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Published on January 6, 2023
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M6AREG: m6A-centered regulation of disease development and drug response.

Authors: Liu S, Chen L, Zhang Y, Zhou Y, He Y, Chen Z, Qi S, Zhu J, Chen X, Zhang H, Luo Y, Qiu Y, Tao L, Zhu F

Abstract: As the most prevalent internal modification in eukaryotic RNAs, N6-methyladenosine (m6A) has been discovered to play an essential role in cellular proliferation, metabolic homeostasis, embryonic development, etc. With the rapid accumulation of research interest in m6A, its crucial roles in the regulations of disease development and drug response are gaining more and more attention. Thus, a database offering such valuable data on m6A-centered regulation is greatly needed; however, no such database is as yet available. Herein, a new database named 'M6AREG' is developed to (i) systematically cover, for the first time, data on the effects of m6A-centered regulation on both disease development and drug response, (ii) explicitly describe the molecular mechanism underlying each type of regulation and (iii) fully reference the collected data by cross-linking to existing databases. Since the accumulated data are valuable for researchers in diverse disciplines (such as pathology and pathophysiology, clinical laboratory diagnostics, medicinal biochemistry and drug design), M6AREG is expected to have many implications for the future conduct of m6A-based regulation studies. It is currently accessible by all users at: https://idrblab.org/m6areg/.
Published on January 6, 2023
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RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning.

Authors: Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, Craig PA, Crichlow GV, Dalenberg K, Duarte JM, Dutta S, Fayazi M, Feng Z, Flatt JW, Ganesan S, Ghosh S, Goodsell DS, Green RK, Guranovic V, Henry J, Hudson BP, Khokhriakov I, Lawson CL, Liang Y, Lowe R, Peisach E, Persikova I, Piehl DW, Rose Y, Sali A, Segura J, Sekharan M, Shao C, Vallat B, Voigt M, Webb B, Westbrook JD, Whetstone S, Young JY, Zalevsky A, Zardecki C

Abstract: The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to approximately 200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.
Published on January 6, 2023
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TOXRIC: a comprehensive database of toxicological data and benchmarks.

Authors: Wu L, Yan B, Han J, Li R, Xiao J, He S, Bo X

Abstract: The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.
Published on January 6, 2023
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ASCancer Atlas: a comprehensive knowledgebase of alternative splicing in human cancers.

Authors: Wu S, Huang Y, Zhang M, Gong Z, Wang G, Zheng X, Zong W, Zhao W, Xing P, Li R, Liu Z, Bao Y

Abstract: Alternative splicing (AS) is a fundamental process that governs almost all aspects of cellular functions, and dysregulation in this process has been implicated in tumor initiation, progression and treatment resistance. With accumulating studies of carcinogenic mis-splicing in cancers, there is an urgent demand to integrate cancer-associated splicing changes to better understand their internal cross-talks and functional consequences from a global view. However, a resource of key functional AS events in human cancers is still lacking. To fill the gap, we developed ASCancer Atlas (https://ngdc.cncb.ac.cn/ascancer), a comprehensive knowledgebase of aberrant splicing in human cancers. Compared to extant databases, ASCancer Atlas features a high-confidence collection of 2006 cancer-associated splicing events experimentally proved to promote tumorigenesis, a systematic splicing regulatory network, and a suit of multi-scale online analysis tools. For each event, we manually curated the functional axis including upstream splicing regulators, splicing event annotations, downstream oncogenic effects, and possible therapeutic strategies. ASCancer Atlas also houses about 2 million computationally putative splicing events. Additionally, a user-friendly web interface was built to enable users to easily browse, search, visualize, analyze, and download all splicing events. Overall, ASCancer Atlas provides a unique resource to study the functional roles of splicing dysregulation in human cancers.
Published on January 6, 2023
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SPASCER: spatial transcriptomics annotation at single-cell resolution.

Authors: Fan Z, Luo Y, Lu H, Wang T, Feng Y, Zhao W, Kim P, Zhou X

Abstract: In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location and provides novel insights into diverse spatially related biological contexts. Even though two spatial transcriptomics databases exist, they provide limited analytical information. Information such as spatial heterogeneity of genes and cells, cell-cell communication activities in space, and the cell type compositions in the microenvironment are critical clues to unveil the mechanism of tumorigenesis and embryo differentiation. Therefore, we constructed a new spatial transcriptomics database, named SPASCER (https://ccsm.uth.edu/SPASCER), designed to help understand the heterogeneity of tissue organizations, region-specific microenvironment, and intercellular interactions across tissue architectures at multiple levels. SPASCER contains datasets from 43 studies, including 1082 sub-datasets from 16 organ types across four species. scRNA-seq was integrated to deconvolve/map spatial transcriptomics, and processed with spatial cell-cell interaction, gene pattern and pathway enrichment analysis. Cell-cell interactions and gene regulation network of scRNA-seq from matched spatial transcriptomics were performed as well. The application of SPASCER will provide new insights into tissue architecture and a solid foundation for the mechanistic understanding of many biological processes in healthy and diseased tissues.
Published on January 6, 2023
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GPCRdb in 2023: state-specific structure models using AlphaFold2 and new ligand resources.

Authors: Pandy-Szekeres G, Caroli J, Mamyrbekov A, Kermani AA, Keseru GM, Kooistra AJ, Gloriam DE

Abstract: G protein-coupled receptors (GPCRs) are physiologically abundant signaling hubs routing hundreds of extracellular signal substances and drugs into intracellular pathways. The GPCR database, GPCRdb supports >5000 interdisciplinary researchers every month with reference data, analysis, visualization, experiment design and dissemination. Here, we present our fifth major GPCRdb release setting out with an overview of the many resources for receptor sequences, structures, and ligands. This includes recently published additions of class D generic residue numbers, a comparative structure analysis tool to identify functional determinants, trees clustering GPCR structures by 3D conformation, and mutations stabilizing inactive/active states. We provide new state-specific structure models of all human non-olfactory GPCRs built using AlphaFold2-MultiState. We also provide a new resource of endogenous ligands along with a larger number of surrogate ligands with bioactivity, vendor, and physiochemical descriptor data. The one-stop-shop ligand resources integrate ligands/data from the ChEMBL, Guide to Pharmacology, PDSP Ki and PubChem database. The GPCRdb is available at https://gpcrdb.org.
Published on January 6, 2023
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FerrDb V2: update of the manually curated database of ferroptosis regulators and ferroptosis-disease associations.

Authors: Zhou N, Yuan X, Du Q, Zhang Z, Shi X, Bao J, Ning Y, Peng L

Abstract: Ferroptosis is a mode of regulated cell death characterized by iron-dependent accumulation of lipid peroxidation. It is closely linked to the pathophysiological processes in many diseases. Since our publication of the first ferroptosis database in 2020 (FerrDb V1), many new findings have been published. To keep up with the rapid progress in ferroptosis research and to provide timely and high-quality data, here we present the successor, FerrDb V2. It contains 1001 ferroptosis regulators and 143 ferroptosis-disease associations manually curated from 3288 articles. Specifically, there are 621 gene regulators, of which 264 are drivers, 238 are suppressors, 9 are markers, and 110 are unclassified genes; and there are 380 substance regulators, with 201 inducers and 179 inhibitors. Compared to FerrDb V1, curated articles increase by >300%, ferroptosis regulators increase by 175%, and ferroptosis-disease associations increase by 50.5%. Circular RNA and pseudogene are novel regulators in FerrDb V2, and the percentage of non-coding RNA increases from 7.3% to 13.6%. External gene-related data were integrated, enabling thought-provoking and gene-oriented analysis in FerrDb V2. In conclusion, FerrDb V2 will help to acquire deeper insights into ferroptosis. FerrDb V2 is freely accessible at http://www.zhounan.org/ferrdb/.
Published on January 6, 2023
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DrugMAP: molecular atlas and pharma-information of all drugs.

Authors: Li F, Yin J, Lu M, Mou M, Li Z, Zeng Z, Tan Y, Wang S, Chu X, Dai H, Hou T, Zeng S, Chen Y, Zhu F

Abstract: The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.
Published on January 6, 2023
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DRESIS: the first comprehensive landscape of drug resistance information.

Authors: Sun X, Zhang Y, Li H, Zhou Y, Shi S, Chen Z, He X, Zhang H, Li F, Yin J, Mou M, Wang Y, Qiu Y, Zhu F

Abstract: Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named 'DRESIS' was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/.
Published on January 6, 2023
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CovInter: interaction data between coronavirus RNAs and host proteins.

Authors: Amahong K, Zhang W, Zhou Y, Zhang S, Yin J, Li F, Xu H, Yan T, Yue Z, Liu Y, Hou T, Qiu Y, Tao L, Han L, Zhu F

Abstract: Coronavirus has brought about three massive outbreaks in the past two decades. Each step of its life cycle invariably depends on the interactions among virus and host molecules. The interaction between virus RNA and host protein (IVRHP) is unique compared to other virus-host molecular interactions and represents not only an attempt by viruses to promote their translation/replication, but also the host's endeavor to combat viral pathogenicity. In other words, there is an urgent need to develop a database for providing such IVRHP data. In this study, a new database was therefore constructed to describe the interactions between coronavirus RNAs and host proteins (CovInter). This database is unique in (a) unambiguously characterizing the interactions between virus RNA and host protein, (b) comprehensively providing experimentally validated biological function for hundreds of host proteins key in viral infection and (c) systematically quantifying the differential expression patterns (before and after infection) of these key proteins. Given the devastating and persistent threat of coronaviruses, CovInter is highly expected to fill the gap in the whole process of the 'molecular arms race' between viruses and their hosts, which will then aid in the discovery of new antiviral therapies. It's now free and publicly accessible at: https://idrblab.org/covinter/.