Biology subjects

Su, C. T.-T.

Publications and source records attributed to Su, C. T.-T..

6 recordsLinked to original sources

bioRxiv2026

Ara h 2 and FcϵRIα binding elicit state-dependent allostery in IgE

Background: Peanuts are among the most prevalent food allergens responsible for anaphylaxis, particularly in children. Allergic responses may be mitigated by disrupting molecular interactions between immunoglobulin-E (IgE) and its binding partners: peanut allergen (Ara h 2) and IgE receptors (Fc{epsilon}RI). However, the structural dynamics of IgE upon engaging these factors are not yet fully elucidated. Objective: To characterize how Ara h 2 and/or Fc{epsilon}RI binding influence IgE flexibility and its interdomain communications. Methods: The structural dynamics of full-length IgE in various unbound and bound states were examined by combining molecular dynamics (MD) simulations with cross linking mass spectrometry (XLMS). Causal relationship between IgE domains were characterized to infer allosteric communication pathways, which were assessed through hydrogen-deuterium exchange mass spectrometry (HDX-MS). Results: Non canonical bent IgE conformations were identified. We demonstrate that Fc{epsilon}RI binding immobilizes IgE predominantly by stabilizing and restricting Fc flexibility, whereas Ara h 2 binding induces specific conformational changes within the Fc{epsilon}RI-binding region. Our results further suggest that the C{epsilon} domains retain sufficient freedom to enable causal dynamics by the Fabs in a binding-dependent manner, thereby modulating the receptor engagement potential of IgE. Notably, concurrent binding of Ara h 2 and Fc{varepsilon}RI to IgE shifts the allosteric communication between the Fabs and Fc regions from the C{epsilon}3 to C{epsilon}4 domain. Conclusion: Our findings provide new insights into the dynamic regulation of IgE and its role in allergic responses. This could serve as a structural framework for understanding IgE signaling in peanut allergy and suggest potential targets for therapeutic interventions

immunology↗

bioRxiv2025

Epi4Ab: A data-driven prediction model of conformational epitopes for specific antibody VH/VL families and CDR H3/L1 sequences.

Antibodies recognize antigens via complementary and structurally dependent mechanisms. Therefore, inclusion of antibody inputs is crucial for accurate epitope prediction. Given limited availability of antibody-antigen complex structures, it is necessary that any epitope prediction model requires minimal yet sufficient antibody inputs to ensure precise epitope identification. To address this need, we introduce Epi4Ab, an antibody-specific epitope prediction model, which focuses on identifying unique in-contact antigen residues for a given antibody. Epi4Ab requires minimal antibody inputs, specifically VH/VL families and CDR H3/L1 sequences.

bioinformatics↗

bioRxiv2024

epiTCR-KDA: Knowledge Distillation model on Dihedral Angles for TCR-peptide prediction

MotivationAntigen recognition by T-cell receptors (TCRs) triggers cascades of immune responses. Successful predictions of the TCR and antigen (as peptide) bindings therefore signify the advancements in immunotherapy. However, most of current TCR-peptide interaction predictors fail to predict unseen data. This limitation may be derived from the conventional usage of TCR and/or peptide sequences as input, which may not adequately reflect their structural characteristics. Therefore, incorporating the TCR and peptide structural information into the prediction model to improve the generalizability is necessary. ResultsWe presented epiTCR-KDA as a new predictor of TCR-peptide binding that utilises structural information, specifically the dihedral angles between the residues of both the peptide and the TCR. This structural descriptor was integrated into a model constructed using knowledge distillation to enhance its generalizability. The epiTCR-KDA demonstrated competitive prediction performance, with an AUC of 0.99 for seen data and AUC of 0.86 for unseen data. Across multiple public datasets, epiTCR-KDA consistently outperformed other predictors, such as epiTCR, NetTCR, BERTrand, TEIM-Seq, TEINet, and ImRex, maintaining a median AUC of 0.9 (ranging from 0.82 to 0.91). Further analysis of epiTCR-KDA performance indicated that the cosine similarity of the dihedral angle vectors between the unseen testing data and training data is crucial for its stable performance. In conclusion, our epiTCR-KDA model, with its capacity to predict for unseen data, has brought us one step closer toward the development of a highly effective pipeline for affordable antigen-based immunotherapy. Availability and implementationepiTCR-KDA is available on GitHub (https://github.com/ddiem-ri-4D/epiTCR-KDA)

bioinformatics↗

bioRxiv2020

Antibody Signal peptides and essential amino acid counts on recombinant antibody production

Boosting the production of recombinant therapeutic antibodies is crucial in both academic and industry settings. In this work, we investigated the usage of varying signal peptides by antibody genes and their roles in recombinant transient production. Comparing myeloma and the native signal peptides of both heavy and light chains in 168 antibody permutation variants, we performed a systematic analysis, finding amino acids counts to be involved in antibody production to construct a model for predicting co-transfection transient recombinant antibody production rates using the HEK293 system. The findings also provide insights into the usage of the large repertoire of antibody signal peptides.

molecular biology↗

bioRxiv2020

Molecular insight into Nickel(II) binding by Her2-specific IgE: a possible mechanistic insight into the pathogenesis of Type I nickel hypersensitivity

Nickel (Ni) allergy has been reported in contact dermatitis Type IV (Ni-specific T cells mediated) and asthmatic Type I (IgE-mediated) hypersensitivities. Associations between the two hypersensitivities have been found in some patients, but the underlying mechanisms remain enigmatic. Using Her2-specific IgEs as models, we found additional binding to Ni-NTA without observable changes in binding to Her2 and that glutamine, together with the canonical Ni2+-binding histidine, could form Ni2+ binding signatures. This mechanism may underlie Type I hypersensitivity in the selection of anti-Ni2+ IgEs. This mechanism may also underlie Type IV hypersensitivity and the interaction of immunoglobulin proteins with other heavy metal ions. Our findings shed light to how Ni hypersensitivities can occur and how they can be avoided in therapeutics design, or even incorporated for biotechnological purification purposes.

immunology↗

bioRxiv2019

Predicting mutations in HIV-1 Gag: Insights from in silico and an in vitro BSL2 platform on thermostability and allosteric effects

While drug resistant mutations in HIV-1 is largely credited to its error prone HIV-1 RT, host proteins such as deaminases may also play a role generating mutations. Many HIV-1 RT mutational in vitro studies utilize reporter genes (LacZ) as template, leaving out the possible contribution of HIV codon usage and gene-specific effects. To address this gap, we studied HIV-1 RT mutation rates and bias on its own Gag, protease, and RT p66 genes in an in-vitro selection pressure free system. We found rare clinical mutations with a general avoidance of crucial functional sites in the background mutations rates for Gag, protease and RT p66 at 4.71 x 10-5, 6.03 x 10-5, and 7.09 x 10-5 mutations/bp respectively. Gag and p66 genes showed a large number of A to G hypermutations likely due to cellular adenosine deaminases. Comparisons with silently mutated p66 sequences showed an increase in mutation rates (1.88 x 10-4 mutations/bp) and that A to G mutations occurred in regions reminiscent of ADAR neighbour preferences. Mutational free energies by the A to G mutations revealed an avoidance of destabilizing effects with the natural p66 gene codon usage providing barriers to ADAR effects. Our study demonstrates the importance of studying mutation emergence in HIV genes to understand how fast drug resistance can emerge, sometimes with contributions of host deaminases, providing transferable applications to how new viral diseases and drug resistances can emerge.

microbiology↗