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Published on January 7, 2022
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ViMIC: a database of human disease-related virus mutations, integration sites and cis-effects.

Authors: Wang Y, Tong Y, Zhang Z, Zheng R, Huang D, Yang J, Zong H, Tan F, Xie Y, Huang H, Zhang X

Abstract: Molecular mechanisms of virus-related diseases involve multiple factors, including viral mutation accumulation and integration of a viral genome into the host DNA. With increasing attention being paid to virus-mediated pathogenesis and the development of many useful technologies to identify virus mutations (VMs) and viral integration sites (VISs), much research on these topics is available in PubMed. However, knowledge of VMs and VISs is widely scattered in numerous published papers which lack standardization, integration and curation. To address these challenges, we built a pilot database of human disease-related Virus Mutations, Integration sites and Cis-effects (ViMIC), which specializes in three features: virus mutation sites, viral integration sites and target genes. In total, the ViMIC provides information on 31 712 VMs entries, 105 624 VISs, 16 310 viral target genes and 1 110 015 virus sequences of eight viruses in 77 human diseases obtained from the public domain. Furthermore, in ViMIC users are allowed to explore the cis-effects of virus-host interactions by surveying 78 histone modifications, binding of 1358 transcription regulators and chromatin accessibility on these VISs. We believe ViMIC will become a valuable resource for the virus research community. The database is available at http://bmtongji.cn/ViMIC/index.php.
Published on January 7, 2022
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NPCDR: natural product-based drug combination and its disease-specific molecular regulation.

Authors: Sun X, Zhang Y, Zhou Y, Lian X, Yan L, Pan T, Jin T, Xie H, Liang Z, Qiu W, Wang J, Li Z, Zhu F, Sui X

Abstract: Natural product (NP) has a long history in promoting modern drug discovery, which has derived or inspired a large number of currently prescribed drugs. Recently, the NPs have emerged as the ideal candidates to combine with other therapeutic strategies to deal with the persistent challenge of conventional therapy, and the molecular regulation mechanism underlying these combinations is crucial for the related communities. Thus, it is urgently demanded to comprehensively provide the disease-specific molecular regulation data for various NP-based drug combinations. However, no database has been developed yet to describe such valuable information. In this study, a newly developed database entitled 'Natural Product-based Drug Combination and Its Disease-specific Molecular Regulation (NPCDR)' was thus introduced. This database was unique in (a) providing the comprehensive information of NP-based drug combinations & describing their clinically or experimentally validated therapeutic effect, (b) giving the disease-specific molecular regulation data for a number of NP-based drug combinations, (c) fully referencing all NPs, drugs, regulated molecules/pathways by cross-linking them to the available databases describing their biological or pharmaceutical characteristics. Therefore, NPCDR is expected to have great implications for the future practice of network pharmacology, medical biochemistry, drug design, and medicinal chemistry. This database is now freely accessible without any login requirement at both official (https://idrblab.org/npcdr/) and mirror (http://npcdr.idrblab.net/) sites.
Published on January 7, 2022
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CTR-DB, an omnibus for patient-derived gene expression signatures correlated with cancer drug response.

Authors: Liu Z, Liu J, Liu X, Wang X, Xie Q, Zhang X, Kong X, He M, Yang Y, Deng X, Yang L, Qi Y, Li J, Liu Y, Yuan L, Diao L, He F, Li D

Abstract: To date, only some cancer patients can benefit from chemotherapy and targeted therapy. Drug resistance continues to be a major and challenging problem facing current cancer research. Rapidly accumulated patient-derived clinical transcriptomic data with cancer drug response bring opportunities for exploring molecular determinants of drug response, but meanwhile pose challenges for data management, integration, and reuse. Here we present the Cancer Treatment Response gene signature DataBase (CTR-DB, http://ctrdb.ncpsb.org.cn/), a unique database for basic and clinical researchers to access, integrate, and reuse clinical transcriptomes with cancer drug response. CTR-DB has collected and uniformly reprocessed 83 patient-derived pre-treatment transcriptomic source datasets with manually curated cancer drug response information, involving 28 histological cancer types, 123 drugs, and 5139 patient samples. These data are browsable, searchable, and downloadable. Moreover, CTR-DB supports single-dataset exploration (including differential gene expression, receiver operating characteristic curve, functional enrichment, sensitizing drug search, and tumor microenvironment analyses), and multiple-dataset combination and comparison, as well as biomarker validation function, which provide insights into the drug resistance mechanism, predictive biomarker discovery and validation, drug combination, and resistance mechanism heterogeneity.
Published on January 7, 2022
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RNAInter v4.0: RNA interactome repository with redefined confidence scoring system and improved accessibility.

Authors: Kang J, Tang Q, He J, Li L, Yang N, Yu S, Wang M, Zhang Y, Lin J, Cui T, Hu Y, Tan P, Cheng J, Zheng H, Wang D, Su X, Chen W, Huang Y

Abstract: Establishing an RNA-associated interaction repository facilitates the system-level understanding of RNA functions. However, as these interactions are distributed throughout various resources, an essential prerequisite for effectively applying these data requires that they are deposited together and annotated with confidence scores. Hence, we have updated the RNA-associated interaction database RNAInter (RNA Interactome Database) to version 4.0, which is freely accessible at http://www.rnainter.org or http://www.rna-society.org/rnainter/. Compared with previous versions, the current RNAInter not only contains an enlarged data set, but also an updated confidence scoring system. The merits of this 4.0 version can be summarized in the following points: (i) a redefined confidence scoring system as achieved by integrating the trust of experimental evidence, the trust of the scientific community and the types of tissues/cells, (ii) a redesigned fully functional database that enables for a more rapid retrieval and browsing of interactions via an upgraded user-friendly interface and (iii) an update of entries to >47 million by manually mining the literature and integrating six database resources with evidence from experimental and computational sources. Overall, RNAInter will provide a more comprehensive and readily accessible RNA interactome platform to investigate the regulatory landscape of cellular RNAs.
Published on January 7, 2022
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Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents.

Authors: Zhou Y, Zhang Y, Lian X, Li F, Wang C, Zhu F, Qiu Y, Chen Y

Abstract: Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes (a) 34 861 poor binders and 12 683 non-binders of 1308 targets; (b) 534 prodrug-drug pairs for 121 targets; (c) 1127 co-targets of 672 targets regulated by 642 approved and 624 clinical trial drugs; (d) the collective structure-activity landscapes of 427 262 active agents of 1565 targets; (e) the profiles of drug-like properties of 33 598 agents of 1102 targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, 159 and 1658 newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. The database is accessible without login requirement at: https://idrblab.org/ttd/.
Published on January 7, 2022
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CovPDB: a high-resolution coverage of the covalent protein-ligand interactome.

Authors: Gao M, Moumbock AFA, Qaseem A, Xu Q, Gunther S

Abstract: In recent years, the drug discovery paradigm has shifted toward compounds that covalently modify disease-associated target proteins, because they tend to possess high potency, selectivity, and duration of action. The rational design of novel targeted covalent inhibitors (TCIs) typically starts from resolved macromolecular structures of target proteins in their apo or holo forms. However, the existing TCI databases contain only a paucity of covalent protein-ligand (cP-L) complexes. Herein, we report CovPDB, the first database solely dedicated to high-resolution cocrystal structures of biologically relevant cP-L complexes, curated from the Protein Data Bank. For these curated complexes, the chemical structures and warheads of pre-reactive electrophilic ligands as well as the covalent bonding mechanisms to their target proteins were expertly manually annotated. Totally, CovPDB contains 733 proteins and 1,501 ligands, relating to 2,294 cP-L complexes, 93 reactive warheads, 14 targetable residues, and 21 covalent mechanisms. Users are provided with an intuitive and interactive web interface that allows multiple search and browsing options to explore the covalent interactome at a molecular level in order to develop novel TCIs. CovPDB is freely accessible at http://www.pharmbioinf.uni-freiburg.de/covpdb/ and its contents are available for download as flat files of various formats.
Published on January 7, 2022
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BrainBase: a curated knowledgebase for brain diseases.

Authors: Liu L, Zhang Y, Niu G, Li Q, Li Z, Zhu T, Feng C, Liu X, Zhang Y, Xu T, Chen R, Teng X, Zhang R, Zou D, Ma L, Zhang Z

Abstract: Brain is the central organ of the nervous system and any brain disease can seriously affect human health. Here we present BrainBase (https://ngdc.cncb.ac.cn/brainbase), a curated knowledgebase for brain diseases that aims to provide a whole picture of brain diseases and associated genes. Specifically, based on manual curation of 2768 published articles along with information retrieval from several public databases, BrainBase features comprehensive collection of 7175 disease-gene associations spanning a total of 123 brain diseases and linking with 5662 genes, 16 591 drug-target interactions covering 2118 drugs/chemicals and 623 genes, and five types of specific genes in light of expression specificity in brain tissue/regions/cerebrospinal fluid/cells. In addition, considering the severity of glioma among brain tumors, the current version of BrainBase incorporates 21 multi-omics datasets, presents molecular profiles across various samples/conditions and identifies four groups of glioma featured genes with potential clinical significance. Collectively, BrainBase integrates not only valuable curated disease-gene associations and drug-target interactions but also molecular profiles through multi-omics data analysis, accordingly bearing great promise to serve as a valuable knowledgebase for brain diseases.
Published on January 6, 2022
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Identification of Druggable Genes for Asthma by Integrated Genomic Network Analysis.

Authors: Adikusuma W, Chou WH, Lin MR, Ting J, Irham LM, Perwitasari DA, Chang WP, Chang WC

Abstract: Asthma is a common and heterogeneous disease characterized by chronic airway inflammation. Currently, the two main types of asthma medicines are inhaled corticosteroids and long-acting beta2-adrenoceptor agonists (LABAs). In addition, biological drugs provide another therapeutic option, especially for patients with severe asthma. However, these drugs were less effective in preventing severe asthma exacerbation, and other drug options are still limited. Herein, we extracted asthma-associated single nucleotide polymorphisms (SNPs) from the genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) catalog and prioritized candidate genes through five functional annotations. Genes enriched in more than two categories were defined as "biological asthma risk genes." Then, DrugBank was used to match target genes with FDA-approved medications and identify candidate drugs for asthma. We discovered 139 biological asthma risk genes and identified 64 drugs targeting 22 of these genes. Seven of them were approved for asthma, including reslizumab, mepolizumab, theophylline, dyphylline, aminophylline, oxtriphylline, and enprofylline. We also found 17 drugs with clinical or preclinical evidence in treating asthma. In addition, eleven of the 40 candidate drugs were further identified as promising asthma therapy. Noteworthy, IL6R is considered a target for asthma drug repurposing based on its high target scores. Through in silico drug repurposing approach, we identified sarilumab and satralizumab as the most promising drug for asthma treatment.
Published on January 6, 2022
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Proteomic Characterization and Target Identification Against Streptococcus mutans Under Bacitracin Stress Conditions Using LC-MS and Subtractive Proteomics.

Authors: Zaidi S, Bhardwaj T, Somvanshi P, Khan AU

Abstract: The aim of the present study, is to identify potential targets against the highly pathogenic bacteria Streptococcus mutans that causes dental caries as well as the deadly infection of endocarditis. The powerful and highly sensitive technique of liquid chromatography-mass spectrometry (LC-MS/MS) identified 321 proteins of S. mutans when grown under stressful conditions induced by the antibiotic bacitracin. These 321 proteins were subjected to the insilico method of subtractive proteomics to screen out potential targets by utilizing different analyses like CD-HIT, non-homologous sequence screening, KEGG pathway, essentiality screening, gut-flora non-homology, and codon usage analysis. A database of essential proteins was employed to find sequence homology of non-paralogous proteins to determine proteins which are essential for bacterial survival. Cellular localization analysis of the selected proteins was done to localize them inside the cell along with physico-chemical characterization and druggability analysis. Using computational tools, 22 proteins out of 321, that are functionally distinguishable from their human counterparts and passed the criterion of a potential therapeutic candidate were identified. The selected proteins comprise central energy metabolic proteins, virulence factors, proteins of the sortase family, and essentiality factors. The presented analyses identified proteins of the sortase family, which appear as key therapeutic targets against caries infection. These proteins regulate a number of virulence factors, thus can be simultaneously inhibited to obstruct multiple virulence pathways.
Published on January 6, 2022
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Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes.

Authors: Hindy G, Dornbos P, Chaffin MD, Liu DJ, Wang M, Selvaraj MS, Zhang D, Park J, Aguilar-Salinas CA, Antonacci-Fulton L, Ardissino D, Arnett DK, Aslibekyan S, Atzmon G, Ballantyne CM, Barajas-Olmos F, Barzilai N, Becker LC, Bielak LF, Bis JC, Blangero J, Boerwinkle E, Bonnycastle LL, Bottinger E, Bowden DW, Bown MJ, Brody JA, Broome JG, Burtt NP, Cade BE, Centeno-Cruz F, Chan E, Chang YC, Chen YI, Cheng CY, Choi WJ, Chowdhury R, Contreras-Cubas C, Cordova EJ, Correa A, Cupples LA, Curran JE, Danesh J, de Vries PS, DeFronzo RA, Doddapaneni H, Duggirala R, Dutcher SK, Ellinor PT, Emery LS, Florez JC, Fornage M, Freedman BI, Fuster V, Garay-Sevilla ME, Garcia-Ortiz H, Germer S, Gibbs RA, Gieger C, Glaser B, Gonzalez C, Gonzalez-Villalpando ME, Graff M, Graham SE, Grarup N, Groop LC, Guo X, Gupta N, Han S, Hanis CL, Hansen T, He J, Heard-Costa NL, Hung YJ, Hwang MY, Irvin MR, Islas-Andrade S, Jarvik GP, Kang HM, Kardia SLR, Kelly T, Kenny EE, Khan AT, Kim BJ, Kim RW, Kim YJ, Koistinen HA, Kooperberg C, Kuusisto J, Kwak SH, Laakso M, Lange LA, Lee J, Lee J, Lee S, Lehman DM, Lemaitre RN, Linneberg A, Liu J, Loos RJF, Lubitz SA, Lyssenko V, Ma RCW, Martin LW, Martinez-Hernandez A, Mathias RA, McGarvey ST, McPherson R, Meigs JB, Meitinger T, Melander O, Mendoza-Caamal E, Metcalf GA, Mi X, Mohlke KL, Montasser ME, Moon JY, Moreno-Macias H, Morrison AC, Muzny DM, Nelson SC, Nilsson PM, O'Connell JR, Orho-Melander M, Orozco L, Palmer CNA, Palmer ND, Park CJ, Park KS, Pedersen O, Peralta JM, Peyser PA, Post WS, Preuss M, Psaty BM, Qi Q, Rao DC, Redline S, Reiner AP, Revilla-Monsalve C, Rich SS, Samani N, Schunkert H, Schurmann C, Seo D, Seo JS, Sim X, Sladek R, Small KS, So WY, Stilp AM, Tai ES, Tam CHT, Taylor KD, Teo YY, Thameem F, Tomlinson B, Tsai MY, Tuomi T, Tuomilehto J, Tusie-Luna T, Udler MS, van Dam RM, Vasan RS, Viaud Martinez KA, Wang FF, Wang X, Watkins H, Weeks DE, Wilson JG, Witte DR, Wong TY, Yanek LR, Kathiresan S, Rader DJ, Rotter JI, Boehnke M, McCarthy MI, Willer CJ, Natarajan P, Flannick JA, Khera AV, Peloso GM

Abstract: Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.