Frontiers in bioinformatics最新文献

Accurate prediction of antibody paratopes is a critical challenge in structure-limited, high-throughput discovery workflows. We present ParaDeep, a lightweight and interpretable deep learning framework for residue-level paratope prediction directly from amino acid sequences. ParaDeep integrates bidirectional long short-term memory networks with one-dimensional convolutional layers to capture both long-range sequence context and local binding motifs. We systematically evaluated 30 model configurations varying in encoding schemes, convolutional kernel sizes, and antibody chain types. In five-fold cross-validation, heavy (H) chain models achieved the highest performance (F1 = 0.856 ± 0.014, MCC = 0.842 ± 0.015), outperforming light (L) chain models (F1 = 0.774 ± 0.023, MCC = 0.772 ± 0.022). On an independent blind test set, ParaDeep attained F1 = 0.723 and MCC = 0.685 for H chains, and F1 = 0.607 and MCC = 0.587 for L chains, representing a 27% MCC improvement over the sequence-based baseline Parapred. Chain-specific modeling revealed that heavy chains provide stronger sequence-based predictive signals, while light chains benefit more from structural context. ParaDeep approaches the performance of state-of-the-art structure-based methods on heavy chains while requiring only sequence input, enabling faster and broader applicability without the computational cost of 3D modeling. Its efficiency and scalability make it well-suited for early-stage antibody discovery, repertoire profiling, and therapeutic design, particularly in the absence of structural data. The implementation is freely available at https://github.com/PiyachatU/ParaDeep, with Python (PyTorch) code and a Google Colab interface for ease of use.

Introduction: Recent advances in Alzheimer's research suggest that the brain's immune system plays a critical role in the development and progression of this devastating disease. Microglial cells are vital as immune cells in the brain's defense system. Human Microglia Clone 3 (HMC3) is a cell line developed as a promising experimental model to understand the role of microglial cells in human diseases including Alzheimer's and other neurodegenerative diseases. The frequency of HMC3 cell usage has increased in recent years, with the idea that this cell line could serve as a convenient model for human microglial cell functions.

Methods: We utilized gene-pair ratios from bulk and single-cell RNA sequencing (scRNA-seq) expression data to create predictive models of cell-type origins.

Results: Our model reveals that the HMC3 cell line represents various cell types, with the highest cell similarity score relating to astrocytes, not microglia.

Discussion: These findings suggest that the HMC3 cell line is not a reliable human microglia model and that extreme caution should be taken when interpreting the results of studies using the HMC3 cell line.

Introduction: Numerous biological systems exhibit ordinal connections between categories. Developmental and time-series information inherently depict sequences like "early," "intermediate," and "late" phases, showing that these specific processes follow a progression. Ordinal classification techniques are often applied in biological and medical contexts, ranging from the evaluation of pain intensity, to the detection of evolving diseases, such as cancer. These ranking systems may assist clinicians in establishing diagnoses and developing tailored treatment plans. For instance, tumor staging might guide early detection strategies and targeted therapies, improving patient outcomes. However, applying ordinal classification to biological data presents considerable challenges. In addition to their high dimensionality, these datasets can be highly heterogeneous, often reflecting branching processes that occur simultaneously during progression. Factors such as intratumoral diversity, asynchronous progress, and context-specific signaling activity may interfere with the identification of such alternative development routes.

Methods: To address these challenges, we propose a framework for uncovering ordinal relationships within molecular data. Specifically, directed threshold classifiers are introduced as base learners for ordinal classifier cascades, enabling the detection of both total and partial orderings between molecular states.

Results: This approach preserves the inherent ordinal structure by projecting high-dimensional data onto one single dimension while simultaneously decreasing complexity. Additionally, the distinct features of the resulting thresholds allow the prediction of potential alternative paths among the suborders.

Recent advances in three-dimensional microscopy enable imaging of whole-organ microvascular networks in small animals. Since microvasculature plays a crucial role in tissue development and function, its structure may provide diagnostic biomarkers and insight into disease progression. However, the microscopy community currently lacks benchmarks for scalable algorithms to measure these potential biomarkers. While many algorithms exist for segmenting vessel-like structures and extracting their surface features and connectivity, they have not been thoroughly evaluated on modern gigavoxel-scale images. In this paper, we propose a comprehensive yet compact survey of available algorithms. We focus on essential features for microvascular analysis, including extracting vessel surfaces and the network's associated connectivity. We select a series of algorithms based on popularity and availability and provide a thorough quantitative analysis of their performance on datasets acquired using light sheet fluorescence microscopy (LSFM), knife-edge scanning microscopy (KESM), and X-ray microtomography (µ-CT).

The fields of bioimaging and image analysis are rapidly expanding as new technologies transform biological questions into novel insights. While professionals of varying expertise are essential to achieving these advancements, early-career scientists-a prominent user group within the imaging community-are often assumed to have the prerequisite knowledge and ability to use these tools. This demographic, consisting of students, post-docs, and bioimage analysis trainees, is critical for the field to continue to evolve and flourish. However, obstacles such as geographic location, language barriers, insufficient funding or training, and instrument availability hinder access to resources and introduce significant knowledge gaps, especially for scientists in early-career stages. Democratized resources for bioimaging and analysis such as forums, community organizations, and publicly available datasets have been helpful in overcoming barriers to access for early-career scientists. Here, we discuss the current tools and resources available for early-career researchers, highlight their limitations from the learners' perspective, and propose strategies to better support this group. As bioimage analysis extends broadly into many scientific disciplines, we implore all members of this community, regardless of experience level, to empower next-generation scientists.

Introduction: Colon cancer is a common disease, treated with few chemotherapeutic agents with similar treatment sequencing despite its heterogeneity. A significant proportion of patients are diagnosed with metastasis, and resistance to antineoplastic drugs is associated with disease progression and therapeutic failure. It is known that the tumor microenvironment plays an essential role in cancer progression, contributing to processes that may be associated with therapeutic resistance mechanisms in colon cancer. In this study, we aim to identify a gene expression signature and its relationship with immune cell infiltration in colon cancer, contributing to the identification of potential resistance biomarkers.

Methods: An in silico study was conducted using RNA-seq data from The Cancer Genome Atlas Program (TCGA) samples, subdivided into two groups (treatment-resistant and non-resistant), taking into account the molecular subgroups (CMS1, CMS2, CMS3, and CMS4). The following algorithms were used: i. Limma was applied to identify differentially expressed genes; ii. WGCNA was applied to construct co-expression networks; iii. CIBERSORT was applied to estimate the proportion of infiltrating immune cells; and iv. TIMER was applied to explore the relationship between core genes and immune cell content.

Results: Twenty differentially expressed genes (DEGs) were found, with 18 related to the group considered resistant to oncologic treatment and presenting poorer overall survival. T CD4 memory resting cells and M0 and M2 macrophages were found in more significant proportions in the analyzed samples and more infiltrated in the tumor microenvironment, the higher the expression of some of these resistance DEGs. Additionally, these genes correlate with biological aspects of neuronal differentiation, axogenesis, and synaptic transmission.

Conclusion: The gene expression signature suggests the presence of differentially expressed synaptic membrane genes, which may be involved in neuronal pathways that influence the tumor microenvironment, potentially serving as future biomarkers. Furthermore, the presence of M0 and M2 macrophages and T CD4 memory resting cells suggests a potential interaction that may play a role in therapeutic resistance.

This perspective paper examines the profound cognitive and methodological parallels between scientific and artistic research, challenging the traditional distinction between the two domains. While science and art use different languages, both emerge from the human drive for creativity and understanding. We argue that scientific inquiry, often presented as strictly objective and methodical, inherently shares with art the need for imagination, flexibility, and interpretative thinking. Drawing on neuroscience, education, design theory, and the visual arts, we highlight how artistic practices, particularly in the visual arts, can enhance scientific learning, innovation, and public engagement. We advocate integrating art into scientific training and research to foster a more creative and inclusive epistemology. Through examples in microbiology, education, and data visualization, we show how the arts can support deeper understanding, cross-disciplinary collaboration, and more effective science communication. Ultimately, we call for a shift toward a more integrated approach that embraces the complementary strengths of both art and science in advancing knowledge and societal impact.

Introduction: Neurodegenerative diseases pose significant challenges owing to the limited number of effective therapies. Nerve growth factor (NGF) plays a crucial role in neuronal survival and differentiation through tropomyosin receptor kinase A (TrkA). Although snake venom NGF (sNGF) has been studied for its ability to activate TrkA, the binding modes and associated dynamics remain unclear compared to those of human NGF (hNGF). Herein, we explored the possibilities of NGFs from Daboia russelii and Naja naja as potential therapeutic alternatives to hNGF by comparing the structural similarities and conserved binding residues.

Methods: The active sites were identified through a literature review, molecular docking was performed using HADDOCK, and molecular dynamics simulation was performed to analyse the stabilities of the complexes; then, PRODIGY and molecular mechanics Poisson-Boltzmann surface area were used to determine the binding affinities.

Results: The different sNGFs exhibited stronger binding affinities and stabilities than hNGF, while principal component analysis and the free energy landscape indicated constrained conformational flexibilities suggestive of an adaptive mechanism in sNGF for effective receptor engagement. A network coevolutionary analysis was performed, which showed the pattern in which the amino acids were coevolved and conserved throughout the simulations.

Discussion: These findings indicate that NGFs from D. russelii and N. naja are promising therapeutic candidates for treating neurodegenerative disorders and warrant further in vivo validation.

Introduction: Drug repositioning-finding new therapeutic uses for existing drugs-can dramatically reduce development time and cost, but requires efficient computational frameworks to generate and validate repositioning hypotheses. Network-based methods can uncover drug communities with shared pharmacological properties, while molecular docking offers mechanistic insights by predicting drug-target binding.

Methods: We introduce an end-to-end, fully automated pipeline that (1) constructs a tripartite drug-gene-disease network from DrugBank and DisGeNET, (2) projects it into a drug-drug similarity network for community detection, (3) labels communities via Anatomical Therapeutic Chemical (ATC) codes to generate repositioning hints and identify relevant targets, (4) validates hints through automated literature searches, and (5) prioritizes candidates via targeted molecular docking.

Results: After filtering for connectivity and size, 12 robust communities emerged from the initial 34 clusters. The pipeline correctly matched 53.4% of drugs to their ATC level 1 community label via database entries; literature validation confirmed an additional 20.2%, yielding 73.6% overall accuracy. The remaining 26.4% of drugs were flagged as repositioning candidates. To illustrate the advantages of our pipeline, molecular docking studies of chloramphenicol demonstrated stable binding and interaction profiles similar to those of known inhibitors, reinforcing its potential as an anticancer agent.

Conclusion: Our integrated pipeline effectively integrates network-based community analysis and automated ATC labeling with literature and docking analysis, narrowing the search space for in silico and experimental follow-up. The chloramphenicol example illustrates its utility for uncovering non-obvious repositioning opportunities. Future work will extend similarity definitions (e.g., to higher-order network motifs) and incorporate wet-lab validation of top candidates.