Frontiers in bioinformatics最新文献

Structure-based drug design (SBDD) is enhanced by machine learning (ML) to improve both virtual screening and de novo design. Despite advances in ML tools for both strategies, screening remains bounded by time and computational cost, while generative models frequently produce invalid and synthetically inaccessible molecules. Screening time can be improved with pharmacophore search, which quickly identifies ligands in a database that match a pharmacophore query. In this work, we introduce PharmacoForge, a diffusion model for generating 3D pharmacophores conditioned on a protein pocket. Generated pharmacophore queries identify ligands that are guaranteed to be valid, commercially available molecules. We evaluate PharmacoForge against automated pharmacophore generation methods using the LIT-PCBA benchmark and ligand generative models through a docking-based evaluation framework. We further assess pharmacophore quality through a retrospective screening of the DUD-E dataset. PharmacoForge surpasses other pharmacophore generation methods in the LIT-PCBA benchmark, and resulting ligands from pharmacophore queries performed similarly to de novo generated ligands when docking to DUD-E targets and had lower strain energies compared to de novo generated ligands.

Aggressive cancers, such as pancreatic ductal adenocarcinoma (PDAC), are often characterized by a complex and desmoplastic tumor microenvironment, a stroma rich supportive connective tissue composed primarily of extracellular matrix (ECM) and non-cancerous cells. Desmoplasia, a dense deposition of stroma, is a major reason for therapy resistance, acting both as a physical barrier that interferes with drug penetration and as a supportive niche that protects cancer cells through diverse mechanisms. Precise understanding of spatial cell interactions in stroma-rich tumors is essential for optimizing therapeutic responses. It enables detailed mapping of stromal-tumor interfaces, comprehensive cell phenotyping, and insights into changes in tissue architecture, improving assessment of drug responses. Recent advances in multiplexed immunofluorescence imaging have enabled the acquisition of large batches of whole-slide tumor images, but scalable and reproducible methods to analyze the spatial distribution of cell states relative to stromal regions remain limited. To address this gap, we developed an open-source computational pipeline that integrates QuPath, StarDist, and custom Python scripts to quantify biomarker expression at a single- and sub-cellular resolution across entire tumor sections. Our workflow includes: (i) automated nuclei segmentation using StarDist, (ii) machine learning-based cell classification using multiplexed marker expression, (iii) modeling of stromal regions based on fibronectin staining, (iv) sensitivity analyses on classification thresholds to ensure robustness across heterogeneous datasets, and (v) distance-based quantification of the proximity of each cell to the stromal border. To improve consistency across slides with variable staining intensities, we introduce a statistical strategy that translates classification thresholds by propagating a chosen reference percentile across the distribution of marker-related cell measurement in each image. We apply this approach to quantify spatial patterns of distribution of the phosphorylated form of the N-Myc downregulated gene 1 (NDRG1), a novel DNA repair protein that conveys signals from the ECM to the nucleus to maintain replication fork homeostasis, and a known cell proliferation marker Ki67 in fibronectin-defined stromal regions in PDAC xenografts. The pipeline is applicable for the analysis of markers of interest in stroma-rich tissues and is publicly available.

Introduction: Single-cell multi-omics has transformed T-cell biology by enabling the simultaneous analysis of T-cell receptor (TCR) sequences, transcriptomes, and surface proteins at the resolution of individual cells. These capabilities are critical for identifying antigen-specific T-cells and accelerating the development of TCR-based immunotherapies.

Methods: Here, we introduce TCRscape, an open-source Python 3 tool designed for high-resolution T-cell receptor clonotype discovery and quantification, optimized for BD Rhapsody™ single-cell multi-omics data.

Results: TCRscape integrates full-length TCR sequence data with gene expression profiles and surface protein expression to enable multimodal clustering of αβ and γδ T-cell populations. It also outputs Seurat-compatible matrices, facilitating downstream visualization and analysis in standard single-cell analysis environments.

Discussion: By bridging clonotype detection with immune cell transcriptome, proteome, and antigen specificity profiling, TCRscape supports rapid identification of dominant T-cell clones and their functional phenotypes, offering a powerful resource for immune monitoring and TCR-engineered therapeutic development. TCRscape can be found at https://github.com/Perik-Zavodskii/TCRscape/.

Proteolytic digestion is an essential process in mass spectrometry-based proteomics for converting proteins into peptides, hence crucial for protein identification and quantification. In a typical proteomics experiment, digestion reagents are selected without prior evaluation of their optimality for detecting proteins or peptides of interest, partly due to the lack of comprehensive and user-friendly predictive tools. In this work, we introduce Protein Cleaver, a web-based application that systematically assesses regions of proteins that are likely or unlikely to be identified, along with extensive sequence and structure annotation and visualization features. We showcase practical examples of Protein Cleaver's usability in drug discovery and highlight proteins that are typically difficult to detect using the most common proteolytic enzymes. We evaluate trypsin and chymotrypsin for identifying G-protein-coupled receptors and discover that chymotrypsin produces significantly more identifiable peptides than trypsin. We perform a bulk digestion analysis and assess 36 proteolytic enzymes for their ability to detect most of cysteine-containing peptides in the human proteome. We anticipate Protein Cleaver to be a valuable auxiliary tool for proteomics scientists.

How can we be sure that there is sufficient data for our model, such that the predictions remain reliable on unseen data and the conclusions drawn from the fitted model would not vary significantly when using a different sample of the same size? We answer these and related questions through a systematic approach that examines the data size and the corresponding gains in accuracy. Assuming the sample data are drawn from a data pool with no data drift, the law of large numbers ensures that a model converges to its ground truth accuracy. Our approach provides a heuristic method for investigating the speed of convergence with respect to the size of the data sample. This relationship is estimated using sampling methods, which introduces a variation in the convergence speed results across different runs. To stabilize results-so that conclusions do not depend on the run-and extract the most reliable information encoded in the available data regarding convergence speed, the presented method automatically determines a sufficient number of repetitions to reduce sampling deviations below a predefined threshold, thereby ensuring the reliability of conclusions about the required amount of data.

Suffix trees are fundamental data structures in stringology and have wide applications across various domains. In this work, we propose two linear-time algorithms for indexing strings under each internal node in a suffix tree while preserving the ability to track similarities and redundancies across different internal nodes. This is achieved through a novel tree structure derived from the suffix tree, along with new indexing concepts. The resulting indexes offer practical solutions in several areas, including DNA sequence analysis and approximate pattern matching.

Protein interatomic contacts, defined by spatial proximity and physicochemical complementarity at atomic resolution, are fundamental to characterizing molecular interactions and bonding. Methods for calculating contacts are generally categorized as cutoff-dependent, which rely on Euclidean distances, or cutoff-independent, which utilize Delaunay and Voronoi tessellations. While cutoff-dependent methods are recognized for their simplicity, completeness, and reliability, traditional implementations remain computationally expensive, posing significant scalability challenges in the current Big Data era of bioinformatics. Here, we introduce COC $\alpha$ DA (COntact search pruning by C $\alpha$ Distance Analysis), a Python-based command-line tool for improving search pruning in large-scale interatomic protein contact analysis using alpha-carbon (C $\alpha$ ) distance matrices. COC $\alpha$ DA detects intra- and inter-chain contacts, and classifies them into seven different types: hydrogen and disulfide bonds; hydrophobic effects; attractive, repulsive, and salt-bridge interactions; and aromatic stackings. To evaluate our tool, we compared it with three traditional approaches in the literature: all-against-all atom distance calculation ("brute-force"), static C $\alpha$ distance cutoff (SC), and Biopython's NeighborSearch class (NS). COC $\alpha$ DA demonstrated superior performance compared to the other methods, achieving on average 6x faster computation times than advanced data structures like k-d trees from NS, in addition to being simpler to implement and fully customizable. The presented tool facilitates exploratory and large-scale analyses of interatomic contacts in proteins in a simple and efficient manner, also enabling the integration of results with other tools and pipelines. The COC $\alpha$ DA tool is freely available at https://github.com/LBS-UFMG/COCaDA.

The increasing adoption of high-throughput "omics" technologies has heightened the demand for standardized, scalable, and reproducible bioinformatics workflows. Nextflow and nf-core provide a robust framework for researchers, particularly early- and mid-career researchers (EMCRs), to navigate complex data analysis. At The Kids Research Institute Australia, we implemented a structured approach to bioinformatics capacity building using these tools. This perspective presents nine practical rules derived from lessons learnt, which facilitated the successful adoption of Nextflow and nf-core, addressing implementation challenges, knowledge gaps, resource allocation, and community support. Our experience serves as a guide for institutions aiming to establish sustainable bioinformatics capabilities and empower EMCRs.