Influenza B virus causes considerable disease burden worldwide annually, highlighting the limitations of current influenza vaccines and antiviral drugs. In recent years, broadly neutralizing antibodies (bnAbs) against hemagglutinin (HA) have emerged as a new approach for combating influenza. We describe the generation and characterization of a chimeric monoclonal antibody, C12G6, that cross-neutralizes representative viruses spanning the 76 years of influenza B antigenic evolution since 1940, including viruses belonging to the Yamagata, Victoria, and earlier lineages. Notably, C12G6 exhibits broad cross-lineage hemagglutination inhibition activity against influenza B viruses and has higher potency and breadth of neutralization when compared to four previously reported influenza B bnAbs. In vivo, C12G6 confers stronger cross-protection against Yamagata and Victoria lineages of influenza B viruses in mice and ferrets than other bnAbs or the anti-influenza drug oseltamivir and has an additive antiviral effect when administered in combination with oseltamivir. Epitope mapping indicated that C12G6 targets a conserved epitope that overlaps with the receptor binding site in the HA region of influenza B virus, indicating why it neutralizes virus so potently. Mechanistic analyses revealed that C12G6 inhibits influenza B viruses via multiple mechanisms, including preventing viral entry, egress, and HA-mediated membrane fusion and triggering antibody-dependent cell-mediated cytotoxicity and complement-dependent cytotoxicity responses. C12G6 is therefore a promising candidate for the development of prophylactics or therapeutics against influenza B infection and may inform the design of a truly universal influenza vaccine.

The presence of neutralizing epitopes in human papillomavirus (HPV) L1 virus-like particles (VLPs) is the structural basis of prophylactic vaccines. An anti-HPV16 neutralizing monoclonal antibody (N-mAb) 26D1 was isolated from a memory B cell of a human vaccinee. The pre-binding of heparan sulfate to VLPs inhibited the binding of both N-mAbs to the antigen, indicating that the epitopes are critical for viral cell attachment/entry. Hybrid VLP binding with surface loop swapping between types indicated the essential roles of the DE and FG loops for both 26D1 (DEa in particular) and H16.V5 binding. Specifically, Tyr135 and Val141 on the DEa loop were shown to be critical residues for 26D1 binding via site-directed mutagenesis. Partially overlap between the epitopes between 26D1 and H16.V5 was shown using pairwise epitope mapping, and their binding difference is demonstrated to be predominantly in DE loop region. In addition, 26D1 epitope is immunodominant epitope recognized by both antibodies elicited by the authentic virus from infected individuals and polyclonal antibodies from vaccinees. Overall, a partially overlapping but distinct neutralizing epitope from that of H16.V5 was identified using a human N-mAb, shedding lights to the antibody arrays as part of human immune response to vaccination and infection.

Structural information pertaining to antigen-antibody interactions is fundamental in immunology, and benefits structure-based vaccine design. Modeling of antigen-antibody immune complexes from co-crystal structures or molecular docking simulations provides an extensive profile of the epitope at the interface; however, the key amino acids involved in the interaction must be further clarified, often through the use of experimental mutagenesis and subsequent binding assays. Here, we describe an in silico mutagenesis method to identify key sites at antigen-antibody interfaces, using significant increase in pH-dependency energy among saturated point mutations. Through a comprehensive analysis of the crystal structures of three antigen-antibody immune complexes, we show that a cutoff value of 1 kcal/mol of increased interaction energy provides good congruency with the experimental non-binding mutations conducted in vitro. This in silico mutagenesis strategy, in association with energy calculations, may provide an efficient tool for antibody-antigen interface analyses, epitope optimization, and/or conformation prediction in structure-based vaccine design.

Generative adversarial networks (GANs) are one of the most popular innovations in machine learning. GANs provide a way to learn deep representations without extensively annotated training data. They achieve this by deriving backpropagation signals through a process involving a pair of networks. GANs are generative models since they are able to create data instances that resemble the training data. Besides, GANs provide a way to learn deep representations without annotated training data. They achieve this by deriving backpropagation signals through a looping process involving multiple networks. Those representations learned by GANs can be used in various fields such as Image Generation, Abnormal Detection, Video Repair, using GAN for Infrared to RGB, Image Inpainting, etc. This review paper provides a clear overview of GANs and their application into the Image Inpainting process. Furthermore, it points out both the advantages and disadvantages of GANs in the machine learning field.

P24 antigen is the main structural protein of HIV-1, its detection provide a means to aid the early diagnosis of HIV-1 infection. The aim of this study was to improve the selectivity and sensitivity of the HIV P24 diagnostic assay by developing a cohort of 9E8 affinity-matured antibodies through in vitro phage affinity maturation which was performed by complementarity determining region (CDR)-hot spot mutagenesis strategy. Antibody 9E8-491 had an affinity constant of 5.64 × 10(-11) M, which was 5.7-fold higher than that of the parent antibody (9E8). Furthermore, the affinity, sensitivity and specificity of 9E8-491 were higher than those of 9E8, which indicate that 9E8-491 is a good candidate detection antibody for HIV P24 assay. Structure analysis of matured variants revealed that most hydrogen bonds resided in HCDR3. Among the antibody-antigen predicted binding residues, Tyr(100A/100B) was the original conserved residue that was commonly present in HCDR3 of 9E8 and variants. Arg(100)/Asp(100C) was the major variant substitution that most likely influenced the binding differences among variants and 9E8 monoclonal antibody. Both efficient library panning and predicted structural data were in agreement that the binding residues were mostly located in HCDR3 and enabled identification of key residues that influence antibody affinity.