Gorka Prieto1, Asier Fullaondo, Jose A Rodriguez. 1. Department of Communications Engineering, University of the Basque Country (UPV/EHU), Alda. Urquijo s/n Bilbao, 48013 and Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n Leioa, 48940, Spain.
Abstract
MOTIVATION: Leucine-rich nuclear export signals (NESs) are short amino acid motifs that mediate binding of cargo proteins to the nuclear export receptor CRM1, and thus contribute to regulate the localization and function of many cellular proteins. Computational prediction of NES motifs is of great interest, but remains a significant challenge. RESULTS: We have developed a novel approach for amino acid motif searching that can be used for NES prediction. This approach, termed Wregex (weighted regular expression), combines regular expressions with a position-specific scoring matrix (PSSM), and has been implemented in a web-based, freely available, software tool. By making use of a PSSM, Wregex provides a score to prioritize candidates for experimental testing. Key features of Wregex include its flexibility, which makes it useful for searching other types of protein motifs, and its fast execution time, which makes it suitable for large-scale analysis. In comparative tests with previously available prediction tools, Wregex is shown to offer a good rate of true-positive motifs, while keeping a smaller number of potential candidates.
MOTIVATION: Leucine-rich nuclear export signals (NESs) are short amino acid motifs that mediate binding of cargo proteins to the nuclear export receptor CRM1, and thus contribute to regulate the localization and function of many cellular proteins. Computational prediction of NES motifs is of great interest, but remains a significant challenge. RESULTS: We have developed a novel approach for amino acid motif searching that can be used for NES prediction. This approach, termed Wregex (weighted regular expression), combines regular expressions with a position-specific scoring matrix (PSSM), and has been implemented in a web-based, freely available, software tool. By making use of a PSSM, Wregex provides a score to prioritize candidates for experimental testing. Key features of Wregex include its flexibility, which makes it useful for searching other types of protein motifs, and its fast execution time, which makes it suitable for large-scale analysis. In comparative tests with previously available prediction tools, Wregex is shown to offer a good rate of true-positive motifs, while keeping a smaller number of potential candidates.
Authors: Justin Taylor; Maria Sendino; Alexander N Gorelick; Alessandro Pastore; Matthew T Chang; Alexander V Penson; Elena I Gavrila; Connor Stewart; Ella M Melnik; Florisela Herrejon Chavez; Lillian Bitner; Akihide Yoshimi; Stanley Chun-Wei Lee; Daichi Inoue; Bo Liu; Xiao J Zhang; Anthony R Mato; Ahmet Dogan; Michael G Kharas; Yuhong Chen; Demin Wang; Rajesh K Soni; Ronald C Hendrickson; Gorka Prieto; Jose A Rodriguez; Barry S Taylor; Omar Abdel-Wahab Journal: Cancer Discov Date: 2019-07-08 Impact factor: 39.397
Authors: Selvin Noé Palacios-Rápalo; Luis Adrián De Jesús-González; José Manuel Reyes-Ruiz; Juan Fidel Osuna-Ramos; Carlos Noe Farfan-Morales; Ana Lorena Gutiérrez-Escolano; Rosa María Del Ángel Journal: Arch Virol Date: 2021-03-08 Impact factor: 2.574
Authors: Igor Arregi; Jorge Falces; Anne Olazabal-Herrero; Marián Alonso-Mariño; Stefka G Taneva; José A Rodríguez; María A Urbaneja; Sonia Bañuelos Journal: PLoS One Date: 2015-06-19 Impact factor: 3.240