Biology subjects

No, K. T.

Publications and source records attributed to No, K. T..

7 recordsLinked to original sources

bioRxiv2026

Targeting Noncanonical TEAD Axis to Overcome DNA Repair-Driven Chemoresistance

How transcription factors contribute to DNA damage repair (DDR) independent of canonical gene regulation remains poorly understood. Here, we show that DNA double-strand break (DSB) triggers a functional state transition in TEAD, disengaging it from YAP-dependent transcription and translocating it to DNA lesions as a chromatin-associated DDR factor. Upon genotoxic stress, TEAD is rapidly recruited to damaged chromatin through a biphasic mechanism involving early poly(ADP-ribose)-dependent recruitment followed by ATM-driven {gamma}H2AX-mediated retention. At DNA lesions, TEAD constrains chromatin over-relaxation, suppresses excessive end resection, and promotes non-homologous end joining (NHEJ) independent of its canonical function. Conserved residues within the TEA domain mediate this noncanonical chromatin engagement of TEAD and define a druggable N-terminal interface. Pharmacological targeting of this interface impaired TEAD-dependent DSB repair and sensitized tumors to chemotherapy. Together, these findings establish noncanonical TEAD as a critical DNA repair factor and a clinically actionable driver of chemoresistance, while providing a conceptual framework for understanding how broader families of transcription factors may be repurposed as targetable DNA damage regulators in cancer.

cancer biology↗

bioRxiv2025

Cryo-EM structures of an anti-MLC1 Fab in apo and peptide-bound states reveal the structural basis of antigen recognition

Monoclonal antibodies are indispensable tools in structural biology and biomedical research, but defining the molecular basis of their specificity remains challenging. Here, we developed a novel monoclonal antibody (37E5) against the astrocytic membrane protein MLC1, a component of gliovascular signaling implicated in megalencephalic leukoencephalopathy with subcortical cysts. 37E5 demonstrated high specificity and versatility across biochemical, cellular, and histological assays, enabling reliable detection of MLC1 in both human and mouse tissue. Using single-particle cryo-EM, we determined [~]3 [A] resolution structures of the 37E5 Fab in apo and antigen-bound states, despite the small molecular mass ([~]50 kDa), close to the lower size limit of cryo-EM. The antigen-bound structure revealed continuous density for an MLC1-derived peptide and enabled atomic mapping of polar and non-polar interaction networks. Conformational changes in CDR-L1 and CDR-L2 indicated an induced-fit mechanism of recognition. Comparison with AlphaFold-predicted models underscored the accuracy of Fab backbone prediction but revealed major limitations in modeling epitope-paratope geometry. These findings establish 37E5 as a versatile antibody for mechanistic studies of gliovascular biology and MLC disease, while demonstrating that cryo-EM can achieve atomic-level characterization of small Fab-antigen complexes, thereby expanding the methodological frontier of antibody-antigen structural biology. SignificanceThis study defines the molecular basis of MLC1 recognition by a novel monoclonal antibody, establishes 37E5 as a versatile reagent for mechanistic and translational research, and demonstrates the feasibility of cryo-EM to resolve dynamic features of small Fab-antigen complexes.

biochemistry↗

bioRxiv2025

High-resolution cryo-EM structures of small protein-ligand complexes near the theoretical size limit

Cryo-electron microscopy (cryo-EM) is widely used to determine macromolecular structures at atomic resolution. The theoretical size lower limit of particles for cryo-EM analysis is 38 kDa, limited by factors such as contrast and particle alignment accuracy. To date, no cryo-EM structures have been reported for proteins near this size limit. This study presents cryo-EM structures of two protein-ligand complexes around 40 kDa. The structure of maltose-binding protein (43 kDa) was determined at 2.32 [A] resolution, clearly revealing the bound maltose and water molecules. Additionally, the kinase domain of human PLK1 (37 kDa), slightly below the theoretical limit, was determined at 3.04 [A] resolution, allowing the identification of the bound ligand, onvansertib. These findings demonstrate that cryo-EM can be effectively employed for structure determination and structure-based drug screening of small proteins or domains.

biophysics↗

bioRxiv2024

Exploring the conformational space of protein-protein complex with transformer-based generative model

Protein-protein interactions are the basis of many protein functions, and understanding the contact and conformational changes of protein-protein interactions is crucial for linking protein structure to biological function. Although difficult to detect experimentally, molecular dynamics (MD) simulations are widely used to study the conformational ensembles and dynamics of protein-protein complexes, but there are significant limitations in sampling efficiency and computational costs. In this study, a generative neural network was trained on protein-protein complex conformations obtained from molecular simulations to directly generate novel conformations with physical realism. We demonstrated the use of a deep learning model based on the transformer architecture to explore the conformational ensembles of protein-protein complexes through MD simulations. The results showed that the learned latent space can be used to generate unsampled conformations of protein-protein complexes for obtaining new conformations complementing pre-existing ones, which can be used as an exploratory tool for the analysis and enhancement of molecular simulations of protein-protein complexes.

bioinformatics↗

bioRxiv2023

Multimodal generation of astrocyte by integrating single-cell multi-omics data via deep learning

Obtaining positive and negative samples to examining several multifaceted brain diseases in clinical trials face significant challenges. We propose an innovative approach known as Adaptive Conditional Graph Diffusion Convolution (ACGDC) model. This model is tailored for the fusion of single cell multi-omics data and the creation of novel samples. ACGDC customizes a new array of edge relationship categories to merge single cell sequencing data and pertinent meta-information gleaned from annotations. Afterward, it employs network node properties and neighborhood topological connections to reconstruct the relationship between edges and their properties among nodes. Ultimately, it generates novel single-cell samples via inverse sampling within the framework of conditional diffusion model. To evaluate the credibility of the single cell samples generated through the new sampling approach, we conducted a comprehensive assessment. This assessment included comparisons between the generated samples and real samples across several criteria, including sample distribution space, enrichment analyses (GO term, KEGG term), clustering, and cell subtype classification, thereby allowing us to rigorously validate the quality and reliability of the single-cell samples produced by our novel sample method. The outcomes of our study demonstrated the effectiveness of the proposed method in seamlessly integrating single-cell multi-omics data and generating innovative samples that closely mirrored both the spatial distribution and bioinformatic significance observed in real samples. Thus, we suggest that the generation of these reliable control samples by ACGDC holds substantial promise in advancing precision research on brain diseases. Additionally, it offers a valuable tool for classifying and identifying astrocyte subtypes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/569500v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1749dd8org.highwire.dtl.DTLVardef@1270912org.highwire.dtl.DTLVardef@1c4a65dorg.highwire.dtl.DTLVardef@18651b5_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

An interface-based molecular generative framework for protein-protein interaction inhibitors

Protein-protein interactions (PPIs) play a crucial role in numerous biochemical and biological processes. Although several structure-based molecular generative models have been developed, PPI interfaces and compounds targeting PPIs exhibit distinct physicochemical properties compared to traditional binding pockets and small-molecule drugs. As a result, generating compounds that effectively target PPIs, particularly by considering PPI complexes or interface hotspot residues, remains a significant challenge. In this work, we constructed a comprehensive dataset of PPI interfaces with active and inactive compound pairs. Based on this, we propose a novel molecular generative framework tailored to PPI interfaces, named GENiPPI. Our evaluation demonstrates that GENiPPI captures the implicit relationships between the PPI interfaces and the active molecules, and can generate novel compounds that target these interfaces. Moreover, GENiPPI can generate structurally diverse novel compounds with limited PPI interface modulators. To the best of our knowledge, this is the first exploration of a structure-based molecular generative model focused on PPI interfaces, which could facilitate the design of PPI modulators. The PPI interface-based molecular generative model enriches the existing landscape of structure-based (pocket/interface) molecular generative model.

bioinformatics↗

bioRxiv2023

PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science

SummaryThe nature of taxonomic science and the scientific nomenclature system makes it difficult to use scientific names as identifiers without running into complications. To facilitate high-throughput analysis of biological data involving scientific names, we designed PhyloSophos, a Python package that takes into account the properties of scientific names and taxonomic systems to map name inputs to the entries within the reference database of choice. We would like to present three case-studies which demonstrates how our implementations, including rule-based pre-processing and recursive mapping could improve mapping performance and information availability. We expect PhyloSophos to help with the systematic processing of poorly digitized and curated biological data, such as biodiversity information and ethnopharmacological resources, thus enabling full-scale bioinformatics analysis using these data. Availability and implementationPhyloSophos is available at GitHub https://github.com/mhcho4096/phylosophos. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗