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Nguyen, D. D. A.

Publications and source records attributed to Nguyen, D. D. A..

2 recordsLinked to original sources

Multi-omic dereplication of antibiotic production by diffusion chamber isolated bacteria from Australian soils

Soil bacteria are a major source of clinically useful antibiotics, yet the majority of soil-dwelling microorganisms remain uncultivable by standard laboratory methods. To access this untapped microbial diversity, we employed microbial diffusion chambers to isolate bacteria from ten Australian soil samples. A total of 1,218 bacterial isolates were recovered, representing a diverse collection spanning 61 genera from 32 families, covering the major known phyla of soil bacteria. Antibiotic activity screening revealed that 16% of isolates inhibited the growth of at least one of E. coli or S. aureus, with 120 isolates displaying activity against multidrug-resistant pathogens including methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium (VRE). Mass spectrometry-based dereplication using GNPS identified known antibiotics in 33% of bioactive strains, including actinomycin D, nonactins, and valinomycin. Genomic analysis confirmed the presence of corresponding biosynthetic gene clusters (BGCs), while targeted analysis of selected strains uncovered production of additional antibiotics such as nigericin and streptothricin that were not initially detected by mass spectrometry. Our results demonstrate that diffusion chambers enhance bacterial recovery from soil and show the benefits of a combined pipeline including bioactivity screening, mass spectrometry, and genomics for effective antibiotic dereplication and discovery. Impact StatementThis study delivers a significant advance in natural product discovery by demonstrating that microbial diffusion chambers can dramatically improve the recovery of diverse soil bacteria with antibiotic-producing potential from Australian soil samples. By integrating in situ cultivation with high-throughput screening, mass spectrometry-based dereplication and genome mining, this study yielded more than 1,200 bacterial isolates--including many with activity against multidrug-resistant pathogens--and confirms the production of both known and previously undetected antibiotics. The discovery of streptothricin production via genomics, despite its absence from mass spectrometry data, underscores the power of a multi-layered dereplication strategy. This work not only validates the utility of diffusion chambers for unlocking the "rare biosphere" of uncultivable microbes but also highlights critical methodological refinements to diversify antibiotic-producing strains beyond Streptomyces. The findings will inform and accelerate future efforts to discover urgently needed antibiotics from environmental microbiomes. Data SummaryThe whole-genome sequences were deposited in the National Center for Biotechnology Information National Library of Medicine under BioProject accession number PRJNA1272227.

microbiology↗

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↗