Literature DB >> 21184613

MIND-BEST: Web server for drugs and target discovery; design, synthesis, and assay of MAO-B inhibitors and theoretical-experimental study of G3PDH protein from Trichomonas gallinae.

Humberto González-Díaz1, Francisco Prado-Prado, Xerardo García-Mera, Nerea Alonso, Paula Abeijón, Olga Caamaño, Matilde Yáñez, Cristian R Munteanu, Alejandro Pazos, María Auxiliadora Dea-Ayuela, María Teresa Gómez-Muñoz, M Magdalena Garijo, José Sansano, Florencio M Ubeira.   

Abstract

Many drugs with very different affinity to a large number of receptors are described. Thus, in this work, we selected drug-target pairs (DTPs/nDTPs) of drugs with high affinity/nonaffinity for different targets. Quantitative structure-activity relationship (QSAR) models become a very useful tool in this context because they substantially reduce time and resource-consuming experiments. Unfortunately, most QSAR models predict activity against only one protein target and/or they have not been implemented on a public Web server yet, freely available online to the scientific community. To solve this problem, we developed a multitarget QSAR (mt-QSAR) classifier combining the MARCH-INSIDE software for the calculation of the structural parameters of drug and target with the linear discriminant analysis (LDA) method in order to seek the best model. The accuracy of the best LDA model was 94.4% (3,859/4,086 cases) for training and 94.9% (1,909/2,012 cases) for the external validation series. In addition, we implemented the model into the Web portal Bio-AIMS as an online server entitled MARCH-INSIDE Nested Drug-Bank Exploration & Screening Tool (MIND-BEST), located at http://miaja.tic.udc.es/Bio-AIMS/MIND-BEST.php . This online tool is based on PHP/HTML/Python and MARCH-INSIDE routines. Finally, we illustrated two practical uses of this server with two different experiments. In experiment 1, we report for the first time a MIND-BEST prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of eight rasagiline derivatives, promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, sample preparation, 2-DE, MALDI-TOF and -TOF/TOF MS, MASCOT search, 3D structure modeling with LOMETS, and MIND-BEST prediction for different peptides as new protein of the found in the proteome of the bird parasite Trichomonas gallinae, which is promising for antiparasite drug targets discovery.

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Year:  2011        PMID: 21184613     DOI: 10.1021/pr101009e

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  15 in total

1.  In silico design of multi-target inhibitors for C-C chemokine receptors using substructural descriptors.

Authors:  Alejandro Speck-Planche; Valeria V Kleandrova
Journal:  Mol Divers       Date:  2011-10-22       Impact factor: 2.943

2.  Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates.

Authors:  Nerea Alonso; Olga Caamaño; Francisco J Romero-Duran; Feng Luan; M Natália D S Cordeiro; Matilde Yañez; Humberto González-Díaz; Xerardo García-Mera
Journal:  ACS Chem Neurosci       Date:  2013-07-29       Impact factor: 4.418

3.  Multiscale modelling of relationships between protein classes and drug behavior across all diseases using the CANDO platform.

Authors:  Geetika Sethi; Gaurav Chopra; Ram Samudrala
Journal:  Mini Rev Med Chem       Date:  2015       Impact factor: 3.862

4.  Evaluation of model quality predictions in CASP9.

Authors:  Andriy Kryshtafovych; Krzysztof Fidelis; Anna Tramontano
Journal:  Proteins       Date:  2011-10-14

5.  Fragment-based optimization of small molecule CXCL12 inhibitors for antagonizing the CXCL12/CXCR4 interaction.

Authors:  Joshua J Ziarek; Yan Liu; Emmanuel Smith; Guolin Zhang; Francis C Peterson; Jun Chen; Yongping Yu; Yu Chen; Brian F Volkman; Rongshi Li
Journal:  Curr Top Med Chem       Date:  2012       Impact factor: 3.295

6.  Drug-target interaction prediction through domain-tuned network-based inference.

Authors:  Salvatore Alaimo; Alfredo Pulvirenti; Rosalba Giugno; Alfredo Ferro
Journal:  Bioinformatics       Date:  2013-05-29       Impact factor: 6.937

7.  Prediction of chemical-protein interactions network with weighted network-based inference method.

Authors:  Feixiong Cheng; Yadi Zhou; Weihua Li; Guixia Liu; Yun Tang
Journal:  PLoS One       Date:  2012-07-16       Impact factor: 3.240

8.  Insights into the mutation-induced HHH syndrome from modeling human mitochondrial ornithine transporter-1.

Authors:  Jing-Fang Wang; Kuo-Chen Chou
Journal:  PLoS One       Date:  2012-01-26       Impact factor: 3.240

Review 9.  Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.

Authors:  Maryam Bagherian; Elyas Sabeti; Kai Wang; Maureen A Sartor; Zaneta Nikolovska-Coleska; Kayvan Najarian
Journal:  Brief Bioinform       Date:  2021-01-18       Impact factor: 11.622

10.  Prediction of drug-target interactions and drug repositioning via network-based inference.

Authors:  Feixiong Cheng; Chuang Liu; Jing Jiang; Weiqiang Lu; Weihua Li; Guixia Liu; Weixing Zhou; Jin Huang; Yun Tang
Journal:  PLoS Comput Biol       Date:  2012-05-10       Impact factor: 4.475

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