Literature DB >> 18038206

CancerLectinDB: a database of lectins relevant to cancer.

Deepa Damodaran1, Justin Jeyakani, Alok Chauhan, Nirmal Kumar, Nagasuma R Chandra, Avadhesha Surolia.   

Abstract

The role of lectins in mediating cancer metastasis, apoptosis as well as various other signaling events has been well established in the past few years. Data on various aspects of the role of lectins in cancer is being accumulated at a rapid pace. The data on lectins available in the literature is so diverse, that it becomes difficult and time-consuming, if not impossible to comprehend the advances in various areas and obtain the maximum benefit. Not only do the lectins vary significantly in their individual functional roles, but they are also diverse in their sequences, structures, binding site architectures, quaternary structures, carbohydrate affinities and specificities as well as their potential applications. An organization of these seemingly independent data into a common framework is essential in order to achieve effective use of all the data towards understanding the roles of different lectins in different aspects of cancer and any resulting applications. An integrated knowledge base (CancerLectinDB) together with appropriate analytical tools has therefore been developed for lectins relevant for any aspect of cancer, by collating and integrating diverse data. This database is unique in terms of providing sequence, structural, and functional annotations for lectins from all known sources in cancer and is expected to be a useful addition to the number of glycan related resources now available to the community. The database has been implemented using MySQL on a Linux platform and web-enabled using Perl-CGI and Java tools. Data for individual lectins pertain to taxonomic, biochemical, domain architecture, molecular sequence and structural details as well as carbohydrate specificities. Extensive links have also been provided for relevant bioinformatics resources and analytical tools. Availability of diverse data integrated into a common framework is expected to be of high value for various studies on lectin cancer biology. CancerLectinDB can be accessed through http://proline.physics.iisc.ernet.in/cancerdb .

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Year:  2007        PMID: 18038206     DOI: 10.1007/s10719-007-9085-5

Source DB:  PubMed          Journal:  Glycoconj J        ISSN: 0282-0080            Impact factor:   2.916


  27 in total

1.  SCOP: a structural classification of proteins database.

Authors:  L Lo Conte; B Ailey; T J Hubbard; S E Brenner; A G Murzin; C Chothia
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

3.  Lectins: Carbohydrate-Specific Proteins That Mediate Cellular Recognition.

Authors:  Halina Lis; Nathan Sharon
Journal:  Chem Rev       Date:  1998-04-02       Impact factor: 60.622

4.  The Gene Ontology (GO) database and informatics resource.

Authors:  M A Harris; J Clark; A Ireland; J Lomax; M Ashburner; R Foulger; K Eilbeck; S Lewis; B Marshall; C Mungall; J Richter; G M Rubin; J A Blake; C Bult; M Dolan; H Drabkin; J T Eppig; D P Hill; L Ni; M Ringwald; R Balakrishnan; J M Cherry; K R Christie; M C Costanzo; S S Dwight; S Engel; D G Fisk; J E Hirschman; E L Hong; R S Nash; A Sethuraman; C L Theesfeld; D Botstein; K Dolinski; B Feierbach; T Berardini; S Mundodi; S Y Rhee; R Apweiler; D Barrell; E Camon; E Dimmer; V Lee; R Chisholm; P Gaudet; W Kibbe; R Kishore; E M Schwarz; P Sternberg; M Gwinn; L Hannick; J Wortman; M Berriman; V Wood; N de la Cruz; P Tonellato; P Jaiswal; T Seigfried; R White
Journal:  Nucleic Acids Res       Date:  2004-01-01       Impact factor: 16.971

Review 5.  Classification of plant lectins in families of structurally and evolutionary related proteins.

Authors:  W J Peumans; E J Van Damme; A Barre; P Rougé
Journal:  Adv Exp Med Biol       Date:  2001       Impact factor: 2.622

6.  The SWISS-PROT protein sequence data bank.

Authors:  A Bairoch; B Boeckmann
Journal:  Nucleic Acids Res       Date:  1992-05-11       Impact factor: 16.971

Review 7.  Lectins in cancer cells.

Authors:  R Lotan; A Raz
Journal:  Ann N Y Acad Sci       Date:  1988       Impact factor: 5.691

Review 8.  Lectins as bioactive plant proteins: a potential in cancer treatment.

Authors:  Elvira González De Mejía; Valentin I Prisecaru
Journal:  Crit Rev Food Sci Nutr       Date:  2005       Impact factor: 11.176

9.  Structural similarity and functional diversity in proteins containing the legume lectin fold.

Authors:  N R Chandra; M M Prabu; K Suguna; M Vijayan
Journal:  Protein Eng       Date:  2001-11

10.  The COG database: an updated version includes eukaryotes.

Authors:  Roman L Tatusov; Natalie D Fedorova; John D Jackson; Aviva R Jacobs; Boris Kiryutin; Eugene V Koonin; Dmitri M Krylov; Raja Mazumder; Sergei L Mekhedov; Anastasia N Nikolskaya; B Sridhar Rao; Sergei Smirnov; Alexander V Sverdlov; Sona Vasudevan; Yuri I Wolf; Jodie J Yin; Darren A Natale
Journal:  BMC Bioinformatics       Date:  2003-09-11       Impact factor: 3.169

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  9 in total

1.  Sequence-based predictive modeling to identify cancerlectins.

Authors:  Hong-Yan Lai; Xin-Xin Chen; Wei Chen; Hua Tang; Hao Lin
Journal:  Oncotarget       Date:  2017-04-25

2.  Analysis and prediction of cancerlectins using evolutionary and domain information.

Authors:  Ravi Kumar; Bharat Panwar; Jagat S Chauhan; Gajendra Ps Raghava
Journal:  BMC Res Notes       Date:  2011-07-20

Review 3.  Carbohydrate recognition by boronolectins, small molecules, and lectins.

Authors:  Shan Jin; Yunfeng Cheng; Suazette Reid; Minyong Li; Binghe Wang
Journal:  Med Res Rev       Date:  2010-03       Impact factor: 12.944

4.  Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

Authors:  Jieru Zhang; Ying Ju; Huijuan Lu; Ping Xuan; Quan Zou
Journal:  Int J Genomics       Date:  2016-07-13       Impact factor: 2.326

5.  A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique.

Authors:  Runtao Yang; Chengjin Zhang; Lina Zhang; Rui Gao
Journal:  Biomed Res Int       Date:  2018-02-07       Impact factor: 3.411

6.  Identification of Cancerlectins Using Support Vector Machines With Fusion of G-Gap Dipeptide.

Authors:  Lili Qian; Yaping Wen; Guosheng Han
Journal:  Front Genet       Date:  2020-04-03       Impact factor: 4.599

Review 7.  Okra (Abelmoschus Esculentus) as a Potential Dietary Medicine with Nutraceutical Importance for Sustainable Health Applications.

Authors:  Abd Elmoneim O Elkhalifa; Eyad Alshammari; Mohd Adnan; Jerold C Alcantara; Amir Mahgoub Awadelkareem; Nagat Elzein Eltoum; Khalid Mehmood; Bibhu Prasad Panda; Syed Amir Ashraf
Journal:  Molecules       Date:  2021-01-28       Impact factor: 4.411

8.  35 years in plant lectin research: a journey from basic science to applications in agriculture and medicine.

Authors:  Els J M Van Damme
Journal:  Glycoconj J       Date:  2021-08-24       Impact factor: 3.009

9.  Predicting cancerlectins by the optimal g-gap dipeptides.

Authors:  Hao Lin; Wei-Xin Liu; Jiao He; Xin-Hui Liu; Hui Ding; Wei Chen
Journal:  Sci Rep       Date:  2015-12-09       Impact factor: 4.379

  9 in total

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