Biology subjects

Huang, H.-D.

Publications and source records attributed to Huang, H.-D..

8 recordsLinked to original sources

bioRxiv2026

MetExPred: A Comprehensive Prediction Framework with Protein-Context-Aware Multi-view Learning for Drug Metabolism and Excretion

Within the ADMET continuum, metabolism and excretion (ME) form a critical bridge between drug exposure established by absorption and distribution and downstream efficacy and toxicity. However, no existing framework for drug ME prediction has simultaneously achieved broad coverage of endpoints and robust data recency and completeness. Here, we developed MetExPred, a multi-view prediction framework that comprises 17 classification endpoints and two regression endpoints, covering the overall drug ME process. The framework combines sequence-based and graph-based molecular representations, while optionally incorporating ESM-2 protein representations when experimentally annotated targets are available. A protein-aware masking strategy enables the same architecture to operate in both molecular-only and target-enhanced settings, and multi-view attention adaptively integrates the available representations. Across the classification tasks, MetExPred achieved mean AUROC, AUPRC and F1 scores of 0.844, 0.734 and 0.697, respectively. For clearance and half-life prediction, the model achieved RMSE values of 0.686 and 0.668. MetExPred showed the strongest average performance among the evaluated baselines, while ablation studies confirmed the complementary contributions of molecular sequence, graph and protein information. These results provide a unified and flexible modeling framework for systematic ME prediction of drug compounds, enabling early-stage virtual screening and pharmacokinetic assessment during lead optimization.

bioinformatics↗

bioRxiv2026

MIRCID: Inferred Hub-miRNAs Drive Cross-Task Improvements in Drug Mechanistic Modeling

Drug mechanism-of-action (MoA) modeling commonly relies on perturbational transcriptomes, but matched microRNA (miRNA) measurements are often unavailable. Inferred regulatory features offer a scalable way to reuse these data. Here, we present MIRCID, a framework comparing gene expression with inferred transcription factor (TF) activity and miRNA expression across pathway classification and similarity-based MoA retrieval. HubmiRNet infers 414 pan-cancer hub miRNAs (HubmiRs) from 977 L1000 landmark genes, achieving a Pearson correlation coefficient of 87.72\%; its 1,298-output variant also outperformed SiCmiR on the full-miRNA task (71.21\% versus 67.30\%). In the evaluated comparisons, miRNA augmentation provided more consistent gains than TF activity. Generic embedding controls showed model-dependent utility, while complementarity analyses identified a distinct, partially linearly recoverable representation that retained gene-derived structure. Illustrative rescue cases linked improved classification to biologically plausible miRNA patterns in samples with weak transcriptional signatures. These findings support inferred HubmiRs as a biologically informed recoding of transcriptomic data for perturbational drug modeling, while leaving recovery of measured perturbational miRNA responses to further validation.

bioinformatics↗

bioRxiv2026

Microbial Antioxidants Reduce ROS In Human Skin Cells Under Oxidative Stress

Reactive oxygen species (ROS) play a dual role in cellular homeostasis, but excessive levels of ROS lead to oxidative stress, accelerating skin aging. Environmental stressors like UV radiation induce ROS overproduction, overwhelming endogenous antioxidant defenses and causing cellular damage. While the skin possesses an intrinsic antioxidant network that provides moderate protection, excessive oxidative stress can trigger inflammatory responses, thereby necessitating exogenous antioxidant intervention. Microbe-derived antioxidants (MA), produced via probiotic fermentation of sea buckthorn and chestnut rose, have shown promise in mitigating ROS-induced damage. In this study, we evaluated two MA formulations, MA1 and MA2, for their ability to scavenge free radicals and alleviate hydrogen peroxide (H2O2)-induced oxidative stress in human dermal fibroblasts (HDF) and dermal papilla cells (HDP). Both formulations displayed dose-dependent DPPH radical scavenging activity and enhanced cell viability at low concentrations. Under H2O2-induced oxidative stress, MA1 and MA2 effectively restored intracellular ROS to baseline levels, demonstrating significant cytoprotective effects. UHPLC-MS/MS profiling identified 12 compounds shared by both formulations, and Gene Ontology Biological Process enrichment analysis revealed that their associated target genes were significantly enriched in antioxidant-related pathways. Five compounds--adenosine, citric acid, 5-hydroxymethylfurfural, myricetin, and phenylalanine--emerged as key contributors to the observed antioxidative effects. Together, these findings highlight the potential of fermented microbial antioxidants to re-establish redox homeostasis in human skin cells and support their further development as therapeutic or cosmetic interventions targeting oxidative stress and skin aging. Given the heightened oxidative sensitivity of aged fibroblasts, MAs ability to alleviate ROS may offer novel therapeutic strategies against skin aging and related pathologies.

molecular biology↗

bioRxiv2026

The Red Rice Bran Extract (RRBE) Mitigates Photoaging by Targeting Mitochondrial Oxidative Stress and Regulating Thermal Damage Responses

The photoprotective efficacy of natural skin-active complexes is well recognized, yet their ap-plication is often hindered by the challenge of deciphering their complex, multi-component, and multi-target mechanisms. To bridge the gap between established phenotypes and molecular mechanisms, we developed an AI-driven integrated platform that combines phytochemical profiling, network pharmacology, and deep learning-based target prediction with rigorous biophysical validation. We applied this platform to investigate RRBE, a bioactive complex refined from red rice bran extract. In vivo clinical studies confirmed that RRBE significantly accelerates the resolution of UV-induced erythema, while cellular and 3D tissue models demonstrated robust suppression of oxidative stress and DNA damage responses. To decode its material basis, the platform deconstructed RRBE into 10 distinct chemical modules. Leveraging our SCOPE-DTI deep learning model for global target prediction, we identified flavonoids (Module 1) and phenolic acids (Module 10) as the primary bioactive drivers relevant to photoprotection. These computational predictions were structurally supported by molecular docking and definitively validated in a physiological environment via Cellular Thermal Shift Assay-Mass Spectrometry (CETSA-MS). Mechanistically, RRBE functions through a synergistic polypharmacology: (1) Module 1 components, represented by procyanidin B2, target NDUFA7 to stabilize mitochondrial function and mitigate ROS; (2) Module 10 components, exemplified by caffeic acid, ferulic acid, and p-coumaric acid, engage FKBP11 and HSP90AA1 to regulate protein homeostasis and stress responses. This work not only deciphers the polypharmacological basis of RRBEs photoprotective action but also validates a scalable "AI-guided, cell-validated" discovery pipeline, offering a rational paradigm for uncovering the protective benefits of complex natural extracts.

systems biology↗

bioRxiv2025

DeepADR: Multi-modal Prediction of Adverse Drug Reaction Frequency by Integrating Early-Stage Drug Discovery Information via Kolmogorov-Arnold Networks

Adverse drug reactions (ADRs) are a major cause of clinical trial failure and post-market withdrawal, posing significant risks to public health and impeding drug development. While computational methods offer an alternative to costly preclinical testing, existing models often fail with novel compounds by requiring pre-existing information such as drug-ADR associations or by inadequately integrating diverse data sources. Here, we introduce DeepADR, a multi-modal deep learning framework for predicting both the occurrence and frequency of ADRs using early-stage, readily available data. DeepADR integrates chemical structures and biological target profiles with semantic representations of ADR terms derived from a large language model. These heterogeneous parameters are fused using a Kolmogorov-Arnold Network (KAN), which effectively models complex, non-linear cross-modal interactions to capture underlying toxicological mechanisms. Our model outperforms existing methods in predicting both ADR occurrence and frequency, demonstrating robust generalization to new chemical entities. By effectively integrating chemical, biological, and semantic datasets, DeepADR provides a powerful, scalable tool for the early-stage safety assessment and candidate prioritization. This framework not only facilitates the prioritization of safer drug candidates but also offers a methodology for predicting the toxicity of other hazardous materials, holding significant promise for advancing public health.

bioinformatics↗

bioRxiv2025

Imputation Disparities Driven by Recent Selectionand Their Impact on Disease Risk Estimation in East and Southeast Asian Populations

Using genotype data consisting of 8,316 individuals, we systematically evaluated imputation performance across six state-of-the-art reference panels for Chinese and Thai populations. A substantial proportion of variants identified through whole-genome sequencing, especially low-frequency variants, remained undetected by existing reference panels. In the Chinese population, the TOPMed panel required an R2 threshold of 0.60-0.70 to achieve comparable imputation accuracy of the ChinaMAP panel without R2 filtering, challenging the standard practice of applying a fixed R2 threshold for downstream analyses. Regional analysis highlighted the role of recent selection in imputation discrepancies and revealed an enrichment of immune-related genes in poorly imputed regions. In addition, we showed that the selection of reference panels and R2 thresholds could significantly influence estimation of polygenic risk score for disease prediction. These findings underscore the importance of developing ancestrally diverse reference panels and provide valuable guidelines for improving genotype imputation in East and Southeast Asian populations.

genomics↗

bioRxiv2021

KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

The purpose of this work is to enhance KinasePhos, a machine-learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProt, GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models were observed to be more effective than other prediction tools. For example, the prediction of sites phosphorylated by the Akt, CKT, and PKA families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the Shapley additive explanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned with the goal of providing comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/index.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.

bioinformatics↗

bioRxiv2020

DNMT3b Dysfunction Promotes DNA cleavages at Centromeric R-loops to Increase Centromere Instability

This study investigates how DNA methyltransferase 3b (DNMT3b) dysfunction causes genome instability. We showed that in DNMT3b deficient cells, R-loops contribute to prominent {gamma}H2AX signal, which was mapped to repetitive satellite sequences including centromere regions. By ChIP and DRIP analyses, our data revealed that centromeric R-loops in DNMT3b deficient cells are removed by XPG/XPF, thus generating DNA breaks in centromeres to increase mitotic aberration. In immunodeficiency-centromeric instability-facial anomalies (ICF) patient cells carrying the loss-of-function mutation at DNMT3b, knockdown of XPG/XPF in ICF cells also reduces DNA breaks in centromere while bringing up centromeric R-loop to the level similar to that in wild-type cells. These results suggest that DNMT3b has a critical function in preventing XPG/XPF-mediated cleavages at centromeric R-loop sites. Finally, we showed the involvement of non-homologous end-joining repair at centromeric sites in ICF cells. Thus, DNA cleavages at centromeric R-loops with error-prone repair undermine centromere stability in ICF cells.

molecular biology↗