Chiti, F. | bioRxiv Science

bioRxiv2025

Engineering a Dimeric Single-Domain Antibody for Improved Detection and neutralization of Amyloid-β oligomers

Soluble A{beta} oligomers are regarded as major neurotoxic agents in Alzheimers disease. Several monoclonal antibodies have been developed to target A{beta} oligomers, but most of them show limited specificity binding also to monomers and fibrils. To generate an antibody with high specificity for the oligomers, we aimed to increase the efficiency and sensitivity of a humanized A{beta}-oligomer-specific single domain antibody, called DesAb-O. We engineered a dimeric DesAb-O variant, DiDesAb-O, which showed a significantly improved binding properties for A{beta} oligomers as compared to the monomeric sdAb. Furthermore, DiDesAb-O detected A{beta}42 oligomers in cells, prevented their binding to cell membranes and the A{beta}42 oligomers-induced neurotoxicity from both synthetic A{beta}42 samples and cerebrospinal fluid of Alzheimers patients at lower concentrations compared to DesAb-O. Overall, our findings indicate that the rational engineering of dimeric sdAb variants is an effective strategy to improve their binding properties offering new opportunities for the development of clinical molecules in the early diagnosis and cure of Alzheimers disease. Significance StatementProtein aggregates, characterized by high structural heterogeneity, are central to numerous neurodegenerative diseases, yet the development of aggregate-specific molecular probes and therapeutic agents remains a significant challenge. We present a novel approach to enhance the binding of single-domain antibodies by engineering multivalent, specifically dimeric, molecules to exploit the avidity effect. As a proof of concept, we developed a dimeric version of a previously studied single-domain antibody named DesAb-O, which exhibits enhanced binding sensitivity and specificity to toxic amyloid-{beta} oligomers and greater efficacy in inhibiting their toxicity. These findings provide a robust foundation for creating next-generation antibody fragments with enhanced binding to heterogeneous protein aggregates, opening new avenues for innovative diagnostic tools and therapeutic strategies in neurodegenerative disease research and treatment.

biochemistry↗

bioRxiv2024

Leveraging Machine Learning-Guided Molecular Simulations Coupled with Experimental Data to Decipher Membrane Binding Mechanisms of Aminosterols

Understanding the molecular mechanisms of the interactions between specific compounds and cellular membranes is essential for numerous biotechnological applications, including targeted drug delivery, elucidation of drug mechanism of action, pathogen identification, and novel antibiotic development. However, the estimation of the free energy landscape associated with solute binding to realistic biological systems is still a challenging task. In this work, we leverage the Time-lagged Independent Component Analysis (TICA) in combination with neural networks (NN) through the Deep-TICA approach for determining the free energy associated with the membrane insertion processes of two natural aminosterol compounds, trodusquemine (TRO) and squalamine (SQ). These compounds are particularly noteworthy because they interact with the outer layer of neuron membranes protecting them from the toxic action of misfolded proteins involved in neurodegenerative disorders, both in their monomeric and oligomeric forms. We demonstrate how this strategy could be used to generate an effective collective variable for describing solute absorption in the membrane and for estimating free energy landscape of translocation via On-the-fly probability enhanced sampling (OPES) method. In this context, the computational protocol allowed an exhaustive characterization of the aminosterols entry pathway into a neuron-like lipid bilayer. Furthermore, it provided accurate prediction of membrane binding affinities, in close agreement with the experimental binding data obtained by using fluorescently-labelled aminosterols and large unilamellar vesicles (LUVs). The findings contribute significantly to our comprehension of aminosterol entry pathways and aminosterol-lipid membrane interactions. Finally, the deployed computational methods in this study further demonstrate considerable potential for investigating membrane binding processes.

biophysics↗