Identifying binding sites is crucial for expanding our knowledge of various biological processes, supporting drug discovery, and repositioning strategies, particularly in the early research phases of target hopping. This is especially important for neglected diseases, which predominantly impact vulnerable populations in developing regions. To address this, we created BENDER DB, a database designed to map predicted protein binding sites within the proteomes of pathogens associated with neglected diseases. Utilizing AlphaFold-predicted structures, BENDER DB integrates results from five leading binding site prediction tools, resulting in over one million binding sites across over 100,000 proteins from 10 different proteomes. BENDER DB offers unique features, such as integrating multiple predictors, interactive visualization tools, and comprehensive graphical representations, allowing for a detailed visual comparative analysis of different prediction methods and the predicted binding sites. We combined the computational approaches to offer a consensus of binding site predictions, leveraging the strengths of multiple techniques. Additionally, we introduce BENDER AI, a meta-predictor that integrates the outputs of the individual predictors into a unified, consensus-based binding site classification. By combining the five methods, BENDER AI provides a high-confidence classification that achieved an MCC of 0.64 and an AUC of 0.89, offering reliable predictions that minimize false positives while remaining competitive with the best individual predictors. By consolidating these results, BENDER DB enables detailed comparative analyses of prediction methods and integrates interactive visualization tools to provide users with an intuitive platform for binding site exploration. The database aims to accelerate drug discovery efforts, particularly in underdeveloped regions, by providing a robust and detailed resource for studying protein binding sites. BENDER DB is available at https://benderdb.ufv.br .

Accurate identification of medicinal and wild macrofungi remains a major challenge due to high intra-species variability, visually overlapping morphologies, and inconsistent field-level imaging conditions. Existing deep learning studies typically focus on limited taxonomic scopes (5-12 classes) and lack systematic comparisons across modern convolutional and transformer architectures, leaving a significant gap in fine-grained fungal classification. This study introduces a comprehensive evaluation framework covering 46 macrofungal species, integrating 18 single backbones and three feature-level fusion strategies under a unified, fully reproducible protocol. All models were trained with identical preprocessing, stratified partitions, deterministic seeds, and a two-stage fine-tuning pipeline to ensure fair architectural comparison. Among single models, ConvNeXt-Base achieved the strongest performance with 96.59% accuracy, 0.966 macro-F1, 0.965 MCC, 0.9997 macro-AUC, and 0.1054 log-loss, exhibiting a near-diagonal confusion matrix where 25 classes reached perfect recall. Although feature-level fusion did not exceed the best single model in overall accuracy, it improved stability for several visually difficult taxa. The best ensemble, ConvNeXt-Base + DeiT-Base, achieved 95.85% accuracy, 0.958 macro-F1, and 0.9995 macro-AUC, remaining close to the ConvNeXt-Base baseline while providing complementary class-level robustness. The reported latency of ≈0.0021 s/image refers only to the precomputed-feature mode, in which backbone features are already cached and only the shared MLP head is evaluated. Therefore, this value should not be interpreted as full end-to-end inference latency for newly acquired field images. Explainability analyses using Grad-CAM and Integrated Gradients demonstrated that both single and fused models rely on biologically meaningful structures such as gill edges, pore surfaces, cap margins, and hymenophore textures. Overall, this study provides one of the most extensive quantitative benchmarks for macrofungal recognition to date and establishes a reproducible, architecture-agnostic foundation for future multimodal, large-scale, and explainable AI systems in fungal taxonomy.

RNA modifications have emerged as key regulators of virtually all aspects of RNA biology, driving growing interest in computational approaches for their transcriptome-wide detection. For some RNA modifications, the search space can be reduced by exploiting characteristic sequence motifs, as exemplified by the canonical DRACH motif associated with m[6]A. However, comparable sequence preferences remain largely unknown for most other RNA modifications, making systematic motif discovery considerably more challenging. Here, we applied exponential population analysis to direct RNA sequencing data from HEK293T cells and peripheral human blood to identify candidate motif patterns for pseudouridine (Ψ), inosine (I), and 5-methylcytosine (m[5]C). Our results demonstrate that exponential population analysis provides a systematic framework for identifying candidate motif patterns from nanopore-derived RNA modification predictions, thereby prioritizing sequence contexts for future biological validation.

Per- and polyfluoroalkyl substances (PFAS) are persistent and ubiquitous environmental contaminants associated with diverse adverse health outcomes. Despite prior extensive molecular analyses, mechanistic understanding of PFAS hepatotoxicity remains incomplete. In recent years, human liver spheroids have emerged as a useful tool in this space for investigating human-relevant responses to PFAS exposure across longer-term exposure periods. Here, we reprocessed publicly available PFAS RNA-sequencing data from human liver spheroids using the R-ODAF pipeline to generate a high-confidence set of differentially expressed genes (DEGs) and applied an integrated multimodal platform combining cheminformatic target prediction and transcription factor (TF) activity inference within a liver-specific prior knowledge network. While conventional pathway enrichment recapitulated known PFAS effects on SREBF- and PPAR-driven lipid metabolism, it provided limited insight for compounds with fewer DEGs, such as PFBS. Multimodal integration revealed compound-specific regulatory programs, with PFOS and PFOA exhibiting a time- and dose-dependent suppression of a conserved TF module comprising SREBF, CEBP, and HNF family members. Cheminformatic protein target predictions for the various PFAS compounds highlighted several common targets, including MAP3K5 and PLCG1, as well as several compound-specific target predictions. Incorporating these predictions with network biology analyses elucidated upstream cascades differentiating PFOA- versus PFOS-mediated regulation of lipid metabolism, with the PFOA-specific inhibition of PTPN7 being a discriminating feature, highlighting potential molecular bases for compound-specific toxicological profiles. Collectively, our findings demonstrate that integrating structural, transcriptomic, and network-biology approaches enhances mechanistic resolution beyond traditional enrichment analyses, providing testable hypotheses for PFAS-induced perturbations and guiding experimental validation of key targets.