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microProtein Prediction Program (miP3)

Authors :: Enrico Magnani
Michael Banf
Niek de Klein
Seung Y. Rhee
Department of Plant Biology [Carnegie Institution]
Carnegie Institution for Science [Washington]
Department of Genetics
University Medical Center Groningen [Groningen] (UMCG)
Institut Jean-Pierre Bourgin (IJPB)
Institut National de la Recherche Agronomique (INRA)-AgroParisTech
Techniekbeurs of the Hogeschool Arnhem Nijmegen National Science Foundation MCB-1052348 IOS-1026003 MCB-0820823 Department of Energy BER65472 Association for Independent Plant Research Institutes (AIPI)
Department of Plant Biology [Carnegie] (DPB)
Source :: International journal of genomics, International journal of genomics, Hindawi Publishing Corporation, 2015, 2015, ⟨10.1155/2015/734147⟩, International Journal of Genomics, 2015:734147, International Journal of Genomics, Vol 2015 (2015), International Journal of Genomics (2015), . (2015), International Journal of Genomics
Publication Year :: 2015
Abstract: An emerging concept in transcriptional regulation is that a class of truncated transcription factors (TFs), called microProteins (miPs), engages in protein-protein interactions with TF complexes and provides feedback controls. A handful of miP examples have been described in the literature but the extent of their prevalence is unclear. Here we present an algorithm that predicts miPs and their target TFs from a sequenced genome. The algorithm is called miP prediction program (miP3), which is implemented in Python. The software will help shed light on the prevalence, biological roles, and evolution of miPs. Moreover, miP3 can be used to predict other types of miP-like proteins that may have evolved from other functional classes such as kinases and receptors. The program is freely available and can be applied to any sequenced genome.