicity and allergenicity prediction, respectively. The ToxinPred web server was used to investigate the peptide toxicity. Each peptide was docked with its corresponding receptors.

RESULTS

No peptides were expected to be toxic. P315 and P2941 are predicted to have robust antigenic properties, lowest allergenicity, and minimal sOPEP energies. In turn, P315 (derived from gpE1) formed the highest hydrophobic bonds with the BCR and CD81 receptors that will elicit B cell function. P2941 (derived from NS5B) was shown to strongly bind to both CD4 and CD8 receptors that will elicit T cell function.

CONCLUSION

P315 successfully bound to B cell (BCR and CD81) receptors. Also, P2941 is strongly bound to T cell (CD4 and CD8) receptors.

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.

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction

Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.

Investigating RNA-protein recognition mechanisms through supervised molecular dynamics (SuMD) simulations

Ribonucleic acid (RNA) plays a key regulatory role within the cell, cooperating with proteins to control the genome expression and several biological processes. Due to its characteristic structural features, this polymer can mold itself into different three-dimensional structures able to recognize target biomolecules with high affinity and specificity, thereby attracting the interest of drug developers and medicinal chemists. One successful example of the exploitation of RNA's structural and functional peculiarities is represented by aptamers, a class of therapeutic and diagnostic tools that can recognize and tightly bind several pharmaceutically relevant targets, ranging from small molecules to proteins, making use of the available structural and conformational freedom to maximize the complementarity with their interacting counterparts. In this scientific work, we present the first application of Supervised Molecular Dynamics (SuMD), an enhanced sampling Molecular Dynamics-based method for the study of receptor-ligand association processes in the nanoseconds timescale, to the study of recognition pathways between RNA aptamers and proteins, elucidating the main advantages and limitations of the technique while discussing its possible role in the rational design of RNA-based therapeutics.

Accurate determination of epitope for antibodies with unknown 3D structures

MAbTope is a docking-based method for the determination of epitopes. It has been used to successfully determine the epitopes of antibodies with known 3D structures. However, during the antibody discovery process, this structural information is rarely available. Although we already have evidence that homology models of antibodies could be used instead of their 3D structure, the choice of the template, the methodology for homology modeling and the resulting performance still have to be clarified. Here, we show that MAbTope has the same performance when working with homology models of the antibodies as compared to crystallographic structures. Moreover, we show that even low-quality models can be used. We applied MAbTope to determine the epitope of dupilumab, an anti- interleukin 4 receptor alpha subunit therapeutic antibody of unknown 3D structure, that we validated experimentally. Finally, we show how the MAbTope-determined epitopes for a series of antibodies targeting the same protein can be used to predict competitions, and demonstrate the accuracy with an experimentally validated example.

3D: three-dimensionalRMSD: root mean square deviationCDR: complementary-determining regionCPU: central processing unitsVH: heavy chain variable regionVL: light chain variable regionscFv: single-chain variable fragmentsVHH: single-chain antibody variable regionIL4Rα: Interleukin 4 receptor alpha chainSPR: surface plasmon resonancePDB: protein data bankHEK293: Human embryonic kidney 293 cellsEDTA: Ethylenediaminetetraacetic acidFBS: Fetal bovine serumANOVA: Analysis of varianceEGFR: Epidermal growth factor receptorPE: PhycoerythrinAPC: AllophycocyaninFSC: forward scatterSSC: side scatterWT: wild type

Keywords: MAbTope, Epitope Mapping, Molecular docking, Antibody modeling, Antibody-antigen docking

A Predictive Model of Antibody Binding in the Presence of IgG-Interacting Bacterial Surface Proteins

Many bacteria can interfere with how antibodies bind to their surfaces. This bacterial antibody targeting makes it challenging to predict the immunological function of bacteria-associated antibodies. The M and M-like proteins of group A streptococci (GAS) exhibit IgGFc-binding regions, which they use to reverse IgG binding orientation depending on the host environment. Unraveling the mechanism behind these binding characteristics may identify conditions under which bound IgG can drive an efficient immune response. Here, we have developed a biophysical model for describing these complex protein-antibody interactions. We show how the model can be used as a tool for studying the binding behavior of various IgG samples to M protein by performing in silico simulations and correlating this data with experimental measurements. Besides its use for mechanistic understanding, this model could potentially be used as a tool to aid in the development of antibody treatments. We illustrate this by simulating how IgG binding to GAS in serum is altered as specified amounts of monoclonal or pooled IgG is added. Phagocytosis experiments link this altered antibody binding to a physiological function and demonstrate that it is possible to predict the effect of an IgG treatment with our model. Our study gives a mechanistic understanding of bacterial antibody targeting and provides a tool for predicting the effect of antibody treatments in the presence of bacteria with IgG-modulating surface proteins.

SARS-CoV-2 neutralizing human recombinant antibodies selected from pre-pandemic healthy donors binding at RBD-ACE2 interface

COVID-19 is a severe acute respiratory disease caused by SARS-CoV-2, a new recently emerged sarbecovirus. This virus uses the human ACE2 enzyme as receptor for cell entry, recognizing it with the receptor binding domain (RBD) of the S1 subunit of the viral spike protein. We present the use of phage display to select anti-SARS-CoV-2 spike antibodies from the human naïve antibody gene libraries HAL9/10 and subsequent identification of 309 unique fully human antibodies against S1. 17 antibodies are binding to the RBD, showing inhibition of spike binding to cells expressing ACE2 as scFv-Fc and neutralize active SARS-CoV-2 virus infection of VeroE6 cells. The antibody STE73-2E9 is showing neutralization of active SARS-CoV-2 as IgG and is binding to the ACE2-RBD interface. Thus, universal libraries from healthy human donors offer the advantage that antibodies can be generated quickly and independent from the availability of material from recovering patients in a pandemic situation.

Characterization and Modeling of Reversible Antibody Self-Association Provide Insights into Behavior, Prediction, and Correction

Reversible antibody self-association, while having major developability and therapeutic implications, is not fully understood or readily predictable and correctable. For a strongly self-associating humanized mAb variant, resulting in unacceptable viscosity, the monovalent affinity of self-interaction was measured in the low μM range, typical of many specific and biologically relevant protein-protein interactions. A face-to-face interaction model extending across both the heavy-chain (HC) and light-chain (LC) Complementary Determining Regions (CDRs) was apparent from biochemical and mutagenesis approaches as well as computational modeling. Light scattering experiments involving individual mAb, Fc, Fab, and Fab'2 domains revealed that Fabs self-interact to form dimers, while bivalent mAb/Fab'2 forms lead to significant oligomerization. Site-directed mutagenesis of aromatic residues identified by homology model patch analysis and self-docking dramatically affected self-association, demonstrating the utility of these predictive approaches, while revealing a highly specific and tunable nature of self-binding modulated by single point mutations. Mutagenesis at these same key HC/LC CDR positions that affect self-interaction also typically abolished target binding with notable exceptions, clearly demonstrating the difficulties yet possibility of correcting self-association through engineering. Clear correlations were also observed between different methods used to assess self-interaction, such as Dynamic Light Scattering (DLS) and Affinity-Capture Self-Interaction Nanoparticle Spectroscopy (AC-SINS). Our findings advance our understanding of therapeutic protein and antibody self-association and offer insights into its prediction, evaluation and corrective mitigation to aid therapeutic development.

A novel strategy for engineering of a smart generation of immune ribonucleases against EGFR+ cells

The overexpression of epidermal growth factor receptor (EGFR) could result in the development of solid tumors of prostate, breast, gastric, colorectal, ovarian, and head and neck, leading to carcinoma. Antibody therapies are ideal methods to overcome malignant diseases. However, immunoribonucleases are a new generation of antibodies in which an RNase binds to a specific antibody and shows a stronger ability to terminate cancer cells. In this study, we engineered Rana pipiens RNase to bind to the scFv of human antiepidermal growth factor receptor antibody. The molecular dynamic simulations confirmed protein stability and the ability of scFv-ranpirnase (rantoxin) to bind to epidermal growth factor receptor protein. Then, the rantoxin construct was synthesized in a pCDNA 3.1 Neo vector. CHO-K1 cells were used as expression hosts and the construct was transfected. Cells were selected by antibiotic therapies using neomycin, 120 mg/ml, and the high-yield colony was screened by real-time polymerase chain reaction (PCR) methods. Then, the recombinant protein production was confirmed using the sodium dodecyl sulfate polyacrylamide gel electrophoresis and western blot analyses. The molecular dynamic simulation (MDS) confirmed that the I467, S468, Q408, and H409 amino acids of EGFR bonded well to rantoxin. As revealed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and western blot analyses, the rantoxin production and PCR analysis showed that the T3 colony can produce rantoxin messenger RNA fourfold higher than the GAPDH gene. The immunotoxin function was assessed in A431 cancer cells and EGFR-negative HEK293 cells, and IC₅₀  values were estimated to be 22.4 ± 3 and >620.4 ± 5 nM, respectively. The results indicated that the immunotoxins produced in this study had the potential for use as anticancer drugs.

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.

Applications of Recombinant Monoclonal Antibodies against Filarial Antigen Proteins

This study investigated the applications of recombinant monoclonal antibodies (rmAbs) produced against two recombinant filarial proteins of diagnostic value. Ab5B and Ab3A were produced against recombinant BmSXP, and Ab4 and Ab4-fragment crystallizable (Fc) against recombinant BmR1. Ab5B and Ab4-Fc were found to be useful as quality control (QC) reagents for two commercial rapid test kits, such as Brugia Rapid^TM and BLF Rapid^® (Reszon Diagnostics International Sdn. Bhd., 47600 Subang Jaya, Selangor, Malaysia), respectively. The two rmAbs reacted positively with the corresponding recombinant proteins lined on the nitrocellulose strips of the cassette tests, thus may replace or reduce the need for patient serum samples as positive controls for QC of the commercial kits. They were also successfully conjugated to gold nanoparticles and reacted positively with the test lines containing the corresponding recombinant proteins when directly applied to the cassette tests. The gold-conjugated reagents can be used to confirm the antigenicity of test lines after the storage of the rapid tests for a prolonged period or under unfavorable conditions. Furthermore, Ab5B and Ab3A were shown to be able to capture the target recombinant proteins through immunoaffinity purification, enabling their use for applications that need very highly purified proteins. In conclusion, this study demonstrated several potential uses of rmAb proteins produced against recombinant filarial proteins.

Antibody Structure and Function: The Basis for Engineering Therapeutics

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

Modeling Antibody-Antigen Complexes by Information-Driven Docking

Antibodies are Y-shaped proteins essential for immune response. Their capability to recognize antigens with high specificity makes them excellent therapeutic targets. Understanding the structural basis of antibody-antigen interactions is therefore crucial for improving our ability to design efficient biological drugs. Computational approaches such as molecular docking are providing a valuable and fast alternative to experimental structural characterization for these complexes. We investigate here how information about complementarity-determining regions and binding epitopes can be used to drive the modeling process, and present a comparative study of four different docking software suites (ClusPro, LightDock, ZDOCK, and HADDOCK) providing specific options for antibody-antigen modeling. Their performance on a dataset of 16 complexes is reported. HADDOCK, which includes information to drive the docking, is shown to perform best in terms of both success rate and quality of the generated models in both the presence and absence of information about the epitope on the antigen.

Zika virus proteins at an atomic scale: how does structural biology help us to understand and develop vaccines and drugs against Zika virus infection?

In Brazil and in other tropical areas Zika virus infection was directly associated with clinical complications as microcephaly in newborn children whose mothers were infected during pregnancy and the Guillain-Barré syndrome in adults. Recently, research has been focused on developing new vaccines and drug candidates against Zika virus infection since none of those are available. In order to contribute to vaccine and drug development efforts, it becomes important the understanding of the molecular basis of the Zika virus recognition, infection and blockade. To this purpose, it is essential the structural determination of the Zika virus proteins. The genome sequencing of the Zika virus identified ten proteins, being three structural (protein E, protein C and protein prM) and seven non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5). Together, these proteins are the main targets for drugs and antibody recognition. Here we examine new discoveries on high-resolution structural biology of Zika virus, observing the interactions and functions of its proteins identified via state-of-art structural methodologies as X-ray crystallography, nuclear magnetic resonance spectroscopy and cryogenic electronic microscopy. The aim of the present study is to contribute to the understanding of the structural basis of Zika virus infection at an atomic level and to point out similarities and differences to others flaviviruses.

A cross-reactive human monoclonal antibody targets the conserved H7 antigenic site A from fifth wave H7N9-infected humans

Subtype H7 avian influenza viruses have been found to be associated with human infection and represent a risk for global public health. In 2013, the emergence of H7N9 virus in human beings and persistent human infection in China raised the most serious pandemic threat. Here we identified a human monoclonal antibody, P52E03, targeting the hemagglutinin (HA) of subtype H7 influenza viruses (H7 antigen), from a convalescent patient infected with H7N9 in 2017. P52E03 showed in vitro hemagglutination inhibiting (HI) and neutralizing activity against subtype H7 viruses belonging to both North American and Eurasian lineages. Moreover, it could prophylactically protect mice against weight loss and death caused by challenge with lethal H7N9 viruses in vivo and, therefore, is a candidate for development of antiviral agent against H7N9 infection. By generating escape mutant variants, we found that a single G151E substitution in the viral H7 antigenic site A could abort the neutralizing activity. Computational structural prediction of the P52E03/H7 complex revealed that residues including G151 in and around the conserved antigenic site A region are important for antigen recognition by the H7 cross-reactive antibody. Finally, we found that the P52E03 germline precursor (gHgL) antibody recognizes HA with measurable affinity, suggesting that its epitope is vulnerable to the human immune system and might elicit neutralizing antibodies (nAbs) in vivo after vaccination.

Trends in therapeutic antibody affinity maturation: From in-vitro towards next-generation sequencing approaches

Current advances in antibody engineering driving the strongest growth area in biotherapeutic agents development. Affinity improvement that is mainly important for biological activity and clinical efficacy of therapeutic antibodies, has still remained a challenging task. In the human body, during a course of immune response affinity maturation increase antibody activity by several rounds of somatic hypermutation and clonal selection in the germinal center. The final outputs are antibodies representing higher affinity and specificity against a particular antigen. In the realm of biotechnology, exploring of mutations which improve antibody affinity while preserving its specificity and stability is an extremely time-consuming and laborious process. Recent advances in computational algorithms and DNA sequencing technologies help researchers to redesign antibody structure to achieve desired properties such as improved binding affinity. In this review, we briefly described the principle of affinity maturation and different corresponding in vitro techniques. Also, we recapitulated the most recent advancements in the field of antibody affinity maturation including computational approaches and next-generation sequencing (NGS).

Conformational changes in antibody Fab fragments upon binding and their consequences on the performance of docking algorithms

BACKGROUND

The existence of conformational changes in antibodies upon binding has been previously established. However, existing analyses focus on individual cases and no quantitative study provides a more global view of potential moves and repacking, especially on recent data. The present study focuses on analyzing the conformational changes in various antibodies upon binding, providing quantitative observations to be exploited for antibody-related modeling.

METHODS

Cartesian and dihedral Root-Mean-Squared Deviations were calculated for different subparts of 27 different antibodies, for which X-ray structures in the bound and unbound states are available. Elbow angle variations were also calculated. Previously reported results of four docking algorithms were condensed into one score giving overall docking success for each of 16 antibody-antigen cases.

RESULTS

Very diverse movements are observed upon binding. While many loops stay very rigid, several others display side-chain repacking or backbone rearrangements, or both, at many different levels. Large conformational changes restricted to one or more antibody hypervariable loops were found to be a better indicator of docking difficulty than overall conformational variation at the antibody-antigen interface. However, the failure of docking algorithms on some almost-rigid cases shows that scoring is still a major bottleneck in docking pose prediction.

CONCLUSIONS

This study is aimed to help scientists working on antibody analysis and design by giving insights into the nature and the extent of conformational changes at different levels upon antigen binding.

Computational study on the origin of the cancer immunotherapeutic potential of B and T cell epitope peptides

Immune therapy is generally seen as the future of cancer treatment. The discovery of tumor-associated antigens and cytotoxic T lymphocyte epitope peptides spurned intensive research into effective peptide-based cancer vaccines. One of the major obstacles hindering the development of peptide-based cancer vaccines is the lack of humoral response induction. As of now, very limited work has been performed to identify epitope peptides capable of inducing both cellular and humoral anticancer responses. In addition, no research has been carried out to analyze the structure and properties of peptides responsible for such immunological activities. This study utilizes a machine learning method together with interpretable descriptors in an attempt to identify parameters determining the immunotherapeutic activity of cancer epitope peptides.

Mapping Antibody Epitopes by Solution NMR Spectroscopy: Practical Considerations

Identifying an epitope, the region of the antigen in contact with an antibody, is useful in both basic and pharmaceutical research, as well as in vaccine design. Solution NMR spectroscopy is particularly well suited to the residue level characterization of intermolecular interfaces, including antibody-antigen interactions, and thus to epitope identification. Here, we describe the use of NMR for residue level characterization of protein epitopes, focusing on experimental protocols and practical considerations, highlighting advantages and drawbacks of the approach.

Ofatumumab Monoclonal Antibody Affinity Maturation Through in silico Modeling

Background:

Ofatumumab, an anti-CD20 mAb, was approved in 2009 for the treatment of chronic lymphocytic leukemia. This mAb acts through immune-mediated mechanisms, in particular complement-dependent cytotoxicity and antibody-dependent cellular cytotoxicity by natural killer cells as well as antibody-dependent phagocytosis by macrophages. Apoptosis induction is another mechanism of this antibody. Computational docking is the method of predicting the conformation of an antibody-antigen from its separated elements. Validation of the designed antibodies is carried out by docking tools. Increased affinity enhances the biological action of the antibody, which in turn improves the therapeutic effects. Furthermore, the increased antibody affinity can reduce the therapeutic dose of the antibody, resulting in lower toxicity and handling cost.

Methods:

Considering the importance of this issue, using in silico analysis such as docking and molecular dynamics, we aimed to find the important amino acids of the Ofatumumab antibody and then replaced these amino acids with others to improve antibody-binding affinity. Finally, we examined the binding affinity of antibody variants to antigen.

Results:

Our findings showed that variant 3 mutations have improved the characteristics of antibody binding compared to normal Ofatumumab antibodies.

Conclusion:

The designed anti-CD20 antibodies showed potentiality for improved affinity in comparison to commercial Ofatumumab.

A Human Bi-specific Antibody against Zika Virus with High Therapeutic Potential

Zika virus (ZIKV), a mosquito-borne flavivirus, causes devastating congenital birth defects. We isolated a human monoclonal antibody (mAb), ZKA190, that potently cross-neutralizes multi-lineage ZIKV strains. ZKA190 is highly effective in vivo in preventing morbidity and mortality of ZIKV-infected mice. NMR and cryo-electron microscopy show its binding to an exposed epitope on DIII of the E protein. ZKA190 Fab binds all 180 E protein copies, altering the virus quaternary arrangement and surface curvature. However, ZIKV escape mutants emerged in vitro and in vivo in the presence of ZKA190, as well as of other neutralizing mAbs. To counter this problem, we developed a bispecific antibody (FIT-1) comprising ZKA190 and a second mAb specific for DII of E protein. In addition to retaining high in vitro and in vivo potencies, FIT-1 robustly prevented viral escape, warranting its development as a ZIKV immunotherapy.

Balance of Anti-CD123 Chimeric Antigen Receptor Binding Affinity and Density for the Targeting of Acute Myeloid Leukemia

Chimeric antigen receptor (CAR)-redirected T lymphocytes are a promising immunotherapeutic approach and object of pre-clinical evaluation for the treatment of acute myeloid leukemia (AML). We developed a CAR against CD123, overexpressed on AML blasts and leukemic stem cells. However, potential recognition of low CD123-positive healthy tissues, through the on-target, off-tumor effect, limits safe clinical employment of CAR-redirected T cells. Therefore, we evaluated the effect of context-dependent variables capable of modulating CAR T cell functional profiles, such as CAR binding affinity, CAR expression, and target antigen density. Computational structural biology tools allowed for the design of rational mutations in the anti-CD123 CAR antigen binding domain that altered CAR expression and CAR binding affinity without affecting the overall CAR design. We defined both lytic and activation antigen thresholds, with early cytotoxic activity unaffected by either CAR expression or CAR affinity tuning but later effector functions impaired by low CAR expression. Moreover, the anti-CD123 CAR safety profile was confirmed by lowering CAR binding affinity, corroborating CD123 is a good therapeutic target antigen. Overall, full dissection of these variables offers suitable anti-CD123 CAR design optimization for the treatment of AML.

Pharmacokinetic de-risking tools for selection of monoclonal antibody lead candidates

Pharmacokinetic studies play an important role in all stages of drug discovery and development. Recent advancements in the tools for discovery and optimization of therapeutic proteins have created an abundance of candidates that may fulfill target product profile criteria. Implementing a set of in silico, small scale in vitro and in vivo tools can help to identify a clinical lead molecule with promising properties at the early stages of drug discovery, thus reducing the labor and cost in advancing multiple candidates toward clinical development. In this review, we describe tools that should be considered during drug discovery, and discuss approaches that could be included in the pharmacokinetic screening part of the lead candidate generation process to de-risk unexpected pharmacokinetic behaviors of Fc-based therapeutic proteins, with an emphasis on monoclonal antibodies.

Antibody H3 Structure Prediction

Antibodies are proteins of the immune system that are able to bind to a huge variety of different substances, making them attractive candidates for therapeutic applications. Antibody structures have the potential to be useful during drug development, allowing the implementation of rational design procedures. The most challenging part of the antibody structure to experimentally determine or model is the H3 loop, which in addition is often the most important region in an antibody's binding site. This review summarises the approaches used so far in the pursuit of accurate computational H3 structure prediction.

Protein-Protein Docking

Protein-protein docking algorithms are powerful computational tools, capable of analyzing the protein-protein interactions at the atomic-level. In this chapter, we will review the theoretical concepts behind different protein-protein docking algorithms, highlighting their strengths as well as their limitations and pointing to important case studies for each method. The methods we intend to cover in this chapter include various search strategies and scoring techniques. This includes exhaustive global search, fast Fourier transform search, spherical Fourier transform-based search, direct search in Cartesian space, local shape feature matching, geometric hashing, genetic algorithm, randomized search, and Monte Carlo search. We will also discuss the different ways that have been used to incorporate protein flexibility within the docking procedure and some other future directions in this field, suggesting possible ways to improve the different methods.

Computer-Aided Antibody Design: An Overview

The use of monoclonal antibody as the next generation protein therapeutics with remarkable success has surged the development of antibody engineering to design molecules for optimizing affinity, better efficacy, greater safety and therapeutic function. Therefore, computational methods have become increasingly important to generate hypotheses, interpret and guide experimental works. In this chapter, we discussed the overall antibody design by computational approches.

Translating genomic sequences into antibody efficacy and safety against influenza toward clinical trial outcomes: a case study

Antibodies (Abs) are regarded as a newly emerging form of therapeutics that can provide passive protection against influenza. Although the application of genomics in clinics has increased dramatically, the number of therapeutics available for the treatment of many diseases remains insufficient. To translate genomics into medicines, we established a computational workflow to reconstruct 3D structures of hemagglutinin [HA, antigen (Ag)] and Ab for modeling Ab-HA interactions, based on their protein sequences. This platform was capable of testing the validity of bioinformatics predictions against viral neutralization titers for four Abs: CH65, CR8020, C05, and 5J8. By considering off-target effects, CR8020, the only successful candidate in clinical trials, was prospectively identified. Our approach could facilitate the discovery of Ab drugs against infectious diseases.

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.

SAbPred: a structure-based antibody prediction server

SAbPred is a server that makes predictions of the properties of antibodies focusing on their structures. Antibody informatics tools can help improve our understanding of immune responses to disease and aid in the design and engineering of therapeutic molecules. SAbPred is a single platform containing multiple applications which can: number and align sequences; automatically generate antibody variable fragment homology models; annotate such models with estimated accuracy alongside sequence and structural properties including potential developability issues; predict paratope residues; and predict epitope patches on protein antigens. The server is available at http://opig.stats.ox.ac.uk/webapps/sabpred.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

Prophylactic and postexposure efficacy of a potent human monoclonal antibody against MERS coronavirus

Middle East Respiratory Syndrome (MERS) is a highly lethal pulmonary infection caused by a previously unidentified coronavirus (CoV), likely transmitted to humans by infected camels. There is no licensed vaccine or antiviral for MERS, therefore new prophylactic and therapeutic strategies to combat human infections are needed. In this study, we describe, for the first time, to our knowledge, the isolation of a potent MERS-CoV-neutralizing antibody from memory B cells of an infected individual. The antibody, named LCA60, binds to a novel site on the spike protein and potently neutralizes infection of multiple MERS-CoV isolates by interfering with the binding to the cellular receptor CD26. Importantly, using mice transduced with adenovirus expressing human CD26 and infected with MERS-CoV, we show that LCA60 can effectively protect in both prophylactic and postexposure settings. This antibody can be used for prophylaxis, for postexposure prophylaxis of individuals at risk, or for the treatment of human cases of MERS-CoV infection. The fact that it took only 4 mo from the initial screening of B cells derived from a convalescent patient for the development of a stable chinese hamster ovary (CHO) cell line producing neutralizing antibodies at more than 5 g/L provides an example of a rapid pathway toward the generation of effective antiviral therapies against emerging viruses.

Structural and electrostatic analysis of HLA B-cell epitopes: inference on immunogenicity and prediction of humoral alloresponses

PURPOSE OF REVIEW

The immunogenic capacity of donor human leukocyte antigen (HLA) to induce humoral immune responses is not an intrinsic property of the mismatched alloantigen but depends on the HLA phenotype of the recipient. In recent years, advances in molecular sequence technology and information from X-ray crystallography have enabled structural comparison of donor and recipient HLA type providing an opportunity for a more rational approach for determining HLA compatibility. In this article, we review studies investigating the molecular basis of antibody-antigen interactions and present computational approaches to determine the complex physiochemical and structural properties of B-cell epitopes.

RECENT FINDINGS

The relative immunogenicity of individual HLA mismatches may be predicted from analysis of polymorphic amino acids at continuous and discontinuous HLA sequence positions. The use of alloantigen sequence information alone, however, provides limited insight into key determinants of B-cell epitope immunogenicity, such as the orientation, accessibility and physiochemical properties of amino acid side chains. Advances in computational molecular modelling techniques now enable assessment of HLA-alloantibody interactions at the atomic level. Recent evidence supports a strong link between HLA B-cell epitope surface electrostatic potential and their immunogenicity.

SUMMARY

Assessment of the surface electrostatic properties of HLA alloantigens and computational analyses of HLA-alloantibody interactions represent a promising area for future research into the molecular basis of HLA immunogenicity and antigenicity.

In silico approach towards designing virtual oligopeptides for HRSV

HRSV (human respiratory syncytial virus) is a serious cause of lower respiratory tract illness in infants and young children. Designing inhibitors from the proteins involved in virus replication and infection process provides target for new therapeutic treatments. In the present study, in silico docking was performed using motavizumab as a template to design motavizumab derived oligopeptides for developing novel anti-HRSV agents. Additional simulations were conducted to study the conformational propensities of the oligopeptides and confirmed the hypothesis that the designed oligopeptide is highly flexible and capable of assuming stable confirmation. Our study demonstrated the best specific interaction of GEKKLVEAPKS oligopeptide for glycoprotein strain A among various screened oligopeptides. Encouraged by the results, we expect that the proposed scheme will provide rational choices for antibody reengineering which is useful for systematically identifying the possible ways to improve efficacy of existing antibody drugs.

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

Antibodies (or immunoglobulins) are crucial for defending organisms from pathogens, but they are also key players in many medical, diagnostic and biotechnological applications. The ability to predict their structure and the specific residues involved in antigen recognition has several useful applications in all of these areas. Over the years, we have developed or collaborated in developing a strategy that enables researchers to predict the 3D structure of antibodies with a very satisfactory accuracy. The strategy is completely automated and extremely fast, requiring only a few minutes (∼10 min on average) to build a structural model of an antibody. It is based on the concept of canonical structures of antibody loops and on our understanding of the way light and heavy chains pack together.

Flexible docking-based molecular dynamics/steered molecular dynamics calculations of protein-protein contacts in a complex of cytochrome P450 1A2 with cytochrome b5

Formation of transient complexes of cytochrome P450 (P450) with another protein of the endoplasmic reticulum membrane, cytochrome b5 (cyt b5), dictates the catalytic activities of several P450s. Therefore, we examined formation and binding modes of the complex of human P450 1A2 with cyt b5. Docking of soluble domains of these proteins was performed using an information-driven flexible docking approach implemented in HADDOCK. Stabilities of the five unique binding modes of the P450 1A2-cyt b5 complex yielded by HADDOCK were evaluated using explicit 10 ns molecular dynamics (MD) simulations in aqueous solution. Further, steered MD was used to compare the stability of the individual P450 1A2-cyt b5 binding modes. The best binding mode was characterized by a T-shaped mutual orientation of the porphyrin rings and a 10.7 Å distance between the two redox centers, thus satisfying the condition for a fast electron transfer. Mutagenesis studies and chemical cross-linking, which, in the absence of crystal structures, were previously used to deduce specific P450-cyt b5 interactions, indicated that the negatively charged convex surface of cyt b5 binds to the positively charged concave surface of P450. Our simulations further elaborate structural details of this interface, including nine ion pairs between R95, R100, R138, R362, K442, K455, and K465 side chains of P450 1A2 and E42, E43, E49, D65, D71, and heme propionates of cyt b5. The universal heme-centric system of internal coordinates was proposed to facilitate consistent classification of the orientation of the two porphyrins in any protein complex.

In silico structural homology modelling and docking for assessment of pandemic potential of a novel H7N9 influenza virus and its ability to be neutralized by existing anti-hemagglutinin antibodies

The unpredictable nature of pandemic influenza and difficulties in early prediction of pandemic potential of new isolates present a major challenge for health planners. Vaccine manufacturers, in particular, are reluctant to commit resources to development of a new vaccine until after a pandemic is declared. We hypothesized that a structural bioinformatics approach utilising homology-based molecular modelling and docking approaches would assist prediction of pandemic potential of new influenza strains alongside more traditional laboratory and sequence-based methods. The newly emerged Chinese A/Hangzhou/1/2013 (H7N9) influenza virus provided a real-life opportunity to test this hypothesis. We used sequence data and a homology-based approach to construct a 3D-structural model of H7-Hangzhou hemagglutinin (HA) protein. This model was then used to perform docking to human and avian sialic acid receptors to assess respective binding affinities. The model was also used to perform docking simulations with known neutralizing antibodies to assess their ability to neutralize the newly emerged virus. The model predicted H7N9 could bind to human sialic acid receptors thereby indicating pandemic potential. The model also confirmed that existing antibodies against the HA head region are unable to neutralise H7N9 whereas antibodies, e.g. Cr9114, targeting the HA stalk region should bind with high affinity to H7N9. This indicates that existing stalk antibodies initially raised against H5N1 or other influenza A viruses could be therapeutically beneficial in prevention and/or treatment of H7N9 infections. The subsequent publication of the H7N9 HA crystal structure confirmed the accuracy of our in-silico structural model. Antibody docking studies performed using the H7N9 HA crystal structure supported the model's prediction that existing stalk antibodies could cross-neutralise the H7N9 virus. This study demonstrates the value of using in-silico structural modelling approaches to complement physical studies in characterization of new influenza viruses.

Protein engineering strategies for the development of viral vaccines and immunotherapeutics

Vaccines that elicit a protective broadly neutralizing antibody (bNAb) response and monoclonal antibody therapies are critical for the treatment and prevention of viral infections. However, isolation of protective neutralizing antibodies has been challenging for some viruses, notably those with high antigenic diversity or those that do not elicit a bNAb response in the course of natural infection. Here, we discuss recent work that employs protein engineering strategies to design immunogens that elicit bNAbs or engineer novel bNAbs. We highlight the use of rational, computational, and combinatorial strategies and assess the potential of these approaches for the development of new vaccines and immunotherapeutics.

Redesign of a cross-reactive antibody to dengue virus with broad-spectrum activity and increased in vivo potency

Affinity improvement of proteins, including antibodies, by computational chemistry broadly relies on physics-based energy functions coupled with refinement. However, achieving significant enhancement of binding affinity (>10-fold) remains a challenging exercise, particularly for cross-reactive antibodies. We describe here an empirical approach that captures key physicochemical features common to antigen-antibody interfaces to predict protein-protein interaction and mutations that confer increased affinity. We apply this approach to the design of affinity-enhancing mutations in 4E11, a potent cross-reactive neutralizing antibody to dengue virus (DV), without a crystal structure. Combination of predicted mutations led to a 450-fold improvement in affinity to serotype 4 of DV while preserving, or modestly increasing, affinity to serotypes 1-3 of DV. We show that increased affinity resulted in strong in vitro neutralizing activity to all four serotypes, and that the redesigned antibody has potent antiviral activity in a mouse model of DV challenge. Our findings demonstrate an empirical computational chemistry approach for improving protein-protein docking and engineering antibody affinity, which will help accelerate the development of clinically relevant antibodies.

Rational engineering of a human anti-dengue antibody through experimentally validated computational docking

Antibodies play an increasing pivotal role in both basic research and the biopharmaceutical sector, therefore technology for characterizing and improving their properties through rational engineering is desirable. This is a difficult task thought to require high-resolution x-ray structures, which are not always available. We, instead, use a combination of solution NMR epitope mapping and computational docking to investigate the structure of a human antibody in complex with the four Dengue virus serotypes. Analysis of the resulting models allows us to design several antibody mutants altering its properties in a predictable manner, changing its binding selectivity and ultimately improving its ability to neutralize the virus by up to 40 fold. The successful rational design of antibody mutants is a testament to the accuracy achievable by combining experimental NMR epitope mapping with computational docking and to the possibility of applying it to study antibody/pathogen interactions.

Characterization of epitopes recognized by monoclonal antibodies: experimental approaches supported by freely accessible bioinformatic tools

Monoclonal antibodies (mAbs) have been used successfully both in research and for clinical purposes. The possible use of protective mAbs directed against different microbial pathogens is currently being considered. The fine definition of the epitope recognized by a protective mAb is an important aspect to be considered for possible development in epitope-based vaccinology. The most accurate approach to this is the X-ray resolution of mAb/antigen crystal complex. Unfortunately, this approach is not always feasible. Under this perspective, several surrogate epitope mapping strategies based on the use of bioinformatics have been developed. In this article, we review the most common, freely accessible, bioinformatic tools used for epitope characterization and provide some basic examples of molecular visualization, editing and computational analysis.