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MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and Target prediction

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Acta Pharmaceutica Sinica B 2021年第10期11卷 3092-3104页

作者： Le Zhang Lei Zhang Yue Guo Ming Xiao Lu Feng Chengcan Yang Guan Wang Liang Ouyang Innovation Center of Nursing Research West China Biomedical Big Data CenterState Key Laboratory of Biotherapy and Cancer CenterWest China HospitalCollege of Computer Scienceand Collaborative Innovation Center of BiotherapySichuan UniversityChengdu 610065China Nursing Key Laboratory of Sichuan Province West China HospitalSichuan UniversityChengdu 610041China Pera Corporation Ltd. Beijing 100025China

Mitotic catastrophe(MC)is a form of programmed cell death induced by mitotic process disorders,which is very important in tumor prevention,development,and drug resistance.Because rapidly increased data for MC is vigorously promoting the tumor-related biomedical and clinical study,it is urgent for us to develop a professional and comprehensive database to curate MC-related data.Mitotic Catastrophe Database(MCDB)consists of 1214 genes/proteins and 5014 compounds collected and organized from more than 8000 research articles.Also,MCDB defines the confidence level,classification criteria,and uniform naming rules for MC-related data,which greatly improves data reliability and retrieval convenience.Moreover,MCDB develops protein sequence alignment and Target prediction functions.The former can be used to predict new potential MC-related genes and proteins,and the latter can facilitate the identification of potential target proteins of unknown MC-related compounds.In short,MCDB is such a proprietary,standard,and comprehensive database for MC-relate data that will facilitate the exploration of MC from chemists to biologists in the fields of medicinal chemistry,molecular biology,bioinformatics,oncology and so on.The MCDB is distributed on http://www.***.online/MCDB/indexhtml/.

关键词： Mitotic catastrophe Database Protein sequence analysis Target prediction Data mining

One Decade of Development and Evolution of MicroRNA Target prediction Algorithms

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Genomics, Proteomics & Bioinformatics 2012年第5期10卷 254-263页

作者： Paula H.Reyes~Herrera Elisa Ficarra School of Electronic and Biomedical Engineering Universidad Antonio Narino Department of Control and Computer Engineering Politecnico di Torino

Nearly two decades have passed since the publication of the first study reporting the discovery of microRNAs （miRNAs）. The key role of miRNAs in post-transcriptional gene regulation led to the performance of an increasing number of studies focusing on origins, mech- anisms of action and functionality of miRNAs. In order to associate each miRNA to a specific functionality it is essential to unveil the rules that govern miRNA action. Despite the fact that there has been significant improvement exposing structural characteristics of the miRNA-mRNA interaction, the entire physical mechanism is not yet fully understood. In this respect, the development of computa- tional algorithms for miRNA Target prediction becomes increasingly important. This manuscript summarizes the research done on miRNA Target prediction. It describes the experimental data currently available and used in the field and presents three lines of compu tational approaches for Target prediction. Finally, the authors put forward a number of considerations regarding current challenges and future directions.

关键词： MicroRNAs MicroRNA recognition elements Target prediction

Drug-target interaction prediction with deep learning

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中国药理学与毒理学杂志 2019年第10期 855-855页

作者： YANG Shuo LI Shi-liang LI Hong-lin Shanghai Key Laboratory of New Drug Design State Key Laboratory of Bioreactor Engineering School of Pharmacy East China University of Science and Technology

OBJECTIVE The identification of drugtarget interaction(DTI) is an important process for drug discovery. It has been a hot research topic in the field of drug design to develop different computational models to predict potential DTIs and provide powerful complementary tools for biological experiments. METHODS Artificial intelligence has been applied to predict DTIs, however,there are still many obstacles that need to be overcome,such as low prediction accuracy, unreasonable selection of negative samples, and poor data reliability. In this paper, we developed a deep learning based method named DLDTIs to predict DTIs. By introducing high dimensional molecular fingerprints and protein descriptors, and then applying a probability matrix decomposition algorithm to generate negative sample data sets, we constructed a promising DTI classification model. RESULTS DLDTIs was comparable or superior to other methods against the test sets by achieving more than 90% of accuracy, specificity, sensitivity and area under the curve, respectively. CONCLUSION The probability matrix decomposition algorithm was beneficial for generating negative DTIs. DLDTIs may be served as a powerful tool for future drug Target prediction.

关键词： drug-target interaction deep learning probability matrix decomposition Target prediction

Design,Synthesis,in Silico ADME-Tox Filtering,Target prediction,and Molecular Docking Study Chrysin Derivatives as Hemostatic and Anti-thrombosis drug

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Design,Synthesis,in Silico ADME-Tox Filtering,Target Predict...

Target prediction based on the Maximum 2D- and 3D-ligand similarity models

中国化学会第十七届全国有机分析与生物分析学术研讨会

作者：黄成进 Qian Jin-rong Zhang Mi 蒋炳丽 School of Chemistry and Chemical Engineering Guangxi University

＜正＞Recent advances in systems biology and chemical biology exhibit great challenges to thecurrent drug discovery paradigm that drugs selectively bind with one or two targets and showthat most drugs interact with multiple targets,that is polypharmacology.Base on themulti-drug-multi-target theory,we designed to introduce some hemostatic groups into chrysinwhich possess certain effects in promoting blood circulation to remove blood stasis for discoveryof hemostatic and anti-thrombosis drug.

关键词： Chrysin Target prediction Docking ADME-Tox Filtering

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Target Prediction based on the Maximum 2D- and 3D-ligand sim...

Exploration of target Spaces in the Human Genome for Protein and Peptide Drugs

第五届全国生物信息学与系统生物学学术大会暨国际生物信息...

作者： Xian Liu Jianlong Peng Qiancheng Shen Jing Lu Jing Li Lili Liu Yin Lu Xiaomin Luo Mingyue Zheng Drug Discovery and Design Center(DDDC) Shanghai Institute of Materia Mediea

Background:Most of drugs can bind to multiple targets,among which the primary one is typically related to disease treatment and others are responsible for adverse events. For this reason,a computational approach to predict potential targets of ligand could enable researchers to discover new chemical entities with more favorable activity and safety profile. Methods:We developed a maximum similarity model,on the assumption that the similar molecule may have similar bio-profile and tends to interact with the same target. targets related to FDA-approved drugs were collected from DrugBank and each target was presented by its ligand set(drugs or active molecule) as a template.Next,we explored 2D fingerprints,3D method(combining shape overlapping and feature matching) to calculate the similarity between a query molecule and all the ligand sets that correspond to different targets. For the each set,we only keep the maximum similarity score,which was used to rank all possible interacting targets for the query molecule.The high ranked targets were collected and analyzed.Furthermore,by pair-wise comparison of similarities between representative ligand sets of different drug targets,we constructed a network of target-target interaction,which can be used for exploring target relationship systematically. Results:Our proposed model was tested by retrieving targets for the active ligands in DUD(directory of useful decoys) dataset,showing AUC(area under curve) values above 0.85 for 20 targets(out of a total of 30 targets). Conclusions:The ligand-based Target prediction model presented here can be used in following applications:One is to target-fishing for orphan ligands and the other is repurposing for known drugs or drug candidates.The primary advantage of the method is fast and without relying on the target structure.

关键词： Target prediction off-target polypharmacology drug repurposing

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Genomics, Proteomics & Bioinformatics 2022年第4期20卷 780-794页

作者： Zhongyang Liu Honglei Li Zhaoyu Jin Yang Li Feifei Guo Yangzhige He Xinyue Liu Yaning Qi Liying Yuan Fuchu He Dong Li State Key Laboratory of Proteomics Beijing Proteome Research CenterNational Center for Protein Sciences(Beijing)Beijing Institute of LifeomicsBeijing 102206China School of Basic Medical Sciences Anhui Medical UniversityHefei 230032China College of Chemistry and Environmental Science Hebei UniversityBaoding 071002China Suzhou Geneworks Technology Co. Ltd.Suzhou 215028China Institute of Chinese Materia Medica China Academy of Chinese Medical SciencesBeijing 100700China Department of Medical Research Center Peking Union Medical College HospitalChinese Academy of Medical Science&Peking Union Medical CollegeBeijing 100730China College of Life Sciences Hebei UniversityBaoding 071002China

After decades of development,protein and peptide drugs have now grown into a major drug class in the marketplace.target identification and validation are crucial for the discovery of protein and peptide drugs,and bioinformatics prediction of targets based on the characteristics of known target proteins will help improve the efficiency and success rate of target selection.However,owing to the developmental history in the pharmaceutical industry,previous systematic exploration of the target spaces has mainly focused on traditional small-molecule drugs,while studies related to protein and peptide drugs are lacking.Here,we systematically explore the target spaces in the human genome specifically for protein and peptide drugs.Compared with other proteins,both successful protein and peptide drug targets have many special characteristics,and are also significantly different from those of small-molecule drugs in many aspects.Based on these features,we develop separate effective genome-wide Target prediction models for protein and peptide drugs.Finally,a user-friendly web server,Predictor Of Protein and Pept Ide drugs’therapeutic targets(POPPIT)(http://***/),is established,which provides not only Target prediction specifically for protein and peptide drugs but also abundant annotations for predicted targets.

关键词： Protein drug Peptide drug target analysis Target prediction Web server

D3targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19

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Acta Pharmaceutica Sinica B 2020年第7期10卷 1239-1248页

作者： Yulong Shi Xinben Zhang Kaijie Mu Cheng Peng Zhengdan Zhu Xiaoyu Wang Yanqing Yang Zhijian Xu Weiliang Zhu CAS Key Laboratory of Receptor Research Drug Discovery and Design CenterShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai 201203China School of Pharmacy University of Chinese Academy of SciencesBeijing 100049China Nano Science and Technology Institute University of Science and Technology of ChinaSuzhou 215123China

A highly effective medicine is urgently required to cure coronavirus disease 2019(COVID-19).For the purpose,we developed a molecular docking based webserver,namely D3 targets-2019-nCoV,with two functions,one is for predicting drug targets for drugs or active compounds observed from clinic or in vitro/in vivo studies,the other is for identifying lead compounds against potential drug targets via docking.This server has its unique features,(1)the potential target proteins and their different conformations involving in the whole process from virus infection to replication and release were included as many as possible;(2)all the potential ligand-binding sites with volume larger than 200 A^3 on a protein structure were identified for docking;(3)correlation information among some conformations or binding sites was annotated;(4)it is easy to be updated,and is accessible freely to public(https://www.d3 ***/D3 targets-2019-nCoV/index.php).Currently,the webserver contains 42 proteins[20 severe acute respiratory syndrome-related coronavirus 2(SARS-CoV-2)encoded proteins and 22 human proteins involved in virus infection,replication and release]with 69 different conformations/structures and 557 potential ligand-binding pockets in total.With 6 examples,we demonstrated that the webserver should be useful to medicinal chemists,pharmacologists and clinicians for efficiently discovering or developing effective drugs against the SARS-CoV-2 to cure COVID-19.

关键词： COVID-19 SARS-CoV-2 Target prediction Multi-conformation Multi-site Docking D3targets-2019-nCoV

Correlating Bladder Cancer Risk Genes with Their targeting MicroRNAs Using MMiRNA-Tar

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Genomics, Proteomics & Bioinformatics 2015年第3期13卷 177-182页

作者： Yang Liu Steve Baker Hui Jiang Gary Stuart Yongsheng Bai Department of Electrical and Computer Engineering Rose-Hulman Institute of Technology Department of Math and Computer Science Indiana State University Department of Biostatistics University of Michigan Department of Biology Indiana State University The Center for Genomic Advocacy Indiana State University

The Cancer Genome Arias （TCGA）（http：//cancergenome.***.gov） is a valuable data resource focused on an increasing number of well-characterized cancer genomes. In part, TCGA provides detailed information about cancer-dependent gene expression changes, including changes in the expression of transcription-regulating microRNAs. We developed a web interface tool MMiRNA-Tar （http：f/bioinfl.***.edufMMiRNA-Tar） that can calculate and plot the correlation of expression for mRNA-microRNA pairs across samples or over a time course for a list of pairs under different prediction confidence cutoff criteria. prediction confidence was estab- lished by requiring that the proposed mRNA-microRNA pair appears in at least one of three Target prediction databases： targetProfiler, targetScan, or miRanda. We have tested our MMiRNA-Tar tool through analyzing 53 tumor and 11 normal samples of bladder urothelial carcinoma （BLCA） datasets obtained from TCGA and identified 204 microRNAs. These microRNAs were correlated with the mRNAs of five previously-reported bladder cancer risk genes and these selected pairs exhib- ited correlations in opposite direction between the tumor and normal samples based on the cus- tomized cutoff criterion of prediction. Furthermore, we have identified additional 496 genes （830 pairs） potentially targeted by 79 significant microRNAs out of 204 using three cutoff criteria, i.e.,false discovery rate （FDR）〈 0.1, opposite correlation coefficient between the tumor and normal samples, and predicted by at least one of three Target prediction databases. Therefore, MMiRNA- Tar provides researchers a convenient tool to visualize the co-relationship between microRNAs and mRNAs and to predict their targeting relationship. We believe that correlating expression profiles for microRNAs and mRNAs offers a complementary approach for elucidating their interactions.

关键词： The Cancer Genome Atlas Bladder cancer MicroRNA mRNA Correlation Target prediction

prediction of Cancer-Associated piRNA–mRNA and piRNA–lncRNA Interactions by Integrated Analysis of Expression and Sequence Data

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Tsinghua Science and Technology 2018年第2期23卷 115-125页

作者： Yajun Liu Junying Zhang Aimin Li Zhaowen Liu Zhongzhen He Xiguo Yuan Shouheng Tuo Xi'an University of Technology Xi'an 710048 China. School of Computer Science and Technology Xidian University Xi'an 710071China

piwi-interactingRNAs(piRNAs) are valuable biomarkers, but functional studies are still very limited.Recent research shows that piRNA-mediated cleavage acts on Transposable Elements(TEs), messengerRNAs(mRNAs), and long non-codingRNAs(lncRNAs). This study aimed to predict cancer-associated piRNA-mRNA and piRNA-lncRNA interactions as well as piRNA regulatory functions. Four cancer types(BRCA, HNSC, KIRC,and LUAD) were investigated. Interactions were identified by integrated analysis of the expression and sequence data. For the expression analysis, only piRNA–mRNA and piRNA–lncRNA pairs with expression profiles that were significantly inversely correlated were retained to reduce false-positive rates during the prediction. For the sequence analysis, miRanda was used for the Target prediction. We identified 198 piRNA–mRNA and 10 piRNA–lncRNA pairs. Unlike mRNA and lncRNA expressions, the piRNA expression was relatively consistent across the cancer types. Furthermore, the identified piRNAs were consistent with previously published cancer biomarkers, such as piRNA-36741, piR-21032, and piRNA-57125. More importantly, predicted piRNA functions were determined by constructing an interaction network, and piRNA targets were placed in gene ontology categories related to the cancer hallmarks "activating invasion and metastasis" and "sustained angiogenesis".

关键词： co-expression piRNA-mRNA interaction piRNA-lncRNA interaction integrated analysis Target prediction