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Chua, A. C. Y.

Publications and source records attributed to Chua, A. C. Y..

3 recordsLinked to original sources

A chromosome-scale Plasmodium cynomolgi Berok genome reveals a distinct subtelomeric architecture and a highly diverged primate malaria lineage

Plasmodium cynomolgi is the closest relative of P. vivax and the primary experimental model for relapsing malaria, hypnozoite biology, and blood-stage drug susceptibility. Yet existing reference genomes remain fragmented, leaving structurally complex, AT-rich regions largely unresolved. We generated a chromosome-scale genome assembly for the K4-A7 cloned line of P. cynomolgi Berok by combining Hi-C chromosome conformation capture, Oxford Nanopore long reads, PacBio, and Illumina sequencing. The assembly spans 14 chromosomes plus mitochondrial and apicoplast genomes, with only seven unplaced minor contigs, the fewest for any non-P. falciparum Plasmodium genome, and an N50 of 3.06 Mb. Critically, this hybrid strategy resolved approximately 8 Mb of extremely AT-rich (~20% GC) sequence onto chromosomes 4, 8, and 13, anchoring what were previously unplaced or absent contigs into a continuous chromosomal framework. These subtelomere-like expansions (SLEs) constitute ~26.5% of the chromosomal genome and are enriched for PIR/VIR, STP1, variable surface antigen, and methyltransferase pseudogene families. Despite low gene density, SLE-encoded genes are transcriptionally active and show stage-specific expression across the erythrocytic cycle. Integrated lifecycle transcriptomics across 7,006 genes revealed a ~54-hour erythrocytic cycle with a "just-in-time" transcriptional cascade closely resembling that of P. vivax. Phylogenomic analyses and pairwise amino acid comparisons across more than 2,600 single-copy orthologs show that Berok forms a deeply diverged P. cynomolgi lineage, suggesting a distinct subspecies. This assembly establishes a high-resolution genomic foundation for comparative malaria biology, drug discovery, and the study of subtelomeric architecture, host adaptation, and lineage boundaries in primate Plasmodium.

microbiology↗

AI-guided competitive docking for virtual screening and compound efficacy prediction

Machine learning has transformed how we predict protein structures and interactions, but its full potential in drug discovery is only beginning to be realized. In this study, we demonstrate that advanced deep learning tools --such as AlphaFold3 and Boltz-1/2-- not only predict protein-ligand interactions with high accuracy but can also separate active drug compounds from inactive ones. We present a straightforward strategy called pairwise competitive docking, which ranks drug candidates by directly comparing how well they bind to a proteins target site. When applied to both bacterial and human enzymes, this method produced rankings that closely matched experimental results. We further show how this approach can guide the design of improved antibiotics and speed up the discovery of promising drug candidates from large chemical libraries. Overall, our findings highlight how machine learning can make structure-based drug design faster, more reliable, and more cost-effective.

bioinformatics↗

Extended blood stage sensitivity profiles of Plasmodium vivax to doxycycline and tafenoquine using Plasmodium cynomolgi as a model

Testing Plasmodium vivax antimicrobial sensitivity is limited to ex vivo schizont maturation assays, which precludes determining the IC50s of delayed action antimalarials such as doxycycline. Using Plasmodium cynomolgi as a model for P. vivax, we determined the physiologically significant delayed death effect induced by doxycycline (IC50(96h), 1401 {+/-} 607 nM). As expected, IC50(96 h) to chloroquine (20.4 nM), piperaquine (12.6 {micro}M) and tafenoquine (1424 nM) were not affected by extended exposure.

microbiology↗