Bivalent single domain antibody constructs for effective neutralization of Venezuelan equine encephalitis

Venezuelan equine encephalitis virus (VEEV) is a mosquito borne alphavirus which leads to high viremia in equines followed by lethal encephalitis and lateral spread to humans. In addition to naturally occurring outbreaks, VEEV is a potential biothreat agent with no approved human vaccine or therapeutic currently available. Single domain antibodies (sdAb), also known as nanobodies, have the potential to be effective therapeutic agents. Using an immune phage display library derived from a llama immunized with an equine vaccine that included inactivated VEEV, five sdAb sequence families were identified that showed varying ability to neutralize VEEV. One of the sequence families had been identified previously in selections against chikungunya virus, a related alphavirus of public health concern. A key advantage of sdAb is the ability to optimize properties such as neutralization capacity through protein engineering. Neutralization of VEEV was improved by two orders of magnitude by genetically linking sdAb. One of the bivalent constructs showed effective neutralization of both VEEV and chikungunya virus. Several of the bivalent constructs neutralized VEEV in cell-based assays with reductions in the number of plaques by 50% at protein concentrations of 1 ng/mL or lower, making future evaluation of their therapeutic potential compelling.

Probing the structure and function of the protease domain of botulinum neurotoxins using single-domain antibodies

Botulinum neurotoxins (BoNTs) are among the deadliest of bacterial toxins. BoNT serotype A and B in particular pose the most serious threat to humans because of their high potency and persistence. To date, there is no effective treatment for late post-exposure therapy of botulism patients. Here, we aim to develop single-domain variable heavy-chain (VHH) antibodies targeting the protease domains (also known as the light chain, LC) of BoNT/A and BoNT/B as antidotes for post-intoxication treatments. Using a combination of X-ray crystallography and biochemical assays, we investigated the structures and inhibition mechanisms of a dozen unique VHHs that recognize four and three non-overlapping epitopes on the LC of BoNT/A and BoNT/B, respectively. We show that the VHHs that inhibit the LC activity occupy the extended substrate-recognition exosites or the cleavage pocket of LC/A or LC/B and thus block substrate binding. Notably, we identified several VHHs that recognize highly conserved epitopes across BoNT/A or BoNT/B subtypes, suggesting that these VHHs exhibit broad subtype efficacy. Further, we identify two novel conformations of the full-length LC/A, that could aid future development of inhibitors against BoNT/A. Our studies lay the foundation for structure-based engineering of protein- or peptide-based BoNT inhibitors with enhanced potencies and cross-subtypes properties.

Classification of lower limb motor imagery based on iterative EEG source localization and feature fusion

Motor imagery (MI) brain–computer interface (BCI) systems have broad application prospects in rehabilitation and other fields. However, to achieve accurate and practical MI-BCI applications, there are still several critical issues, such as channel selection, electroencephalogram (EEG) feature extraction and EEG classification, needed to be better resolved. In this paper, these issues are studied for lower limb MI which is more difficult and less studied than upper limb MI. First, a novel iterative EEG source localization method is proposed for channel selection. Channels FC1, FC2, C1, C2 and Cz, instead of the commonly used traditional channel set (TCS) C3, C4 and Cz, are selected as the optimal channel set (OCS). Then, a multi-domain feature (MDF) extraction algorithm is presented to fuse single-domain features into multi-domain features. Finally, a particle swarm optimization based support vector machine (SVM) method is utilized to classify the EEG data collected by the lower limb MI experiment designed by us. The results show that the classification accuracy is 88.43%, 3.35–5.41% higher than those of using traditional SVM to classify single-domain features on the TCS, which proves that the combination of OCS and MDF can not only reduce the amount of data processing, but also retain more feature information to improve the accuracy of EEG classification.

Purification Process of a Recombinant Human Follicle Stimulating Hormone Biosimilar (Primapur®) to Yield a Pharmaceutical Product with High Batch-to-Batch Consistency

Recombinant human follicle stimulating hormone (r-hFSH) is widely used for infertility treatment and is subject to the development of biosimilars. There are different purification strategies that can yield r-hFSH of pharmaceutical quality from Chinese hamster ovary cell culture broth. We developed a purification process for r-hFSH centered on immunoaffinity chromatography with single-domain recombinant camelid antibodies. The resulting downstream process is simple and devoid of ultrafiltration operations. Studies on chromatography resin resource and ligand leakage showed that the immunoaffinity matrix employed was suitable for industrial use and stable for at least 40 full chromatography cycles, and the leaked single-domain antibody ligand was completely removed by subsequent purification steps. All chromatography resins employed withstood the same 40 cycles of use without significant changes in separation efficiency and product binding capacity. The resulting industrial purification process yielded batches of r-hFSH with consistent levels of purity and bioactivity.

Broad neutralization of SARS-CoV-2 variants by an inhalable bispecific single-domain antibody

The effectiveness of SARS-CoV-2 vaccines and therapeutic antibodies has been limited by the continuous emergence of viral variants, and by the restricted diffusion of antibodies from circulation into the sites of respiratory virus infection. Here, we report the identification of two highly conserved regions on Omicron variant RBD recognized by broadly neutralizing antibodies. Based on this finding, we generated a bispecific single-domain antibody that was able to simultaneously and synergistically bind these two regions on a single Omicron variant RBD as revealed by Cryo-EM structures. This inhalable antibody exhibited exquisite neutralization breadth and therapeutic efficacy in mouse models of SARS-CoV-2 infections. The structures also deciphered an uncommon cryptic epitope within the spike trimeric interface that may have implications for the design of broadly protective SARS-CoV-2 vaccines and therapeutics.

Intelligent fault diagnosis using image representation of multi-domain features

Deep learning (DL) algorithms, especially the convolutional neural network (CNN), have been proven as a newly developed tool in machinery intelligent diagnosis. However, the current CNN-based fault diagnosis studies usually consider features or images extracted from a single domain as model input. This single domain information may not reflect fault patterns comprehensively, leading to low modeling accuracy and inaccurate diagnostic results. To overcome this limitation, this paper proposes a new CNN-based fault diagnosis approach using image representation considering multi-domain features of vibration signals. First, multi-domain features of vibration signals are extracted. These extracted features are then used to construct a n × n matrix, and subsequently to form images by RGB color transformations. This image transformation technique allows for capturing complementary and rich diagnostic information from multiple domains. At last, these images associated with different mechanical defects are fed into a CNN model that is improved based on the classic LeNet-5 CNN architecture for fault diagnosis and identification. Comparative experiments with the traditional feature extraction methods as well as state-of-the-art CNN-based methods are also investigated. Experimental studies on rolling bearings validate the effectiveness and superiorities of the proposed approach.