Biology subjects

Amlashi, P. B.

Publications and source records attributed to Amlashi, P. B..

2 recordsLinked to original sources

bioRxiv2025

Development and application of nbLIBRA-seq for high-throughput discovery of antigen-specific nanobodies

Nanobodies are of high interest in many fields of medicine and biotechnology due to their high stability, tissue penetration, and engineering adaptability compared to monoclonal antibodies. However, nanobody discovery has been limited by technologies that rely on laborious library generation, panning, and clone screening techniques. Here, we demonstrate the successful adaptation of Linking B-Cell Receptor to Antigen Specificity through Sequencing (LIBRA-seq) to immunized alpacas for the rapid identification of antigen-specific nanobodies, derived from heavy-chain antibodies. We validated for nanobody discovery (nbLIBRA-seq) in two different disease settings. First, we identified over 300 antigen-specific heavy chain antibodies against human Transferrin Receptor (TfR1), also known as CD71, from a single alpaca blood sample. Experimental validation showed nbLIBRA-seq was able to identify nanobodies that exhibit specific binding to CD71, with two nanobodies also showing receptor internalization on human T cells. In a separate experiment, we tested the ability of nbLIBRA-seq to perform nanobody discovery with multiple antigens in the antigen screening library. Using fusion glycoproteins from the related respiratory syncytial virus (RSV) and human metapneumovirus (hMPV), 1,125 antigen-specific heavy-chain expressing B cells were recovered via nbLIBRA-seq. A subset of these nanobodies was validated experimentally to possess the target antigen specificity. Together, our results illustrate the potential of nbLIBRA-seq to rapidly identify antigen-specific heavy chain antibodies for a range of diverse targets, a capability that will be of critical significance for the effective and efficient development of novel nanobody-based therapeutics against targets of biomedical significance.

immunology↗

bioRxiv2025

Contrastive Learning Enables Epitope Overlap Predictions for Targeted Antibody Discovery

Computational epitope prediction remains an unmet need for therapeutic antibody development. We present three complementary approaches for predicting epitope relationships from antibody amino acid sequences. First, we analyze [~]18 million antibody pairs targeting [~]250 protein families and establish that a threshold of >70% CDRH3 sequence identity among antibodies sharing both heavy and light chain V-genes reliably predicts overlapping-epitope antibody pairs. Next, we develop a supervised contrastive fine-tuning framework for antibody large language models which results in embeddings that better correlate with epitope information than those from pre-trained models. Applying this contrastive learning approach to SARS-CoV-2 receptor binding domain antibodies, we achieve 82.7% balanced accuracy in distinguishing same-epitope versus different-epitope antibody pairs and demonstrate the ability to predict relative levels of structural overlap from learning on functional epitope bins (Spearman{rho} = 0.25). Finally, we create AbLang-PDB, a generalized model for predicting overlapping-epitope antibodies for a broad range of protein families. AbLang-PDB achieves five-fold improvement in average precision for predicting overlapping-epitope antibody pairs compared to sequence-based methods, and effectively predicts the amount of epitope overlap among overlapping-epitope pairs ({rho} = 0.81). In an antibody discovery campaign searching for overlapping-epitope antibodies to the HIV-1 broadly neutralizing antibody 8ANC195, 70% of computationally selected candidates demonstrated HIV-1 specificity, with 50% showing competitive binding with 8ANC195. Together, the computational models presented here provide powerful tools for epitope-targeted antibody discovery, while demonstrating the efficacy of contrastive learning for improving epitope-representation.

immunology↗