Biology subjects

Ahn, M.-J.

Publications and source records attributed to Ahn, M.-J..

6 recordsLinked to original sources

bioRxiv2025

An integrated in silico-in vitro workflow for discovering high-affinity, selective antibodies to the KRAS(G12D)-MHC I complex

Antibodies that recognize peptide-loaded class I major histocompatibility complex (pMHC I) molecules could enable therapeutic targeting of intracellular oncogenic proteins, yet their discovery has been hampered by the small size of peptide antigens and allele-specificity. We describe an integrated in silico-in vitro workflow for generating high-affinity, selective antibodies to KRAS(G12D)10 presented by HLA-C*08:02, a clinically validated cancer neoantigen. In silico, multiple human antibody-derived variable fragments (Fvs) plausibly docked to the target pMHC were generated, followed by limited complementarity-determining region (CDR) sequence design. In vitro, CDR diversity was introduced at 3-4 positions per Fv to construct yeast surface display library for iterative selections. This workflow yielded antibodies with exclusive binding to KRAS(G12D)10/HLA-C*08:02 without cross-reactivity. Affinity maturation achieved nanomolar dissociation constants, and incorporation into chimeric antigen receptor T cells enabled specific activation against target-positive cells. This study establishes a practical design-to-function pipeline for TCR-like antibody discovery, and demonstrates the feasibility of therapeutic targeting against KRAS(G12D)-driven malignancies.

biochemistry↗

bioRxiv2025

Computational design of monomeric Fc variants with distinct pH-responsive FcRn-binding profiles

IgG1 and IgG4 antibodies form a [~]150 kDa homodimer through dimerization of the Fc domain, which prolongs their in vivo half-life via pH-dependent binding to the neonatal Fc receptor (FcRn). Conformationally stable, half-life-extended monomeric Fc (mFc) variants offer a promising platform for antibodies and Fc-fusion therapeutics, enabling deeper tissue penetration, reduced toxicity, and simplified manufacturing. Due to the loss of binding avidity, mFc requires significantly enhanced FcRn-binding affinity at pH 6.0, but retaining weak binding at neutral pH to achieve comparable serum half-life, making engineering such mFc variants highly challenging. Mainly by computational design approach, we created mFc mutants with diverse human FcRn-binding profiles, including two variants that exhibit 17- and 47-fold stronger FcRn binding at pH 6.0 (KD of 30.7 nM and 88 nM) compared to a baseline mFc, while maintaining greater than 213-fold weaker binding at pH 7.4 (KD of 6,549 nM and 39,150 nM). These variants are highly soluble and display a melting temperature greater than 60.6 {degrees}C, underscoring their potential as platforms for extending the in vivo half-life of therapeutic modalities. Other mFc variants with different pH-responsive FcRn-binding profiles would potentially fit for other therapeutic needs. Moreover, transferring the same variations into IgG4 Fc generated IgG4 mFc variants with FcRn-binding properties similar to those of the parent IgG1 mFc variants. Furthermore, incorporating the FcRn-binding affinity-enhancing substitutions into native Fc produced a dimeric Fc variant that exhibits strong, pH-responsive FcRn-binding affinities, promising an extended half-life in serum.

bioengineering↗

bioRxiv2024

Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer

This study investigates the variability among patients with non-small cell lung cancer (NSCLC) in their responses to immune checkpoint inhibitors (ICI). Recognizing that patients with advanced-stage NSCLC rarely qualify for surgical interventions, it becomes crucial to identify biomarkers that influence responses to ICI therapy. We conducted an analysis of single-cell transcriptomes from 33 lung cancer biopsy samples, with a particular focus on 14 core samples taken before the initiation of palliative ICI treatment. Our objective was to link tumor and immune cell profiles with patient responses to ICI. We discovered that ICI non-responders exhibited a higher presence of CD4+ regulatory T cells, resident memory T cells, and TH17 cells. This contrasts with the diverse activated CD8+ T cells found in responders. Furthermore, tumor cells in non-responders frequently showed heightened transcriptional activity in the NF-kB and STAT3 pathways, suggesting a potential inherent resistance to ICI therapy. Through the integration of immune cell profiles and tumor molecular signatures, we achieved an discriminative power (AUC) exceeding 95% in identifying patient responses to ICI treatment. These results underscore the crucial importance of the interplay between tumor and immune microenvironment, including within metastatic sites, in affecting the effectiveness of ICIs in NSCLC.

bioinformatics↗

bioRxiv2022

Predicting Lung Cancer in Korean Never-Smokers with Polygenic Risk Scores

In the last few decades, genome-wide association studies (GWAS) with more than 10,000 subjects have identified several loci associated with lung cancer. Hence, recently, genetic data have been used to develop novel risk prediction tools for cancer. The present study aimed to establish a lung cancer prediction model for Korean never-smokers using polygenic risk scores (PRSs). PRSs were calculated using a thresholding-pruning-based approach based on 11 genome-wide significant single nucleotide polymorphisms (SNPs). Overall, the odds ratios tended to increase as PRSs were larger, with the odds ratio of the top 5% PRSs being 1.71 (95% confidence interval: 1.31-2.23), and the area under the curve (AUC) of the prediction model being of 0.76 (95% confidence interval: 0.747-0.774). The receiver operating characteristic (ROC) curves of the prediction model with and without PRSs as covariates were compared using DeLongs test, and a significant difference was observed. Our results suggest that PRSs can be valuable tools for predicting the risk of lung cancer.

bioinformatics↗

bioRxiv2022

Noncovalent antibody catenation on a target surface drastically increases the antigen-binding avidity

Immunoglobulin G (IgG) antibodies are widely used for diagnosis and therapy. Given the unique dimeric structure of IgG, we hypothesized that, by genetically fusing a homodimeric protein (catenator) to the C-terminus of IgG, reversible catenation of antibody molecules could be induced on a surface where target antigen molecules are abundant, and that it could be an effective way to greatly enhance the antigen-binding avidity. A thermodynamic simulation shows that quite low homodimerization affinity of a catenator, e.g. dissociation constant of 100 M, can enhance nanomolar antigen-binding avidity to a picomolar level, and that the fold enhancement sharply depends on the density of the antigen. In a proof-of-concept experiment where antigen molecules are immobilized on a biosensor tip, C-terminal fusion of a weakly homodimerizing protein to two different antibodies enhanced the antigen-binding avidity by at least 210 to 5,120 folds from the intrinsic binding avidity. Thus, the homodimerization-induced antibody catenation would be a simple, powerful and general approach to improve many antibody applications, including the detection of scarce biomarkers and targeted anticancer therapies.

biophysics↗

bioRxiv2019

JuLI: accurate detection of DNA fusions in clinical sequencing for precision oncology

Accurate detection of genomic fusions by high-throughput sequencing in clinical samples with inadequate tumor purity and formalin-fixed paraffin embedded (FFPE) tissue is an essential task in precise oncology. We developed the fusion detection algorithm Junction Location Identifier (JuLI) for optimization of high-depth clinical sequencing. We implemented novel filtering steps to minimize false positives and a joint calling function to increase sensitivity in clinical setting. We comprehensively validated the algorithm using high-depth sequencing data from cancer cell lines and clinical samples and whole genome sequencing data from NA12878. We showed that JuLI outperformed state-of-the-art fusion callers in cases with high-depth clinical sequencing and rescued a driver fusion from false negative in plasma cell-free DNA. JuLI is freely available via GitHub (https://github.com/sgilab/JuLI).

bioinformatics↗