Biology subjects

Yeo, K.

Publications and source records attributed to Yeo, K..

4 recordsLinked to original sources

bioRxiv2026

LEN-Seek: Fast and scalable ligand binding-site similarity search in the latent space of an SE(3)-invariant graph VAE

MotivationLigand binding-site similarity search is a crucial step in drug discovery that reduces the conformational search space for docking and other downstream tasks by comparing a target protein against experimentally identified binding sites. Existing methods rely on either direct structural alignment or lossy compression of structural information, producing a trade-off between scalability and precision. ResultsWe propose LEN-Seek, a ligand binding-site search method based on a graph neural network (GNN)-driven variational autoencoder (VAE) that encodes the 3D structural and physicochemical context of a binding site into a probabilistic latent space, enabling similarity search within a low-dimensional vector space. A binding site is modeled as a graph of amino acid residues, with node features adopted from the protein language model, Ankh, and edges encoded as SE(3)-invariant (roto-translational invariant) geometric relationships, thereby avoiding expensive data augmentation or SE(3)-equivariant models. Compared to ProBiS, the purely geometric graph-clique based method, LEN-Seek successfully retrieves a substantial portion of similar binding sites with a roughly 3,400-fold lower per-comparison cost, demonstrating its potential as a scalable approach to template-based ligand binding-site search in large-scale protein structure databases. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2025

N-cadherin in osteolineage cells restrains breast cancer cell growth via inhibition of a PI3K-dependent, Tgf-β1-driven feed-forward loop

Tumor growth and metastases are affected by interactions between tumor and microenvironment cells. We have reported the presence of Sp7-positive cells with an osteogenic signature in primary mouse breast cancer, where they stimulate tumor growth; and genetic ablation of Cdh2 (encoding N-cadherin) in these cells enhances their pro-tumorigenic action. To study the molecular mechanisms of this biologic system, we used MC3T3 cells, phenotypically similar to tumor associated osteolineage cells. Ablation of Cdh2 in MC3T3 cells enhances PI3K-Akt-{beta}-catenin signaling in response to transforming growth factor-{beta}1 (Tgf-{beta}1), resulting in increased production of Tgf-{beta}1. Interference with PI3K activity is mediated by N-cadherin binding to PI3K components, p85 and p100, resulting in reduced activation of PI3K-Akt-{beta}-catenin signaling. Downstream, Cdh2 ablation enhances Tgf-{beta}1-induced binding of Sp1 and Lef-1 to the Tgfb1 promoter, leading to increased promoter activity and enhanced Tgf-{beta}1 production. This is associated with miR-21 up-regulation and decreased expression of Pten, a PI3K inhibitor. MC3T3 cells promote growth of breast cancer cells (BCC) when co-cultured in vitro or co-injected in mouse mammary fat pad, and Cdh2-deficiency enhances this pro-tumorigenic effect. Notably, genetic ablation of the Tgf-{beta}1 receptor subunit, Tgfbr1, in BCC abrogates the pro-tumorigenic action of MC3T3 cells and its enhancement by Cdh2 ablation. Finally, Sp7-driven Tgfbr1 ablation in mice also reduces the growth of BCC in the mammary fat pad. Thus, autocrine Tgf-{beta}1 production via PI3K-Akt-{beta}-catenin signaling is a key mechanism by which osteolineage cells promote BCC growth, an action restrained by Ncad via interference with PI3K components. HighlightsO_LIIn tumor-associated osteolineage cells, N-cadherin reduces the growth of breast tumors; it also inhibits Tgf-{beta}1-activated PI3K/AKT/{beta}-catenin signaling C_LIO_LITgf-{beta}1 produced by tumor microenvironment cells stimulates breast cancer cell growth and further autocrine production of Tgf-{beta}1 C_LIO_LIThe anti-tumorigenic action of N-cadherin is mediated by a braking effect on a Tgf-{beta}1-driven, pro-tumorigenic, feed-forward cycle between microenvironment and tumor cells. C_LI

cancer biology↗

bioRxiv2024

A comparison between full-length 16S rRNA Oxford Nanopore sequencing and Illumina V3-V4 16S rRNA sequencing in head and neck cancer tissues

IntroductionDescribing the microbial community within the tumour has been a key aspect in understanding the pathophysiology of the tumour microenvironment. In head and neck cancer (HNC), most studies on tissue samples have only performed 16S ribosomal RNA (rRNA) short-read sequencing (SRS) on V3-V5 region. SRS is mostly limited to genus level identification. In this study, we compared full-length 16S rRNA long-read sequencing (FL-ONT) from Oxford Nanopore Technology (ONT) to V3-V4 Illumina SRS (V3V4-Illumina). To date, this is the largest study using HNC tissues samples to perform FL-ONT of the 16S rRNA using ONT. MethodsSequencing of the full-length and the V3-V4 16S rRNA region was conducted on tumour samples from 26 HNC patients, using ONT and Illumina technologies respectively. Paired sample analysis was applied to compare differences in diversities and abundance of microbial communities. Further validation was also performed using culture-based methods in 16 bacterial isolates obtained from 4 patients using MALDI-TOF MS. ResultsWe observed similar alpha diversity indexes between FL-ONT and V3V4-Illumina technologies. However, beta-diversity was significantly different between techniques (PERMANOVA - R2 = 0.083, p < 0.0001). At higher taxonomic levels (Phylum to Family), all metrics were more similar among sequencing techniques, while lower taxonomy displayed more discrepancies. At higher taxonomic levels, correlation in microbial abundance from FL-ONT and V3V4-Illumina were higher, while this correlation decreased at lower levels. Finally, FL-ONT was able to identify more isolates at the species level that were identified using MALDI-TOF MS (81.3% v.s. 62.5%). ConclusionsFL-ONT was able to identify lower taxonomic levels at a better resolution as compared to V3V4-Illumina 16S rRNA sequencing. Depending on application purposes, both methods are suitable for identification of microbial communities, with FL-ONT being more superior at species level.

microbiology↗

bioRxiv2022

Context-Aware Transcript Quantification from Long Read RNA-Seq data with Bambu

Most approaches to transcript quantification rely on fixed reference annotations. However, the transcriptome is dynamic, and depending on the context, such static annotations contain inactive isoforms for some genes while they are incomplete for others. To address this, we have developed Bambu, a method that performs machine-learning based transcript discovery to enable quantification specific to the context of interest using long-read RNA-Seq data. To identify novel transcripts, Bambu employs a precision-focused threshold referred to as the novel discovery rate (NDR), which replaces arbitrary per-sample thresholds with a single interpretable parameter. Bambu retains the full-length and unique read counts, enabling accurate quantification in presence of inactive isoforms. Compared to existing methods for transcript discovery, Bambu achieves greater precision without sacrificing sensitivity. We show that context-aware annotations improve abundance estimates for both novel and known transcripts. We apply Bambu to human embryonic stem cells to quantify isoforms from repetitive HERVH-LTR7 retrotransposons, demonstrating the ability to estimate transcript expression specific to the context of interest.

bioinformatics↗