Briefings in bioinformatics最新文献

Computational methods for therapeutic target discovery face challenges in integrating multi-scale biological data and predicting system-wide therapeutic effects. We present a computational framework that integrates single-cell transcriptomics, weighted gene co-expression network analysis (WGCNA), and computational perturbation simulation to systematically discover novel therapeutic targets. The framework constructs a knowledge graph comprising 29 896 nodes (23 530 genes and 6366 pathways) with 322 136 edges, integrating gene-gene, gene-pathway, and module relationships. Using perturbation simulation algorithms, we systematically explored 265 candidate targets, scoring each based on perturbation magnitude, module response, therapeutic effect, and statistical significance. Applied to single-cell RNA sequencing data from 38 patients (51 415 cells), the framework identified 66 novel therapeutic targets, including nine very high novelty targets. Computational validation demonstrates efficient scalability (knowledge graph construction: <5 min; 265 perturbation simulations: <2 min) and robust performance across different module sizes. This approach represents a novel computational method for systems-level therapeutic target discovery, with generalizable applications to other complex diseases.

Accurately predicting protein-ligand interactions is vital for structure-based drug discovery. Although deep learning (DL) models have shown strong performance, the potential of traditional statistical potentials under data-limited conditions remains underexplored. Here, we systematically assess several statistical potential models in docking and virtual screening. We find that docking benefits from distance-dependent pairwise atom-atom potentials with clear physical meanings, while screening relies more on orientation-dependent atom-residue potentials that capture local chemical environments. Based on these findings, we propose HybridSP, a hybrid potential combining distance-dependent atom-atom, atom-residue, and orientation-dependent atom-residue terms. An affinity-weighted scheme is applied to correct biases in statistical distributions. On the CASF-2016 benchmark, HybridSP achieves a 91.6% docking success rate and an enrichment factor of 29.35 at the top 1%, rivaling and even surpassing state-of-the-art DL models. Its strong screening ability is further validated on directory of useful decoys-enhanced and directory of useful decoys-adjusted. These results demonstrate that well-designed statistical potentials can achieve high performance and interpretability without complex DL architectures, offering an efficient alternative for scoring function design. The models are available at: https://github.com/zelixirSH/HybridSP.git.

Despite aging being a fundamental biological process that profoundly influences health and disease, the interplay between tissue-specific aging and mortality remains underexplored. This study applies machine learning on GTEx transcriptomic data to model tissue-specific biological ages across 12 different types of tissues and introduces an age-gap metric to quantify deviations from the chronological age. We use several modeling techniques optimized with three feature selection strategies: Pearson correlation, age-related differentially expressed genes, and tissue-enriched genes (expressed at least four-fold higher in a specific tissue). Among these, Pearson correlation combined with elastic net regression yields the best performance, with models achieving an average root mean squared error of 6.44 years and an R2 of 0.64. To quantify deviations from chronological age relative to the population, we train neural networks to regress predicted ages against chronological ages, and subtract their outputs from the predicted ages to calculate a metric that we call the age-gap. Age-gap statistics reveal significant tissue-specific aging patterns, identifying extreme agers and correlations between extreme aging and mortality. About 20% of subjects are found to exhibit extreme aging in one tissue, while 1% show multi-organ aging. Further analysis reveals that accelerated aging in specific tissues correlates with greater risk of death from illness. These findings greatly emphasize the role of transcriptomics in aging research and its implications for health and longevity.

Comprehensive pan-domain metagenomic classification is increasingly constrained by the memory and runtime costs of building and querying the rapidly expanding reference genome space. We introduce Kun-peng, a taxonomic classifier powered by an intelligent block-partitioned database structure and optimized search strategies, enabling ultra-scalable, memory-efficient pan-domain profiling. Using the Critical Assessment of Metagenome Interpretation II benchmark, Kun-peng substantially reduces the memory usage of database-building and querying by up to 24-fold, and accelerates sample classification by up to 4.73-fold compared with Kraken2. Kun-peng achieves competitive accuracy with fewer false positives than Kraken2, Centrifuger, and even KrakenUniq, while maintaining consistently high sensitivity across diverse datasets. In a real-world evaluation of 586 metagenomic samples spanning air, water, soil, and human-associated environments, we performed classification using a 4.3 TB pan-domain database comprising 204,477 genomes, which was built by Kun-peng with only 4.1 GB peak memory. Kun-peng processed each sample in 0.2-11.2 min with 4.0-35.4 GB peak memory, corresponding to a 54-473-fold reduction in memory usage relative to Kraken2. Compared with Sylph, Kun-peng achieved up to a 46-fold speedup while requiring 21-fold less memory. Kun-peng classified 69.8%-94.3% of reads, improving coverage by 20%-60% over the standard Kraken2 database with 62,026 genomes. This improvement reflects expanded reference coverage, although a small fraction of false positives is inherent to k-mer-based methods. Overall, Kun-peng effectively eliminates the long-standing memory bottleneck in pan-domain database building and classification, enabling rapid and scalable pan-domain taxonomic analysis of complex environmental, ecological, and exposomic sequencing datasets.

The pathological aggregation of α-synuclein (α-syn) constitutes a pivotal hallmark in the progression of neurodegenerative disorders, including Parkinson's disease, underscoring the imperative need for identifying site-specific ligands. This study presents, for the first time, an advanced deep learning framework specifically designed for the prediction of molecular properties associated with α-syn. The framework integrates graph-based contextual attention mechanisms, structural feature aggregation protocols, and dual-channel feature integration, complemented by a composite regularization strategy that synergizes mean squared error minimization, Kullback-Leibler divergence-induced latent space regularization, and L2 norm penalization, thereby delivering outstanding predictive accuracy on the independent test dataset with MSE of 0.1812. Mechanistic insights derived from GNNExplainer analysis and molecular docking studies (PDB: 6A6B) elucidated that aromatic ring systems (benzene ring significance: 0.737) and hydrogen bond donor groups (amino group significance: 0.438) play critical roles in mediating high-affinity ligand-receptor interactions through π-π stacking within the hydrophobic pocket formed by Val82 and Ala89 residues, as well as directed hydrogen bonding involving catalytic residues Ser42 and Lys45. These findings not only enhance the understanding of inhibitor mechanisms but also establish a novel framework for the preliminary screening of small-molecule therapeutics, thereby laying a rigorous groundwork for structure-guided drug optimization and rational molecular design.

Cancer classification is pivotal for precision oncology, yet traditional methods struggle with the molecular heterogeneity of tumors. Our study introduces a self-attention based Conv1D machine learning network designed for panel capture sequencing data, which is more commonly used in clinical settings. Combining clinical capture sequencing data and The Cancer Genome Atlas data, we achieved an overall classification accuracy of over 90%, with precision rates reaching 100% for cervical and gastric cancers. Additionally, recall rates were highest at 95.79% for gastric cancer and lowest at 77.46% for cervical cancer, demonstrating robust performance across various cancer types. The model identified key genes such as C3orf36, JHY, and TASP1, showing significant differences in mutation counts across cancers. High-impact gene enrichment analysis highlighted critical pathways like acute myeloid leukemia and adipocytokine signaling. This approach not only significantly improves the precision of cancer classification, demonstrating the potential for clinical application, but also enhances our understanding of cancer biology.

Spatially variable genes (SVGs) are essential for elucidating tissue organization within spatially resolved transcriptomics. While a number of computational methods have been developed for SVG identification, their reliance on algorithm-specific assumptions, such as predefined kernel functions or spatial neighborhood graphs, often results in substantial variability in sensitivity and inflated false discovery rates (FDRs) across heterogeneous datasets. To address this challenge, we here develop Castl, an ensemble-based framework for SVG identification that integrates multiple detection methods through statistically designed aggregation modules. Comprehensive evaluations on both simulated and real-world data demonstrate that Castl consistently identifies biologically meaningful spatial expression patterns, mitigates method-specific biases and effectively controls FDRs across various biological contexts, resolutions, and spatial technologies. This flexible, assumption-free framework offers a robust and standardized foundation for spatially informed feature discovery in complex biological systems.

The development of single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for elucidating cell heterogeneity and gene expression. Identifying and discovering cell types through cell clustering is a crucial step in analyzing scRNA-seq data. However, the high-dimensionality nature and frequent dropout events of the data raise great challenges for cell clustering. Here, we propose a novel contrastive clustering framework called scSCCNIA (Similarity-matrix-based Contrastive Clustering with Neighbor Information Aggregation), for the accurate identification of cell clusters from scRNA-seq data. scSCCNIA adopts a Laplacian filter to conduct neighbor information aggregation, constructs different graph views by using special un-shared parameters Siamese encoders for data augmentation, and learns the latent low-dimensional embedding representations via similarity-matrix-based contrastive learning. Comparative analyses of multiple scRNA-seq datasets from different platforms and with varying cell numbers demonstrate that scSCCNIA outperforms existing methods in terms of cell clustering and marker gene identification. Furthermore, scSCCNIA reveals the heterogeneity and functional specificity of various cell types through Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Overall, scSCCNIA is an effective algorithm for learning latent features from scRNA-seq data, enhancing cell type identification accuracy and facilitating downstream analyses of scRNA-seq data.

G protein-coupled receptors (GPCRs) are among the most important drug targets, and peptide therapeutics are rapidly emerging. However, accurate prediction of peptide-GPCR interactions (PepGI) remains challenging due to the scarcity of high-quality data and the poor generalization of existing drug-target interaction (DTI) models, which are largely trained on small molecule data. Here, we introduce a progressive fine-tuning framework with a dynamic parameter selection strategy that adaptively selects critical fine-tuning parameters using Fisher information. Our method begins with pretraining on a large small molecule-GPCR dataset, followed by intermediate fine-tuning on peptide-target data to alleviate the representation mismatch across heterogeneous ligand modalities. Finally, the task-specific fine-tuning is performed on the low-resource PepGI scenario. Extensive experiments show that our approach significantly outperforms baselines across multiple evaluation metrics, and exhibits robust generalization under few-shot and practical cold-start settings. Overall, this work offers an effective solution for low-resource peptide-GPCR prediction and presents a transferable framework for cross-structure DTI modeling.