OptMAVEn-2.0: De novo Design of Variable Antibody Regions against Targeted Antigen Epitopes

Technological advancements in viral vector designing and optimization for therapeutic applications

Viral vector engineering is critical to the advancement of several sectors of biotechnology, gene therapy, and vaccine development. These vectors were produced from viruses, were employed to deliver therapeutic genes or to alter biological processes. The potential for viral vectors to improve the precision, safety, and efficiency of therapeutic interventions has boosted their demand. The dynamic interplay between technological advancements and computational tools in establishing the landscape of viral vector engineering and vector optimization for therapeutic reasons is discussed in this chapter. It also emphasizes the importance of in silico techniques in maximizing vector potential for therapeutics and many phases of viral vector engineering, from genomic analysis to computer modelling and advancements to improve precise gene delivery. High-throughput screening propels the expedited process of vector selection, and computational techniques to analyze complex omics data to further enhance vector capabilities have been discussed. As in silico models reveal insights into off-target effects and integration sites, vector safety (biodistribution and toxicity) remains a crucial part and bridges the gap between preclinical and clinical investigations. Despite the limitations, this chapter depicts a future in which technology and computing merge to catapult viral vector therapy into an era of boundless possibilities.

Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies' Recognition of TRBC1 and TRBC2

T cell receptor β-chain constant (TRBC) is a promising class of cancer targets consisting of two highly homologous proteins, TRBC1 and TRBC2. Developing targeted antibody therapeutics against TRBC1 or TRBC2 is expected to eradicate the malignant T cells and preserve half of the normal T cells. Recently, several antibody engineering strategies have been used to modulate the TRBC1 and TRBC2 specificity of antibodies. Here, we used molecular simulation and artificial intelligence methods to quantify the affinity difference in antibodies with various mutations for TRBC1 and TRBC2. The affinity of the existing mutants was verified by FEP calculations aided by the AI. We also performed long-time molecular dynamics simulations to reveal the dynamical antigen recognition mechanisms of the TRBC antibodies.

Exploring rigid-backbone protein docking in biologics discovery: a test using the DARPin scaffold

Accurate protein-protein docking remains challenging, especially for artificial biologics not coevolved naturally against their protein targets, like antibodies and other engineered scaffolds. We previously developed ProPOSE, an exhaustive docker with full atomistic details, which delivers cutting-edge performance by allowing side-chain rearrangements upon docking. However, extensive protein backbone flexibility limits its practical applicability as indicated by unbound docking tests. To explore the usefulness of ProPOSE on systems with limited backbone flexibility, here we tested the engineered scaffold DARPin, which is characterized by its relatively rigid protein backbone. A prospective screening campaign was undertaken, in which sequence-diversified DARPins were docked and ranked against a directed epitope on the target protein BCL-W. In this proof-of-concept study, only a relatively small set of 2,213 diverse DARPin interfaces were selected for docking from the huge theoretical library from mutating 18 amino-acid positions. A computational selection protocol was then applied for enrichment of binders based on normalized computed binding scores and frequency of binding modes against the predefined epitope. The top-ranked 18 designed DARPin interfaces were selected for experimental validation. Three designs exhibited binding affinities to BCL-W in the nanomolar range comparable to control interfaces adopted from known DARPin binders. This result is encouraging for future screening and engineering campaigns of DARPins and possibly other similarly rigid scaffolds against targeted protein epitopes. Method limitations are discussed and directions for future refinements are proposed.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Improved HIV-1 neutralization breadth and potency of V2-apex antibodies by in silico design

Broadly neutralizing antibodies (bNAbs) against HIV can reduce viral transmission in humans, but an effective therapeutic will require unusually high breadth and potency of neutralization. We employ the OSPREY computational protein design software to engineer variants of two apex-directed bNAbs, PGT145 and PG9RSH, resulting in increases in potency of over 100-fold against some viruses. The top designed variants improve neutralization breadth from 39% to 54% at clinically relevant concentrations (IC80 < 1 μg/mL) and improve median potency (IC80) by up to 4-fold over a cross-clade panel of 208 strains. To investigate the mechanisms of improvement, we determine cryoelectron microscopy structures of each variant in complex with the HIV envelope trimer. Surprisingly, we find the largest increases in breadth to be a result of optimizing side-chain interactions with highly variable epitope residues. These results provide insight into mechanisms of neutralization breadth and inform strategies for antibody design and improvement.

Computational and artificial intelligence-based methods for antibody development

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.

De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD)

The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of "human Abs" based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed "forward folding" with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.

Binding affinity prediction for antibody-protein antigen complexes: A machine learning analysis based on interface and surface areas

Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibody‒protein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for protein‒protein binding affinity usually fail to predict the binding affinity of an antibody‒protein antigen complex with a comparable level of accuracy. Here, new models specific for antibody‒antigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibody‒antigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibody‒protein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibody‒protein antigen binding affinity prediction are superior to the previously-used general models for predicting the protein‒protein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibody‒protein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibody‒protein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics.

Towards an optimal monoclonal antibody with higher binding affinity to the receptor-binding domain of SARS-CoV-2 spike proteins from different variants

A highly efficient and robust multiple scales in silico protocol, consisting of atomistic Molecular Dynamics (MD), coarse-grain (CG) MD, and constant-pH CG Monte Carlo (MC), has been developed and used to study the binding affinities of selected antigen-binding fragments of the monoclonal antibody (mAbs) CR3022 and several of its here optimized versions against 11 SARS-CoV-2 variants including the wild type. Totally 235,000 mAbs structures were initially generated using the RosettaAntibodyDesign software, resulting in top 10 scored CR3022-like-RBD complexes with critical mutations and compared to the native one, all having the potential to block virus-host cell interaction. Of these 10 finalists, two candidates were further identified in the CG simulations to be the best against all SARS-CoV-2 variants. Surprisingly, all 10 candidates and the native CR3022 exhibited a higher affinity for the Omicron variant despite its highest number of mutations. The multiscale protocol gives us a powerful rational tool to design efficient mAbs. The electrostatic interactions play a crucial role and appear to be controlling the affinity and complex building. Studied mAbs carrying a more negative total net charge show a higher affinity. Structural determinants could be identified in atomistic simulations and their roles are discussed in detail to further hint at a strategy for designing the best RBD binder. Although the SARS-CoV-2 was specifically targeted in this work, our approach is generally suitable for many diseases and viral and bacterial pathogens, leukemia, cancer, multiple sclerosis, rheumatoid, arthritis, lupus, and more.

MutDock: A computational docking approach for fixed-backbone protein scaffold design

Despite the successes of antibodies as therapeutic binding proteins, they still face production and design challenges. Alternative binding scaffolds of smaller size have been developed to overcome these issues. A subset of these alternative scaffolds recognizes target molecules through mutations to a set of surface resides, which does not alter their backbone structures. While the computational design of antibodies for target epitopes has been explored in depth, the same has not been done for alternative scaffolds. The commonly used dock-and-mutate approach for binding proteins, including antibodies, is limited because it uses a constant sequence and structure representation of the scaffold. Docking fixed-backbone scaffolds with a varied group of surface amino acids increases the chances of identifying superior starting poses that can be improved with subsequent mutations. In this work, we have developed MutDock, a novel computational approach that simultaneously docks and mutates fixed backbone scaffolds for binding a target epitope by identifying a minimum number of hydrogen bonds. The approach is broadly divided into two steps. The first step uses pairwise distance alignment of hydrogen bond-forming areas of scaffold residues and compatible epitope atoms. This step considers both native and mutated rotamers of scaffold residues. The second step mutates clashing variable interface residues and thermodynamically unfavorable residues to create additional strong interactions. MutDock was used to dock two scaffolds, namely, Affibodies and DARPins, with ten randomly selected antigens. The energies of the docked poses were minimized and binding energies were compared with docked poses from ZDOCK and HADDOCK. The top MutDock poses consisted of higher and comparable binding energies than the top ZDOCK and HADDOCK poses, respectively. This work contributes to the discovery of novel binders based on smaller-sized, fixed-backbone protein scaffolds.

Inactivation Mechanism and Efficacy of Grape Seed Extract for Human Norovirus Surrogate

Proper disinfection of harvested food and water is critical to minimize infectious disease. Grape seed extract (GSE), a commonly used health supplement, is a mixture of plant-derived polyphenols. Polyphenols possess antimicrobial and antifungal properties, but antiviral effects are not well-known. Here we show that GSE outperformed chemical disinfectants (e.g., free chlorine and peracetic acids) in inactivating Tulane virus, a human norovirus surrogate. GSE induced virus aggregation, a process that correlated with a decrease in virus titers. This aggregation and disinfection were not reversible. Molecular docking simulations indicate that polyphenols potentially formed hydrogen bonds and strong hydrophobic interactions with specific residues in viral capsid proteins. Together, these data suggest that polyphenols physically associate with viral capsid proteins to aggregate viruses as a means to inhibit virus entry into the host cell. Plant-based polyphenols like GSE are an attractive alternative to chemical disinfectants to remove infectious viruses from water or food. IMPORTANCE Human noroviruses are major food- and waterborne pathogens, causing approximately 20% of all cases of acute gastroenteritis cases in developing and developed countries. Proper sanitation or disinfection are critical strategies to minimize human norovirus-caused disease until a reliable vaccine is created. Grape seed extract (GSE) is a mixture of plant-derived polyphenols used as a health supplement. Polyphenols are known for antimicrobial, antifungal, and antibiofilm activities, but antiviral effects are not well-known. In studies presented here, plant-derived polyphenols outperformed chemical disinfectants (i.e., free chlorine and peracetic acids) in inactivating Tulane virus, a human norovirus surrogate. Based on data from molecular assays and molecular docking simulations, the current model is that the polyphenols in GSE bind to the Tulane virus capsid, an event that triggers virion aggregation. It is thought that this aggregation prevents Tulane virus from entering host cells.

Affinity maturation of TCR-like antibodies using phage display guided by structural modeling

TCR-like antibodies represent a unique type of engineered antibodies with specificity toward pHLA, a ligand normally restricted to the sensitive recognition by T cells. Here, we report a phage display-based sequential development path of such antibodies. The strategy goes from initial lead identification through in silico informed CDR engineering in combination with framework engineering for affinity and thermostability optimization, respectively. The strategy allowed the identification of HLA-DQ2.5 gluten peptide-specific TCR-like antibodies with low picomolar affinity. Our method outlines an efficient and general method for development of this promising class of antibodies, which should facilitate their utility including translation to human therapy.

Redesigning an antibody H3 loop by virtual screening of a small library of human germline-derived sequences

The design of superior biologic therapeutics, including antibodies and engineered proteins, involves optimizing their specific ability to bind to disease-related molecular targets. Previously, we developed and applied the Assisted Design of Antibody and Protein Therapeutics (ADAPT) platform for virtual affinity maturation of antibodies (Vivcharuk et al. in PLoS One 12(7):e0181490, 10.1371/journal.pone.0181490, 2017). However, ADAPT is limited to point mutations of hot-spot residues in existing CDR loops. In this study, we explore the possibility of wholesale replacement of the entire H3 loop with no restriction to maintain the parental loop length. This complements other currently published studies that sample replacements for the CDR loops L1, L2, L3, H1 and H2. Given the immense sequence space theoretically available to H3, we focused on the virtual grafting of over 5000 human germline-derived H3 sequences from the IGMT/LIGM database increasing the diversity of the sequence space when compared to using crystalized H3 loop sequences. H3 loop conformations are generated and scored to identify optimized H3 sequences. Experimental testing of high-ranking H3 sequences grafted into the framework of the bH1 antibody against human VEGF-A led to the discovery of multiple hits, some of which had similar or better affinities relative to the parental antibody. In over 75% of the tested designs, the re-designed H3 loop contributed favorably to overall binding affinity. The hits also demonstrated good developability attributes such as high thermal stability and no aggregation. Crystal structures of select re-designed H3 variants were solved and indicated that although some deviations from predicted structures were seen in the more solvent accessible regions of the H3 loop, they did not significantly affect predicted affinity scores.

Challenges in Detection of Serum Oncoprotein: Relevance to Breast Cancer Diagnostics

Breast cancer is a highly prevalent malignancy that shows improved outcomes with earlier diagnosis. Current screening and monitoring methods have improved survival rates, but the limitations of these approaches have led to the investigation of biomarker evaluation to improve early diagnosis and treatment monitoring. The enzyme-linked immunosorbent assay (ELISA) is a specific and robust technique ideally suited for the quantification of protein biomarkers from blood or its constituents. The continued clinical relevancy of this assay format will require overcoming specific technical challenges, including the ultra-sensitive detection of trace biomarkers and the circumventing of potential assay interference due to the expanding use of monoclonal antibody (mAb) therapeutics. Approaches to increasing the sensitivity of ELISA have been numerous and include employing more sensitive substrates, combining ELISA with the polymerase chain reaction (PCR), and incorporating nanoparticles as shuttles for detection antibodies and enzymes. These modifications have resulted in substantial boosts in the ability to detect extremely low levels of protein biomarkers, with some systems reliably detecting antigen at sub-femtomolar concentrations. Extensive utilization of mAb therapies in oncology has presented an additional contemporary challenge for ELISA, particularly when both therapeutic and assay antibodies target the same protein antigen. Resolution of issues such as epitope overlap and steric hindrance requires a rational approach to the design of diagnostic antibodies that takes advantage of modern antibody generation pipelines, epitope binning techniques and computational methods to strategically target biomarker epitopes. This review discusses technical strategies in ELISA implemented to date and their feasibility to address current constraints on sensitivity and problems with interference in the clinical setting. The impact of these recent advancements will depend upon their transformation from research laboratory protocols into facile, reliable detection systems that can ideally be replicated in point-of-care devices to maximize utilization and transform both the diagnostic and therapeutic monitoring landscape.

IsAb: a computational protocol for antibody design

The design of therapeutic antibodies has attracted a large amount of attention over the years. Antibodies are widely used to treat many diseases due to their high efficiency and low risk of adverse events. However, the experimental methods of antibody design are time-consuming and expensive. Although computational antibody design techniques have had significant advances in the past years, there are still some challenges that need to be solved, such as the flexibility of antigen structure, the lack of antibody structural data and the absence of standard antibody design protocol. In the present work, we elaborated on an in silico antibody design protocol for users to easily perform computer-aided antibody design. First, the Rosetta web server will be applied to generate the 3D structure of query antibodies if there is no structural information available. Then, two-step docking will be used to identify the binding pose of an antibody-antigen complex when the binding information is unknown. ClusPro is the first method to be used to conduct the global docking, and SnugDock is applied for the local docking. Sequentially, based on the predicted binding poses, in silico alanine scanning will be used to predict the potential hotspots (or key residues). Finally, computational affinity maturation protocol will be used to modify the structure of antibodies to theoretically increase their affinity and stability, which will be further validated by the bioassays in the future. As a proof of concept, we redesigned antibody D44.1 and compared it with previously reported data in order to validate IsAb protocol. To further illustrate our proposed protocol, we used cemiplimab antibody, a PD-1 checkpoint inhibitor, as an example to showcase a step-by-step tutorial.

Applications of Machine and Deep Learning in Adaptive Immunity

Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide-MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics.

Spike protein-based epitopes predicted against SARS-CoV-2 through literature mining

BACKGROUND

With the diffusion of SARS-CoV-2 around the world, human health is being threatened. As there is no effective vaccine yet, the development of the vaccine is urgently in progress.

MATERIALS AND METHODS

Immunoinformatics methods were applied to predict epitopes from the Spike protein through mining literature associated with B- and T-cell epitopes prediction published or preprinted since the outbreak of the virus till June 1, 2020. 3D structure of the Spike protein were obtained (PDB ID: 6VSB) for prediction of discontinuous B-cell epitopes and localization of epitopes in the hotspot regions.

RESULTS

Methods provided by the Immune Epitope Database (IEDB) server were the most frequently used to predict epitopes. Sequence alignment of the epitopes extracted from literature with the Spike protein demonstrated that the epitopes in different studies converged to multiple short hotspot regions. There were three hotspot regions found in RBD of the Spike protein harboring B-cell linear epitopes ('RQIAPGQTGKIADYNYKLPD', 'SYGFQPTNGVGYQ' and 'YAWNRKRISNCVA') predicted to have high antigenicity score. Two T-cell epitopes ('KPFERDISTEIYQ' and 'NYNYLYRLFR') predicted to be highly antigenic in the original studies were discovered in the hotspot region. Toxicity and allergenicity analysis confirmed all the five epitopes are of non-toxin, and four of them are of non-allergen. The five epitopes identified in hotspot regions of RBD were found fully exposed based on the 3D structure of the Spike protein.

CONCLUSION

The five epitopes we discovered from literature mining may be potential candidates for diagnostics and vaccine development against SARS-CoV-2.

Computational approaches to therapeutic antibody design: established methods and emerging trends

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.

Computational biophysical characterization of the SARS-CoV-2 spike protein binding with the ACE2 receptor and implications for infectivity

SARS-CoV-2 is a novel highly virulent pathogen which gains entry to human cells by binding with the cell surface receptor - angiotensin converting enzyme (ACE2). We computationally contrasted the binding interactions between human ACE2 and coronavirus spike protein receptor binding domain (RBD) of the 2002 epidemic-causing SARS-CoV-1, SARS-CoV-2, and bat coronavirus RaTG13 using the Rosetta energy function. We find that the RBD of the spike protein of SARS-CoV-2 is highly optimized to achieve very strong binding with human ACE2 (hACE2) which is consistent with its enhanced infectivity. SARS-CoV-2 forms the most stable complex with hACE2 compared to SARS-CoV-1 (23% less stable) or RaTG13 (11% less stable). Notably, we calculate that the SARS-CoV-2 RBD lowers the binding strength of angiotensin 2 receptor type I (ATR1) which is the native binding partner of ACE2 by 44.2%. Strong binding is mediated through strong electrostatic attachments with every fourth residue on the N-terminus alpha-helix (starting from Ser19 to Asn53) as the turn of the helix makes these residues solvent accessible. By contrasting the spike protein SARS-CoV-2 Rosetta binding energy with ACE2 of different livestock and pet species we find strongest binding with bat ACE2 followed by human, feline, equine, canine and finally chicken. This is consistent with the hypothesis that bats are the viral origin and reservoir species. These results offer a computational explanation for the increased infection susceptibility by SARS-CoV-2 and allude to therapeutic modalities by identifying and rank-ordering the ACE2 residues involved in binding with the virus.

IPRO+/-: Computational Protein Design Tool Allowing for Insertions and Deletions

Insertions and deletions (indels) in protein sequences alter the residue spacing along the polypeptide backbone and consequently open up possibilities for tuning protein function in a way that is inaccessible by amino acid substitution alone. We describe an optimization-based computational protein redesign approach centered around predicting beneficial combinations of indels along with substitutions and also obtain putative substrate-docked structures for these protein variants. This modified algorithmic capability would be of interest for enzyme engineering and broadly inform other protein design tasks. We highlight this capability by (1) identifying active variants of a bacterial thioesterase enzyme ('TesA) with experimental corroboration, (2) recapitulating existing active TEM-1 β-Lactamase sequences of different sizes, and (3) identifying shorter 4-Coumarate:CoA ligases with enhanced in vitro activities toward non-native substrates. A separate PyRosetta-based open-source tool, Indel-Maker (http://www.maranasgroup.com/software.htm), has also been created to construct computational models of user-defined protein variants with specific indels and substitutions.

Multiepitope-Based Subunit Vaccine Design and Evaluation against Respiratory Syncytial Virus Using Reverse Vaccinology Approach

Respiratory syncytial virus (RSV) is primarily associated with respiratory disorders globally. Despite the availability of information, there is still no competitive vaccine available for RSV. Therefore, the present study has been designed to develop a multiepitope-based subunit vaccine (MEV) using a reverse vaccinology approach to curb RSV infections. Briefly, two highly antigenic and conserved proteins of RSV (glycoprotein and fusion protein) were selected and potential epitopes of different categories (B-cell and T-cell) were identified from them. Eminently antigenic and overlapping epitopes, which demonstrated strong associations with their respective human leukocyte antigen (HLA) alleles and depicted collective ~70% coverage of the world's populace, were shortlisted. Finally, 282 amino acids long MEV construct was established by connecting 13 major histocompatibility complex (MHC) class-I with two MHC class-II epitopes with appropriate adjuvant and linkers. Adjuvant and linkers were added to increase the immunogenic stimulation of the MEV. Developed MEV was stable, soluble, non-allergenic, non-toxic, flexible and highly antigenic. Furthermore, molecular docking and molecular dynamics (MD) simulations analyses were carried out. Results have shown a firm and robust binding affinity of MEV with human pathogenic toll-like receptor three (TLR3). The computationally mediated immune response of MEV demonstrated increased interferon-γ production, a significant abundance of immunoglobulin and activation of macrophages which are essential for immune-response against RSV. Moreover, MEV codons were optimized and in silico cloning was performed, to ensure its increased expression. These outcomes proposed that the MEV developed in this study will be a significant candidate against RSV to control and prevent RSV-related disorders if further investigated experimentally.

Pluripotent Stem Cell-Based Models: A Peephole into Virus Infections during Early Pregnancy

The rubella virus (RV) was the first virus shown to be teratogenic in humans. The wealth of data on the clinical symptoms associated with congenital rubella syndrome is in stark contrast to an incomplete understanding of the forces leading to the teratogenic alterations in humans. This applies not only to RV, but also to congenital viral infections in general and includes (1) the mode of vertical transmission, even at early gestation, (2) the possible involvement of inflammation as a consequence of an activated innate immune response, and (3) the underlying molecular and cellular alterations. With the progress made in the development of pluripotent stem cell-based models including organoids and embryoids, it is now possible to assess congenital virus infections on a mechanistic level. Moreover, antiviral treatment options can be validated, and newly emerging viruses with a potential impact on human embryonal development, such as that recently reflected by the Zika virus (ZIKV), can be characterized. Here, we discuss human cytomegalovirus (HCMV) and ZIKV in comparison to RV as viruses with well-known congenital pathologies and highlight their analysis on current models for the early phase of human development. This includes the implications of their genetic variability and, as such, virus strain-specific properties for their use as archetype models for congenital virus infections. In this review, we will discuss the use of induced pluripotent stem cells (iPSC) and derived organoid systems for the study of congenital virus infections with a focus on their prominent aetiologies, HCMV, ZIKV, and RV. Their assessment on these models will provide valuable information on how human development is impaired by virus infections; it will also add new insights into the normal progression of human development through the analysis of developmental pathways in the context of virus-induced alterations. These are exciting perspectives for both developmental biology and congenital virology.

Artificial water channels enable fast and selective water permeation through water-wire networks

Artificial water channels are synthetic molecules that aim to mimic the structural and functional features of biological water channels (aquaporins). Here we report on a cluster-forming organic nanoarchitecture, peptide-appended hybrid[4]arene (PAH[4]), as a new class of artificial water channels. Fluorescence experiments and simulations demonstrated that PAH[4]s can form, through lateral diffusion, clusters in lipid membranes that provide synergistic membrane-spanning paths for a rapid and selective water permeation through water-wire networks. Quantitative transport studies revealed that PAH[4]s can transport >109 water molecules per second per molecule, which is comparable to aquaporin water channels. The performance of these channels exceeds the upper bound limit of current desalination membranes by a factor of ~104, as illustrated by the water/NaCl permeability-selectivity trade-off curve. PAH[4]'s unique properties of a high water/solute permselectivity via cooperative water-wire formation could usher in an alternative design paradigm for permeable membrane materials in separations, energy production and barrier applications.

A consensus protocol for the in silico optimisation of antibody fragments

We present an in silico mutagenetic protocol for improving the binding affinity of single domain antibodies (or nanobodies, VHHs). The method iteratively attempts random mutations in the interacting region of the protein and evaluates the resulting binding affinity towards the target by scoring, with a collection of scoring functions, short explicit solvent molecular dynamics trajectories of the binder-target complexes. The acceptance/rejection of each attempted mutation is carried out by a consensus decision-making algorithm, which considers all individual assessments derived from each scoring function. The method was benchmarked by evolving a single complementary determining region (CDR) of an anti-HER2 VHH hit obtained by direct panning of a phage display library. The optimised VHH mutant showed significantly enhanced experimental affinity with respect to the original VHH it matured from. The protocol can be employed as it is for the optimization of peptides, antibody fragments, and (given enough computational power) larger antibodies.

7 Log Virus Removal in a Simple Functionalized Sand Filter

Viral contamination of drinking water due to fecal contamination is difficult to detect and treat effectively, leading to frequent outbreaks worldwide. The purpose of this paper is to report on the molecular mechanism for unprecedented high virus removal from a practical sand filter. Sand filters functionalized using a water extract of Moringa oleifera (MO) seeds, functionalized sand (f-sand) filters, achieved a ∼7 log₁₀ virus removal. These tests were conducted with MS2 bacteriophage, a recognized surrogate for pathogenic norovirus and rotavirus. We studied the molecular mechanism of this high removal since it can have important implications for sand filtration, the most common water treatment technology worldwide. Our data reveal that the virus removal activity of f-sand is due to the presence of a chitin-binding protein, M. oleifera chitin-binding protein (MoCBP) on f-sand. Standard column experiments were supported by proteomic analysis and molecular docking simulations. Our simulations show that MoCBP binds preferentially to MS2 capsid proteins demonstrating that specific molecular interactions are responsible for enhanced virus removal. In addition, we simplified the process of making f-sand and evinced how it could be regenerated using saline water. At present, no definitive solution exists for the challenge of treating fecally contaminated drinking and irrigation water for viruses without using technologies that demand high energy or chemical consumption. We propose functionalized sand (f-sand) filters as a highly effective, energy-efficient, and practical technology for virus removal applicable to both developing and developed countries.

Computer-Assisted and Data Driven Approaches for Surveillance, Drug Discovery, and Vaccine Design for the Zika Virus

Human life has been at the edge of catastrophe for millennia due diseases which emerge and reemerge at random. The recent outbreak of the Zika virus (ZIKV) is one such menace that shook the global public health community abruptly. Modern technologies, including computational tools as well as experimental approaches, need to be harnessed fast and effectively in a coordinated manner in order to properly address such challenges. In this paper, based on our earlier research, we have proposed a four-pronged approach to tackle the emerging pathogens like ZIKV: (a) Epidemiological modelling of spread mechanisms of ZIKV; (b) assessment of the public health risk of newly emerging strains of the pathogens by comparing them with existing strains/pathogens using fast computational sequence comparison methods; (c) implementation of vaccine design methods in order to produce a set of probable peptide vaccine candidates for quick synthesis/production and testing in the laboratory; and (d) designing of novel therapeutic molecules and their laboratory testing as well as validation of new drugs or repurposing of drugs for use against ZIKV. For each of these stages, we provide an extensive review of the technical challenges and current state-of-the-art. Further, we outline the future areas of research and discuss how they can work together to proactively combat ZIKV or future emerging pathogens.

PoreDesigner for tuning solute selectivity in a robust and highly permeable outer membrane pore

Monodispersed angstrom-size pores embedded in a suitable matrix are promising for highly selective membrane-based separations. They can provide substantial energy savings in water treatment and small molecule bioseparations. Such pores present as membrane proteins (chiefly aquaporin-based) are commonplace in biological membranes but difficult to implement in synthetic industrial membranes and have modest selectivity without tunable selectivity. Here we present PoreDesigner, a design workflow to redesign the robust beta-barrel Outer Membrane Protein F as a scaffold to access three specific pore designs that exclude solutes larger than sucrose (>360 Da), glucose (>180 Da), and salt (>58 Da) respectively. PoreDesigner also enables us to design any specified pore size (spanning 3-10 Å), engineer its pore profile, and chemistry. These redesigned pores may be ideal for conducting sub-nm aqueous separations with permeabilities exceeding those of classical biological water channels, aquaporins, by more than an order of magnitude at over 10 billion water molecules per channel per second.