Biology subjects

Jang, W.

Publications and source records attributed to Jang, W..

9 recordsLinked to original sources

bioRxiv2026

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening

MotivationStructure-based virtual screening (SBVS) is limited by the rigid-receptor assumption, which is particularly problematic for kinases that adopt multiple active-site conformations but are experimentally biased toward a single state. Although ensemble screening can address this limitation, it remains computationally expensive. ResultsWe introduce KASSPer (Kinase Active Site Structure Predictor), a framework that predicts kinase active-site conformational states using protein and compound language models. Given a kinase amino acid sequence and a ligand SMILES string, KASSPer enables ligand-specific conformer selection prior to SBVS, substantially reducing the computational cost associated with ensemble screening. Benchmarking on the DUD-E kinase subset demonstrates that KASSPer-guided screening consistently outperforms ensemble-based approaches across all evaluation metrics. Availability and ImplementationThe implementation for model loading and inference is available at the GitHub repository https://github.com/kucm-lsbi/KASSPer

bioinformatics↗

bioRxiv2025

CoBRA: Compound Binding Site Prediction using RNA Language Model

Ribonucleic acid (RNA) molecules perform a variety of functions within cells and thus implicated in various human diseases such as cancer. The fact that the proteins constitute a small portion of mRNAs and the ability of RNA to form highly specific three-dimensional binding pockets for small molecules have generated considerable interest in developing therapeutic agents that target RNAs as an alternative target for small-molecule drugs. Thus, like proteins, precise prediction of small-molecule binding sites across different classes of RNA targets could act as an starting point for drug discovery. In this study, we present a lightweight deep learning framework called Compound Binding Site Prediction for Ribonucleic Acid (CoBRA). This framework can predict ligand-binding nucleotides without requiring any explicit structural information. Our approach uses residue-level embeddings obtained from a pre-trained RNA language model. These embeddings encapsulate the contextual and statistical properties of each nucleotide and are used in a frozen state as the input for a multi-layer perceptron classifier performing binary classification of binding versus non-binding residues. The model was trained using combinations of ten distinct RNA language models and six different loss functions, using the TR60 and HARIBOSS datasets, and tested on four independent benchmark sets (RB9, JL10, TL12, and TE18). The performance of CoBRA demonstrates a relative improvement of 22.1% in Matthew correlation coefficient and an increase in sensitivity of 45.6% compared to existing state-of-the-art RNA- ligand binding site prediction methods with structural information. These results show that sequence-based language model embeddings alone, which do not require any explicit coordinate or distance information, can match or outperform structure-based methods. This makes it a flexible tool for predicting binding sites across diverse RNA targets. CoBRA is available at https://github.com/WonkyeongJang/CoBRA.

bioinformatics↗

bioRxiv2024

Inefficient Endocytosis Limits Heterotrimeric G Protein Abundance on Endosomes

Classical G protein-coupled receptor (GPCR) signaling takes place in response to extracellular stimuli and involves receptors and heterotrimeric G proteins located at the plasma membrane. It has recently been established that GPCR signaling can also take place from intracellular membrane compartments, including endosomes that contain internalized receptors and ligands. While the mechanisms of GPCR endocytosis are well understood, it is not clear how well internalized receptors are supplied with G proteins. To address this gap we use gene editing, confocal microscopy, and bioluminescence resonance energy transfer to study the distribution and trafficking of endogenous G proteins. We show here that constitutive endocytosis is sufficient to supply newly internalized endocytic vesicles with 20-30% of the G protein density found at the plasma membrane. We find that G proteins are present on early, late, and recycling endosomes, are abundant on lysosomes, but are virtually undetectable on the endoplasmic reticulum, mitochondria, and the medial Golgi apparatus. Receptor activation does not change heterotrimer abundance on endosomes. Our findings provide a subcellular map of endogenous G protein distribution, suggest that G proteins may be partially excluded from nascent endocytic vesicles, and are likely to have implications for GPCR signaling from endosomes and other intracellular compartments.

pharmacology and toxicology↗

bioRxiv2024

Formation of a giant unilocular vacuole via macropinocytosis-like process confers anoikis resistance

Cell survival in metazoans depends on cell attachment to the extracellular matrix (ECM) or to neighboring cells. Loss of such attachment triggers a type of programmed cell death known as anoikis, the acquisition of resistance to which is a key step in cancer development. The mechanisms underlying anoikis resistance remain unclear, however. The intracellular F-actin cytoskeleton plays a key role in sensing the loss of cell-ECM attachment, but how its disruption affects cell fate during such stress is not well understood. Here, we reveal a cell survival strategy characterized by the formation of a giant unilocular vacuole (GUVac) in the cytoplasm of the cells whose actin cytoskeleton is disrupted during loss of matrix attachment. Time-lapse imaging and electron microscopy showed that large vacuoles with a diameter of >500 nm accumulated early after inhibition of actin polymerization in cells in suspension culture, and that these vacuoles subsequently coalesced to form a GUVac. GUVac formation was found to result from a variation of a macropinocytosis-like process, characterized by the presence of inwardly curved membrane invaginations. This phenomenon relies on both F-actin depolymerization and the recruitment of septin proteins for micron-sized plasma membrane invagination. The vacuole fusion step during GUVac formation requires PI(3)P produced by VPS34 and PI3K-C2 on the surface of vacuoles. Furthermore, its induction after loss of matrix attachment conferred anoikis resistance. Our results thus show that the formation of a previously unrecognized organelle promotes cell survival in the face of altered actin and matrix environments.

cell biology↗

bioRxiv2023

An in vitro model of acute horizontal basal cell activation reveals dynamic gene regulatory networks underlying the acute activation phase

Horizontal basal cells (HBCs) activate only in response to severe olfactory epithelium (OE) injury. This activation is mediated by the loss of the transcription factor TP63. Using the compound phorbol 12-myristate 13-acetate (PMA), we find that we can model the process of acute HBC activation. First, we find that PMA treatment induces a rapid loss in TP63 protein and induces the expression of HOPX and the nuclear translocation of RELA, previously identified to mediate HBC activation. Using bulk RNA sequencing, we find that PMA-treated HBCs pass through various stages of acute activation identifiable by transcriptional regulatory signatures that mimic stages identified in vivo. These temporal stages are associated with varying degrees of engraftment and differentiation potential in transplantation assays. Together, this data shows that our model can model physiologically relevant features of HBC activation and identifies new candidates for mechanistic testing.

neuroscience↗

bioRxiv2022

Nucleotide-decoupled G proteins reveal the role of G protein conformation in receptor-G protein selectivity

G protein-coupled receptors (GPCRs) selectively activate at least one of the four families of heterotrimeric G proteins to transduce environmental cues, but the mechanistic basis of coupling selectivity remains unclear. Structural studies have emphasized structural complementarity of GPCR complexes with nucleotide-free G proteins, but it has also been suggested that selectivity may be determined by intermediate activation processes that occur prior to nucleotide release. To test these ideas we have studied coupling to nucleotide- decoupled G protein variants, which can adopt conformations similar to receptor-bound G proteins without the need for nucleotide release. We find that selectivity is significantly degraded when nucleotide release is not required for GPCR-G protein complex formation, to the extent that most GPCRs interact with most nucleotide-decoupled G proteins. These findings demonstrate the absence of absolute structural incompatibility between most GPCRs and G proteins, and are consistent with the hypothesis that high-energy intermediate state complexes are involved in coupling selectivity.

pharmacology and toxicology↗

bioRxiv2021

ABCnet : Self-Attention based Atom, Bond Message Passing Network for Predicting Drug-Target Interaction

Drug-target interaction (DTI) is a methodology for predicting the binding affinity between a compound and a target protein, and a key technology in the derivation of candidate substances in drug discovery. As DTI experiments have progressed for a long time, a substantial volume of chemical, biomedical, and pharmaceutical data have accumulated. This accumulation of data has occurred contemporaneously with the advent of the field of big data, and data-based machine learning methods could significantly reduce the time and cost of drug development. In particular, the deep learning method shows potential when applied to the fields of vision and speech recognition, and studies to apply deep learning to various other fields have emerged. Research applying deep learning is underway in drug development, and among various deep learning models, a graph-based model that can effectively learn molecular structures has received more attention as the SOTA in experimental results were achieved. Our study focused on molecular structure information among graph-based models in message passing neural networks. In this paper, we propose a self-attention-based bond and atom message passing neural network which predicts DTI by extracting molecular features through a graph model using an attention mechanism. Model validation experiments were performed after defining binding affinity as a regression and classification problem: binary classification to predict the presence or absence of binding to the drug-target, and regression to predict binding affinity to the drug-target. Classification was performed with BindingDB, and regression was performed with the DAVIS dataset. In the classification problem, ABCnet showed higher performance than MPNN, as it does in the existing study, and in regression, the potential of ABCnet was checked compared to that of SOTA. According to experiments, for Binary classification ABCnet have an average performance improvement of 1% for higher performance on DTI task than other MPNN and for regresssion ABCnet have CI with an average 0.01 to 0.02 performance degradation compared to SOTA. https://www.overleaf.com/project/618a05533676801d8f68ccf6

bioinformatics↗

bioRxiv2021

Constitutive G protein coupling profiles of understudied orphan GPCRs

A large number of GPCRs are potentially valuable drug targets but remain understudied. Many of these lack well-validated activating ligands and are considered "orphan" receptors, and G protein coupling profiles have not been defined for many orphan GPCRs. Here we asked if constitutive receptor activity can be used to determine G protein coupling profiles of orphan GPCRs. We monitored nucleotide-sensitive interactions between 48 understudied orphan GPCRs and five G proteins (240 combinations) using bioluminescence resonance energy transfer (BRET). No receptor ligands were used, but GDP was used as a common G protein ligand to disrupt receptor-G protein complexes. Constitutive BRET between the same receptors and {beta}-arrestins was also measured. We found sufficient GDP-sensitive BRET to generate G protein coupling profiles for 22 of the 48 receptors we studied. Altogether we identified 48 coupled receptor-G protein pairs, many of which have not been described previously. We conclude that receptor-G protein complexes that form spontaneously in the absence of guanine nucleotides can be used to profile G protein coupling of constitutively active GPCRs. This approach may prove useful for studying G protein coupling of other GPCRs for which activating ligands are not available.

pharmacology and toxicology↗

bioRxiv2020

Combined analysis of the time-resolved transcriptome and proteome of the pathogenicity-activated plant pathogen Xanthomonas oryzae pv. oryzae

Xanthomonas oryzae pv. oryzae (Xoo) is a plant pathogen responsible for causing bacterial blight in rice. The immediate alterations in Xoo upon initial contact with rice are essential for pathogenesis. We studied time-resolved genome-wide gene expression in pathogenicity-activated Xoo cells at the transcriptome and proteome levels. The early response genes of Xoo include genes related to cell motility, inorganic ion transport, and effectors. The alteration of gene expression is initiated as early as few minutes after the initial interaction and changes with time. The time-resolved comparison of the transcriptome and proteome shows the differences between transcriptional and translational expression peaks in many genes, although the overall expression pattern of mRNAs and proteins is conserved. The discrepancy suggests an important role of translational regulation in Xoo at the early stages of pathogenesis. The gene expression analysis using time-resolved transcriptome and proteome provides unprecedented valuable information regarding Xoo pathogenesis.

plant biology↗