Biology subjects

Myung, Y.

Publications and source records attributed to Myung, Y..

8 recordsLinked to original sources

bioRxiv2026

A cargo receptor entrapment complex is a therapeutic node for genetically and clinically distinct proteinopathies

Severe proteinopathies--such as retinitis pigmentosa, a form of inherited blindness--are driven by genetic mutations that overwhelm the quality control of the post-endoplasmic reticulum (post-ER) secretory pathway, causing toxic protein accumulation. Here, we identify a therapeutic node defined by a hetero-oligomeric cargo receptor complex consisting of TMED7, 2, 9, and 10. This "entrapment complex" anchors structurally and functionally diverse mutant clients within the early secretory pathway via TMED7 binding to the integral Golgi protein GRASP55. Disruption of the entrapment complex results in the clearance of accumulated protein cargoes. In vivo ablation of the entrapment node via inducible genetic deletion or via the small molecule BRD7635 reverses histopathological hallmarks and rescues functional deficits in clinically distinct proteinopathies of the kidney and the eye, including mitigating vision loss in a mouse model of retinitis pigmentosa.

cell biology↗

bioRxiv2026

CharacTERT: A machine learning tool for classifying hTERT missense variants

Missense mutations in TERT, the gene encoding the human telomerase catalytic subunit hTERT, are associated with Telomere Biology Disorders (TBDs). Experimentally elucidating the effects of all possible missense variants would be time-consuming and technically challenging. Moreover, current computational predictors are not hTERT-specific and primarily rely on sequence information, failing to capture the complex biological and structural context of the telomerase enzyme. In this work, we developed three machine learning models integrating both sequence- and structure-based features to account for the biological mechanisms of hTERT. Compared to state-of-the-art methods, our best-performing models achieved a higher Matthews Correlation Coefficient of 0.88 on ClinVar and gnomAD curated variants and demonstrated robust sensitivity (0.75) on a dataset curated according to guidelines from the American College of Medical Genetics and Genomics and Association for Molecular Pathology (ACMG/AMP). Feature interpretation highlighted hTERT residue conservation and changes in hydrophobic and weak polar interactions as critical determinants of pathogenicity. Finally, in silico saturation mutagenesis was performed to present a mutational landscape of TERT, available in a user-friendly web server, CharacTERT, which could offer valuable insights into the molecular mechanisms driving TBDs, aid in early diagnosis, as well as guide personalized treatment strategies. CharacTERT is freely available at https://biosig.lab.uq.edu.au/charactert/.

bioinformatics↗

bioRxiv2026

Benchmarking 80 binary phenotypes from the openSNP dataset using deep learning algorithms and polygenic risk score tools

Genotype-phenotype prediction plays a crucial role in identifying disease-causing single nucleotide polymorphisms and precision medicine. In this manuscript, we benchmark the performance of various machine/deep learning algorithms and polygenic risk score tools on 80 binary phenotypes extracted from the openSNP dataset. After cleaning and extraction, the genotype data for each phenotype is passed to PLINK for quality control, after which it is transformed separately for each of the considered tools/algorithms. To compute polygenic risk scores, we used the quality control measures for the test data and the genome-wide association studies summary statistic file, along with various combinations of clumping and pruning. For the machine learning algorithms, we used p-value thresholding on the training data to select the single nucleotide polymorphisms, and the resulting data was passed to the algorithm. Our results report the average 5-fold Area Under the Curve (AUC) for 29 machine learning algorithms, 80 deep learning algorithms, and 3 polygenic risk scores tools with 675 different clumping and pruning parameters. Machine learning outperformed for 44 phenotypes, while polygenic risk score tools excelled for 36 phenotypes. The results give us valuable insights into which techniques tend to perform better for certain phenotypes compared to more traditional polygenic risk scores tools.

bioinformatics↗

bioRxiv2025

Transfer learning across molecular graphs for predicting protein-ligand affinities and their changes upon mutations

Predicting protein-ligand binding affinity and mutation-induced affinity changes ({Delta}{Delta}G) remains challenging due to limited data and complex interaction mechanisms. Here we present DDMuffin, a deep learning framework that integrates structural, sequence, and interaction graph features, employing transfer learning and stringent dataset partitioning to achieve reliable generalization. DDMuffin demonstrates strong predictive accuracy on the rigorous LP-PDBBind benchmark (Pearson r up to 0.70 after excluding top 10% outliers; RMSE = 1.48 kcal mol-{superscript 1}). In evaluating mutation-induced affinity changes for clinically relevant kinase inhibitors, DDMuffin achieves competitive average performance (mean Spearman{rho} = 0.39), outperforming or matching several established methods. The approach provides valuable insights into mutation-driven drug resistance and interpretable ligand binding mechanisms, particularly advantageous for guiding inhibitor design and personalized therapeutic strategies. To facilitate broad application, we deployed DDMuffin as an accessible web server at https://biosig.lab.uq.edu.au/ddmuffin/, promoting systematic exploration of protein-ligand interactions in drug discovery research.

bioinformatics↗

bioRxiv2021

VIVID: a web application for variant interpretation and visualisation in multidimensional analyses

Large-scale comparative genomics- and population genetic studies generate enormous amounts of polymorphism data in the form of DNA variants. Ultimately, the goal of many of these studies is to associate genetic variants to phenotypes or fitness. We introduce VIVID, an interactive, user-friendly web application that integrates a wide range of approaches for encoding genotypic to phenotypic information in any organism or disease, from an individual or population, in three-dimensional (3D) space. It allows mutation mapping and annotation, calculation of interactions and conservation scores, prediction of harmful effects, analysis of diversity and selection, and 3-dimensional (3D) visualisation of genotypic information encoded in Variant Call Format (VCF) on AlphaFold2 protein models. VIVID enables the rapid assessment of genes of interest in the study of adaptive evolution and the genetic load, and it helps prioritising targets for experimental validation. We demonstrate the utility of VIVID by exploring the evolutionary genetics of the parasitic protist Plasmodium falciparum, revealing geographic variation in the signature of balancing selection in potential targets of functional antibodies.

bioinformatics↗

bioRxiv2021

Sequence grammar underlying unfolding and phase separation of globular proteins

Aberrant phase separation of globular proteins is associated with many diseases. Here, we use a model protein system to understand how unfolded states of globular proteins drive phase separation and the formation of unfolded protein deposits (UPODs). For UPODs to form, the concentrations of unfolded molecules must be above a threshold value. Additionally, unfolded molecules must possess appropriate sequence grammars to drive phase separation. While UPODs recruit molecular chaperones, their compositional profiles are also influenced by synergistic physicochemical interactions governed by the sequence grammars of unfolded proteins and sequence features of cellular proteins. Overall, we find that the driving forces for phase separation and the compositional profiles of UPODs are governed by the sequence grammar of unfolded proteins. Our studies highlight the need for uncovering the sequence grammars of unfolded proteins that drive UPOD formation and lead to gain-of-function interactions whereby proteins are aberrantly recruited into UPODs. HighlightsO_LIUnfolded states of globular proteins phase separate to form UPODs in cells C_LIO_LIThe fraction of unfolded molecules and the sticker grammar govern phase separation C_LIO_LIHydrophobic residues act as stickers that engage in intermolecular interactions C_LIO_LISticker grammar also influences gain-of-function recruitment into aberrant UPODs C_LI

biophysics↗

bioRxiv2020

Mercury methylation by metabolically versatile and cosmopolitan marine bacteria

Microbes transform aqueous mercury (Hg) into methylmercury (MeHg), a potent neurotoxin in terrestrial and marine food webs. This process requires the gene pair hgcAB, which encodes for proteins that actuate Hg methylation, and has been well described for anoxic environments. However, recent studies report potential MeHg formation in suboxic seawater, although the microorganisms involved remain poorly understood. In this study, we conducted large-scale multi-omic analyses to search for putative microbial Hg methylators along defined redox gradients in Saanich Inlet (SI), British Columbia, a model natural ecosystem with previously measured Hg and MeHg concentration profiles. Analysis of gene expression profiles along the redoxcline identified several putative Hg methylating microbial groups, including Calditrichaeota, SAR324 and Marinimicrobia, with the last by far the most active based on hgc transcription levels. Marinimicrobia hgc genes were identified from multiple publicly available marine metagenomes, consistent with a potential key role in marine Hg methylation. Computational homology modelling predicted that Marinimicrobia HgcAB proteins contain the highly conserved structures required for functional Hg methylation. Furthermore, a number of terminal oxidases from aerobic respiratory chains were associated with several SI putative novel Hg methylators. Our findings thus reveal potential novel marine Hg-methylating microorganisms with a greater oxygen tolerance and broader habitat range than previously recognised.

microbiology↗

bioRxiv2020

COVID-3D: An online resource to explore the structural distribution of genetic variation in SARS-CoV-2 and its implication on therapeutic development

The emergence of the COVID-19 pandemic has spurred a global rush to uncover basic biological mechanisms, to inform effective vaccine and drug development. Despite viral novelty, global sequencing efforts have already identified genomic variation across isolates. To enable easy exploration and spatial visualization of the potential implications of SARS-CoV-2 mutations on infection, host immunity and drug development we have developed COVID-3D (http://biosig.unimelb.edu.au/covid3d/).

bioinformatics↗