Biology subjects

Ljungars, A.

Publications and source records attributed to Ljungars, A..

5 recordsLinked to original sources

bioRxiv2026

Accelerating multi-objective VHH discovery via integrated high-throughput selection and AlphaFold3-guided structure prediction

Discovering therapeutic antibodies that bind multiple related targets with high affinity and favourable biophysical properties remains challenging and resource intensive. For snakebite antivenoms, this challenge is critical as treatments must neutralise toxins across multiple snake species. We developed a pipeline combining high-throughput yeast screening, deep sequencing, and AlphaFold3 structure prediction to rapidly identify poly-specific variable domains of heavy-chain-only antibodies (VHHs) against long-chain -neurotoxins. Multiplexed yeast display screening generated a dataset of diverse candidates with varying binding specificities. AlphaFold3-generated VHH-toxin complex predictions enabled structure-based prioritisation that accurately predicted poly-specific binders targeting conserved epitopes across multiple toxins. These structural insights enabled computational optimisation of both affinity and solubility without disrupting target recognition. Experimental validation confirmed improved variants maintained broad specificity across toxins. This integrated approach accelerates multi-objective antibody discovery by predicting which candidates will bind multiple targets before extensive laboratory testing, providing a generalisable strategy applicable beyond antivenoms to any therapeutic requiring broad target coverage.

bioengineering↗

bioRxiv2025

Generalizable direct protein sequencing with InstaNexus

Protein-based therapeutics, such as antibodies and nanobodies, are not encoded in reference genomes, challenging their accurate characterization via standard proteomics. Current methods rely on indirect inference, fragmented outputs, and labor-intensive workflows, which hinder functional insights and routine application. Here, we present a generalizable, end-to-end workflow for direct protein sequencing, combining streamlined sample preparation, AI-driven de novo peptide sequencing, and tailored assembly to reconstruct contiguous protein sequences. A novel composite scoring framework prioritises longer assemblies and coverage, enhancing accuracy and reducing ambiguity. Validation across diverse protein modalities demonstrates its utility and ability to robustly sequence functionally critical regions of selected proteins. This workflow represents an advance in precision proteomics with promising applications in therapeutic discovery, immune profiling, and protein science.

bioinformatics↗

bioRxiv2023

De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments

Bottom-up mass spectrometry-based proteomics is challenged by the task of identifying the peptide that generates a tandem mass spectrum. Traditional methods that rely on known peptide sequence databases are limited and may not be applicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to the spectra without prior information, is valuable for various biological applications; yet, due to a lack of accuracy, it remains challenging to apply this approach in many situations. Here, we introduce InstaNovo, a transformer neural network with the ability to translate fragment ion peaks into the sequence of amino acids that make up the studied peptide(s). The model was trained on 28 million labelled spectra matched to 742k human peptides from the ProteomeTools project. We demonstrate that InstaNovo outperforms current state-of-the-art methods on benchmark datasets and showcase its utility in several applications. Building upon human intuition, we also introduce InstaNovo+, a multinomial diffusion model that further improves performance by iterative refinement of predicted sequences. Using these models, we could de novo sequence antibody-based therapeutics with unprecedented coverage, discover novel peptides, and detect unreported organisms in different datasets, thereby expanding the scope and detection rate of proteomics searches. Finally, we could experimentally validate tryptic and non-tryptic peptides with targeted proteomics, demonstrating the fidelity of our predictions. Our models unlock a plethora of opportunities across different scientific domains, such as direct protein sequencing, immunopeptidomics, and exploration of the dark proteome. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/555055v3_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@20063org.highwire.dtl.DTLVardef@1679c01org.highwire.dtl.DTLVardef@1332940org.highwire.dtl.DTLVardef@1d9339f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Phage display assisted discovery of a pH-dependent anti-alpha-cobratoxin antibody from a natural variable domain library

Recycling antibodies can bind to their target antigen at neutral pH in the blood stream and release them upon endocytosis when pH levels drop, allowing the antibodies to be recycled into circulation via FcRn-mediated pathway, while the antigens undergo lysosomal degradation. This enables recycling antibodies to achieve the same therapeutic effect at lower doses than their non-recyclable counterparts. The development of such antibodies is typically achieved by histidine doping of the variable regions of specific antibodies or by performing in vitro antibody selection campaigns utilizing histidine doped libraries. While often successful, these strategies may introduce sequence liabilities, as they often involve mutations that may render the resultant antibodies to be non-natural. Here, we present a methodology that employs a naive antibody phage display library, consisting of natural variable domains, to discover antibodies that bind -cobratoxin from the venom of Naja kaouthia in a pH-dependent manner. Upon screening of the discovered antibodies with immunoassays and bio-layer interferometry, a pH-dependent antibody was discovered that exhibits an 8-fold higher dissociation rate at pH 5.5 than 7.4. Interestingly, the variable domains of the pH-dependent antibody were found to be entirely devoid of histidines, demonstrating that pH-dependency may not always be driven by this amino acid. Further, given the high diversity available in a naive antibody library, the methodology presented here can likely be applied to discover pH-dependent antibodies against different targets ab initio without the need of histidine doping. For broader audienceHere, we present the discovery of an -cobratoxin targeting pH-dependent antibody, with a variable region devoid of histidines, from a naive antibody library with natural variable domains. Our findings suggest that the commonly taken approach of histidine doping to find pH-dependent antibodies may not always be required, and thus offer an alternative strategy for the discovery of pH-dependent antibodies.

bioengineering↗

bioRxiv2020

Activation of the myeloid checkpoint inhibitor LILRB3 (ILT5) elicits profound immununomodulation

Despite advances in identifying the key immunoregulatory roles of many of the human leukocyte immunoglobulin (Ig)-like receptor (LILR) family members, the function of the inhibitory molecule LILRB3 (ILT5, CD85a, LIR3) remains unclear. Studies indicate a predominant myeloid expression; however, high homology within the LILR family and a relative paucity of reagents have hindered progress for this receptor. To investigate its function and potential immunomodulatory capacity, a panel of LILRB3-specific monoclonal antibodies (mAb) was generated. LILBR3-specific mAb bound to discrete epitopes in either Ig-like domain two or four. LILRB3 ligation on primary human monocytes by agonistic mAb resulted in phenotypic and functional changes, leading to potent inhibition of immune responses in vitro, including significant reduction in T cell proliferation. Importantly, agonizing LILRB3 in humanized mice induced tolerance and permitted efficient engraftment of allogeneic cells. Our findings reveal powerful immunosuppresive functions of LILRB3 and identify it as an important myeloid checkpoint receptor.

immunology↗