Database: The Journal of Biological Databases and Curation最新文献

{"title":"I-NOWJ: adaptive context selection and knowledge-guided learning for substance use named entity recognition.","authors":"Quoc-Viet-Anh Tran, Gia-Bao Duong, Huu-Huy-Hoang Tran, Thi-Hai-Yen Vuong, Hoang-Quynh Le","doi":"10.1093/database/baag056","DOIUrl":"10.1093/database/baag056","url":null,"abstract":"<p><p>Substance use information in clinical narratives is clinically important but is typically documented in unstructured and informal text, making automatic extraction challenging, particularly in low-resource and non-English settings. The BioCreative IX ToxHabits shared task addresses this problem in Spanish clinical text by identifying substance use triggers and their arguments. In this paper, we introduce I-NOWJ, which addresses these challenges through two complementary strategies: a low-resource strategy based on domain knowledge expansion, controlled data augmentation with quality-aware self-training, and an adaptive context selection mechanism that determines the appropriate contextual scope for each instance. Experimental results on the ToxHabits benchmark show that I-NOWJ achieves an F1 score of 94.93% on trigger detection and 91.30% on argument extraction. These results demonstrate the effectiveness of knowledge-guided modelling and adaptive context selection for robust clinical named entity recognition in low-resource settings. Our implementation and resources are publicly available at: https://github.com/candleMind/toxhabit.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13613236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149036728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"huSA: a comprehensive database for multi-dimensional resolution of bulk, single cell and spatial transcription profiles in skin diseases.","authors":"Meiling Zheng, Bao Qian, Zhi Hu, Xingyu Wei, Ke Sun, Wenjuan Jiang, Changxing Gao, Ming Zhao","doi":"10.1093/database/baag009","DOIUrl":"10.1093/database/baag009","url":null,"abstract":"<p><strong>Background: </strong>Skin diseases are among the most prevalent conditions worldwide, posing significant threats to human health by causing physical discomfort, psychological distress, and reduced quality of life. With the rapid advancement of high-throughput technologies, a substantial number of transcriptomic datasets, including single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA-seq, have been generated in the field of dermatology over the past decade. However, the lack of effective integration and standardized analysis pipelines limits the full utilization of these valuable resources in skin disease research.</p><p><strong>Objectives: </strong>To address this gap, we aimed to construct a comprehensive, integrative, and user-friendly atlas that enables systematic exploration of skin transcriptomic data across multiple diseases and modalities.</p><p><strong>Methods: </strong>We developed the Human Skin Atlas (huSA) ('https://humanskinatlas.com/index.html'), a publicly accessible database that incorporates data from 17 skin diseases and 63 independent datasets, including 1 434 scRNA-seq, 63 spatial transcriptomics, and 1 502 bulk RNA-seq samples. The database provides standardized cell-type annotations, differential gene expression analysis, cell-cell interaction mapping, pathway and metabolic module enrichment, transcription factor regulatory inference, and differentiation state assessment for scRNA-seq data. Data from identical skin diseases were further integrated to enhance biological signal detection. For visualization, we embedded the 'cell × gene' and 'Cirrocumulus' platforms, offering interactive and customizable gene expression visualizations at both single-cell and spatial levels with user-defined parameters.</p><p><strong>Results: </strong>The huSA enables both individual dataset analysis and cross-dataset integration, providing robust, consistent, and scalable insights into skin disease biology. Demonstration analyses confirmed that results derived from either single datasets or aggregated multi-dataset integrations exhibited high reliability and biological relevance. The platform successfully supports diverse research needs, including cell-type-specific expression profiling, regulatory network construction, and spatial transcriptomic exploration.</p><p><strong>Conclusions: </strong>The Human Skin Atlas (huSA) represents a state-of-the-art integrative resource for the skin research community. By offering multiscale analyses and interactive visualization tools, the huSA accelerates the discovery of molecular mechanisms underlying skin diseases and facilitates translational research efforts aimed at improving skin health.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12923168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146257469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring protein glycosylation across GlyGen, UniProtKB, and related knowledgebases for data integration.","authors":"Jeet Vora, Kate Warner, Preethi Vasudev, Sandra Orchard, Sujeet Kulkarni, Raja Mazumder, Maria Martin","doi":"10.1093/database/baag064","DOIUrl":"https://doi.org/10.1093/database/baag064","url":null,"abstract":"<p><p>Protein glycosylation plays a crucial role in cellular function, protein stability, and disease mechanisms, making the accurate annotation and integration of glycobiology data essential for biological research. However, the inherent complexity of glycosylation and the heterogeneity of glycans present significant challenges for data integration, annotation, and accessibility across biological databases. In this paper, we explore the protein glycosylation information present in GlyGen, a glycoscience knowledgebase, Universal Protein Resource Knowledgebase (UniProtKB), a proteomics knowledgebase, and other resources such as Chemical Entities of Biological Interest (ChEBI) and MatrixDB from International Molecular Exchange (IMEx). We detail the systematic approaches used to annotate glycoproteins, glycosyltransferases, and glycohydrolases across these databases, highlighting the controlled vocabularies and standardized methodologies employed. The integration of data between these resources enables comprehensive access to glycobiology data, from protein sequence and modification sites to detailed glycan structures and biochemical reactions. We also present tools developed through collaboration with UniProt, including the GlyGen Isoform Mapper and enhanced visualization capabilities through ProtVista, which facilitate the exploration of glycosylation in the context of protein structure and function. Through extensive cross-referencing between glycomics and proteomics resources, we establish a robust, interoperable data ecosystem that supports researchers in studying glycan biosynthesis, protein-glycan interactions, and their broader biological implications. Our work demonstrates how database harmonization and collective efforts can drive advancements in glycobiology, providing researchers with high-quality, interconnected data resources to explore and facilitate biomedical research in health and disease.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149243365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TogoMCP: natural language querying of life-science knowledge graphs via schema-guided LLMs and the Model Context Protocol.","authors":"Akira R Kinjo, Yasunori Yamamoto, Samuel Bustamante-Larriet, Jose-Emilio Labra-Gayo, Takatomo Fujisawa","doi":"10.1093/database/baag042","DOIUrl":"10.1093/database/baag042","url":null,"abstract":"<p><p>Querying the RDF Portal knowledge graph maintained by DBCLS-which aggregates ~60 life-science databases-requires proficiency in both SPARQL and database-specific RDF schemas, placing this resource beyond the reach of most researchers. Large Language Models (LLMs) can, in principle, translate natural-language questions into executable SPARQL, but without schema-level context, they frequently fabricate non-existent predicates or fail to resolve entity names to database-specific identifiers. We present TogoMCP, a system that recasts the LLM as a protocol-driven inference engine orchestrating specialized tools via the Model Context Protocol (MCP). Two mechanisms are essential to its design: (i) the MIE (Metadata-Interoperability-Exchange) file, a concise YAML document that dynamically supplies the LLM with each target database's structural and semantic context at query time; and (ii) a two-stage workflow separating entity resolution via external REST APIs from schema-guided SPARQL generation. On a benchmark of 50 biologically grounded questions spanning five types and 23 databases, TogoMCP achieved a large improvement over an unaided baseline (Cohen's $d = 1.82$, Wilcoxon $P lt .001$), with win rates exceeding 80% for question types with precise, verifiable answers. An ablation study shows that all component configurations deliver significant improvements, with MIE schema files providing the largest marginal contribution on mean per-question score ($Delta = +0.50$ relative to a no-MIE condition, two-sided Wilcoxon $P = .067$; 90% bootstrap CI $[+0.04,,,+0.94]$ excludes zero); a one-line instruction to load the relevant MIE file recovers the same mean improvement as a full procedural protocol, while the protocol additionally reduces downside risk (loss rate 1.6% vs. 4.8%, Fisher $P = .036$). These results suggest a general design principle: concise, dynamically delivered schema context is more valuable than complex orchestration logic for mean score performance, while procedural guidance plays a complementary role in narrowing variance. Database URL:  https://togomcp.rdfportal.org/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13397537/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148577436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a web-based platform for Eragrostis tef resource management and analysis.","authors":"Badasa Firdisa Fufa, Adugna Abdi Woldesemayat, Abiy Zegeye","doi":"10.1093/database/baag051","DOIUrl":"10.1093/database/baag051","url":null,"abstract":"<p><p>Developing a web-based platform is beneficial for data accessibility, centralization, security, management, and scalability. Eragrostis tef is an indigenous cereal crop in Ethiopia that currently lacks specialized or unified web-based platforms compared with major crops. This gap hinders genomic research, crop improvement programs, and the efficient utilization of tef genetic resources. Consequently, challenges include data fragmentation and the absence of a centralized database for accessing and managing tef-related data. In this study, a centralized platform called the Tef Database (TefDB) was developed. The platform integrates 77 795 tef-specific biological records, including 1 372 literature entries, 6 144 genome-related records, 66 455 gene models, 3 447 protein sequences, and 377 chemical/pathway entries. Advanced features were included along with the platform's interactive, visualization, responsive, searchable and filterable, downloadable, and analysis tools. The platform was built using modern web technologies: React.js for the frontend, Django and Python for backend processing, and PostgreSQL for data management. The Design Science Research (DSR) methodology guided development, incorporating iterative input from potential users. The platform's usability, performance, and functionality were validated through testing. TefDB offers bioinformatics analysis through natively embedded tools (BLAST and JBrowse 2) and externally linked tools (Primer3Plus and Clustal Omega) that open in new browser tabs. This integration reduces workflow fragmentation, allowing users to move seamlessly from data retrieval to analysis without exporting datasets to external platforms. Future work should focus on platform upgrades, data updates and integration, integration of local tef variety data (including SNPs and VCFs), advanced analytical modules, and collaborative user features.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13504265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TEITbase: a database for transposable element (TE)-initiated transcripts in human cancers.","authors":"Yun Zhang, Jianqi She, Xueyan Hu, Yueqi Jin, Changyu Tao, Minghao Du, Ence Yang","doi":"10.1093/database/baag025","DOIUrl":"10.1093/database/baag025","url":null,"abstract":"<p><p>Transposable elements (TEs) are abundant and play a crucial regulatory role in the human genome. Functioning as alternative promoters, TEs can be reactivated to produce TE-initiated transcripts. In cancer, TE-initiated transcription may upregulate oncogene expression and generate novel tumour-specific antigens, which could serve as potential targets for immunotherapy. However, there remains a lack of comprehensive databases that systematically investigate TE-initiated transcription in cancer. To address this gap, we developed a deep learning-based method and identified 38 995 TE-initiated transcripts across 33 cancer types from The Cancer Genome Atlas. Among these, 6203 were tumour-specifically expressed and strongly associated with the tumourigenesis. Using these annotations, we created TEITbase (http://teitbase.medbioinfo.org/), a user-friendly database that provides researchers with tools to conduct various downstream analyses and investigations for TE-initiated transcripts, including 546 novel onco-exaptation events. The establishment of TEITbase provides new insights into transcriptional reprogramming in cancers and enables further investigation into the potential roles of TE-initiated transcripts in cancer diagnostics and therapeutic strategies.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13184619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147971099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OzButterflies - a high quality open database of multispectral images and spectra of tropical to temperate Australian butterflies.","authors":"Marilia Fernandes Erickson, Hansani S S Daluwatta Galappaththige, Donald James Mclean, Diogo Jackson de Aquino Silva, Mariel Fulham, Zoe Wild, Michael B J Kelly, Christopher J Irving, Daniele Carlesso, Georgina E Binns, Sanni A Silvasti, Liisa Hämäläinen, Louis G O'Neill, Kiara L'Herpiniere, Johanna Mappes, David W Kikuchi, Hannah M Rowland, Michelle Power, Thomas E White, Darrell J Kemp, Marie E Herberstein","doi":"10.1093/database/baag040","DOIUrl":"10.1093/database/baag040","url":null,"abstract":"<p><p>Butterflies have been a model system for studying the evolution of colour. This is partly due to their complex patterns that reflect human-visible (VIS) and ultraviolet (UV) light, which are perceived by conspecifics and predators. Many studies have sourced data from publicly available images, but most of these images only consider the visible spectrum of light. Including the UV spectrum is crucial for fully understanding the evolution of butterfly morphology and behavioural ecology. Here we provide standardized images (VIS and UV) of over 4 000 individuals from 16 communities of Australian butterflies. These communities represent different climates and urbanization levels spanning over 2 500 km. The dataset contains at least one individual of 125 different species from five families, constituting over one quarter of Australian butterfly diversity. In addition to photographs, we provide spectral measurements of butterfly wings for at least one individual of each species and sex, and Cytochrome Oxidase subunit 1 (CO1) sequences of 1 635 individuals. All these data are accessible in Zenodo and an associated R package simplifies the download of subsets of the database. This database will be of use to evolutionary biologists and ecologists interested in a broad range of topics related to phenotypic variation.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13397536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148577393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative analysis of growth and diversification in venom data using database metrics.","authors":"Kim N Kirchhoff, Tobias Senoner, Selin Tuerkoglu, Ivan Koludarov, Björn M von Reumont, Mandë Holford","doi":"10.1093/database/baag032","DOIUrl":"10.1093/database/baag032","url":null,"abstract":"<p><p>Animal venomics is a growing field of research with evolutionary and biotechnological significance. Yet, fundamental questions regarding the origin, diversification, and bioactivity of venoms remain unresolved. Here, we analysed venom tissue-related data curated in Tox-Prot, currently the most comprehensive database for animal venoms, across three snapshots spanning two decades (2005, 2015, 2025). We assessed the taxonomic landscape related to Tox-Prot entries, sequence length distribution, protein family abundances, and habitat-specific venom patterns. Our results consistently show that snakes, spiders, cone snails, and scorpions, along with their associated protein families such as Phospholipase A2, Snake Three Finger Toxin and Long 4(C-C) scorpion toxin family, dominate across Tox-Prot. Nevertheless, the taxonomic and protein family diversity has been steadily increasing, with 503 new species and 188 new protein families added by 2025 compared to the 2005 dataset. Marine species account for 16%-20% of total species, of which 63%-85% are Neogastropoda reflecting limited marine species diversity coverage; likewise, terrestrial taxa are disproportionately represented by Squamata (39%) and Hymenoptera (20%) relative to their natural diversities of 2.3% and 50.26%, respectively. At the molecular level, half of all entries correspond to mature peptide sequences of 26-75 amino acids, featuring three to four disulfide bridges and C-terminal amidations as the most frequently recorded post-translational modifications. Further, protein language model embeddings infer taxonomical diverse peptide and delimited large enzyme clusters. Our study maps two decades of venom data diversification, reflecting both the field's rapid expansion and the need for robust integrated datasets to propel and disseminate that knowledge.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13221835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148101332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated culturomic and genomic database and analysis platform for methanogenic archaea.","authors":"Jiuyu Chen, Shijie Ren, Zhenye Tong, Zhaofeng Guan, Wenhao Zhang, Siwei Ren, Liqian Ma, Lingdou Kong, Hengyao Chong, Zihao Wang, Xiaoyu Yong, Su Yan, Yuanpeng Wang, Jun Zhou","doi":"10.1093/database/baag055","DOIUrl":"10.1093/database/baag055","url":null,"abstract":"<p><p>Methanogenic archaea research is challenged by limited strain resources, fragmented genomic data, inconsistent genome quality, substantial uncultured lineages, and difficulties in laboratory culturing, hindering advances in biogas production, climate mitigation, and microbial ecology. These archaea play crucial roles in global carbon cycling and anaerobic environments, yet scattered data and unculturable strains limit systematic studies and applications. To address this, we created MethArDB (Methanogenic Archaeal Genome Database), a specialized database for methanogenic archaea, compiling 3919 genomes, 87 host-associated plasmids, and 42 phages, with standardized quality classifications (complete, scaffold, draft), protein sequences, and metadata on geography, habitats, metabolism, and inheritable elements. Integrated MethArCT (Methanogenic Archaeal Culturomics Toolkit) employs a dual-threshold orthologous/paralogous protein analysis to evaluate metabolic pathway completeness, predicting cultivation parameters and suggesting candidate cultivation strategies, including potential medium formulations and conditions, to support strain isolation. Overall, MethArDB and MethArCT form an integrated platform combining genomics and culturomics to facilitate methanogenic archaea research. Database URL:  http://methardb.cn.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13576065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148966745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CysDuF database: annotation and characterization of cysteine residues in domain of unknown function proteins based on cysteine post-translational modifications, their protein microenvironments, biochemical pathways, taxonomy, and diseases.","authors":"Devarakonda Himaja, Debashree Bandyopadhyay","doi":"10.1093/database/baag002","DOIUrl":"10.1093/database/baag002","url":null,"abstract":"<p><p>Experimental characterization and annotation of amino acids belonging to domains of unknown function (DUF) proteins are expensive and time-consuming, which could be complemented by computational methods. Cysteine, being the second most reactive amino acid at the catalytic sites of enzymes, was selected for functional annotation and characterization on DUF proteins. Earlier, we reported functional annotation of cysteine on DUF proteins belonging to the COX-II family. However, holistic characterization of cysteine functions on DUF proteins was not known, to the best of our knowledge. Here, we annotated and characterized cysteine residues based on post-translational modifications (PTMs), biochemical pathways, diseases, taxonomy, and protein microenvironment. The information on uncharacterized DUF proteins was initially obtained from the literature, and the sequence, structure, pathways, taxonomy, and disease information were retrieved from the SCOPe database using DUF IDs. Protein microenvironments (MENV) around cysteine residues from DUF proteins were computed using protein structures (n = 70 342). The cysteine PTMs were predicted using the in-house cysteine-function prediction server, DeepCys https://deepcys.bits-hyderabad.ac.in). The accuracy of the prediction, validated against known experimental cysteine PTMs (n = 18 626), was 0.79. The information was consolidated in the database (https://cysduf.bits-hyderabad.ac.in/), retrievable in downloadable formats (CSV, JSON, or TXT) using the following inputs, DUF ID, PFAM ID, or PDB ID. For the first time, we annotated cysteine PTMs in DUF proteins belonging to seven different biochemical pathways and various species across the taxonomy, notably for the SARS-CoV-2 virus. The nature of MENV around cysteine from DUF proteins was mainly buried and hydrophobic. However, in the SARS-CoV-2 virus, a significant number of functional cysteine residues were exposed on the surface with hydrophilic microenvironment.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12828279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146028686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}