Antibody multispecificity: A necessary evil?

Antibodies represent key effectors of the adaptive immune system. The specificity of antibodies is an established hallmark of the immune response. However, a certain proportion of antibodies exhibit limited promiscuity or multireactivity. Germline antibodies display plasticity which imparts multispecificity to enhance the antibody repertoire. Surprisingly, even affinity matured antibodies display such plasticity and multireactivity enabling their binding to more than one antigen. We propose that antibody multispecificity is a physiological requirement to expand the antibody repertoire at the germline level and to tolerate plasticity in antigens at the mature level. This property of the humoral immune response may attenuate the ability of infectious RNA viruses such as influenza, HIV and SARS-CoV-2 to acquire mutations that render resistance to neutralizing antibodies.

Exploring the role of framework mutations in enabling breadth of a cross-reactive antibody (CR3022) against the SARS-CoV-2 RBD and its variants of concern.

Cross-reactive and broadly neutralizing antibodies against surface proteins of diverse strains of rapidly evolving viral pathogens like SARS-CoV-2 can prevent infection and therefore are crucial for the development of effective universal vaccines. While antibodies typically incorporate mutations in their complementarity determining regions during affinity maturation, mutations in the framework regions have been reported as players in determining properties of broadly neutralizing antibodies against HIV and the Influenza virus. We propose an increase in the cross-reactive potential of CR3022 against the emerging SARS- CoV-2 variants of concern through enhanced conformational flexibility. In this study, we use molecular dynamics simulations, in silico mutagenesis, structural modeling, and docking to explore the role of light chain FWR mutations in CR3022, a SARS-CoV anti-spike (S)-protein antibody cross-reactive to the S-protein receptor binding domain of SARS-CoV-2. Our study shows that single substitutions in the light chain framework region of CR3022 with conserved epitopes across SARS-CoV strains allow targeting of diverse antibody epitope footprints that align with the epitopes of recently-categorized neutralizing antibody classes while enabling binding to more than one strain of SARS-CoV-2. Our study has implications for rapid and evolution-based engineering of broadly neutralizing antibodies and reaffirms the role of framework mutations in effective change of antibody orientation and conformation via improved flexibility.Communicated by Ramaswamy H. Sarma.

Epitope-directed anti-SARS-CoV-2 scFv engineered against the key spike protein region could block membrane fusion.

The newly emerged SARS-CoV-2 causing coronavirus disease (COVID-19) resulted in >500 million infections. A great deal about the molecular processes of virus infection in the host is getting uncovered. Two sequential proteolytic cleavages of viral spike protein by host proteases are prerequisites for the entry of the virus into the host cell. The first cleavage occurs at S1/S2 site by the furin protease, and the second cleavage at a fusion activation site, the S2' site, by the TMPRSS2 protease. S2' cleavage site is present in the S2 domain of spike protein followed by a fusion peptide. Given the S2' site to be conserved among all the SARS-CoV-2 variants, we chose an S2' epitope encompassing the S2' cleavage site and generated single-chain antibodies (scFvs) through an exhaustive phage display library screening. Crystal structure of a scFv in complex with S2' epitope was determined. Incidentally, S2' epitope in the scFv bound structure adopts an alpha-helical conformation equivalent to the conformation of the epitope in the spike protein. Furthermore, these scFvs can bind to the spike protein expressed either in vitro or on the mammalian cell surface. We illustrate a molecular model based on structural and biochemical insights into the antibody-S2' epitope interaction emphasizing scFvs mediated blocking of virus entry into the host cell by restricting the access of TMPRSS2 protease and consequently inhibiting the S2' cleavage competitively.

Antibody Class(es) Predictor for Epitopes (AbCPE): A Multi-Label Classification Algorithm.

Development of vaccines and therapeutic antibodies to deal with infectious and other diseases are the most perceptible scientific interventions that have had huge impact on public health including that in the current Covid-19 pandemic. From inactivation methodologies to reverse vaccinology, vaccine development strategies of 21st century have undergone several transformations and are moving towards rational design approaches. These developments are driven by data as the combinatorials involved in antigenic diversity of pathogens and immune repertoire of hosts are enormous. The computational prediction of epitopes is central to these developments and numerous B-cell epitope prediction methods developed over the years in the field of immunoinformatics have contributed enormously. Most of these methods predict epitopes that could potentially bind to an antibody regardless of its type and only a few account for antibody class specific epitope prediction. Recent studies have provided evidence of more than one class of antibodies being associated with a particular disease. Therefore, it is desirable to predict and prioritize 'peptidome' representing B-cell epitopes that can potentially bind to multiple classes of antibodies, as an open problem in immunoinformatics. To address this, AbCPE, a novel algorithm based on multi-label classification approach has been developed for prediction of antibody class(es) to which an epitope can potentially bind. The epitopes binding to one or more antibody classes (IgG, IgE, IgA and IgM) have been used as a knowledgebase to derive features for prediction. Multi-label algorithms, Binary Relevance and Label Powerset were applied along with Random Forest and AdaBoost. Classifier performance was assessed using evaluation measures like Hamming Loss, Precision, Recall and F1 score. The Binary Relevance model based on dipeptide composition, Random Forest and AdaBoost achieved the best results with Hamming Loss of 0.1121 and 0.1074 on training and test sets respectively. The results obtained by AbCPE are promising. To the best of our knowledge, this is the first multi-label method developed for prediction of antibody class(es) for sequential B-cell epitopes and is expected to bring a paradigm shift in the field of immunoinformatics and immunotherapeutic developments in synthetic biology. The AbCPE web server is available at http://bioinfo.unipune.ac.in/AbCPE/Home.html.

Glycoprotein molecular dynamics analysis: SARS-CoV-2 spike glycoprotein case study.

Molecular Dynamics (MD) is a method used to calculate the movement of atoms and molecules broadly applied to several aspects of science. It involves computational simulation, which makes it, at first glance, not easily accessible. The rise of several automated tools to perform molecular simulations has allowed researchers to navigate through the various steps of MD. This enables to elucidate structural properties of proteins that could not be analyzed otherwise, such as the impact of glycosylation. Glycosylation dictates the physicochemical and biological properties of a protein modulating its solubility, stability, resistance to proteolysis, interaction partners, enzymatic activity, binding and recognition. Given the high conformational and compositional diversity of the glycan chains, assessing their influence on the protein structure is challenging using conventional analytical techniques. In this manuscript, we present a step-by-step workflow to build and perform MD analysis of glycoproteins focusing on the SPIKE glycoprotein of SARS-CoV-2 to appraise the impact of glycans in structure stabilization and antibody occlusion.

Biosensors for the Determination of SARS-CoV-2 Virus and Diagnosis of COVID-19 Infection.

Monitoring and tracking infection is required in order to reduce the spread of the coronavirus disease 2019 (COVID-19), induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To achieve this goal, the development and deployment of quick, accurate, and sensitive diagnostic methods are necessary. The determination of the SARS-CoV-2 virus is performed by biosensing devices, which vary according to detection methods and the biomarkers which are inducing/providing an analytical signal. RNA hybridisation, antigen-antibody affinity interaction, and a variety of other biological reactions are commonly used to generate analytical signals that can be precisely detected using electrochemical, electrochemiluminescence, optical, and other methodologies and transducers. Electrochemical biosensors, in particular, correspond to the current trend of bioanalytical process acceleration and simplification. Immunosensors are based on the determination of antigen-antibody interaction, which on some occasions can be determined in a label-free mode with sufficient sensitivity.

Affinity Sensors for the Diagnosis of COVID-19.

The coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was proclaimed a global pandemic in March 2020. Reducing the dissemination rate, in particular by tracking the infected people and their contacts, is the main instrument against infection spreading. Therefore, the creation and implementation of fast, reliable and responsive methods suitable for the diagnosis of COVID-19 are required. These needs can be fulfilled using affinity sensors, which differ in applied detection methods and markers that are generating analytical signals. Recently, nucleic acid hybridization, antigen-antibody interaction, and change of reactive oxygen species (ROS) level are mostly used for the generation of analytical signals, which can be accurately measured by electrochemical, optical, surface plasmon resonance, field-effect transistors, and some other methods and transducers. Electrochemical biosensors are the most consistent with the general trend towards, acceleration, and simplification of the bioanalytical process. These biosensors mostly are based on the determination of antigen-antibody interaction and are robust, sensitive, accurate, and sometimes enable label-free detection of an analyte. Along with the specification of biosensors, we also provide a brief overview of generally used testing techniques, and the description of the structure, life cycle and immune host response to SARS-CoV-2, and some deeper details of analytical signal detection principles.