- -

METADOCK 2: a high-throughput parallel metaheuristic scheme for molecular docking

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

  • Estadisticas de Uso

METADOCK 2: a high-throughput parallel metaheuristic scheme for molecular docking

Show full item record

Imbernón, B.; Serrano, A.; Bueno-Crespo, A.; Abellán, JL.; Pérez-Sánchez, H.; Cecilia-Canales, JM. (2020). METADOCK 2: a high-throughput parallel metaheuristic scheme for molecular docking. Bioinformatics. 1-6. https://doi.org/10.1093/bioinformatics/btz958

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/166348

Files in this item

Item Metadata

Title: METADOCK 2: a high-throughput parallel metaheuristic scheme for molecular docking
Author: Imbernón, Baldomero Serrano, Antonio Bueno-Crespo, Andrés Abellán, José L. Pérez-Sánchez, Horacio Cecilia-Canales, José María
UPV Unit: Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
Issued date:
Abstract:
[EN] Motivation Molecular docking methods are extensively used to predict the interaction between protein-ligand systems in terms of structure and binding affinity, through the optimization of a physics-based scoring ...[+]
Copyrigths: Reserva de todos los derechos
Source:
Bioinformatics. (issn: 1367-4803 )
DOI: 10.1093/bioinformatics/btz958
Publisher:
Oxford University Press
Publisher version: https://doi.org/10.1093/bioinformatics/btz958
Project ID:
info:eu-repo/grantAgreement/f SéNeCa//20813%2FPI%2F18/
info:eu-repo/grantAgreement/f SéNeCa//20524%2FPDC%2F18/
info:eu-repo/grantAgreement/f SéNeCa//20988%2FPI%2F18/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CTQ2017-87974-R/ES/DESARROLLO DE TECNICAS AVANZADAS DE DESCUBRIMIENTO DE FARMACOS, SU IMPLEMENTACION EN HERRAMIENTAS SOFTWARE Y WEB, Y SU APLICACION A CONTEXTOS DE RELEVANCIA FARMACOLOGICA/
info:eu-repo/grantAgreement/MINECO//TIN2016-78799-P/ES/DESARROLLO HOLISTICO DE APLICACIONES EMERGENTES EN SISTEMAS HETEROGENEOS/
Thanks:
This work was partially supported by the Fundación Séneca del Centro de Coordinación de la Investigación de la Región de Murcia [Projects 20813/PI/ 18, 20988/PI/18, 20524/PDC/18] and by the Spanish Ministry of ...[+]
Type: Artículo

References

Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J. (2008). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2), 239-287. doi:10.1007/s11047-008-9098-4

Cecilia, J. M., Llanes, A., Abellán, J. L., Gómez-Luna, J., Chang, L.-W., & Hwu, W.-M. W. (2018). High-throughput Ant Colony Optimization on graphics processing units. Journal of Parallel and Distributed Computing, 113, 261-274. doi:10.1016/j.jpdc.2017.12.002

Desiraju, G., & Steiner, T. (2001). The Weak Hydrogen Bond. doi:10.1093/acprof:oso/9780198509707.001.0001 [+]
Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J. (2008). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2), 239-287. doi:10.1007/s11047-008-9098-4

Cecilia, J. M., Llanes, A., Abellán, J. L., Gómez-Luna, J., Chang, L.-W., & Hwu, W.-M. W. (2018). High-throughput Ant Colony Optimization on graphics processing units. Journal of Parallel and Distributed Computing, 113, 261-274. doi:10.1016/j.jpdc.2017.12.002

Desiraju, G., & Steiner, T. (2001). The Weak Hydrogen Bond. doi:10.1093/acprof:oso/9780198509707.001.0001

Eisenberg, D., & McLachlan, A. D. (1986). Solvation energy in protein folding and binding. Nature, 319(6050), 199-203. doi:10.1038/319199a0

Ewing, T. J. A., Makino, S., Skillman, A. G., & Kuntz, I. D. (2001). Journal of Computer-Aided Molecular Design, 15(5), 411-428. doi:10.1023/a:1011115820450

Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., … Shenkin, P. S. (2004). Glide:  A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. Journal of Medicinal Chemistry, 47(7), 1739-1749. doi:10.1021/jm0306430

Guerrero, G. D., Imbernón, B., Pérez-Sánchez, H., Sanz, F., García, J. M., & Cecilia, J. M. (2014). A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing. BioMed Research International, 2014, 1-8. doi:10.1155/2014/474219

Hauser, A. S., & Windshügel, B. (2016). LEADS-PEP: A Benchmark Data Set for Assessment of Peptide Docking Performance. Journal of Chemical Information and Modeling, 56(1), 188-200. doi:10.1021/acs.jcim.5b00234

Llanes, A., Muñoz, A., Bueno-Crespo, A., García-Valverde, T., Sánchez, A., Arcas-Túnez, F., … M. Cecilia, J. (2016). Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures. Current Drug Targets, 17(14), 1626-1648. doi:10.2174/1389450117666160201114028

McIntosh-Smith, S., Price, J., Sessions, R. B., & Ibarra, A. A. (2014). High performance in silico virtual drug screening on many-core processors. The International Journal of High Performance Computing Applications, 29(2), 119-134. doi:10.1177/1094342014528252

Mehler, E. L., & Solmajer, T. (1991). Electrostatic effects in proteins: comparison of dielectric and charge models. «Protein Engineering, Design and Selection», 4(8), 903-910. doi:10.1093/protein/4.8.903

Morris, G. M., Goodsell, D. S., Halliday, R. S., Huey, R., Hart, W. E., Belew, R. K., & Olson, A. J. (1998). Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry, 19(14), 1639-1662. doi:10.1002/(sici)1096-987x(19981115)19:14<1639::aid-jcc10>3.0.co;2-b

Mysinger, M. M., Carchia, M., Irwin, J. J., & Shoichet, B. K. (2012). Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking. Journal of Medicinal Chemistry, 55(14), 6582-6594. doi:10.1021/jm300687e

O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Open Babel: An open chemical toolbox. Journal of Cheminformatics, 3(1). doi:10.1186/1758-2946-3-33

Sakurai, Y., Kolokoltsov, A. A., Chen, C.-C., Tidwell, M. W., Bauta, W. E., Klugbauer, N., … Davey, R. A. (2015). Two-pore channels control Ebola virus host cell entry and are drug targets for disease treatment. Science, 347(6225), 995-998. doi:10.1126/science.1258758

Sánchez-Linares, I., Pérez-Sánchez, H., Cecilia, J. M., & García, J. M. (2012). High-Throughput parallel blind Virtual Screening using BINDSURF. BMC Bioinformatics, 13(S14). doi:10.1186/1471-2105-13-s14-s13

Sliwoski, G., Kothiwale, S., Meiler, J., & Lowe, E. W. (2013). Computational Methods in Drug Discovery. Pharmacological Reviews, 66(1), 334-395. doi:10.1124/pr.112.007336

Sörensen, K. (2013). Metaheuristics-the metaphor exposed. International Transactions in Operational Research, 22(1), 3-18. doi:10.1111/itor.12001

Yuan, S., Chan, J. F.-W., den-Haan, H., Chik, K. K.-H., Zhang, A. J., Chan, C. C.-S., … Yuen, K.-Y. (2017). Structure-based discovery of clinically approved drugs as Zika virus NS2B-NS3 protease inhibitors that potently inhibit Zika virus infection in vitro and in vivo. Antiviral Research, 145, 33-43. doi:10.1016/j.antiviral.2017.07.007

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record