Alemtuzumab scFv fragments and CD52 interaction study through molecular dynamics simulation and binding free energy

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.

In silico analysis of the different variable domain oriented single-chain variable fragment antibody-antigen complexes

Single-chain variable fragment (scFv) antibodies hold great potential as diagnostic tools and therapeutic agents, especially for tumor cells. Since these applications require their production with improved properties, the design strategy of scFvs is crucial for their active, soluble, and high yield expression with high affinity towards their antigens. The order of VL and VH domains is one of the important parameters that affect the expression and binding affinity properties of scFvs. In addition, the optimum order of VL and VH domains could change for each scFv. In the present study, we used computer simulation tools to evaluate the effect of variable domain orientation on structure, stability, interacting residues of scFvs, and binding free energies of scFv-antigen complexes. We selected anti-HER2 scFv, which is specific for human epidermal growth receptor 2 (HER2) overexpressed in breast cancer, and anti-IL-1β scFv against IL-1β which is an important inflammatory biomarker, as model scFvs. Molecular dynamics simulations of the scFv-antigen complexes for 100 ns resulted in stability and compactness for both scFv constructs. Interaction and binding free energies calculated by the Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) approach suggested that the relative binding energies of anti-HER2 scFv-VLVH and anti-HER2 scFv-VHVL constructs had similar binding affinity towards HER2, while a relatively more negative binding free energy obtained between anti-IL-1β scFv-VHVL and IL-1β pointed to a higher binding affinity. The in silico approach and the results obtained here could be applied as a guide for future experimental interaction studies for highly specific scFvs used as biotechnological tools.Communicated by Ramaswamy H. Sarma.

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.

Adsorption abilities and mechanisms of Lactobacillus on various nanoplastics

As a new type of pollutants, nanoplastics (NPs), which are easily ingested by humans from food wraps, salt, drinking water, have been widely detected in various water environments, and are a threat to human health. It is therefore urgent to develop an efficient method to remove NPs from the diet or relief its harm. In the present study, the possibility of a well-known human probiotic, lactic acid bacteria (LAB), was evaluated to remove NPs from food as an absorbent. The results indicated that LAB from infant feces could efficiently absorb three types NPs, i.e. polypropylene (PP), polyethylene (PE), and polyvinyl chloride (PVC) with the adsorption rates of PP > PE > PVC (PP 78.57%, PE 71.59%, PVC 66.57%) and the Nile red-stained NPs being aggregated on the surfaces of Lactobacillus cells. The smaller the particle size, the stronger the ability of NP adsorption on the cell surface. The hydrophobicity of NPs and bacterial cells affected the adsorption process. The measurement of adsorption rates of different cell components indicated that the overall adsorption effect of cell was better than that of individual cell component. The results of molecular dynamics analysis revealed that adsorption was mainly caused by electrostatic interactions, van der Waals forces, and hydrogen bonds. The hydrophobic interaction was also involved in adsorption process. Overall, this research may provide new information for developing new strategies for NPs removal in intestinal environment.

Preparation and application of a specific single-chain variable fragment against artemether

Artemether, an artemisinin derivative, is a component of the commonly used artemisinin-based combination therapy, artemether-lumefantrine. In this study, we cloned the VH and VL genes of a cell line (mAb 2G12E1) producing a monoclonal antibody specific to artemether, and used to construct a recombinant DNA of single-chain variable fragment (scFv). The scFv was constructed into prokaryotic expression vectors pET32a (+), pET22b (+), pGEX-2T, and pMAL-p5x, respectively. However, only the pMAL-p5x/scFv could be induced to express soluble scFv with comparable sensitivity and specificity to that of mAb 2G12E1. Based on the anti-artemether scFv, an indirect competitive enzyme-linked immunosorbent assay (icELISA) was developed. The 50% of inhibition concentration (IC₅₀) value and the working range based on IC₂₀ to IC₈₀ were 4.33 ng mL^-1 and 1.05-22.65 ng mL^-1, respectively. The artemether content in different drugs were determined by the developed icELISA, and the results were consistent to those determined by ultra performance liquid chromatography (UPLC). The anti-artemether scFv prepared in the current study could be a valuable genetically engineered antibody applied for artemether monitoring and specific binding mechanism studying.

Insights into Modern Therapeutic Approaches in Pediatric Acute Leukemias

Pediatric cancers predominantly constitute lymphomas and leukemias. Recently, our knowledge and awareness about genetic diversities, and their consequences in these diseases, have greatly expanded. Modern solutions are focused on mobilizing and impacting a patient’s immune system. Strategies to stimulate the immune system, to prime an antitumor response, are of intense interest. Amid those types of therapies are chimeric antigen receptor T (CAR-T) cells, bispecific antibodies, and antibody–drug conjugates (ADC), which have already been approved in the treatment of acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML). In addition, immune checkpoint inhibitors (ICIs), the pattern recognition receptors (PRRs), i.e., NOD-like receptors (NLRs), Toll-like receptors (TLRs), and several kinds of therapy antibodies are well on their way to showing significant benefits for patients with these diseases. This review summarizes the current knowledge of modern methods used in selected pediatric malignancies and presents therapies that may hold promise for the future.

Engineering of single-domain antibodies for next-generation snakebite antivenoms

Given the magnitude of the global snakebite crisis, strategies to ensure the quality of antivenom, as well as the availability and sustainability of its supply are under development by several research groups. Recombinant DNA technology has allowed the engineering of monoclonal antibodies and recombinant fragments as alternatives to conventional antivenoms. Besides having higher therapeutic efficacy, with broad neutralization capacity against local and systemic toxicity, novel antivenoms need to be safe and cost-effective. Due to the biological and physical chemical properties of camelid single-domain antibodies, with high volume of distribution to distal tissue, their modular format, and their versatility, their biotechnological application has grown considerably in recent decades. This article presents the most up-to-date developments concerning camelid single-domain-based antibodies against major toxins from snake venoms, the main venomous animals responsible for reported envenoming cases and related human deaths. A brief discussion on the composition, challenges, and perspectives of antivenoms is presented, as well as the road ahead for next-generation antivenoms based on single-domain antibodies.