Biology subjects

Chuang, K.

Publications and source records attributed to Chuang, K..

3 recordsLinked to original sources

bioRxiv2026

Chemoinformatics-guided discovery of food-grade anionic stabilizers for phycocyanin under acidic conditions

Phycocyanin (PC) is the principal natural blue pigment used in functional beverages, but it rapidly loses color and aggregates under acidic conditions (pH {approx} 3). Experimental screening of stabilizers is costly and combinatorially intractable. Here we develop a chemoinformatics framework -- descriptor-based QSPR, a chemistry-prior heuristic, and virtual screening -- that learns from three rounds of commissioned screening (48 compounds, 6% hit rate) to predict stabilizer efficacy directly from molecular structure. In this genuinely small-data regime (3 positives), a LightGBM classifier built from 10 RDKit descriptors and 11 domain-expert charge/polymer features attained a leave-one-out AUC of 0.73, only marginally above a single-feature charge-density baseline (AUC 0.67); LOO sensitivity was 1/3 at threshold 0.5. A complementary chemistry-prior heuristic encoding anion-type priors substantially outperformed both, reaching AUC 0.95, indicating that explicit chemical knowledge captures information that descriptor-based ML cannot readily recover at this dataset size. SHAP analysis of the QSPR identified effective negative-charge density per unit, log molecular weight, polyphosphate identity, and functional-group density as the dominant features (jointly {approx}97% of mean |SHAP|), recovering the electrostatic-complexation mechanism without it being supplied as a prior. Virtual screening of 30 generally recognized as safe (GRAS) food additives nominated the pyrophosphate family -- led by sodium pyrophosphate decahydrate and sodium hexametaphosphate (SHMP), both at P {approx} 0.99 -- and the heuristic additionally flagged sodium phytate (IP{square}), which the descriptor model under-ranked at P = 0.045. Experimental validation at pH 3 and 46 {degrees}C for 7 days confirmed SHMP 2:1 (78.1 {+/-} 11.3% color retention), TSPP 2:1 (54.1 {+/-} 10.6%) and IP{square} 1:1 (52.7 {+/-} 9.0%), while a ternary IP{square} + STPP combination reached 83.8 {+/-} 11.9%, surpassing all single-component formulations. {zeta}-Potential measurements indicated a predominantly electrostatic origin for the protection (Pearson r = -0.82 between {zeta} and CR{square} {square} {square}; n = 24; p = 1 x 10{square} {square}). The framework, dataset and code are released to accelerate stabilizer discovery for other acid-sensitive food colorants and to provide a candid small-data benchmark.

biochemistry↗

bioRxiv2024

A high-throughput phenotypic screen combined with an ultra-large-scale deep learning-based virtual screening reveals novel scaffolds of antibacterial compounds

The proliferation of multi-drug-resistant bacteria underscores an urgent need for novel antibiotics. Traditional discovery methods face challenges due to limited chemical diversity, high costs, and difficulties in identifying structurally novel compounds. Here, we explore the integration of small molecule high-throughput screening with a deep learning-based virtual screening approach to uncover new antibacterial compounds. Leveraging a diverse library of nearly 2 million small molecules, we conducted comprehensive phenotypic screening against a sensitized Escherichia coli strain that, at a low hit rate, yielded thousands of hits. We trained a deep learning model, GNEprop, to predict antibacterial activity, ensuring robustness through out-of-distribution generalization techniques. Virtual screening of over 1.4 billion compounds identified potential candidates, of which 82 exhibited antibacterial activity, illustrating a 90X improved hit rate over the high-throughput screening experiment GNEprop was trained on. Importantly, a significant portion of these newly identified compounds exhibited high dissimilarity to known antibiotics, indicating promising avenues for further exploration in antibiotic discovery.

bioinformatics↗

bioRxiv2020

Identification of Retinal Ganglion Cell Types and Brain Nuclei expressing the transcription factor Brn3c/Pou4f3 using a Cre recombinase knock-in allele.

Members of the POU4F/Brn3 transcription factor family have an established role in the development of retinal ganglion cell types (RGCs), the projection sensory neuron conveying visual information from the mammalian eye to the brain. Our previous work using sparse random recombination of a conditional knock-in reporter allele expressing Alkaline Phosphatase (AP) and intersectional genetics had identified three types of Pou4f3/Brn3c positive (Brn3c+) RGCs. Here, we describe a novel Brn3cCre mouse allele generated by serial Dre to Cre recombination. We use this allele to explore the expression overlap of Brn3c with Brn3a and Brn3b and the dendritic arbor morphologies and visual stimulus properties of Brn3c+ RGC types. Furthermore, we explore Brn3c-expressing brain nuclei. Our analysis reveals a much larger number of Brn3c+ RGCs and more diverse set of RGC types than previously reported. The majority of RGCs having expressed Brn3c during development are still Brn3c positive in the adult, and all of them express Brn3a while only about half express Brn3b. Intersection of Brn3b and Brn3c expression highlights an area of increased RGC density, similar to an area centralis, corresponding to part of the binocular field of view of the mouse. Brn3c+ neurons and projections are present in multiple brain nuclei. Brn3c+ RGC projections can be detected in the Lateral Geniculate Nucleus (LGN), Pretectal Area (PTA) and Superior Colliculus (SC) but also in the thalamic reticular nucleus (TRN), a visual circuit station that was not previously described to receive retinal input. Most Brn3c+ neurons of the brain are confined to the pretectum and the dorsal midbrain. Amongst theses we identify a previously unknown Brn3c+ subdivision of the deep mesencephalic nucleus (DpMe). Thus, our newly generated allele provides novel biological insights into RGC type classification, brain connectivity and midbrain cytoarchitectonic, and opens the avenue for specific characterization and manipulation of these structures.

neuroscience↗